thesis on machine translation

Machine translation and its evaluation: a study

• Published: 19 February 2023
• Volume 56 , pages 10137–10226, ( 2023 )

Cite this article

• Subrota Kumar Mondal   ORCID: orcid.org/0000-0002-0008-7797 1 ,
• Haoxi Zhang 1 ,
• H. M. Dipu Kabir 2 ,
• Kan Ni 1 &
• Hong-Ning Dai 3  

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9 Citations

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Machine translation (namely MT) has been one of the most popular fields in computational linguistics and Artificial Intelligence (AI). As one of the most promising approaches, MT can potentially break the language barrier of people from all over the world. Despite a number of studies in MT, there are few studies in summarizing and comparing MT methods. To this end, in this paper, we principally focus on presenting the two mainstream MT schemes: statistical machine translation (SMT) and neural machine translation (NMT), including their basic rationales and developments. Meanwhile, the detailed translation models are also presented, such as the word-based model, syntax-based model, and phrase-based model in statistical machine translation. Similarly, approaches in NMT, such as the recurrent neural network-based, attention mechanism-based, and transformer-based models are presented. Last but not least, the evaluation approaches also play an important role in helping developers to improve their methods better in MT. The prevailing machine translation evaluation methodologies are also presented in this article.

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Neural machine translation: past, present, and future

A Comprehensive Survey on Various Fully Automatic Machine Translation Evaluation Metrics

Text-Text Neural Machine Translation: A Survey

SYSTRAN https://www.systran.net/en/translate/ .

Apertium https://www.apertium.org/index.eng.html .

GramTrans https://gramtrans.com/ .

Google AI Blog https://ai.googleblog.com/2016/09/a-neural-network-for-machine.html .

Microsoft Translator Blog https://www.microsoft.com/en-us/translator/blog/2016/11/15/microsoft-translator-launching-neural-network-based-translations-for-all-its-speech-languages/ .

Moses https://www.statmt.org/moses/ .

www.promt.com.

www.systransoft.com.

Machine Translation Research at Google https://research.google/pubs/?area=machine-translation .

Machine Translation Research at Microsoft https://www.microsoft.com/en-us/research/group/machine-translation-group/ .

Machine Translation Research at Meta AI https://ai.facebook.com/research/NLP .

OpenNMT https://opennmt.net/ .

Google Translate https://translate.google.com/ .

Europarl Datasets https://www.statmt.org/europarl/ .

WMT’14 Translation Task https://www.statmt.org/wmt14/translation-task.html .

WMT’15 Translation Task https://www.statmt.org/wmt15/translation-task.html .

Hugging Face Transformers Index https://huggingface.co/docs/transformers/index .

C4 Dataset https://www.tensorflow.org/datasets/catalog/c4 .

WMT Workshop https://www.statmt.org/ .

Statistical and Neural Machine Translation \(\rightarrow\) Events https://www.statmt.org/ .

WMT22 https://www.statmt.org/wmt22/ , WMT21, WMT20, and so on.

Google Research: Machine Translation https://research.google/research-areas/machine-translation/ .

Abbreviations

Machine translation

• Natural Language Processing

Rule-based Machine Translation

Corpus-based Machine Translation

• Statistical machine translation

Example-based Machine Translation

Hybrid Machine Translation

• Neural machine translation

Expectation-Maximization

Word-based Machine Translation

Syntax-based Machine Translation

Phrase-based Machine Translation

Context-Free Grammar

Synchronous Context-Free Grammar

Inversion Transduction Grammar

Translation Edit Rat

Human-targeted Translation Edit Rat

Multi-reference TER

Google’s Neural Machine Translation

Backpropagation

Back-Translation

Neural Network

Convolutional Neural Network

Recurrent Neural Network

Transformer Neural Network

Gate Recurrent Unit

Long-Short Term Memory

Bidirectional Encoder Representations from Transformers

Low-Resource Language

High-Resource Language

Automatic Language Processing Advisory Committee

Defense Advanced Research Projects Agency

Bilingual Evaluation Understudy

National Institute of Standards and Technology

Metric for Evaluation of Translation with Explicit ORdering

Recall-Oriented Understudy for Gisting Evaluation

Open Machine Translation Evaluation

Text-To-Text Transfer Transformer

ACL (2022) ACL 2014 NINTH WORKSHOP ON STATISTICAL MACHINE TRANSLATION. Available at https://www.statmt.org/wmt14/translation-task.html . Accessed 05 Apr 2022

ACL 2015 NINTH WORKSHOP ON STATISTICAL MACHINE TRANSLATION (2022) https://www.statmt.org/wmt15/translation-task.html . Accessed 05 Apr 2022

Abid A, Farooqi M, Zou J (2021) Persistent anti-muslim bias in large language models. In: Proceedings of the 2021 AAAI/ACM conference on AI, Ethics, and Society, pp 298–306

Agency DARP (2003) Program Translingual Information Detection, Extraction and Summarization. http://www.darpa.mil/ipto/programs/tides/ . Accessed 08 Apr 2022

Ahmadnia B, Dorr BJ (2019) Augmenting neural machine translation through round-trip training approach. Open Comput Sci 9(1):268–278

Google Scholar  

Ahmadnia B, Serrano J, Haffari G (2017) Persian-Spanish low-resource statistical machine translation through English as pivot language. Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017:24–30

Ahmadnia B, Haffari G, Serrano J (2019) Round-trip training approach for bilingually low-resource statistical machine translation systems. Int J Artif Intell 17(1):167–185

Ahmadnia B, Dorr BJ, Kordjamshidi P (2020) Knowledge graphs effectiveness in neural machine translation improvement. Comput Sci 21:299–318

Ahmed A, Hanneman G (2005) Syntax-based statistical machine translation: a review. Comput Linguist 1:1

Álvaro Rocha, Adeli H, Reis LP, Costanzo S (2018) Trends and advances in information systems and technologies, vol 2. Springer, Berlin

Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z (2007) Dbpedia: a nucleus for a web of open data. In: The semantic web, Springer, pp 722–735

Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, pp 65–72

Bapna A, Firat O, Wang P, Macherey W, Cheng Y, Cao Y (2022) Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation. In: ACL 2022

Barzilay R, Koehn P (2004) Natural Language Processing, Fall 2004, Machine Translation I, Lecture 20. CS an AI Lab, MIT, New York

Bender EM (2019) A typology of ethical risks in language technology with an eye towards where transparent documentation can help. In: Future of artificial intelligence: language, ethics, technology workshop

Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing, Springer, pp 1–4

Bentivogli L, Bisazza A, Cettolo M, Federico M (2016) Neural versus phrase-based machine translation quality: a case study. arXiv preprint arXiv:1608.04631

Besacier L, Blanchon H (2017) Comparing statistical machine translation and neural machine translation performances. https://evaluerlata.hypotheses.org/files/2017/07/Laurent-Besacier-NMTvsSMT.pdf , laboratoire LIG, Université Grenoble Alpes, France

Bick E (2007) Dan2eng: wide-coverage Danish-English machine translation. In: Proceedings of Machine Translation Summit XI: Papers

Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data, pp 1247–1250

Bouchard G (2007) Efficient bounds for the softmax function and applications to approximate inference in hybrid models. In: NIPS 2007 workshop for approximate Bayesian inference in continuous/hybrid systems

Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311

Buck C, Heafield K, Van Ooyen B (2014) N-gram counts and language models from the common crawl. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp 3579–3584

Canfora C, Ottmann A (2020) Risks in neural machine translation. Trans Spaces 9(1):58–77

Casas N, Costa-jussà MR, Fonollosa JA, Alonso JA, Fanlo R (2021) Linguistic knowledge-based vocabularies for Neural Machine Translation. Nat Lang Eng 27(4):485–506

Caswell I, Liang B (2022) Recent Advances in Google Translate. Tutorial https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html . Accessed 05 Apr 2022

Chah N (2018) OK Google, what is your ontology? Or: exploring freebase classification to understand Google’s Knowledge Graph. arXiv preprint arXiv:1805.03885

Chapelle O, Weston J, Bottou L, Vapnik V (2000) Vicinal risk minimization. Adv Neural Inf Process Syst 13:1

Cheng Y (2019) Joint training for pivot-based neural machine translation. In: Joint Training for Neural Machine Translation, Springer, pp 41–54

Cheng Y, Jiang L, Macherey W (2019) Robust Neural Machine Translation with Doubly Adversarial Inputs. In: ACL

Cheng Y, Jiang L, Macherey W, Eisenstein J (2020) AdvAug: Robust Adversarial Augmentation for Neural Machine Translation. In: ACL, https://arxiv.org/abs/2006.11834

Chiang D, Knight K (2006) An introduction to synchronous grammars. Tutorial https://www3.nd.edu/~dchiang/papers/synchtut.pdf . Accessed 4 Jan 2022

Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014a) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014b) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259

Colah (2015) Understanding LSTM networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/ . Accessed 08 Apr 2022

Common Crawl corpus (2022) https://commoncrawl.org/ . Accessed 30 Sep 2022

Corbí-Bellot AM, Forcada ML, Ortiz-Rojas S, Pérez-Ortiz JA, Ramírez-Sánchez G, Sánchez-Martínez F, Alegria I, Mayor A, Sarasola K (2005) An open-source shallow-transfer machine translation engine for the Romance languages of Spain. In: Proceedings of the 10th EAMT conference: practical applications of machine translation

Costa-Jussa MR, Fonollosa JA (2015) Latest trends in hybrid machine translation and its applications. Comput Speech Lang 32(1):3–10

Council NR (1416) Language and Machines: Computers in Translation and Linguistics; a Report. National Research Council, https://books.google.com/books?hl=zh-CN &lr= &id=Q0ErAAAAYAAJ &oi=fnd &pg=PA1 &dq=Languages+and+machines:+computers+in+translation+and+linguistics &ots=NgytafcXa- &sig=Hc733OYAAT89yd4U-3xLdh77gEM#v=onepage &q &f=false . Accessed 08 Apr 2022

Cui Y, Chen Z, Wei S, Wang S, Liu T, Hu G (2016) Attention-over-attention neural networks for reading comprehension. arXiv preprint arXiv:1607.04423

Currey A, Heafield K (2019) Zero-resource neural machine translation with monolingual pivot data. In: Proceedings of the 3rd workshop on neural generation and translation, pp 99–107

Dabre R, Cromieres F, Kurohashi S, Bhattacharyya P (2015) Leveraging small multilingual corpora for smt using many pivot languages. In: Proceedings of the 2015 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1192–1202

Dabre R, Imankulova A, Kaneko M, Chakrabarty A (2021) Simultaneous multi-pivot neural machine translation. arXiv preprint arXiv:2104.07410

Dajun Z, Yun W (2015) Corpus-based machine translation: Its current development and perspectives. In: International Forum of Teaching and Studies, American Scholars Press, Inc., vol 11, p 90

Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39(1):1–22

MathSciNet   MATH   Google Scholar  

Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

Doddington G (2002) Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In: Proceedings of the second international conference on Human Language Technology Research, Morgan Kaufmann Publishers Inc., pp 138–145

Edunov S, Ott M, Auli M, Grangier D (2018) Understanding back-translation at scale. arXiv preprint arXiv:1808.09381

Esplà-Gomis M, Forcada ML, Ramírez-Sánchez G, Hoang H (2019) ParaCrawl: Web-scale parallel corpora for the languages of the EU. In: Proceedings of Machine Translation Summit XVII: Translator, Project and User Tracks, pp 118–119

Europarl (2022) Europarl: European Parliament Proceedings Parallel Corpus. https://www.statmt.org/europarl/ . Accessed 30 Sep 2022

Forcada ML, Ginestí-Rosell M, Nordfalk J, O’Regan J, Ortiz-Rojas S, Pérez-Ortiz JA, Sánchez-Martínez F, Ramírez-Sánchez G, Tyers FM (2011) Apertium: a free/open-source platform for rule-based machine translation. Mach Trans 25(2):127–144

Freitag M, Torres DV, Grangier D, Cherry C, Foster G (2022) A Natural Diet: Towards Improving Naturalness of Machine Translation Output. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Online

Furuse O, Iida H (1992) An example-based method for transfer-driven machine translation. TMI 1992:139–150

Färber M, Ell B, Menne C, Rettinger A (2015) A comparative survey of dbpedia, freebase, opencyc, wikidata, and yago. Semantic Web J 1(1):1–5

Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, JMLR. org, pp 1243–1252

Gheini M, Ren X, May J (2021a) Cross-attention is all you need: adapting pretrained transformers for machine translation. In: Proceedings of the 2021 conference on Empirical Methods in Natural Language Processing, pp 1754–1765

Gheini M, Ren X, May J (2021b) On the strengths of cross-attention in pretrained transformers for machine translation

Gispert Ramis A (2007) Introducing linguistic knowledge into statistical machine translation. Universitat Politècnica de Catalunya

Graves A (2012) Long short-term memory. In: Supervised sequence labelling with recurrent neural networks. Springer, Berlin. pp 37–45

Guo Z, Huang Z, Zhu KQ, Chen G, Zhang K, Chen B, Huang F (2021) Automatically paraphrasing via sentence reconstruction and round-trip translation. IJCAI

Habash N, Zalmout N, Taji D, Hoang H, Alzate M (2017) A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages. In: Proceedings of the 15th conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Association for Computational Linguistics, Valencia, Spain, pp 235–241. https://aclanthology.org/E17-2038

Hardmeier C (2012) Discourse in statistical machine translation. a survey and a case study. Discours Revue de linguistique, psycholinguistique et informatique A journal of linguistics, psycholinguistics and computational linguistics (11)

He D, Xia Y, Qin T, Wang L, Yu N, Liu TY, Ma WY (2016) Dual learning for machine translation. Adv Neural Inf Process Syst 29

Hecht-Nielsen R (1992) Theory of the backpropagation neural network. In: Neural networks for perception, Elsevier, pp 65–93

Hill DC, Gombay C, Sanchez O, Woappi B, Romero Vélez AS, Davidson S, Richardson EZ (2022) Lost in machine translation: The promises and pitfalls of machine translation for multilingual group work in global health education. Discov Educ 1(1):1–5

Horváth I (2022) AI in interpreting: ethical considerations. Across Lang Cult 23(1):1–13

Huck M, Birch A (2015) The Edinburgh machine translation systems for IWSLT 2015. In: Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

Hull DA, Grefenstette G (1996) Querying across languages: a dictionary-based approach to multilingual information retrieval. In: Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, pp 49–57

IWSLT (2018) International Workshop on Spoken Language Translation. https://workshop2018.iwslt.org/ . Accessed 08 Apr 2022

Imankulova A, Sato T, Komachi M (2019) Filtered pseudo-parallel corpus improves low-resource neural machine translation. ACM Trans Asian Low-Resour Lang Inf Process 19(2):1–16

Islam M, Anik M, Hoque S, Islam A et al (2021) Towards achieving a delicate blending between rule-based translator and neural machine translator. Neural Comput Appl 33(18):12141–12167

Jean S, Cho K, Memisevic R, Bengio Y (2015) On Using Very Large Target Vocabulary for Neural Machine Translation. In: Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 1–10

Jehl L, Simianer P, HIrschler J, Riezler S (2015) The Heidelberg university English-German translation system for IWSLT 2015. In: Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

Jian Zhang MZ Ji Wu (2003) The improvement of automatic machine translation evaluation. J Chin Inf Process 17(6):2, http://jcip.cipsc.org.cn/CN/abstract/article_1823.shtml

Johnson M (2020) A scalable approach to reducing gender bias in Google translate, https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html . Google AI Blog Accessed on 30 Sep 2022

Jurafsky D, Martin JH (2022) Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, chapter 10: machine translation and encoder-decoder models, 10.9 Bias and Ethical Issues (3rd (draft) ed.). Accessed on 08 Apr 2022

Kalchbrenner N, Espeholt L, Simonyan K, Oord Avd, Graves A, Kavukcuoglu K (2016) Neural machine translation in linear time. arXiv preprint arXiv:1610.10099

Kaljahi RSZ, Rubino R, Roturier J, Foster J, Park BB (2012) A detailed analysis of phrase-based and syntax-based machine translation: The search for systematic differences. In: Proceedings of AMTA

Karim R (2019) Illustrated attention. https://towardsdatascience.com/attn-illustrated-attention-5ec4ad276ee3 . Accessed 08 Apr 2022

Kenny D, Moorkens J, Do Carmo F (2020) Fair MT: towards ethical, sustainable machine translation. Trans Spaces 9(1):1–11

Kharitonova K (2021) Linguistics4fairness: neutralizing Gender Bias in neural machine translation by introducing linguistic knowledge. Master’s thesis, Universitat Politècnica de Catalunya

Klakow D, Peters J (2002) Testing the correlation of word error rate and perplexity. Speech Commun 38(1–2):19–28

MATH   Google Scholar  

Klein G, Kim Y, Deng Y, Senellart J, Rush AM (2017) Opennmt: Open-source toolkit for neural machine translation. arXiv preprint arXiv:1701.02810

Ko WJ, El-Kishky A, Renduchintala A, Chaudhary V, Goyal N, Guzmán F, Fung P, Koehn P, Diab M (2021) Adapting high-resource NMT models to translate low-resource related languages without parallel data. arXiv preprint arXiv:2105.15071

Koehn P (2009) Statistical machine translation. Cambridge University Press

Koehn P, Chiang D (2019) Special interest group of machine translation. http://www.sigmt.org . Accessed 08 Apr 2022

Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R et al (2007) Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th annual meeting of the association for computational linguistics companion volume proceedings of the demo and poster sessions, pp 177–180

Koehn P (2009) Chapter 4 Word-based models - Statistical Machine Translation. Cambridge University Press, Cambridge. Accessed 08 Apr 2022

Koehn P (2009) Statistical Machine Translation Lecture 5 Syntax-Based Models. Cambridge University Press, Cambridge. Accessed 08 Apr 2022

Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, Association for Computational Linguistics, pp 48–54

Koehn P (2005) Europarl: A parallel corpus for statistical machine translation. In: Proceedings of machine translation summit x: papers, pp 79–86

Koehn P (2009) Statistical Machine Translation Lecture 6 Decoding. Cambridge University Press, Cambridge. Accessed 08 Apr 2022

Kussul E, Baidyk T, Kasatkina L, Lukovich V (2001) Rosenblatt perceptrons for handwritten digit recognition. In: IJCNN’01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222), IEEE, vol 2, pp 1516–1520

Labaka G, España-Bonet C, Màrquez L, Sarasola K (2014) A hybrid machine translation architecture guided by syntax. Mach Trans 28(2):91–125

Lample G, Conneau A (2019) Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291

Lavie A, Sagae K, Jayaraman S (2004) The significance of recall in automatic metrics for MT evaluation. In: Conference of the Association for Machine Translation in the Americas, Springer, pp 134–143

Li Y, Xiong D, Zhang M (2018) A survey of neural machine translation. Chinese Journal of Computers 12:2734

MathSciNet   Google Scholar  

Li Q, Zhang X, Xiong J, Hwu Wm, Chen D (2019) Implementing neural machine translation with bi-directional GRU and attention mechanism on FPGAs using HLS. In: Proceedings of the 24th Asia and South Pacific Design Automation Conference, pp 693–698

Lin CY (2004) Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp 74–81

Lin CY, Hovy E (2003) Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 human language technology conference of the North American chapter of the association for computational linguistics, pp 150–157

Lin CY, Och FJ (2004) Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp 605–612

Liu J (2020) Comparing and analyzing cohesive devices of SMT and NMT from Chinese to English: a diachronic approach. Open J Mod Linguist 10(6):765–772

Liu CH, Silva CC, Wang L, Way A (2018) Pivot machine translation using chinese as pivot language. In: China workshop on machine translation. Springer, Berlin. pp 74–85

Liu X, Wang Y, Wang X, Xu H, Li C, Xin X (2021) Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system. Opt Express 29(4):5923–5933

Lu Y, Zhang J, Zong C (2018) Exploiting knowledge graph in neural machine translation. In: China workshop on machine translation. Springer, Berlin. pp 27–38

Luccioni A, Viviano J (2021) What’s in the box? an analysis of undesirable content in the Common Crawl corpus. In: Proceedings of the 59th annual meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp 182–189

Luong MT, Sutskever I, Le QV, Vinyals O, Zaremba W (2014) Addressing the rare word problem in neural machine translation. arXiv preprint arXiv:1410.8206

Luong MT, Pham H, Manning CD (2015) Effective Approaches to Attention-based Neural Machine Translation. In: Proceedings of the 2015 conference on Empirical Methods in Natural Language Processing, pp 1412–1421

Ma S, Sun X, Wang Y, Lin J (2018) Bag-of-words as target for neural machine translation. arXiv preprint arXiv:1805.04871

Marcus MP, Marcinkiewicz MA, Santorini B (1993) Building a large annotated corpus of English: the penn treebank. Comput Linguist 19(2):313–330

Mehandru N, Robertson S, Salehi N (2022) Reliable and Safe Use of Machine Translation in Medical Settings. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, South Korea)(FAccT’22). Association for Computing Machinery, New York, NY, USA

Meng F, Lu Z, Li H, Liu Q (2016) Interactive attention for neural machine translation. arXiv preprint arXiv:1610.05011

Moon TK (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13(6):47–60

Moon J, Cho H, Park EL (2020) Revisiting round-trip translation for quality estimation. arXiv preprint arXiv:2004.13937

Moorkens J (2022) Ethics and machine translation. Machine translation for everyone: empowering users in the age of artificial intelligence 18:121

Moussallem D, Ngonga Ngomo AC, Buitelaar P, Arcan M (2019) Utilizing knowledge graphs for neural machine translation augmentation. In: Proceedings of the 10th international conference on knowledge capture, pp 139–146

Mueller V (2022) An introduction to synchronous grammars. Tutorial https://medium.com/towards-data-science/attention-please-85bd0abac41 . Accessed 05 Apr 2022

Nießen S, Ney H (2000) Improving SMT quality with morpho-syntactic analysis. In: COLING 2000 Volume 2: the 18th international conference on computational linguistics

Nyberg EH, Mitamura T (1992) The KANT system: Fast, accurate, high-quality translation in practical domains. In: Proceedings of the 14th conference on Computational linguistics-Volume 3, Association for Computational Linguistics, pp 1069–1073

Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1):19–51

Och FJ, Tillmann C, Ney H (1999) Improved alignment models for statistical machine translation. In: 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

Och FJ, Ueffing N, Ney H (2001) An efficient A* search algorithm for statistical machine translation. In: Proceedings of the workshop on Data-driven methods in machine translation-Volume 14, Association for Computational Linguistics, pp 1–8

Och FJ, Gildea D, Khudanpur S, Sarkar A, Yamada K, Fraser A, Kumar S, Shen L, Smith D, Eng K et al (2003) Syntax for statistical machine translation. In: Johns Hopkins University 2003 Summer Workshop on Language Engineering, Center for Language and Speech Processing, Baltimore, MD, Tech. Rep

Of Standards NNI, Technology) (2010) Open Machine Translation Evaluation. https://www.nist.gov/itl/iad/mig/open-machine-translation-evaluation . Accessed 08 Apr 2022

Ortega JE, Castro Mamani R, Cho K (2020) Neural machine translation with a polysynthetic low resource language. Mach Trans 34(4):325–346

Pal SK, Mitra S (1992) Multilayer perceptron, fuzzy sets, and classification. IEEE Trans Neural Netw 3(5):683–697

Papadimitriou CH (2003) Computational complexity. Wiley, New York

Papineni K, Roukos S, Ward T, Zhu WJ (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 311–318

ParaCrwal (2022) ParaCrawl: Web-scale parallel corpora for the languages of the EU. https://paracrawl.eu/ . Accessed 30 Sep 2022

Pierce J (1966) Language and machines: computers in translation and linguistics; a Report. National Research Council

Popović M (2017) chrF++: words helping character n-grams. In: Proceedings of the second conference on machine translation, pp 612–618

Prates MO, Avelar PH, Lamb LC (2020) Assessing gender bias in machine translation: a case study with google translate. Neural Comput Appl 32(10):6363–6381

Pryzant R, Chung Y, Jurafsky D, Britz D (2018) JESC: Japanese-English Subtitle Corpus. In: Proceedings of the eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21:1–67

Ramnath S, Johnson M, Gupta A, Raghuveer A (2021) HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints. In: EMNLP 2021

Ranathunga S, Lee ESA, Skenduli MP, Shekhar R, Alam M, Kaur R (2021) Neural machine translation for low-resource languages: a survey. arXiv preprint arXiv:2106.15115

Ravikumar D, Kodge S, Garg I, Roy K (2020) Exploring Vicinal Risk Minimization for Lightweight Out-of-Distribution Detection. arXiv preprint arXiv:2012.08398

Rebele T, Suchanek F, Hoffart J, Biega J, Kuzey E, Weikum G (2016) YAGO: A multilingual knowledge base from wikipedia, wordnet, and geonames. In: International semantic web conference, Springer, pp 177–185

Rescigno AA, Vanmassenhove E, Monti J, Way A (2020) A case study of natural gender phenomena in translation. A comparison of Google Translate, Bing Microsoft Translator and DeepL for English to Italian, French and Spanish. In: CLiC-it

Richards C, Bouman WP, Seal L, Barker MJ, Nieder TO, T’Sjoen G (2016) Non-binary or genderqueer genders. Int Rev Psychiatry 28(1):95–102

Ringler D, Paulheim H (2017) One knowledge graph to rule them all? Analyzing the differences between DBpedia, YAGO, Wikidata & co. In: Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz), Springer, pp 366–372

Rocktäschel T, Grefenstette E, Hermann KM, Kočiskỳ T, Blunsom P (2015) Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664

Rothman D (2021) Transformers for Natural Language Processing: build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more. Packt Publishing Ltd

Sakamoto A (2019) Unintended consequences of translation technologies: from project managers’ perspectives. Perspectives 27(1):58–73

Sara Stymne GT (2022) Phrase Based Machine Translation. Tutorial https://cl.lingfil.uu.se/kurs/MT19/slides/pbsmt.pdf . Accessed 05 Apr 2022

Saunders D, Sallis R, Byrne B (2020) Neural machine translation doesn’t translate gender coreference right unless you make it. In: Proceedings of the second workshop on gender bias in natural language processing, pp 35–43

Savoldi B, Gaido M, Bentivogli L, Negri M, Turchi M (2021) Gender bias in machine translation. Trans Assoc Comput Linguist 9:845–874

Schiebinger L (2014) Scientific research must take gender into account. Nature 507(7490):9–9

Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

Schwenk H, Chaudhary V, Sun S, Gong H, Guzmán F (2019) Wikimatrix: Mining 135m parallel sentences in 1620 language pairs from wikipedia. arXiv preprint arXiv:1907.05791

Sennrich R, Haddow B, Birch A (2015) Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709

Sennrich R, Haddow B, Birch A (2015) Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909

Shannon CE, Weaver W (1949) The mathematical theory of information. University of Illinois Press

Sharma S, Sharma S, Athaiya A (2017) Activation functions in neural networks towards data science 6(12):310–316

Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le Q, Hinton G, Dean J (2017) Outrageously large neural networks: the sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538

Shieber SM, Schabes Y (1990) Synchronous tree-adjoining grammars. In: Proceedings of the 13th conference on Computational linguistics-Volume 3, pp 253–258

Shieber SM, Schabes Y (1991) Generation and synchronous tree-adjoining grammars. Comput Intell 7(4):220–228

Singh SP, Kumar A, Darbari H, Singh L, Rastogi A, Jain S (2017) Machine translation using deep learning: An overview. In: 2017 international conference on computer, communications and electronics (comptelix), IEEE, pp 162–167

Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of association for machine translation in the Americas, vol 200

Somers H (2005) Round-trip translation: What is it good for? Proc Austral Lang Technol Workshop 2005:127–133

Stanovsky G, Smith NA, Zettlemoyer L (2019) Evaluating gender bias in machine translation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, pp 1679–1684, https://doi.org/10.18653/v1/P19-1164 , https://aclanthology.org/P19-1164

Stasimioti M, Sosoni V, Kermanidis KL, Mouratidis D (2020) Machine Translation Quality: a comparative evaluation of SMT, NMT and tailored-NMT outputs. In: Proceedings of the 22nd annual conference of the European Association for Machine Translation, pp 441–450

Staudemeyer RC, Morris ER (2019) Understanding LSTM–a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586

Steinberger R, Pouliquen B, Widiger A, Ignat C, Erjavec T, Tufis D, Varga D (2006) The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. arXiv preprint arXiv:cs/0609058

Strubell E, Ganesh A, McCallum A (2019) Energy and Policy Considerations for Deep Learning in NLP. In: Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pp 3645–3650

Sun T, Gaut A, Tang S, Huang Y, ElSherief M, Zhao J, Mirza D, Belding E, Chang KW, Wang WY (2019) Mitigating gender bias in natural language processing: literature review. In: Proceedings of the 57th annual meeting of the Association for Computational Linguistics, pp 1630–1640

Sun T, Shah A, Webster K, Johnson M (eds) (2021) They, them, theirs: rewriting with gender-neutral English, arXiv:2102.06788

Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

Tixier AJP (2018) Notes on deep learning for nlp. arXiv preprint arXiv:1808.09772

Toma P (1977) Systran as a multilingual machine translation system. In: Proceedings of the Third European Congress on Information Systems and Networks, overcoming the language barrier, pp 569–581

Tomalin M, Byrne B, Concannon S, Saunders D, Ullmann S (2021) The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing. Ethics Inf Technol 23(3):419–433

Ullmann S (2022) Gender bias in machine translation systems. In: Artificial Intelligence and Its Discontents, Springer, pp 123–144

United Nations Parallel Corpus (2022) https://conferences.unite.un.org/uncorpus . Accessed 30 Sep 2022

van Wynsberghe A (2021) Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics (2021). https://doi.org/10.1007/s43681-021-00043-6

Vandeghinste V, Martens S, Kotzé G, Tiedemann J, Van den Bogaert J, De Smet K, Van Eynde F, Van Noord G (2013) Parse and corpus-based machine translation. Essential Speech and Language Technology for Dutch. Springer, Berlin, pp 305–319

Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:1

Vieira LN, O’Hagan M, O’Sullivan C (2021) Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases. Inf Commun Soc 24(11):1515–1532

Wahler ME (2018) A word is worth a thousand words: legal implications of relying on machine translation technology. Stetson L Rev 48:109

Wang S (2005) Computers and translation: a translator’s guide. Language 81(2):544–545

Wang W, Pan SJ, Dahlmeier D, Xiao X (2017) Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Thirty-First AAAI Conference on Artificial Intelligence

Weidinger L, Mellor J, Rauh M, Griffin C, Uesato J, Huang PS, Cheng M, Glaese M, Balle B, Kasirzadeh A et al (2021) Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359

White JS (1995) Approaches to black box MT evaluation. Citeseer, proceedings of machine translation summit V, vol 10

Wiki (2019) Automatic Language Processing Advisory Committee. https://en.wikipedia.org/wiki/ALPAC . Accessed 08 Apr 2022

Wiki (2019) Long-short term memory. https://en.wikipedia.org/wiki/Long_short-term_memory . Accessed 08 Apr 2022

Wikimatrix (2022) Mining 135m parallel sentences in 1620 language pairs from wikipedia. https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix . Accessed 30 Sep 2022

Wong YW, Mooney R (2007) Learning synchronous grammars for semantic parsing with lambda calculus. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp 960–967

Wu D (1997) Stochastic inversion transduction grammars and bilingual parsing of parallel corpora. Comput Linguist 23(3):377–403

Wu H, Wang H (2007) Pivot language approach for phrase-based statistical machine translation. Mach Trans 21(3):165–181

Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K et al (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144

Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057

Yamada M (2019) The impact of Google Neural Machine Translation on Post-editing by student translators. J Special Trans 31:87–106

Yamada K, Knight K (2001) A syntax-based statistical translation model. In: Proceedings of the 39th annual meeting of the Association for Computational Linguistics, pp 523–530

Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 1480–1489

Yang B, Wang L, Wong DF, Chao LS, Tu Z (2019) Convolutional Self-Attention Networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp 4040–4045

Yun Huang QL Yang Liu (2008) Introduction on machine translation evaluation

Zakir HM, Nagoor MS (2017) A brief study of challenges in machine translation. Int J Comput Sci Issues 14(2):54

Zhang D, Li M, Li CH, Zhou M (2007) Phrase reordering model integrating syntactic knowledge for SMT. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp 533–540

Zhao Y, Xiang L, Zhu J, Zhang J, Zhou Y, Zong C (2020) Knowledge graph enhanced neural machine translation via multi-task learning on sub-entity granularity. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 4495–4505

Zhao Y, Zhang J, Zhou Y, Zong C (2020) Knowledge graphs enhanced neural machine translation. In: IJCAI, pp 4039–4045

Zhou J, Cao Y, Wang X, Li P, Xu W (2016) Deep recurrent models with fast-forward connections for neural machine translation. Trans Assoc Comput Linguist 4:371–383

Zhou W, Ge T, Mu C, Xu K, Wei F, Zhou M (2019) Improving grammatical error correction with machine translation pairs. arXiv preprint arXiv:1911.02825

Ziemski M, Junczys-Dowmunt M, Pouliquen B (2016) The united nations parallel corpus v1. 0. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pp 3530–3534

Zollmann A, Venugopal A, Och FJ, Ponte J (2008) A Systematic Comparison of Phrase-Based, Hierarchical and Syntax-Augmented Statistical MT. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pp 1145–1152

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Acknowledgements

The authors would like to thank the anonymous reviewers for their quality reviews and suggestions. This work was supported in part by The Science and Technology Development Fund of Macao, Macao SAR, China under Grant 0033/2022/ITP and in part by The Faculty Research Grant Projects of Macau University of Science and Technology, Macao SAR, China under Grant FRG-22-020-FI.

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Thang Luong's thesis on Neural Machine Translation

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Thang luong's thesis on neural machine translation.

This repository contains the latest version of my thesis .

Motivated by my advisor (Chris Manning)'s suggestion on extreme openness , I started sharing my thesis writing since day 1. While it is not yet clear how useful it is (smile), you can at least find all of the edits made by my advisor, page by page here . Thanks Chris!

Code, data, and models described in this thesis can be found at our Stanford NMT Project Page .

To save your reading time, let me highlight sections that I have put effort in writing beside my published papers:

• Chapter 7 - Conclusion : this will give you a big picture of what I have achieved in my dissertation and how it influences subsequent work.
• Chapter 6 - NMT future (especially section 6.3 - Future Outlook ): this is where I highlight potential research directions and speculate on the future.

Additionally, one can also read:

• Chapter 2 - Background : this section, I hope, will be useful for any reader who wants to implement NMT by hand like me where I give details on things like derivations for LSTM gradients.
• Chapter 1 - Introduction : I hope to enlighten readers with the history of MT from the 17th century until now.

Acknowledgments

Thanks Gabor Angeli for sharing the thesis setup!

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David Guggenheim

Lecturer, Program Coordinator, Academic Advisor, Environmental Programs (Advanced Academic Programs) 

David Guggenheim is a marine scientist, conservation policy specialist, educator, award-winning author, ocean explorer and manned submersible pilot. He has worked in Cuba for more than 20 years, leading collaborative education, research and conservation efforts focused on coral reef ecosystems. 

Research Professor, Department of Cognitive Science 

John Hale’s expertise lies in computational linguistics. His research centers on language comprehension, asking questions such as how are we able to understand one another, just by hearing a sequence of words? He uses cognitive modeling, analyzing the human mind/brain via computer simulation.  

Simon D. Halliday

• Associate Research Professor and Associate Director at the Center for Economy and Society, SNF Agora Institute 
• Faculty affiliate of the Center for Advanced Study in the Behavioral Sciences, Stanford University (2023-2025) 

Simon Halliday conducts research in behavioral and experimental economics, with a focus on social preferences (reciprocity and social norms) and institutions (ratings, punishment, communication). He also does economics education, and he has co-authored (with Samuel Bowles) an intermediate-level microeconomics textbook: Microeconomics: Competition, Conflict, and Coordination (OUP, 2022). Halliday is also the co-leader of the enCOREage Project. In a new textbook for introductory economics students, Understanding the Economy , the enCOREage team will introduce content that draws students in because it addresses societal problems that students care about while building employability skills, belonging, and inclusion into the curriculum. 

Research Professor, Departments of Biophysics and Biology 

Yuan He is interested in understanding the molecular mechanisms by which large, multi-subunit complexes engage in DNA-centric processes. His research uses cryo-electron microscopy and other biophysical and biochemical approaches.  

William Y. C. Huang

Assistant Professor, Department of Biophysics  

William Huang is broadly interested in biochemical reactions at the cell membrane, especially those involved in signal transduction. The research combines optical methods and kinetic modeling to analyze biochemically reconstituted systems and living cells. The integrated approach has enabled the invention of extensive imaging-based membrane assays, oftentimes revealing unexpected dynamic characteristics unique to membrane signaling configurations. 

Paul Johnson  

Associate Research Professor, Department of Modern Languages and Literatures 

A specialist in 16th- and 17th-century Spanish literature, Paul Johnson draws on the history of emotion, the senses, the body, and performance in his research. His writing on race, gender, translation, and popular culture also places pre-modern Iberia into conversation with urgent contemporary debates, while crossing borders to encompass the Global Hispanophone and larger early modern world. 

Sujung Kim  

Associate Professor, Department of Anthropology 

Sujung Kim’s research focuses on the transnational interactions of Buddhist practices in East Asia by engaging a variety of networks that connect people, places, and praxis in the Buddhist world. After her first monograph, Shinra Myojin and Buddhist Networks of the East Asian “Mediterranean” (University of Hawaii Press, 2019), she is currently working on her second monograph, Korean Magical Medicine: Buddhist Healing Talismans in Choson Korea (under advance contract with the University of Hawai’i Press). 

Chris Kromphardt  

Lecturer, Assistant Program Director, Data Analytics and Policy (Advanced Academic Programs) 

Chris Kromphardt’s research focuses on how citizens view the performance and legitimacy of judicial institutions, how judicial institutions make decisions, and the design and evaluation of experiential learning activities. He has taught courses on public policy, research methods and research design, constitutional law, and American politics. 

Jason Ludden 

Jason Ludden’s research interests include rhetoric of science, environmental communication, composition pedagogy, and creative writing. His work focuses on the role of experts in public policy discourse, and he has examined how forest management discourse shapes public perception of environmental issues.  

Diego Luis     

Rohrbaugh Family Assistant Professor, Department of History 

Diego Luis is a historian of Latin America specializing in the connections between Mexico and the Philippines during the Manila galleon period (1565-1815). His work focuses on the global scope of the early modern Spanish empire by examining the movement of people across the Pacific Ocean and how that movement transformed societies at both the eastern and western termini of the galleon trade. 

Julie K. Lundquist    

Bloomberg Distinguished Professor of Atmospheric Science and Wind Energy, Departments of Earth and Planetary Sciences and Mechanical Engineering (Whiting School of Engineering) 

Julie Lundquist uses observational and computational approaches to understand the atmospheric boundary layer, with an emphasis on atmosphere-wind energy interactions. Her research engages in both the atmospheric influences on wind energy production as well as the atmospheric consequences of wind energy deployment.

Vikash Morar

Lecturer, Biotechnology (Advanced Academic Programs) 

Vikash Morar has a background in research in clinical diagnostics, cellular neuroscience, and machine learning. His interest in learning as an abstraction brought him toward pedagogy, where he has extensively trained and worked to improve student outcomes in the classroom.

Marcelo Nogueira  

Assistant Professor of Spanish and Portuguese, Department of Modern Languages and Literatures

Marcelo Nogueira’s research focuses on modern and contemporary Latin American poetry, popular music, and visual culture, with a special emphasis on Brazil. Drawing on literary history, media theory, sound studies, and ethnomusicology, he investigates the Latin American avant-gardes, Brazilian modernism, concrete poetry, and the art of songwriting. He earned his PhD in Romance Studies from Duke University in 2022. 

Danielle Norcini  

Assistant Professor, William H. Miller III Department of Physics and Astronomy 

Danielle Norcini’s research interests are in particle physics and cosmology. Her main focus is building detectors to discover dark matter and measure neutrinos. Currently, her group is developing single-electron sensors called skipper CCDs to make precision measurements of particle interactions at very low energy thresholds.

Grace Panetti   

Assistant Professor, Department of Chemistry 

Grace Panetti investigates challenges at the interface of photochemistry and inorganic chemistry. She uses inorganic synthesis coupled with photophysical techniques like transient absorption spectroscopy to investigate these new systems.

Mladen Petkov     

Lecturer, Communication (Advanced Academic Programs) 

Mladen Petkov is interested in journalistic roles and practices, media freedom, disinformation, and Artificial Intelligence. He has several years of newsroom experience in the United States and Bulgaria. 

Allison Pugh  

Research Professor, Department of Sociology 

Allison Pugh’s research speaks to central concerns in the sociology of gender, investigating how people forge connections and find meaning and dignity at work and at home. Her work uses qualitative methods to investigate how gender, race, and class inequalities affect the way people negotiate dignity and connection amidst socioeconomic trends such as rationalization, insecurity, and commodification.

Lakshmi Rajkumar   

Senior Lecturer, Program Coordinator, Lab Manager, Biotechnology (Advanced Academic Programs) 

Lakshmi Rajkumar is an alumni of Johns Hopkins University’s M.S. in Biotechnology program specializing in microbiology. She has contributed to cancer research at the University of Maryland’s Translational Core Lab and has worked in the industry as a food microbiologist.

Margaret Renwick    

Associate Research Professor, Department of Cognitive Science 

Margaret Renwick’s research incorporates variation into models of spoken language to answer questions about the nature of phonological contrast, the origins and realization of phonological patterns, regional accents of U.S. English, and linguistic change across generations.

Mona Khadem Sameni 

Senior Lecturer and Program Coordinator, Applied Economics (Advanced Academic Programs)  

Mona Khadem Sameni’s research focuses on health economics, health policy, healthcare systems, and labor economics. She has done studies on global disparities in the impacts of the COVID-19 pandemic on healthcare systems, the application of AI in healthcare worldwide, and the regulation of AI. 

Hale Sirin 

• Assistant Research Professor, Alexander Grass Humanities Institute – Center for Digital Humanities 
• Affiliated faculty, Center for Languages and Speech Processing

Hale Sirin’s research and teaching bring together computational and critical methods to explore questions about narrative, language, and translation across historic and modern languages. She teaches courses on computational methods in the humanities and narrative theory. Sirin’s work has appeared in numerous venues including the Alliance of Digital Humanities Organizations and has been published in technical conferences including the Association for Computational Linguistics.

Sarah Sowden  

Lecturer, Computational Biology (Advanced Academic Programs) 

Sarah Sowden has expertise in data science, research administration, and the ethical and legal implications of data practices. She lectures in bioinformatics and individualized genomics and health.  

Frederick Tan 

Assistant Research Professor, Biology 

Frederick Tan researches mechanisms to effectively train and support diverse populations in genomic data science. He focuses on strategies that increase persistence among undergraduates, graduate students, and faculty. Through collaborative educational programs, he aims to build inclusive communities that advance research using the latest genomic technologies.

Heiko ter Haseborg  

Associate Teaching Professor, Department of Modern Languages and Literatures 

Heiko ter Haseborg’s research interests include curriculum design, curriculum evaluation, assessment, world language pedagogy, teacher, and learner role in language education, second language acquisition, and metacognition.   

Karina A. Vado 

Senior Lecturer, Medicine, Science, and the Humanities Program 

Karina Vado’s research lies at the intersections of Latinx and Latin American literary and cultural studies, science and technology studies, and science fiction studies. She is currently working on her first book project, Latinx DNA: Race, Latinidad, and Gene(ome ). In it, she interrogates scientized representations of Latinx identity across multiple cultural forms—popular science writing, music, speculative memoirs, documentary film, and visual art—and considers the vexed de- and re-codings of Latinidad that these “texts” forward or foreclose. 

Jasmina Wiemann

Assistant Professor, Department of Earth and Planetary Sciences 

Jasmina Wiemann’s research develops new chemical methods and instrument components that comparatively probe biologically and geologically informative patterns in the macromolecular composition of modern and fossil organismal tissues to characterize past, present, and predictable future interactions between life and its changing environments and translates these insights into bio-inspired climate solutions.

Senior Lecturer, Organizational Leadership (Advanced Academic Programs) 

Frances Wu’s research focuses on cross-cultural leadership for global challenges and community-engaged pedagogies for leadership education. She has been awarded research grants on leadership for sustainability in business education in Singapore, higher education innovations in the U.S., and change processes for joint-venture universities between the U.S. and other countries.