For a reading list, see The Machine Translation Reading List

• Philipp Koehn's 2017 Draft Chapter on NMT
• Philipp Koehn's draft book on NMT
• Philipp Koehn's 2020 Book - Neural Machine Translation
• Stahlberg 2019 - Neural Machine Translation: A Review and Survey
• Yang et al 2020 - A Survey of Deep Learning Techniques for Neural Machine Translation
• Tan et al 2020 - Neural Machine Translation: A Review of Methods, Resources, and Tools

• Bahdanau et al 2014 - Neural Machine Translation by Jointly Learning to Align and Translate
• Sennrich et al 2016 - Neural Machine Translation of Rare Words with Subword Units
• Vaswani et al 2017 - Attention Is All You Need

• Wu et al 2016 - Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Levin et al 2017 - Toward a Full-Scale Neural Machine Translation in Production: The Booking.com Use Case
• Britz et al 2017 - Massive Exploration of Neural Machine Translation Architectures
• Arivazhagan et al 2019 - Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges See the list open problems. From Google's MT team, did they deploy this?
• Hassan et al 2018 - Achieving Human Parity on Automatic Chinese to English News Translation
• Costa-jussà et al 2022 - No Language Left Behind: Scaling Human-Centered Machine Translation blog website model demo Transformer encoder-decoder model with sparsely gated mixture of experts. 50B params, and also distilled versions.

• Denkowski & Neubig et al 2017 - Stronger Baselines for Trustable Results in Neural Machine Translation

• Sennrich & Haddow 2016 - Linguistic Input Features Improve Neural Machine Translation
• Nadejde et al 2017 - Predicting Target Language CCG Supertags Improves Neural Machine Translation
• Currey & Heafield 2018 - Multi-Source Syntactic Neural Machine Translation

Multilingual translation is where you build a system to translate between many language pairs (rather than just one).

• Aharoni et al 2019 - Massively Multilingual Neural Machine Translation
• Tang et al 2020 - Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

• Zoph et al 2016 - Transfer Learning for Low-Resource Neural Machine Translation
• Gu et al 2018 - Universal Neural Machine Translation for Extremely Low Resource Languages
• Comparision of SMT vs NMT for low-resource MT
  • Sennrich & Zhang 2019 - Revisiting Low-Resource Neural Machine Translation: A Case Study
  • Duh et al 2020 - Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages
• Costa-jussà et al 2022 - No Language Left Behind: Scaling Human-Centered Machine Translation blog website model demo Transformer encoder-decoder model with sparsely gated mixture of experts. 50B params, and also distilled versions.

• Cherry et al 2018 - Revisiting Character-Based Neural Machine Translation with Capacity and Compression

See also Domain Adaptation.

• Surveys
  • Chu & Wang 2018 - A Survey of Domain Adaptation for Neural Machine Translation
• Thompson et al 2019 - Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation

• Qi et al 2018 - When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
• Tang et al 2020 - Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

• Luo et al 2019 - Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
• Marchisio et al 2020 - When Does Unsupervised Machine Translation Work?
• Marchisio et al 2021 - What Can Unsupervised Machine Translation Contribute to High-Resource Language Pairs?

Before an MT system can be trained, the sentences in the parallel documents need to be aligned to create sentence pairs.

• Thompson & Koehn 2019 - Vecalign: Improved Sentence Alignment in Linear Time and Space
• Mining parallel sentences
  • Some of these methods can be used to mine parallel sentences from large collections of documents
  • Laser
    • Artetxe & Schwenk 2019 - Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond github Used in NLLB
    • Heffernan et al 2022 - Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages

See also Statistical Machine Translation. Recent papers related to SMT:

• 2019 - A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions

For an overview, see Evaluating MT Systems.

See also the metrics task at WMT every year which does a correlation with human evaluations.

• BLEU: Papineni et al 2002 - BLEU: a Method for Automatic Evaluation of Machine Translation
• METEOR: Banerjee & Lavie 2005 - METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments and Lavie & Agarwal 2007, download
• TER: A Study of Translation Edit Rate with Targeted Human Annotation
• Multi-lingual METEOR: Denkowski & Lavie 2014 - Meteor Universal: Language Specific Translation Evaluation for Any Target Language, download
• chrF: Popovic 2015 - chrF: character n-gram F-score for automatic MT evaluation
• chrF++: Popovic 2017 - chrF++: words helping character n-grams, github
• Post 2018 - A Call for Clarity in Reporting BLEU Scores
• BERTscore: Zhang et al 2019 - BERTScore: Evaluating Text Generation with BERT
• Freitag et al 2020 - BLEU might be Guilty but References are not Innocent
• Marie et al 2021 - Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

Note that BLEU is a corpus-level metric, and that averaging BLEU scores computed at the sentence level will not give the same result as corpus-level BLEU. Corpus-level BLEU is the standard one reported in papers.

Notes: To assess length effects (translations being too short), people often report the brevity penalty, BP computed when calculating BLEU. Most BLEU evaluation scripts report this number as BP = .

• SacreBLEU (recommended) paper
  • If you want to simulate SacreBLEU evaluation, but with statistical significance, you can use the mteval-v13a.pl script to tokenize your output and references, and then use MultEval
• Compare-MT Can analyze the differences between two systems and compute statistical significance. paper
• Historical: Moses's multi-bleu.pl
• Jon Clark's MultEval Does automatic boostrap resampling to compute statistical significance. paper

• WMT 2014 En-Fr, etc
• WMT 2016
  • Nice scripts to download and preprocess: wmt16_en_de.sh
• Opus - The Open Parallel Corpus

• IWSLT 2013 MT Datasets English-French (200K sentence pairs), used for example here.
• IWSLT 2014 English-German (160K sentence pairs), used for example here.
• Malagasy-English dataset (80K sentence pairs) Malagasy is a morphologically rich language (WARNING: hasn't been used in a while, no recent neural models to compare to)

• Guzmán et al 2019 dataset Four language pairs: Nepali-English, Sinhala-English, Khmer-English, Pashto-English
• Malagasy-English dataset (Jeff recommends)
• LDMT MURI Data (ask Jeff for it, he has access)
• Flores-101 dataset Paper: Goyal et al 2021 3001 sentences translated into 101 languages
• Flores-200 dataset Paper: Costa-jussà et al 2022
• Cherokee-English dataset Recommended (recent, 2020)

• El-Kishky et al 2019 - CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

See also Tan 2020.

• FairSeq
• OpenNMT
• Sockeye
• Nematus

• Conferences and Workshops
  • WMT (Workshop on Machine Translation, now Conference on Machine Translation)
• Books
  • Koehn 2020 - Neural Machine Translation
• Wikis
  • MT Research Survey Wiki Covers neural methods as well
• Bibliographies
  • MT Reading List (constantly updated)

• Wilker Aziz
• David Chiang
• Orhan Firat
• Kenneth Heafield
• Kevin Knight
• Philipp Koehn
• Graham Neubig
• Matt Post
• Rico Sennrich

• Cross-Lingual Transfer
• Multilinguality
• Noisy Channel Model
• Seq2seq
• Software
• Statistical Machine Translation