---
title: "Improved Lexically Constrained Decoding for — Hu 2019"
description: "Lexically constrained decoding forces generated text to include certain words or phrases (positive constraints) or to avoid them (negative constraints)."
authors: ["J. Edward Hu", "Huda Khayrallah", "Ryan Culkin", "Patrick Xia", "Tongfei Chen", "Matt Post", "Benjamin Van Durme"]
year: 2019
venue: "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)"
source: "https://aclanthology.org/N19-1090/"
doi: "10.18653/v1/n19-1090"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hu-2019-improved-lexically-constrained-decoding-translation/"
---
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# Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting

**Authors:** [J. Edward Hu](https://md.research.elamur.ai/free/en/authors/j-edward-hu/), [Huda Khayrallah](https://md.research.elamur.ai/free/en/authors/huda-khayrallah/), [Ryan Culkin](https://md.research.elamur.ai/free/en/authors/ryan-culkin/), [Patrick Xia](https://md.research.elamur.ai/free/en/authors/patrick-xia/), [Tongfei Chen](https://md.research.elamur.ai/free/en/authors/tongfei-chen/), [Matt Post](https://md.research.elamur.ai/free/en/authors/matt-post/), [Benjamin Van Durme](https://md.research.elamur.ai/free/en/authors/benjamin-van-durme/) · **Year:** 2019 · **Venue:** 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) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N19-1090/) · [PDF](https://aclanthology.org/N19-1090.pdf) · [DOI 10.18653/v1/n19-1090](https://doi.org/10.18653/v1/n19-1090) · [OpenAlex](https://openalex.org/W2945735543)

**Citations:** 113 citations (openalex-snapshot · 2026-10-14) · [85](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Lexically constrained decoding forces generated text to include certain words or phrases (positive constraints) or to avoid them (negative constraints). Earlier methods were slow, and the dynamic beam allocation (DBA) algorithm of Post and Vilar (2018) processed the sentences in a batch one after another. Hu et al. vectorize DBA so that constraint handling runs across a whole batch at once. They also replace the flat-array bookkeeping with multi-state tries, which fix errors that arise when constraints overlap or share prefixes. Decoding with positive constraints becomes about five times faster, and constraints are placed better, with BLEU gains of almost 2 points.

Using the faster decoder, the authors train an improved monolingual sentential rewriter based on PARABANK and release it. They then use it for data augmentation with simple constraint heuristics, either adding paraphrased training data or aggregating predictions over paraphrased test inputs. This yields gains on NLI, QA and MT, and some of the gains add to those from ELMo. The work makes it practical to try many task-specific rewriting strategies.

## Key points
- **Vectorized dynamic beam allocation (VDBA):** the method puts candidate hypotheses in one matrix and sorts them by sentence, number of unmet constraints and score. This makes batched constrained decoding about 5× faster than the sequential approach of Post and Vilar (2018).
- **Trie-based constraint tracking:** counters on end nodes record how many times each constraint must still appear. Multiple states let the decoder notice constraints completed unexpectedly, such as ones with a shared prefix or a non-prefix substring.
- **Released rewriter:** a lexically constrained monolingual rewriter, with augmented data files, is available at nlp.jhu.edu/parabank. Human judges rated it higher than the original PARABANK rewriter for semantic similarity and fluency.
- **QA (TREC-QA, InferSent-style model):**
  - Constraint heuristics kept shared named entities and answer spans, and banned top-k IDF words (k = 2 worked best).
  - Voting over paraphrased candidates raised MAP and MRR in every setting. With ELMo, voting alone raised MAP/MRR from 77.49/81.86 to 80.61/85.65.
  - Adding paraphrases to the training data did not help.
- **MT (WMT 2016 Turkish–English):**
  - Pre-training on half-paraphrased data and then continuing on the original data gave +1.1 BLEU.
  - English→Turkish showed no gain.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

85 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 81 not yet.

- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/)
- [How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
- [Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://md.research.elamur.ai/free/en/papers/thompson-2020-paraphrase-generation-as-zero-shot/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hu2019improved,
  title = {{Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting}},
  author = {Hu, J. Edward and Khayrallah, Huda and Culkin, Ryan and Xia, Patrick and Chen, Tongfei and Post, Matt and Durme, Benjamin Van},
  booktitle = {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)},
  year = {2019},
  doi = {10.18653/v1/n19-1090},
  url = {https://doi.org/10.18653/v1/n19-1090}
}
```

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## References

47 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Charles Chen and Razvan Bunescu. 2017. An exploration of data augmentation and rnn architectures for question ranking in community question answering. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 442–447, Taipei, Taiwan. A… [find](https://scholar.google.com/scholar?q=Charles+Chen+and+Razvan+Bunescu%2E+2017%2E+An+exploration+of+data+augmentation+and+rnn+architectures+for+question+ranking+in+community+question+answering%2E+In+Proceedings+of+the+Eighth+International+Joint+)
2. Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018. Generating natural language adversarial examples. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2890–2896, Brussels, Belgium. Association for Comp… [find](https://scholar.google.com/scholar?q=Moustafa+Alzantot%2C+Yash+Sharma%2C+Ahmed+Elgohary%2C+Bo%2DJhang+Ho%2C+Mani+Srivastava%2C+and+Kai%2DWei+Chang%2E+2018%2E+Generating+natural+language+adversarial+examples%2E+In+Proceedings+of+the+2018+Conference+on+Empiri) [our card](https://md.research.elamur.ai/free/en/papers/alzantot-2018-generating-natural-language-adversarial-examples/)
3. Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes. 2017. Supervised learning of universal sentence representations from natural language inference data. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 670–680. [find](https://scholar.google.com/scholar?q=Alexis+Conneau%2C+Douwe+Kiela%2C+Holger+Schwenk%2C+Lo%C3%AFc+Barrault%2C+and+Antoine+Bordes%2E+2017%2E+Supervised+learning+of+universal+sentence+representations+from+natural+language+inference+data%2E+In+Proceedings+of+) [our card](https://md.research.elamur.ai/free/en/papers/conneau-2017-supervised-learning-universal-sentence-representations/)
4. Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017. Guided open vocabulary image captioning with constrained beam search. In Proceedings of EMNLP 2017, pages 936–945, Copenhagen, Denmark. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Peter+Anderson%2C+Basura+Fernando%2C+Mark+Johnson%2C+and+Stephen+Gould%2E+2017%2E+Guided+open+vocabulary+image+captioning+with+constrained+beam+search%2E+In+Proceedings+of+EMNLP+2017%2C+pages+936%E2%80%93945%2C+Copenhagen%2C+D) [our card](https://md.research.elamur.ai/free/en/papers/anderson-2017-guided-open-vocabulary-image-captioning/)
5. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805. [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2018%2E+Bert%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+CoRR%2C+abs%2F1810%2E04805%2E)
6. Ondřej Bojar, Ondřej Dušek, Tom Kocmi, Jindřich Libovický, Michal Novák, Martin Popel, Roman Sudarikov, and Dušan Variš. 2016a. CzEng 1.6: Enlarged Czech-English Parallel Corpus with Processing Tools Dockered. In Text, Speech, and Dialogue: 19th International Conference, TSD 2016, number 9924 in… [find](https://scholar.google.com/scholar?q=Ondr%CC%8Cej+Bojar%2C+Ondr%CC%8Cej+Du%C5%A1ek%2C+Tom+Kocmi%2C+Jindr%CC%8Cich+Libovick%C3%BD%2C+Michal+Nov%C3%A1k%2C+Martin+Popel%2C+Roman+Sudarikov%2C+and+Du%C5%A1an+Vari%C5%A1%2E+2016a%2E+CzEng+1%2E6%3A+Enlarged+Czech%2DEnglish+Parallel+Corpus+with+Processing+Too)
7. Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018. Understanding back-translation at scale. In Proceedings of EMNLP 2018, pages 489– 500. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sergey+Edunov%2C+Myle+Ott%2C+Michael+Auli%2C+and+David+Grangier%2E+2018%2E+Understanding+back%2Dtranslation+at+scale%2E+In+Proceedings+of+EMNLP+2018%2C+pages+489%E2%80%93+500%2E+Association+for+Computational+Linguistics%2E)
8. Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017. Data augmentation for low-resource neural machine translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 567– 573, Vancouver, Canada. Association for Computation… [find](https://scholar.google.com/scholar?q=Marzieh+Fadaee%2C+Arianna+Bisazza%2C+and+Christof+Monz%2E+2017%2E+Data+augmentation+for+low%2Dresource+neural+machine+translation%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computational+) [our card](https://md.research.elamur.ai/free/en/papers/fadaee-2017-data-augmentation-low-resource-neural/)
9. Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018. Findings of the 2018 conference on machine translation (WMT18). In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 272–303, Belgium, Brussels. A… [find](https://scholar.google.com/scholar?q=Ondr%CC%8Cej+Bojar%2C+Christian+Federmann%2C+Mark+Fishel%2C+Yvette+Graham%2C+Barry+Haddow%2C+Philipp+Koehn%2C+and+Christof+Monz%2E+2018%2E+Findings+of+the+2018+conference+on+machine+translation+%28WMT18%29%2E+In+Proceedings+of+) [our card](https://md.research.elamur.ai/free/en/papers/bojar-2018-findings-2018-conference-machine-translation/)
10. Marzieh Fadaee and Christof Monz. 2018. Back-translation sampling by targeting difficult words in neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 436–446. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Marzieh+Fadaee+and+Christof+Monz%2E+2018%2E+Back%2Dtranslation+sampling+by+targeting+difficult+words+in+neural+machine+translation%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Lang)
11. Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi,… [find](https://scholar.google.com/scholar?q=Ondr%CC%8Cej+Bojar%2C+Rajen+Chatterjee%2C+Christian+Federmann%2C+Yvette+Graham%2C+Barry+Haddow%2C+Matthias+Huck%2C+Antonio+Jimeno+Yepes%2C+Philipp+Koehn%2C+Varvara+Logacheva%2C+Christof+Monz%2C+Matteo+Negri%2C+Aurelie+Neveol%2C+M) [our card](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/)
12. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2013. Ppdb: The paraphrase database. In Proceedings NAACL-HLT 2013, pages 758–764. [find](https://scholar.google.com/scholar?q=Juri+Ganitkevitch%2C+Benjamin+Van+Durme%2C+and+Chris+Callison%2DBurch%2E+2013%2E+Ppdb%3A+The+paraphrase+database%2E+In+Proceedings+NAACL%2DHLT+2013%2C+pages+758%E2%80%93764%2E) [our card](https://md.research.elamur.ai/free/en/papers/ganitkevitch-2013-ppdb-paraphrase-database/)
13. Alex Graves and Jürgen Schmidhuber. 2005. Frame-wise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks, 18(5-6):602–610. [find](https://scholar.google.com/scholar?q=Alex+Graves+and+J%C3%BCrgen+Schmidhuber%2E+2005%2E+Frame%2Dwise+phoneme+classification+with+bidirectional+lstm+and+other+neural+network+architectures%2E+Neural+Networks%2C+18%285%2D6%29%3A602%E2%80%93610%2E)
14. Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, and Berlin Chen. 2019. Looking for ELMo’s friends: Sentence-level pretraining be… [find](https://scholar.google.com/scholar?q=Samuel+R%2E+Bowman%2C+Ellie+Pavlick%2C+Edouard+Grave%2C+Benjamin+Van+Durme%2C+Alex+Wang%2C+Jan+Hula%2C+Patrick+Xia%2C+Raghavendra+Pappagari%2C+R%2E+Thomas+McCoy%2C+Roma+Patel%2C+Najoung+Kim%2C+Ian+Tenney%2C+Yinghui+Huang%2C+Kather)
15. Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016. Incorporating copying mechanism in sequence-to-sequence learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1631–1640, Berlin, Germany. Association for Computat… [find](https://scholar.google.com/scholar?q=Jiatao+Gu%2C+Zhengdong+Lu%2C+Hang+Li%2C+and+Victor+O%2EK%2E+Li%2E+2016%2E+Incorporating+copying+mechanism+in+sequence%2Dto%2Dsequence+learning%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computati) [our card](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/)
16. Chris Callison-Burch, Philipp Koehn, Christof Monz, and Josh Schroeder. 2009. Findings of the 2009 Workshop on Statistical Machine Translation. In Proceedings of the Fourth Workshop on Statistical Machine Translation, pages 1–28, Athens, Greece. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Chris+Callison%2DBurch%2C+Philipp+Koehn%2C+Christof+Monz%2C+and+Josh+Schroeder%2E+2009%2E+Findings+of+the+2009+Workshop+on+Statistical+Machine+Translation%2E+In+Proceedings+of+the+Fourth+Workshop+on+Statistical+Mac) [our card](https://md.research.elamur.ai/free/en/papers/burch-2009-findings-2009-workshop-statistical-machine/)
17. Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post. 2017. Sockeye: A toolkit for neural machine translation. CoRR, abs/1712.05690. pages 110–119, San Diego, California. Association for Computational Linguistics. — [arXiv:1712.05690](https://arxiv.org/abs/1712.05690) [find](https://scholar.google.com/scholar?q=Felix+Hieber%2C+Tobias+Domhan%2C+Michael+Denkowski%2C+David+Vilar%2C+Artem+Sokolov%2C+Ann+Clifton%2C+and+Matt+Post%2E+2017%2E+Sockeye%3A+A+toolkit+for+neural+machine+translation%2E+CoRR%2C+abs%2F1712%2E05690%2E+pages+110%E2%80%93119%2C+Sa)
18. Chris Hokamp and Qun Liu. 2017. Lexically constrained decoding for sequence generation using grid beam search. In Proceedings of ACL, pages 1535– 1546, Vancouver, Canada. [find](https://scholar.google.com/scholar?q=Chris+Hokamp+and+Qun+Liu%2E+2017%2E+Lexically+constrained+decoding+for+sequence+generation+using+grid+beam+search%2E+In+Proceedings+of+ACL%2C+pages+1535%E2%80%93+1546%2C+Vancouver%2C+Canada%2E) [our card](https://md.research.elamur.ai/free/en/papers/hokamp-2017-lexically-constrained-decoding-sequence-generation/)
19. Dekang Lin and Patrick Pantel. 2001. Dirt @sbt@discovery of inference rules from text. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’01, pages 323–328, New York, NY, USA. ACM. [find](https://scholar.google.com/scholar?q=Dekang+Lin+and+Patrick+Pantel%2E+2001%2E+Dirt+%40sbt%40discovery+of+inference+rules+from+text%2E+In+Proceedings+of+the+Seventh+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+and+Data+Mining%2C+KDD+%E2%80%9901)
20. Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018. Sequence-to-sequence data augmentation for dialogue language understanding. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1234–1245, Santa Fe, New Mexico, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yutai+Hou%2C+Yijia+Liu%2C+Wanxiang+Che%2C+and+Ting+Liu%2E+2018%2E+Sequence%2Dto%2Dsequence+data+augmentation+for+dialogue+language+understanding%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational)
21. Minh-Thang Luong and Christopher D. Manning. 2015. Stanford Neural Machine Translation Systems for Spoken Language Domain. In International Workshop on Spoken Language Translation, Da Nang, Vietnam. [find](https://scholar.google.com/scholar?q=Minh%2DThang+Luong+and+Christopher+D%2E+Manning%2E+2015%2E+Stanford+Neural+Machine+Translation+Systems+for+Spoken+Language+Domain%2E+In+International+Workshop+on+Spoken+Language+Translation%2C+Da+Nang%2C+Vietnam%2E)
22. J. Edward Hu, Rachel Rudinger, Matt Post, and Benjamin Van Durme. 2019. P ARA B ANK : Monolingual bitext generation and sentential paraphrasing via lexically-constrained neural machine translation. AAAI. [find](https://scholar.google.com/scholar?q=J%2E+Edward+Hu%2C+Rachel+Rudinger%2C+Matt+Post%2C+and+Benjamin+Van+Durme%2E+2019%2E+P+ARA+B+ANK+%3A+Monolingual+bitext+generation+and+sentential+paraphrasing+via+lexically%2Dconstrained+neural+machine+translation%2E+AA)
23. Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2017. Paraphrasing revisited with neural machine translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 881–893, Valencia, Spain. Association for… [find](https://scholar.google.com/scholar?q=Jonathan+Mallinson%2C+Rico+Sennrich%2C+and+Mirella+Lapata%2E+2017%2E+Paraphrasing+revisited+with+neural+machine+translation%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+the+Association+fo) [our card](https://md.research.elamur.ai/free/en/papers/mallinson-2017-paraphrasing-revisited-neural-machine-translation/)
24. Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018. Adversarial example generation with syntactically controlled paraphrase networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vo… [find](https://scholar.google.com/scholar?q=Mohit+Iyyer%2C+John+Wieting%2C+Kevin+Gimpel%2C+and+Luke+Zettlemoyer%2E+2018%2E+Adversarial+example+generation+with+syntactically+controlled+paraphrase+networks%2E+In+Proceedings+of+the+2018+Conference+of+the+Nort) [our card](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
25. Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016. Natural language inference by tree-based convolution and heuristic matching. In Proc. ACL, page 130. [find](https://scholar.google.com/scholar?q=Lili+Mou%2C+Rui+Men%2C+Ge+Li%2C+Yan+Xu%2C+Lu+Zhang%2C+Rui+Yan%2C+and+Zhi+Jin%2E+2016%2E+Natural+language+inference+by+tree%2Dbased+convolution+and+heuristic+matching%2E+In+Proc%2E+ACL%2C+page+130%2E) [our card](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/)
26. Robin Jia and Percy Liang. 2016. Data recombination for neural semantic parsing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12–22, Berlin, Germany. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Robin+Jia+and+Percy+Liang%2E+2016%2E+Data+recombination+for+neural+semantic+parsing%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+pag)
27. Courtney Napoles, Chris Callison-Burch, and Matt Post. 2016. Sentential paraphrasing as black-box machine translation. In Proceedings of the NAACL 2016, pages 62–66, San Diego, California. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Courtney+Napoles%2C+Chris+Callison%2DBurch%2C+and+Matt+Post%2E+2016%2E+Sentential+paraphrasing+as+black%2Dbox+machine+translation%2E+In+Proceedings+of+the+NAACL+2016%2C+pages+62%E2%80%9366%2C+San+Diego%2C+California%2E+Association)
28. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [find](https://scholar.google.com/scholar?q=Diederik+P+Kingma+and+Jimmy+Ba%2E+2014%2E+Adam%3A+A+method+for+stochastic+optimization%2E+arXiv+preprint+arXiv%3A1412%2E6980%2E)
29. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Lin… [find](https://scholar.google.com/scholar?q=Kishore+Papineni%2C+Salim+Roukos%2C+Todd+Ward%2C+and+Wei%2DJing+Zhu%2E+2002%2E+BLEU%3A+a+method+for+automatic+evaluation+of+machine+translation%2E+In+Proceedings+of+40th+Annual+Meeting+of+the+Association+for+Computat) [our card](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
30. Sosuke Kobayashi. 2018. Contextual augmentation: Data augmentation by words with paradigmatic relations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 452–457, New Orle… [find](https://scholar.google.com/scholar?q=Sosuke+Kobayashi%2E+2018%2E+Contextual+augmentation%3A+Data+augmentation+by+words+with+paradigmatic+relations%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chapter+of+the+Association+for+Comp) [our card](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/)
31. Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolog… [find](https://scholar.google.com/scholar?q=Matthew+Peters%2C+Mark+Neumann%2C+Mohit+Iyyer%2C+Matt+Gardner%2C+Christopher+Clark%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2018%2E+Deep+contextualized+word+representations%2E+In+Proceedings+of+the+2018+Conference+of+t)
32. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open Source Toolkit for Statistical Machine Translation. In Proc… [find](https://scholar.google.com/scholar?q=Philipp+Koehn%2C+Hieu+Hoang%2C+Alexandra+Birch%2C+Chris+Callison%2DBurch%2C+Marcello+Federico%2C+Nicola+Bertoldi%2C+Brooke+Cowan%2C+Wade+Shen%2C+Christine+Moran%2C+Richard+Zens%2C+Chris+Dyer%2C+Ondrej+Bojar%2C+Alexandra+Consta)
33. Matt Post. 2018. A call for clarity in reporting BLEU scores. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186– 191, Belgium, Brussels. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Matt+Post%2E+2018%2E+A+call+for+clarity+in+reporting+BLEU+scores%2E+In+Proceedings+of+the+Third+Conference+on+Machine+Translation%3A+Research+Papers%2C+pages+186%E2%80%93+191%2C+Belgium%2C+Brussels%2E+Association+for+Computa) [our card](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
34. Matt Post and David Vilar. 2018. Fast lexically constrained decoding with dynamic beam allocation for neural machine translation. In Proceedings of NAACL 2018, pages 1314–1324, New Orleans, Louisiana. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Matt+Post+and+David+Vilar%2E+2018%2E+Fast+lexically+constrained+decoding+with+dynamic+beam+allocation+for+neural+machine+translation%2E+In+Proceedings+of+NAACL+2018%2C+pages+1314%E2%80%931324%2C+New+Orleans%2C+Louisiana%2E) [our card](https://md.research.elamur.ai/free/en/papers/post-2018-fast-lexically-constrained-decoding-dynamic/)
35. Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Jin… [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Michel+Galley%2C+Chris+Brockett%2C+Jianfeng+Gao%2C+and+Bill+Dolan%2E+2016%2E+A+diversity%2Dpromoting+objective+function+for+neural+conversation+models%2E+In+Proceedings+of+the+2016+Conference+of+the+North) [our card](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
36. Keisuke Sakaguchi and Benjamin Van Durme. 2018. Efficient online scalar annotation with bounded support. In Proceedings of ACL, pages 208–218, Melbourne, Australia. ACL. [find](https://scholar.google.com/scholar?q=Keisuke+Sakaguchi+and+Benjamin+Van+Durme%2E+2018%2E+Efficient+online+scalar+annotation+with+bounded+support%2E+In+Proceedings+of+ACL%2C+pages+208%E2%80%93218%2C+Melbourne%2C+Australia%2E+ACL%2E)
37. Jason Weston and Chris Watkins. 1999. Support vector machines for multi-class pattern recognition. In ESANN 1999, 7th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 21-23, 1999, Proceedings, pages 219–224. [find](https://scholar.google.com/scholar?q=Jason+Weston+and+Chris+Watkins%2E+1999%2E+Support+vector+machines+for+multi%2Dclass+pattern+recognition%2E+In+ESANN+1999%2C+7th+European+Symposium+on+Artificial+Neural+Networks%2C+Bruges%2C+Belgium%2C+April+21%2D23%2C+19)
38. Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073– 1083, Vancouver, Canada. Association for Compu… [find](https://scholar.google.com/scholar?q=Abigail+See%2C+Peter+J%2E+Liu%2C+and+Christopher+D%2E+Manning%2E+2017%2E+Get+to+the+point%3A+Summarization+with+pointer%2Dgenerator+networks%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computati)
39. John Wieting and Kevin Gimpel. 2018. ParaNMT-50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations. In Proceedings of ACL, pages 451–462. ACL. [find](https://scholar.google.com/scholar?q=John+Wieting+and+Kevin+Gimpel%2E+2018%2E+ParaNMT%2D50M%3A+Pushing+the+limits+of+paraphrastic+sentence+embeddings+with+millions+of+machine+translations%2E+In+Proceedings+of+ACL%2C+pages+451%E2%80%93462%2E+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/wieting-2018-paranmt-50m-pushing-limits-paraphrastic/)
40. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Pa… [find](https://scholar.google.com/scholar?q=Adina+Williams%2C+Nikita+Nangia%2C+and+Samuel+Bowman%2E+2018%2E+A+broad%2Dcoverage+challenge+corpus+for+sentence+understanding+through+inference%2E+In+Proceedings+of+the+2018+Conference+of+the+North+American+Chap)
41. Rico Sennrich and Barry Haddow. 2016. Linguistic input features improve neural machine translation. In Proceedings of the First Conference on Machine Translation, pages 83–91, Berlin, Germany. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Rico+Sennrich+and+Barry+Haddow%2E+2016%2E+Linguistic+input+features+improve+neural+machine+translation%2E+In+Proceedings+of+the+First+Conference+on+Machine+Translation%2C+pages+83%E2%80%9391%2C+Berlin%2C+Germany%2E+Associa) [our card](https://md.research.elamur.ai/free/en/papers/sennrich-2016-linguistic-input-features-improve-neural/)
42. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving neural machine translation models with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 86–96, Berlin, Germany. Association for Computational L… [find](https://scholar.google.com/scholar?q=Rico+Sennrich%2C+Barry+Haddow%2C+and+Alexandra+Birch%2E+2016%2E+Improving+neural+machine+translation+models+with+monolingual+data%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computationa)
43. Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016. Bidirectional attention flow for machine comprehension. CoRR, abs/1611.01603. [find](https://scholar.google.com/scholar?q=Min+Joon+Seo%2C+Aniruddha+Kembhavi%2C+Ali+Farhadi%2C+and+Hannaneh+Hajishirzi%2E+2016%2E+Bidirectional+attention+flow+for+machine+comprehension%2E+CoRR%2C+abs%2F1611%2E01603%2E) [our card](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
44. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Process… [find](https://scholar.google.com/scholar?q=Ashish+Vaswani%2C+Noam+Shazeer%2C+Niki+Parmar%2C+Jakob+Uszkoreit%2C+Llion+Jones%2C+Aidan+N+Gomez%2C+%C5%81ukasz+Kaiser%2C+and+Illia+Polosukhin%2E+2017%2E+Attention+is+all+you+need%2E+In+I%2E+Guyon%2C+U%2E+V%2E+Luxburg%2C+S%2E+Bengio%2C+H%2E+)
45. Mengqiu Wang, Noah A Smith, and Teruko Mitamura. 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). [find](https://scholar.google.com/scholar?q=Mengqiu+Wang%2C+Noah+A+Smith%2C+and+Teruko+Mitamura%2E+2007%2E+What+is+the+Jeopardy+model%3F+A+quasi%2Dsynchronous+grammar+for+QA%2E+In+Proceedings+of+the+2007+Joint+Conference+on+Empirical+Methods+in+Natural+Langu) [our card](https://md.research.elamur.ai/free/en/papers/wang-2007-what-is-jeopardy-model-quasi/)
46. Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018. Switchout: an efficient data augmentation algorithm for neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 856–861. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Xinyi+Wang%2C+Hieu+Pham%2C+Zihang+Dai%2C+and+Graham+Neubig%2E+2018%2E+Switchout%3A+an+efficient+data+augmentation+algorithm+for+neural+machine+translation%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Metho) [our card](https://md.research.elamur.ai/free/en/papers/wang-2018-switchout-efficient-data-augmentation-algorithm/)
47. Hila Weisman, Jonathan Berant, Idan Szpektor, and Ido Dagan. 2012. Learning verb inference rules from [find](https://scholar.google.com/scholar?q=Hila+Weisman%2C+Jonathan+Berant%2C+Idan+Szpektor%2C+and+Ido+Dagan%2E+2012%2E+Learning+verb+inference+rules+from)