publication . Preprint . Conference object . Contribution for newspaper or weekly magazine . 2016

How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation Pairs

Rico Sennrich;
Open Access English
  • Published: 14 Dec 2016
  • Country: United Kingdom
Abstract
Comment: accepted at EACL 2017 (v3: minor fix to table 6 description)
Subjects
free text keywords: Computer Science - Computation and Language, Natural language processing, computer.software_genre, computer, Transliteration, Machine translation, German, language.human_language, language, Encoding (memory), Computer science, Segmentation, Artificial intelligence, business.industry, business
Funded by
EC| QT21
Project
QT21
QT21: Quality Translation 21
  • Funder: European Commission (EC)
  • Project Code: 645452
  • Funding stream: H2020 | RIA
,
EC| SUMMA
Project
SUMMA
Scalable Understanding of Multilingual Media
  • Funder: European Commission (EC)
  • Project Code: 688139
  • Funding stream: H2020 | RIA
Communities
Digital Humanities and Cultural Heritage
Download fromView all 4 versions
OpenAIRE
Preprint . 2016
Provider: OpenAIRE
Edinburgh Research Explorer
Contribution for newspaper or weekly magazine . 2017
17 references, page 1 of 2

[Bahdanau et al.2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine [Loáiciga and Gulordava2016] Sharid Loáiciga and Translation by Jointly Learning to Align and Trans- Kristina Gulordava. 2016. Discontinuous Verb late. In Proceedings of the International Conference Phrases in Parsing and Machine Translation of on Learning Representations (ICLR). English and German. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia.

[Chen and Zhu2014] Boxing Chen and Xiaodan Zhu. 2014. Bilingual Sentiment Consistency for Statistical Machine Translation. In Proceedings of the 14th [Luong and Manning2016] Minh-Thang Luong and Conference of the European Chapter of the Associa- D. Christopher Manning. 2016. Achieving Open tion for Computational Linguistics, pages 607-615, Vocabulary Neural Machine Translation with HyGothenburg, Sweden, April. Association for Com- brid Word-Character Models. In Proceedings of the putational Linguistics. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),

[Chung et al.2016] Junyoung Chung, Kyunghyun Cho, pages 1054-1063. Association for Computational and Yoshua Bengio. 2016. A Character-level De- Linguistics.

[Linzen et al.2016] Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016. Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies. ArXiv e-prints, November.

[Nießen and Ney2000] Sonja Nießen and Hermann Ney. 2000. Improving SMT quality with morphosyntactic analysis. In 18th Int. Conf. on Computational Linguistics, pages 1081-1085.

[Papineni et al.2002] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 311-318, Philadelphia, PA. Association for Computational Linguistics.

[Popovic2011] Maja Popovic. 2011. Hjerson: An Open Source Tool for Automatic Error Classification of Machine Translation Output. Prague Bull. Math. Linguistics, 96:59-68.

[Schmid et al.2004] Helmut Schmid, Arne Fitschen, and Ulrich Heid. 2004. A German Computational Morphology Covering Derivation, Composition, and Inflection. In Proceedings of the IVth International Conference on Language Resources and Evaluation (LREC 2004), pages 1263-1266.

[Sennrich and Haddow2015] Rico Sennrich and Barry Haddow. 2015. A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2081-2087, Lisbon, Portugal. Association for Computational Linguistics.

[Sennrich and Kunz2014] Rico Sennrich and Beat Kunz. 2014. Zmorge: A German Morphological Lexicon Extracted from Wiktionary. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland.

[Sennrich et al.2013] Rico Sennrich, Martin Volk, and Gerold Schneider. 2013. Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing 2013, pages 601-609, Hissar, Bulgaria.

[Sennrich et al.2016a] Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a. Edinburgh Neural Machine Translation Systems for WMT 16. In Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers, pages 368-373, Berlin, Germany, August. Association for Computational Linguistics.

[Sennrich et al.2016b] Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b. Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715-1725, Berlin, Germany. Association for Computational Linguistics.

[Sutskever et al.2014] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, pages 3104-3112, Montreal, Quebec, Canada.

[Wetzel and Bond2012] Dominikus Wetzel and Francis Bond. 2012. Enriching Parallel Corpora for Statistical Machine Translation with Semantic Negation Rephrasing. In Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 20-29, Jeju, Republic of Korea, July. Association for Computational Linguistics.

17 references, page 1 of 2
Abstract
Comment: accepted at EACL 2017 (v3: minor fix to table 6 description)
Subjects
free text keywords: Computer Science - Computation and Language, Natural language processing, computer.software_genre, computer, Transliteration, Machine translation, German, language.human_language, language, Encoding (memory), Computer science, Segmentation, Artificial intelligence, business.industry, business
Funded by
EC| QT21
Project
QT21
QT21: Quality Translation 21
  • Funder: European Commission (EC)
  • Project Code: 645452
  • Funding stream: H2020 | RIA
,
EC| SUMMA
Project
SUMMA
Scalable Understanding of Multilingual Media
  • Funder: European Commission (EC)
  • Project Code: 688139
  • Funding stream: H2020 | RIA
Communities
Digital Humanities and Cultural Heritage
Download fromView all 4 versions
OpenAIRE
Preprint . 2016
Provider: OpenAIRE
Edinburgh Research Explorer
Contribution for newspaper or weekly magazine . 2017
17 references, page 1 of 2

[Bahdanau et al.2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine [Loáiciga and Gulordava2016] Sharid Loáiciga and Translation by Jointly Learning to Align and Trans- Kristina Gulordava. 2016. Discontinuous Verb late. In Proceedings of the International Conference Phrases in Parsing and Machine Translation of on Learning Representations (ICLR). English and German. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia.

[Chen and Zhu2014] Boxing Chen and Xiaodan Zhu. 2014. Bilingual Sentiment Consistency for Statistical Machine Translation. In Proceedings of the 14th [Luong and Manning2016] Minh-Thang Luong and Conference of the European Chapter of the Associa- D. Christopher Manning. 2016. Achieving Open tion for Computational Linguistics, pages 607-615, Vocabulary Neural Machine Translation with HyGothenburg, Sweden, April. Association for Com- brid Word-Character Models. In Proceedings of the putational Linguistics. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),

[Chung et al.2016] Junyoung Chung, Kyunghyun Cho, pages 1054-1063. Association for Computational and Yoshua Bengio. 2016. A Character-level De- Linguistics.

[Linzen et al.2016] Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016. Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies. ArXiv e-prints, November.

[Nießen and Ney2000] Sonja Nießen and Hermann Ney. 2000. Improving SMT quality with morphosyntactic analysis. In 18th Int. Conf. on Computational Linguistics, pages 1081-1085.

[Papineni et al.2002] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: A Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pages 311-318, Philadelphia, PA. Association for Computational Linguistics.

[Popovic2011] Maja Popovic. 2011. Hjerson: An Open Source Tool for Automatic Error Classification of Machine Translation Output. Prague Bull. Math. Linguistics, 96:59-68.

[Schmid et al.2004] Helmut Schmid, Arne Fitschen, and Ulrich Heid. 2004. A German Computational Morphology Covering Derivation, Composition, and Inflection. In Proceedings of the IVth International Conference on Language Resources and Evaluation (LREC 2004), pages 1263-1266.

[Sennrich and Haddow2015] Rico Sennrich and Barry Haddow. 2015. A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2081-2087, Lisbon, Portugal. Association for Computational Linguistics.

[Sennrich and Kunz2014] Rico Sennrich and Beat Kunz. 2014. Zmorge: A German Morphological Lexicon Extracted from Wiktionary. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland.

[Sennrich et al.2013] Rico Sennrich, Martin Volk, and Gerold Schneider. 2013. Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing 2013, pages 601-609, Hissar, Bulgaria.

[Sennrich et al.2016a] Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a. Edinburgh Neural Machine Translation Systems for WMT 16. In Proceedings of the First Conference on Machine Translation, Volume 2: Shared Task Papers, pages 368-373, Berlin, Germany, August. Association for Computational Linguistics.

[Sennrich et al.2016b] Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b. Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1715-1725, Berlin, Germany. Association for Computational Linguistics.

[Sutskever et al.2014] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, pages 3104-3112, Montreal, Quebec, Canada.

[Wetzel and Bond2012] Dominikus Wetzel and Francis Bond. 2012. Enriching Parallel Corpora for Statistical Machine Translation with Semantic Negation Rephrasing. In Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 20-29, Jeju, Republic of Korea, July. Association for Computational Linguistics.

17 references, page 1 of 2
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