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Cited by 8Natural Language Processing TechniquesSpeech Recognition and SynthesisTopic Modeling

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A Joint Dependency Model of Morphological and Syntactic Structure for Statistical Machine Translation

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Sennrich, Rico, Haddow, Barry·2015·Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/d15-1248
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Abstract

When translating between two languages that differ in their degree of morphological synthesis, syntactic structures in one language may be realized as morphological structures in the other, and SMT models need a mechanism to learn such translations.Prior work has used morpheme splitting with flat representations that do not encode the hierarchical structure between morphemes, but this structure is relevant for learning morphosyntactic constraints and selectional preferences.We propose to model syntactic and morphological structure jointly in a dependency translation model, allowing the system to generalize to the level of morphemes.We present a dependency representation of German compounds and particle verbs that results in improvements in translation quality of 1.4-1.8BLEU in the WMT English-German translation task.

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  • The best systems outperform the syntax-based SMT system in terms of BLEU, but not in terms of CHRF3.

    “We observe that our best systems outperform the syntax-based system in terms of BLEU, but not in terms of CHRF3.”

    From Neural Machine Translation of Rare Words with Subword Units (2016) · cites this paper as Sennrich and Haddow, 2015

    Section 5.2 (SMT Results; page not provided) says the paper’s full system outperformed syntax-based submissions by at least 0.8 BLEU. Our copy reports BLEU results but does not report CHRF3 scores or establish that the systems failed to outperform a syntax-based system on CHRF3. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 23

  1. Colin Cherry and George Foster. 2012. Batch Tun- ing Strategies for Statistical Machine Translation. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Compu- tational Linguistics: Human Language Technolo- gies, NAACL HLT '12, pages 427-436, Montreal, Canada. Association for Computational Linguistics.
  2. Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A. Smith. 2011. Better Hypothesis Testing for Statistical Machine Translation: Controlling for Op- timizer Instability. In Proceedings of the 49th An- nual Meeting of the Association for Computational Linguistics, pages 176-181, Portland, Oregon. As- sociation for Computational Linguistics.
  3. Alexander Fraser, Marion Weller, Aoife Cahill, and Fa- bienne Cap. 2012. Modeling Inflection and Word- Formation in SMT. In Proceedings of the 13th Con- ference of the European Chapter of the Association for Computational Linguistics, pages 664-674, Avi- gnon, France. Association for Computational Lin- guistics.
  4. Fabienne Fritzinger and Alexander Fraser. 2010. How to Avoid Burning Ducks: Combining Linguistic Analysis and Corpus Statistics for German Com- pound Processing. In Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR, WMT '10, pages 224-234, Uppsala, Sweden. Association for Computational Linguistics.
  5. Michel Galley, Jonathan Graehl, Kevin Knight, Daniel Marcu, Steve DeNeefe, Wei Wang, and Ignacio Thayer. 2006. Scalable inference and training of context-rich syntactic translation models. In ACL- 44: Proceedings of the 21st International Confer- ence on Computational Linguistics and the 44th an- nual meeting of the Association for Computational Linguistics, pages 961-968, Sydney, Australia. As- sociation for Computational Linguistics.
  6. Matthias Huck, Hieu Hoang, and Philipp Koehn. 2014. Preference Grammars and Soft Syntactic Constraints for GHKM Syntax-based Statistical Ma- chine Translation. In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 148-156, Doha, Qatar. Association for Computational Linguistics.
  7. Sébastien Jean, Orhan Firat, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015. Montreal Neural Machine Translation Systems for WMT'15 . In Proceedings of the Tenth Workshop on Statistical Machine Translation.
  8. Philipp Koehn and Kevin Knight. 2003. Empirical Methods for Compound Splitting. In EACL '03: Proceedings of the Tenth Conference on European Chapter of the Association for Computational Lin- guistics, pages 187-193, Budapest, Hungary. Asso- ciation for Computational Linguistics.
  9. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open Source Toolkit for Statistical Machine Translation. In Proceedings of the ACL-2007 Demo and Poster Sessions, pages 177-180, Prague, Czech Republic. Association for Computational Linguistics.
  10. Klaus Macherey, Andrew Dai, David Talbot, Ashok Popat, and Franz Och. 2011. Language- independent compound splitting with morphological operations. In Proceedings of the 49th Annual Meet- ing of the Association for Computational Linguis- tics: Human Language Technologies, pages 1395- 1404, Portland, Oregon, USA. Association for Com- putational Linguistics.
  11. Sonja Nießen and Hermann Ney. 2000. Improving SMT quality with morpho-syntactic analysis. In 18th Int. Conf. on Computational Linguistics, pages 1081-1085.
  12. Sonja Nießen and Hermann Ney. 2001. Morpho- syntactic analysis for Reordering in Statistical Ma- chine Translation. In Machine Translation Summit, pages 247-252, Santiago de Compostela, Spain.
  13. Maja Popovic, Daniel Stein, and Hermann Ney. 2006. Statistical Machine Translation of German Com- pound Words. In Advances in Natural Language Processing, 5th International Conference on NLP, FinTAL 2006, pages 616-624, Turku, Finland.
  14. Helmut Schmid, Arne Fitschen, and Ulrich Heid. 2004. A German Computational Morphology Cov- ering Derivation, Composition, and Inflection. In Proceedings of the IVth International Conference on Language Resources and Evaluation (LREC 2004), pages 1263-1266.
  15. Rico Sennrich and Beat Kunz. 2014. Zmorge: A Ger- man Morphological Lexicon Extracted from Wik- tionary. In Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland, May.
  16. Rico Sennrich, Martin Volk, and Gerold Schneider. 2013. Exploiting Synergies Between Open Re- sources for German Dependency Parsing, POS- tagging, and Morphological Analysis. In Proceed- ings of the International Conference Recent Ad- vances in Natural Language Processing 2013, pages 601-609, Hissar, Bulgaria.
  17. Rico Sennrich. 2015. Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Transla- tion. Transactions of the Association for Computa- tional Linguistics, 3:169-182.
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  22. Philip Williams, Rico Sennrich, Maria Nadejde, Matthias Huck, Eva Hasler, and Philipp Koehn. 2014. Edinburgh's Syntax-Based Systems at WMT 2014. In Proceedings of the Ninth Workshop on Sta- tistical Machine Translation, pages 207-214, Bal- timore, Maryland, USA. Association for Computa- tional Linguistics.
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Metadata

Authors
Sennrich, R., & Haddow, B.
Affiliations
University of Edinburgh (GB)
ORCID
Sennrich, Rico
Venue
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2015
Pages
2081-2087
DOI
10.18653/v1/d15-1248
OpenAlex
W2251139225
Cited by
8
References
0
Citations by year
2017: 1 · 2016: 6 · 2015: 1
FWCI
1.43
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
  • National Science Foundation
  • European Commission (644333, HORIZON2020, 645452, 644402)
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (148717)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
statistical machine translation, morphological structure, syntactic structure, morphosyntactic constraints, selectional preferences, German compounds, particle verbs, English-German translation, morphological synthesis

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