Mova0.5 pre-release⌕
☰
home / docs / papers / Machine translation / Text Generation from Discourse Representation Structures

Text Generation from Discourse Representation Structures

What it is about

The paper generates document text from DRSs on the Groningen Meaning Bank. Two problems make DRSs hard to generate from. First, the conditions of a box form a set, so one meaning can arrive in many orders; the paper learns a canonical order with a graph model (a graph neural network over conditions followed by a pointer network), which beats a counting baseline that orders pairs by frequency. Second, variable names are arbitrary, so the paper renames each variable by its relative position of first appearance, with repeated mentions as relative offsets. The generator is a sequence decoder over a tree encoder, and the encoder uses a sibling tree-LSTM with two gates instead of one per child, which suits DRS trees that can have up to 180 children. Test BLEU is 59.26, above the graph-to-text comparison systems. The order model gains about 9.6 accuracy points over the one-way graph.

Ideas

For Mova

The ideas fit tools/translate/meta.rs (ids and coreference), en/src/lin.rs (order) and the frame layer of prag. The neural graph model is not for Mova; the counting baseline and the shuffle control are the parts to measure.

Topics: Discourse analysis · Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.naacl-main.35

@inproceedings{liu2021text,
  title = {{Text Generation from Discourse Representation Structures}},
  author = {Liu, Jiangming and Cohen, Shay B. and Lapata, Mirella},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.35},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.35}
}

References

55 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Omri Abend, Shay B. Cohen, and Mark Steedman. In this paper, we have focused on document-level 2015. Lexical event ordering with an edge-factored generation from formal meaning representations. model. In Proceedings of the 2015 Conference of We have adopted DRT as our formalism of choice the North American Chapter of the Association for and highlighted various challenges associated with Computational Linguistics: Human Language Tech-405 nologies (NAACL-HLT), pages 1161–1171, Denver, Colorado, USA. American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 3649–3658, Minneapolis, USA. find
  2. Nicholas Asher and Alex Lascarides. 2003. Logics of conversation. Cambridge University Press. find
  3. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 4th International Conference on Learning Representations (ICLR), San Diego, California. find
  4. Valerio Basile. 2015. From Logic to Language: Natural Language Generation from Logical Forms. Ph.D. thesis, University of Groningen, Netherlands. find
  5. Valerio Basile and Johan Bos. 2011. Towards generating text from discourse representation structures. In Proceedings of the 13th European Workshop on Natural Language Generation, pages 145–150, Nancy, France. find our card
  6. Daniel Beck, Gholamreza Haffari, and Trevor Cohn. 2018. Graph-to-sequence learning using gated graph neural networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 273–283, Melbourne, Australia. find
  7. Johan Bos, Valerio Basile, Kilian Evang, Noortje J Venhuizen, and Johannes Bjerva. 2017. The Groningen meaning bank. In Handbook of Linguistic Annotation, pages 463–496. Springer. find
  8. Deng Cai and Wai Lam. 2020. Graph transformer for graph-to-sequence learning. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pages 7464–7471, New York, USA. find
  9. John Carroll and Stephan Oepen. 2005. High-efficiency realization for a wide-coverage unification grammar. In Robert Dale and Kam-Fai Wong, editors, Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJC-NLP), volume 3651, pages 165–176. Springer, Jeju, Korea. find
  10. Xinchi Chen, Xipeng Qiu, and Xuanjing Huang. 2016. Neural sentence ordering. arXiv preprint arXiv:1607.06952. ↗
  11. Stephen Clark and James Curran. 2007. Wide-coverage efficient statistical parsing with CCG and log-linear models. Computational Linguistics, 33(4):493–552. find our card
  12. Baiyun Cui, Yingming Li, Ming Chen, and Zhongfei Zhang. 2018. Deep attentive sentence ordering network. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4340–4349, Brussels, Belgium. find
  13. Marco Damonte and Shay B. Cohen. 2019. Structural neural encoders for AMR-to-text generation. In Proceedings of the 2019 Conference of the North Federico Fancellu, Sorcha Gilroy, Adam Lopez, and Mirella Lapata. 2019. Semantic graph parsing with recurrent neural network DAG grammars. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2769– 2778, Hong Kong, China. find
  14. Christiane Fellbaum, editor. 1998. WordNet: An Electronic Database. MIT Press, Cambridge, MA. find
  15. Jeffrey Flanigan, Chris Dyer, Noah A. Smith, and Jaime G. Carbonell. 2016. Generation from abstract meaning representation using tree transducers. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 731–739, San Diego, California, USA. find our card
  16. Jingjing Gong, Xinchi Chen, Xipeng Qiu, and Xuanjing Huang. 2016. End-to-end neural sentence ordering using pointer network. arXiv preprint arXiv:1611.04953. ↗
  17. Valerie Hajdik, Jan Buys, Michael Wayne Goodman, and Emily M. Bender. 2019. Neural text generation from rich semantic representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pages 2259–2266, Minneapolis, Minnesota. find
  18. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735–1780. find
  19. Or Honovich, Lucas Torroba Hennigen, Omri Abend, and Shay B. Cohen. 2020. Machine reading of historical events. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7486–7497, Online. Association for Computational Linguistics. find
  20. Matic Horvat, Ann Copestake, and Bill Byrne. 2015. Hierarchical statistical semantic realization for Minimal Recursion Semantics. In Proceedings of the 11th International Conference on Computational Semantics (IWCS), pages 107–117, London, UK. Association for Computational Linguistics. find
  21. Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016. Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), pages 2073–2083, Berlin, Germany. find
  22. Hans Kamp. 1981. A theory of truth and semantic representation. In J. A. G. Groenendijk, T. M. V. Janssen, and M. B. J. Stokhof, editors, Formal Methods in the Study of Language, volume 1, pages 277– 322. find
  23. Mathematisch Centrum, Amsterdam. 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1611–1622, Edinburgh, Scotland, UK. Association for Computational Linguistics. find
  24. Hans Kamp and Uwe Reyle. 1993. From Discourse to Logic; An Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory. Kluwer, Dordrecht. find
  25. Shashi Narayan and Claire Gardent. 2014. Hybrid simplification using deep semantics and machine translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pages 435–445, Baltimore, Maryland. Association for Computational Linguistics. find
  26. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR), Banff, Canada. find
  27. Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017. Neural AMR: Sequence-to-sequence models for parsing and generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pages 146–157, Vancouver, Canada. find our card
  28. Mirella Lapata. 2003. Probabilistic text structuring: Experiments with sentence ordering. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics (ACL), pages 545–552, Sapporo, Japan. find
  29. Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2018. Discourse representation structure parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 429–439, Melbourne, Australia. find
  30. Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2019a. Discourse representation parsing for sentences and documents. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 6248–6262, Florence, Italy. find
  31. Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2019b. Discourse representation structure parsing with recurrent neural networks and the transformer model. In Proceedings of the IWCS Shared Task on Semantic Parsing, Gothenburg, Sweden. Association for Computational Linguistics. find
  32. Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2020. Dscorer: A fast evaluation metric for discourse representation structure parsing. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 4547– 4554. find
  33. Lajanugen Logeswaran, Honglak Lee, and Dragomir Radev. 2018. Sentence ordering and coherence modeling using recurrent neural networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pages 5285–5292, New Orleans, Louisiana, USA. find
  34. Wei Lu and Hwee Tou Ng. 2011. A probabilistic forest-to-string model for language generation from typed lambda calculus expressions. In Proceedings of the 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 (ACL), pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. find our card
  35. Leonardo F.R. Ribeiro, Claire Gardent, and Iryna Gurevych. 2019. Enhancing AMR-to-text generation with dual graph representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3174–3185, Hong Kong, China. find
  36. 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 (ACL), pages 1073–1083, Vancouver, Canada. find
  37. Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In Neural Information Processing Systems (NeurIPS), pages 362–373, Montréal, Canada. Springer. find
  38. Stuart M. Shieber, Gertjan van Noord, Fernando C. N. Pereira, and Robert C. Moore. 1990. Semantic-head-driven generation. Computational Linguistics, 16(1):30–42. find
  39. Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018. A graph-to-sequence model for AMR-to-text generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1616–1626, Melbourne, Australia. find
  40. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP), pages 1556–1566, Beijing, China. find
  41. Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. 2016. Neural headline generation on Abstract Meaning Representation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1054–1059, Austin, Texas. Association for Computational Linguistics. find
  42. Rob A. Van der Sandt. 1992. Presupposition projection as anaphora resolution. Journal of Semantics, 9(4):333–377. find
  43. Rik van Noord, Lasha Abzianidze, Antonio Toral, and Johan Bos. 2018. Exploring neural methods for parsing discourse representation structures. Transactions of the Association for Computational Linguistics, 6:619–633. find
  44. Rik van Noord, Antonio Toral, and Johan Bos. 2019. Linguistic information in neural semantic parsing with multiple encoders. In Proceedings of the 13th International Conference on Computational Semantics (IWCS), pages 24–31, Gothenburg, Sweden. find
  45. Noortje J. Venhuizen, Johan Bos, Petra Hendriks, and Harm Brouwer. 2018. Discourse semantics with information structure. Journal of Semantics, 35(1):127–169. find
  46. Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, and Vadim Sheinin. 2018. SQL-to-text generation with graph-to-sequence model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 931–936, Brussels, Belgium. find
  47. Xuchen Yao, Gosse Bouma, and Yi Zhang. 2012. Semantics-based question generation and implementation. Dialogue and Discourse, 2(3):11–42. find
  48. Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, and Jiebo Luo. 2019. Graph-based neural sentence ordering. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), pages 5387–5393, Macao, China. find
  49. Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019. Modeling graph structure in transformer for better AMR-to-text generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5462–5471, Hong Kong, China. find
  50. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Neural Information Processing Systems (NeurIPS), pages 2692–2700, Montréal, Canada. find
  51. Juen-tin Wang. 1980. On computational sentence generation from logical form. In Proceedings of the 8th International Conference on Computational Linguistics (COLING), pages 405–411, Tokyo, Japan. find
  52. Tianming Wang, Xiaojun Wan, and Hanqi Jin. 2020. AMR-to-text generation with graph transformer. Transactions of the Association for Computational Linguistics, 8:19–33. find
  53. Yaushian Wang, Hung-Yi Lee, and Yun-Nung Chen. 2019. Tree transformer: Integrating tree structures into self-attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1060–1070, Hong Kong, China. find
  54. Michael White, Rajakrishnan Rajkumar, and Scott Martin. 2007. Towards broad coverage surface realization with CCG. In Proceedings of the Workshop on Using Corpora for NLG: Language Generation and Machine Translation (UCNLG+MT), pages 267–276, Copenhagen, Denmark. find our card
  55. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems. find

This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/liu-2021-text-generation-discourse-representation-structures/