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Interpreting Indirect Answers to Yes-No Questions in Multiple Languages (2310.13290v1)

Published 20 Oct 2023 in cs.CL

Abstract: Yes-no questions expect a yes or no for an answer, but people often skip polar keywords. Instead, they answer with long explanations that must be interpreted. In this paper, we focus on this challenging problem and release new benchmarks in eight languages. We present a distant supervision approach to collect training data. We also demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). Experimental results demonstrate that monolingual fine-tuning is beneficial if training data can be obtained via distant supervision for the language of interest (5 languages). Additionally, we show that cross-lingual fine-tuning is always beneficial (8 languages).

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References (57)
  1. Revisiting machine translation for cross-lingual classification. arXiv preprint arXiv:2305.14240.
  2. On the cross-lingual transferability of monolingual representations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4623–4637, Online. Association for Computational Linguistics.
  3. Ron Artstein and Massimo Poesio. 2008. Survey article: Inter-coder agreement for computational linguistics. Computational Linguistics, 34(4):555–596.
  4. Emily M. Bender and Batya Friedman. 2018. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587–604.
  5. BanglaBERT: Language model pretraining and benchmarks for low-resource language understanding evaluation in Bangla. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1318–1327, Seattle, United States. Association for Computational Linguistics.
  6. Penelope Brown and Stephen C Levinson. 1978. Universals in language usage: Politeness phenomena. In Questions and politeness: Strategies in social interaction, pages 56–311. Cambridge University Press.
  7. Alexandra Canavan and George Zipperlen. 1996. CALLFRIEND Spanish-Non-Caribbean Dialect.
  8. Extracting training data from large language models. In USENIX Security Symposium, volume 6.
  9. Automatic Spanish translation of SQuAD dataset for multi-lingual question answering. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5515–5523, Marseille, France. European Language Resources Association.
  10. Improving pretrained cross-lingual language models via self-labeled word alignment. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3418–3430, Online. Association for Computational Linguistics.
  11. XLM-E: Cross-lingual language model pre-training via ELECTRA. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6170–6182, Dublin, Ireland. Association for Computational Linguistics.
  12. QuAC: Question answering in context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2174–2184, Brussels, Belgium. Association for Computational Linguistics.
  13. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In 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), pages 2924–2936, Minneapolis, Minnesota. Association for Computational Linguistics.
  14. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451, Online. Association for Computational Linguistics.
  15. “I’ll be there for you”: The one with understanding indirect answers. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 1–11, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.
  16. MFAQ: a multilingual FAQ dataset. In Proceedings of the 3rd Workshop on Machine Reading for Question Answering, pages 1–13, Punta Cana, Dominican Republic. Association for Computational Linguistics.
  17. BERT: Pre-training of deep bidirectional transformers for language understanding. In 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), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
  18. Nancy Green and Sandra Carberry. 1999. Interpreting and generating indirect answers. Computational Linguistics, 25(3):389–435.
  19. Julia Bell Hirschberg. 1985. A theory of scalar implicature (natural languages, pragmatics, inference). Ph.D. thesis, University of Pennsylvania.
  20. Can you predict responses to yes/no questions? yes, no, and stuff. In Fifth european conference on speech communication and technology. Citeseer.
  21. Cross-language learning with adversarial neural networks. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 226–237, Vancouver, Canada. Association for Computational Linguistics.
  22. Towards realistic practices in low-resource natural language processing: The development set. 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 3342–3349, Hong Kong, China. Association for Computational Linguistics.
  23. Lessons learned from the chameleon testbed. In Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20). USENIX Association.
  24. Moshe Koppel and Noam Ordan. 2011. Translationese and its dialects. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1318–1326, Portland, Oregon, USA. Association for Computational Linguistics.
  25. Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics, 7:452–466.
  26. Using commonsense knowledge to answer why-questions. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1204–1219, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  27. MLQA: Evaluating cross-lingual extractive question answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7315–7330, Online. Association for Computational Linguistics.
  28. Mitigating contradictions in dialogue based on contrastive learning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2781–2788, Dublin, Ireland. Association for Computational Linguistics.
  29. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics.
  30. URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 8–14, Valencia, Spain. Association for Computational Linguistics.
  31. α𝛼\alphaitalic_α-mdf: An attention-based multimodal differentiable filter for robot state estimation. In 7th Annual Conference on Robot Learning.
  32. “I’d rather just go to bed”: Understanding indirect answers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7411–7425, Online. Association for Computational Linguistics.
  33. Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157.
  34. Reframing instructional prompts to GPTk’s language. In Findings of the Association for Computational Linguistics: ACL 2022, pages 589–612, Dublin, Ireland. Association for Computational Linguistics.
  35. Recurrent models of visual attention. Advances in neural information processing systems, 27.
  36. Ariana Negar Mohammadi. 2019. Corpus of Conversational Persian Transcripts.
  37. Learning to answer questions from Wikipedia infoboxes. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1930–1935, Austin, Texas. Association for Computational Linguistics.
  38. I like fish, especially dolphins: Addressing contradictions in dialogue modeling. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1699–1713, Online. Association for Computational Linguistics.
  39. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744.
  40. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392, Austin, Texas. Association for Computational Linguistics.
  41. CoQA: A conversational question answering challenge. Transactions of the Association for Computational Linguistics, 7:249–266.
  42. Synthetic data augmentation for zero-shot cross-lingual question answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7016–7030, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  43. Sebastian Ruder and Avi Sil. 2021. Multi-domain multilingual question answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pages 17–21, Punta Cana, Dominican Republic & Online. Association for Computational Linguistics.
  44. Disentangling indirect answers to yes-no questions in real conversations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4677–4695, Seattle, United States. Association for Computational Linguistics.
  45. Language models are multilingual chain-of-thought reasoners. arXiv preprint arXiv:2210.03057.
  46. Will it blend? blending weak and strong labeled data in a neural network for argumentation mining. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 599–605, Melbourne, Australia. Association for Computational Linguistics.
  47. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26(3):339–374.
  48. Yes, no or IDK: The challenge of unanswerable yes/no questions. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1075–1085, Seattle, United States. Association for Computational Linguistics.
  49. WIQA: A dataset for “what if…” reasoning over procedural text. 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 6076–6085, Hong Kong, China. Association for Computational Linguistics.
  50. Sandra A Thompson. 1986. Questions and responses in english conversation by anna-brita stenström. Language, 62(1):213–214.
  51. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  52. Naturalconv: A chinese dialogue dataset towards multi-turn topic-driven conversation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(16):14006–14014.
  53. A large-scale chinese short-text conversation dataset. In Natural Language Processing and Chinese Computing: 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14–18, 2020, Proceedings, Part I, page 91–103, Berlin, Heidelberg. Springer-Verlag.
  54. Albert Webson and Ellie Pavlick. 2022. Do prompt-based models really understand the meaning of their prompts? In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2300–2344, Seattle, United States. Association for Computational Linguistics.
  55. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics.
  56. Learning disentangled semantic representations for zero-shot cross-lingual transfer in multilingual machine reading comprehension. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 991–1000, Dublin, Ireland. Association for Computational Linguistics.
  57. Consistency regularization for cross-lingual fine-tuning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3403–3417, Online. Association for Computational Linguistics.

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