@inproceedings{wang-etal-2023-interpreting,
title = "Interpreting Indirect Answers to Yes-No Questions in Multiple Languages",
author = "Wang, Zijie and
Hossain, Md and
Mathur, Shivam and
Melo, Terry and
Ozler, Kadir and
Park, Keun and
Quintero, Jacob and
Rezaei, MohammadHossein and
Shakya, Shreya and
Uddin, Md and
Blanco, Eduardo",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.146",
doi = "10.18653/v1/2023.findings-emnlp.146",
pages = "2210--2227",
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, and demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). We show 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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<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, and demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). We show 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).</abstract>
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%0 Conference Proceedings
%T Interpreting Indirect Answers to Yes-No Questions in Multiple Languages
%A Wang, Zijie
%A Hossain, Md
%A Mathur, Shivam
%A Melo, Terry
%A Ozler, Kadir
%A Park, Keun
%A Quintero, Jacob
%A Rezaei, MohammadHossein
%A Shakya, Shreya
%A Uddin, Md
%A Blanco, Eduardo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-interpreting
%X 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, and demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). We show 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).
%R 10.18653/v1/2023.findings-emnlp.146
%U https://aclanthology.org/2023.findings-emnlp.146
%U https://doi.org/10.18653/v1/2023.findings-emnlp.146
%P 2210-2227
Markdown (Informal)
[Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://aclanthology.org/2023.findings-emnlp.146) (Wang et al., Findings 2023)
ACL
- Zijie Wang, Md Hossain, Shivam Mathur, Terry Melo, Kadir Ozler, Keun Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Shakya, Md Uddin, and Eduardo Blanco. 2023. Interpreting Indirect Answers to Yes-No Questions in Multiple Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2210–2227, Singapore. Association for Computational Linguistics.