@inproceedings{dua-etal-2021-generative,
title = "Generative Context Pair Selection for Multi-hop Question Answering",
author = "Dua, Dheeru and
Nogueira dos Santos, Cicero and
Ng, Patrick and
Athiwaratkun, Ben and
Xiang, Bing and
Gardner, Matt and
Singh, Sameer",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.561",
doi = "10.18653/v1/2021.emnlp-main.561",
pages = "7009--7015",
abstract = "Compositional reasoning tasks such as multi-hop question answering require models to learn how to make latent decisions using only weak supervision from the final answer. Crowdsourced datasets gathered for these tasks, however, often contain only a slice of the underlying task distribution, which can induce unanticipated biases such as shallow word overlap between the question and context. Recent works have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. We show that on HotpotQA, while being comparable to the state-of-the-art answering performance, our proposed generative passage selection model has a better performance (4.9{\%} higher than baseline) on adversarial held-out set which tests robustness of model{'}s multi-hop reasoning capabilities.",
}
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<abstract>Compositional reasoning tasks such as multi-hop question answering require models to learn how to make latent decisions using only weak supervision from the final answer. Crowdsourced datasets gathered for these tasks, however, often contain only a slice of the underlying task distribution, which can induce unanticipated biases such as shallow word overlap between the question and context. Recent works have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. We show that on HotpotQA, while being comparable to the state-of-the-art answering performance, our proposed generative passage selection model has a better performance (4.9% higher than baseline) on adversarial held-out set which tests robustness of model’s multi-hop reasoning capabilities.</abstract>
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%0 Conference Proceedings
%T Generative Context Pair Selection for Multi-hop Question Answering
%A Dua, Dheeru
%A Nogueira dos Santos, Cicero
%A Ng, Patrick
%A Athiwaratkun, Ben
%A Xiang, Bing
%A Gardner, Matt
%A Singh, Sameer
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F dua-etal-2021-generative
%X Compositional reasoning tasks such as multi-hop question answering require models to learn how to make latent decisions using only weak supervision from the final answer. Crowdsourced datasets gathered for these tasks, however, often contain only a slice of the underlying task distribution, which can induce unanticipated biases such as shallow word overlap between the question and context. Recent works have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason. We propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts. We show that on HotpotQA, while being comparable to the state-of-the-art answering performance, our proposed generative passage selection model has a better performance (4.9% higher than baseline) on adversarial held-out set which tests robustness of model’s multi-hop reasoning capabilities.
%R 10.18653/v1/2021.emnlp-main.561
%U https://aclanthology.org/2021.emnlp-main.561
%U https://doi.org/10.18653/v1/2021.emnlp-main.561
%P 7009-7015
Markdown (Informal)
[Generative Context Pair Selection for Multi-hop Question Answering](https://aclanthology.org/2021.emnlp-main.561) (Dua et al., EMNLP 2021)
ACL
- Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, and Sameer Singh. 2021. Generative Context Pair Selection for Multi-hop Question Answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7009–7015, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.