@inproceedings{wei-etal-2023-decompositions,
title = "When Do Decompositions Help for Machine Reading?",
author = "Wei, Kangda and
Lawrie, Dawn and
Van Durme, Benjamin and
Chen, Yunmo and
Weller, Orion",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.219/",
doi = "10.18653/v1/2023.emnlp-main.219",
pages = "3599--3606",
abstract = "Answering complex questions often requires multi-step reasoning in order to obtain the final answer. Most research into decompositions of complex questions involves open-domain systems, which have shown success in using these decompositions for improved retrieval. In the machine reading setting, however, work to understand when decompositions are helpful is understudied. We conduct experiments on decompositions in machine reading to unify recent work in this space, using a range of models and datasets. We find that decompositions can be helpful in zero or limited-data settings, giving several points of improvement in exact match. However, we also show that when models are given access to around a few hundred or more examples, decompositions are not helpful (and can actually be detrimental). Thus, our analysis implies that models can learn decompositions implicitly even with limited data."
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<abstract>Answering complex questions often requires multi-step reasoning in order to obtain the final answer. Most research into decompositions of complex questions involves open-domain systems, which have shown success in using these decompositions for improved retrieval. In the machine reading setting, however, work to understand when decompositions are helpful is understudied. We conduct experiments on decompositions in machine reading to unify recent work in this space, using a range of models and datasets. We find that decompositions can be helpful in zero or limited-data settings, giving several points of improvement in exact match. However, we also show that when models are given access to around a few hundred or more examples, decompositions are not helpful (and can actually be detrimental). Thus, our analysis implies that models can learn decompositions implicitly even with limited data.</abstract>
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%0 Conference Proceedings
%T When Do Decompositions Help for Machine Reading?
%A Wei, Kangda
%A Lawrie, Dawn
%A Van Durme, Benjamin
%A Chen, Yunmo
%A Weller, Orion
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wei-etal-2023-decompositions
%X Answering complex questions often requires multi-step reasoning in order to obtain the final answer. Most research into decompositions of complex questions involves open-domain systems, which have shown success in using these decompositions for improved retrieval. In the machine reading setting, however, work to understand when decompositions are helpful is understudied. We conduct experiments on decompositions in machine reading to unify recent work in this space, using a range of models and datasets. We find that decompositions can be helpful in zero or limited-data settings, giving several points of improvement in exact match. However, we also show that when models are given access to around a few hundred or more examples, decompositions are not helpful (and can actually be detrimental). Thus, our analysis implies that models can learn decompositions implicitly even with limited data.
%R 10.18653/v1/2023.emnlp-main.219
%U https://aclanthology.org/2023.emnlp-main.219/
%U https://doi.org/10.18653/v1/2023.emnlp-main.219
%P 3599-3606
Markdown (Informal)
[When Do Decompositions Help for Machine Reading?](https://aclanthology.org/2023.emnlp-main.219/) (Wei et al., EMNLP 2023)
ACL
- Kangda Wei, Dawn Lawrie, Benjamin Van Durme, Yunmo Chen, and Orion Weller. 2023. When Do Decompositions Help for Machine Reading?. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3599–3606, Singapore. Association for Computational Linguistics.