@inproceedings{lee-lee-2023-enhancing,
title = "Enhancing text comprehension for Question Answering with Contrastive Learning",
author = "Lee, Seungyeon and
Lee, Minho",
editor = "Can, Burcu and
Mozes, Maximilian and
Cahyawijaya, Samuel and
Saphra, Naomi and
Kassner, Nora and
Ravfogel, Shauli and
Ravichander, Abhilasha and
Zhao, Chen and
Augenstein, Isabelle and
Rogers, Anna and
Cho, Kyunghyun and
Grefenstette, Edward and
Voita, Lena",
booktitle = "Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.repl4nlp-1.7",
doi = "10.18653/v1/2023.repl4nlp-1.7",
pages = "75--86",
abstract = "Although Question Answering (QA) have advanced to the human-level language skills in NLP tasks, there is still a problem: the QA model gets confused when there are similar sentences or paragraphs. Existing studies focus on enhancing the text understanding of the candidate answers to improve the overall performance of the QA models. However, since these methods focus on re-ranking queries or candidate answers, they fail to resolve the confusion when many generated answers are similar to the expected answer. To address these issues, we propose a novel contrastive learning framework called ContrastiveQA that alleviates the confusion problem in answer extraction. We propose a supervised method where we generate positive and negative samples from the candidate answers and the given answer, respectively. We thus introduce ContrastiveQA, which uses contrastive learning with sampling data to reduce incorrect answers. Experimental results on four QA benchmarks show the effectiveness of the proposed method.",
}
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%0 Conference Proceedings
%T Enhancing text comprehension for Question Answering with Contrastive Learning
%A Lee, Seungyeon
%A Lee, Minho
%Y Can, Burcu
%Y Mozes, Maximilian
%Y Cahyawijaya, Samuel
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Ravfogel, Shauli
%Y Ravichander, Abhilasha
%Y Zhao, Chen
%Y Augenstein, Isabelle
%Y Rogers, Anna
%Y Cho, Kyunghyun
%Y Grefenstette, Edward
%Y Voita, Lena
%S Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lee-lee-2023-enhancing
%X Although Question Answering (QA) have advanced to the human-level language skills in NLP tasks, there is still a problem: the QA model gets confused when there are similar sentences or paragraphs. Existing studies focus on enhancing the text understanding of the candidate answers to improve the overall performance of the QA models. However, since these methods focus on re-ranking queries or candidate answers, they fail to resolve the confusion when many generated answers are similar to the expected answer. To address these issues, we propose a novel contrastive learning framework called ContrastiveQA that alleviates the confusion problem in answer extraction. We propose a supervised method where we generate positive and negative samples from the candidate answers and the given answer, respectively. We thus introduce ContrastiveQA, which uses contrastive learning with sampling data to reduce incorrect answers. Experimental results on four QA benchmarks show the effectiveness of the proposed method.
%R 10.18653/v1/2023.repl4nlp-1.7
%U https://aclanthology.org/2023.repl4nlp-1.7
%U https://doi.org/10.18653/v1/2023.repl4nlp-1.7
%P 75-86
Markdown (Informal)
[Enhancing text comprehension for Question Answering with Contrastive Learning](https://aclanthology.org/2023.repl4nlp-1.7) (Lee & Lee, RepL4NLP 2023)
ACL