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        <title>Too much of product information : Don’t worry, let’s look for evidence!</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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    <abstract>Product question answering (PQA) aims to provide an instant response to customer questions posted on shopping message boards, social media, brand websites and retail stores. In this paper, we propose a distantly supervised solution to answer customer questions by using product information. Auto-answering questions using product information poses two main challenges:(i) labelled data is not readily available (ii)lengthy product information requires attending to various parts of the text to answer the question. To this end, we first propose a novel distant supervision based NLI model to prepare training data without any manual efforts. To deal with lengthy context, we factorize answer generation into two sub-problems. First, given product information, model extracts evidence spans relevant to question. Then, model leverages evidence spans to generate answer. Further, we propose two novelties in fine-tuning approach: (i) First, we jointly fine-tune model for both the tasks in end-to-end manner and showcase that it outperforms standard multi-task fine-tuning. (ii) Next, we introduce an auxiliary contrastive loss for evidence extraction. We show that combination of these two ideas achieves an absolute improvement of 6% in accuracy (human evaluation) over baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.68</identifier>
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%0 Conference Proceedings
%T Too much of product information : Don’t worry, let’s look for evidence!
%A Jain, Aryan
%A Rana, Jitenkumar
%A Aggarwal, Chetan
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jain-etal-2023-much
%X Product question answering (PQA) aims to provide an instant response to customer questions posted on shopping message boards, social media, brand websites and retail stores. In this paper, we propose a distantly supervised solution to answer customer questions by using product information. Auto-answering questions using product information poses two main challenges:(i) labelled data is not readily available (ii)lengthy product information requires attending to various parts of the text to answer the question. To this end, we first propose a novel distant supervision based NLI model to prepare training data without any manual efforts. To deal with lengthy context, we factorize answer generation into two sub-problems. First, given product information, model extracts evidence spans relevant to question. Then, model leverages evidence spans to generate answer. Further, we propose two novelties in fine-tuning approach: (i) First, we jointly fine-tune model for both the tasks in end-to-end manner and showcase that it outperforms standard multi-task fine-tuning. (ii) Next, we introduce an auxiliary contrastive loss for evidence extraction. We show that combination of these two ideas achieves an absolute improvement of 6% in accuracy (human evaluation) over baselines.
%R 10.18653/v1/2023.emnlp-industry.68
%U https://aclanthology.org/2023.emnlp-industry.68
%U https://doi.org/10.18653/v1/2023.emnlp-industry.68
%P 732-738

Markdown (Informal)

[Too much of product information : Don’t worry, let’s look for evidence!](https://aclanthology.org/2023.emnlp-industry.68) (Jain et al., EMNLP 2023)

• Too much of product information : Don’t worry, let’s look for evidence! (Jain et al., EMNLP 2023)

ACL

• Aryan Jain, Jitenkumar Rana, and Chetan Aggarwal. 2023. Too much of product information : Don’t worry, let’s look for evidence!. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 732–738, Singapore. Association for Computational Linguistics.