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.- Anthology ID:
- 2023.emnlp-industry.68
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 732–738
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-industry.68
- DOI:
- 10.18653/v1/2023.emnlp-industry.68
- Cite (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.
- Cite (Informal):
- Too much of product information : Don’t worry, let’s look for evidence! (Jain et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.emnlp-industry.68.pdf