Abstract
This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single- and multi-hop open-domain QA benchmarks, respectively.- Anthology ID:
- P19-1222
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2296–2309
- Language:
- URL:
- https://aclanthology.org/P19-1222
- DOI:
- 10.18653/v1/P19-1222
- Cite (ACL):
- Yair Feldman and Ran El-Yaniv. 2019. Multi-Hop Paragraph Retrieval for Open-Domain Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2296–2309, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (Feldman & El-Yaniv, ACL 2019)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P19-1222.pdf
- Code
- yairf11/MUPPET
- Data
- HotpotQA