Abstract
Traditional Key-value Memory Neural Networks (KV-MemNNs) are proved to be effective to support shallow reasoning over a collection of documents in domain specific Question Answering or Reading Comprehension tasks. However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory. In this paper, we propose a novel mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions. To achieve this, we design a new query updating strategy to mask previously-addressed memory information from the query representations, and introduce a novel STOP strategy to avoid invalid or repeated memory reading without strong annotation signals. This also enables KV-MemNNs to produce structured queries and work in a semantic parsing fashion. Experimental results on benchmark datasets show that our solution, trained with question-answer pairs only, can provide conventional KV-MemNNs models with better reasoning abilities on complex questions, and achieve state-of-art performances.- Anthology ID:
- N19-1301
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2937–2947
- Language:
- URL:
- https://aclanthology.org/N19-1301
- DOI:
- 10.18653/v1/N19-1301
- Cite (ACL):
- Kun Xu, Yuxuan Lai, Yansong Feng, and Zhiguo Wang. 2019. Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2937–2947, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (Xu et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1301.pdf
- Data
- DBpedia