Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
Abstract
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data. However, there is a tradeoff between adding more knowledge data for improved RTE performance and maintaining an efficient RTE system, as such a big database is problematic in terms of the memory usage and computational complexity. In this work, we show the processing time of a state-of-the-art logic-based RTE system can be significantly reduced by replacing its search-based axiom injection (abduction) mechanism by that based on Knowledge Base Completion (KBC). We integrate this mechanism in a Coq plugin that provides a proof automation tactic for natural language inference. Additionally, we show empirically that adding new knowledge data contributes to better RTE performance while not harming the processing speed in this framework.
Cite
Text
Yoshikawa et al. "Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33017410Markdown
[Yoshikawa et al. "Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/yoshikawa2019aaai-combining/) doi:10.1609/AAAI.V33I01.33017410BibTeX
@inproceedings{yoshikawa2019aaai-combining,
title = {{Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference}},
author = {Yoshikawa, Masashi and Mineshima, Koji and Noji, Hiroshi and Bekki, Daisuke},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {7410-7417},
doi = {10.1609/AAAI.V33I01.33017410},
url = {https://mlanthology.org/aaai/2019/yoshikawa2019aaai-combining/}
}