15 research outputs found
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
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.Comment: 9 pages, accepted to AAAI 201