Neural Natural Language Inference Models Enhanced with External Knowledge
This work addresses the challenge of improving NLI models for AI researchers by integrating external knowledge, representing an incremental advancement over existing methods.
The paper tackles the problem of enhancing neural natural language inference models by incorporating external knowledge, resulting in state-of-the-art performance on SNLI and MultiNLI datasets.
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.