Recognizing and Justifying Text Entailment Through Distributional Navigation on Definition Graphs

Authors

  • Vivian Silva University of Passau
  • Siegfried Handschuh University of Passau
  • André Freitas University of Manchester

Keywords:

Text Entailment, Definition Graph, Distributional Navigation, Interpretability

Abstract

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, has become an important component for many natural language processing tasks, such as question answering and information retrieval. For entailments requiring world knowledge, most systems still work as a "black box," providing a yes/no answer that doesn't explain the reasoning behind it. We propose an interpretable text entailment approach that, given a structured definition graph, uses a navigation algorithm based on distributional semantic models to find a path in the graph which links text and hypothesis. If such path is found, it is used to provide a human-readable justification explaining why the entailment holds. Experiments show that the proposed approach present results comparable to some well-established entailment algorithms, while also meeting Explainable AI requirements, supplying clear explanations which allow the inference model interpretation.

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Published

2018-04-26

How to Cite

Silva, V., Handschuh, S., & Freitas, A. (2018). Recognizing and Justifying Text Entailment Through Distributional Navigation on Definition Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11914

Issue

Section

Main Track: NLP and Knowledge Representation