XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Natural Language InferenceSimilar Papers 제목 키워드 기반
Explainable Compliance Detection with Multi-Hop Natural Language Inference on Assurance Case Structure
Ensuring complex systems meet regulations typically requires checking the validity of assurance cases through a claim-argument-evidence framework. Some challenges in this process include the complicated nature of legal a…
Natural Language InferenceFormal Proofs as Structured Explanations: Proposing Several Tasks on Explainable Natural Language Inference
In this position paper, we propose a way of exploiting formal proofs to put forward several explainable natural language inference (NLI) tasks. The formal proofs will be produced by a reliable and high-performing logic-b…
Natural Language InferencePositionUnsupervised Learning of Explainable Parse Trees for Improved Generalisation
Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail…
Natural Language InferenceSentenceSentiment AnalysisDoes External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
Natural language inference (NLI) requires models to learn and apply commonsense knowledge. These reasoning abilities are particularly important for explainable NLI systems that generate a natural language explanation in …
Natural Language InferenceExplainable Multi-hop Verbal Reasoning Through Internal Monologue
Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems. However, these approaches tend to not be interpretable because they do not make the intermediate reasoning…
Language ModelingLanguage ModellingQuestion Answering