X plai NLI : Explainable Natural Language Inference through Visual Analytics (2020)

Abstract: 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.

## introduction we present xplainli, an interactive visualization, web-based interface that computes natural language inference (nli) with three different approaches and provides sketches of explanations for the decision made by each approach. 1 an overview of xplainli is found in figure 1 . the user on the frontend (right) inputs a premise (p) and a hypothesis (h). the pair is passed to the backend (left) where it goes through a symbolic and a deep learning (dl) component, which compute an inference label each. each component also determines the rules and features that lead to the decision: for the symbolic one, we use natural logic @xcite inference rules to explain the inference label, while for the dl approach, we use insights gained from relevant work @xcite @xcite to account for the decision. the complete output enters the hybrid component, which combines the strengths of the symbolic nli engine and the dl model and determines which approach's label should be trusted based on semantic characteristics of the sentences. all output is forwarded to the frontend, where an intuitive visualization encodes the inference labels of the three approaches as well the corresponding explanations. the user can interact further with the interface by adding her own heuristics and by providing feedback on the inference label, which is used for improving the separate components. ## related work work on interpretability for nli is still at an early stage. one strand of research explains the models by "stress-testing" them and revealing the phenomena that the models cannot handle or by detecting bias in the training data @xcite @xcite . another strand of research has approached the task by directly learning natural language explanations along with the inference decision @xcite or creating distributional representations of syntactic and semantic inference rules @xcite and training machine-learning models on them. although all these approaches shed light on the processes behind the reasoning task, the insights gained have not yet been used in their full potential; xplainli seeks to fill this gap. ## xplainli backend model the backend outputs the inference relation for a given pair, as well as the features that lead to that decision, based on each of the following three approaches. the exact backend implementation and the performance of each of the approaches is detailed in @xcite ; this paper focuses on explainability. ## explainable visual interface the user interface (figure 1 , right) features three main components, all emphasizing the role of the human-in-the-loop. two text fields (for p and h) allow users to insert the inference pair to be computed. ## conclusion this paper presented an interactive visualization interface for explainable nli. the interface uses three different approaches to compute inference and visualizes the features that lead to each decision. in contrast to black-box machine-learning models, this approach enables users to get intuitions of the decision-making process @xcite , as well as to distill linguistic knowledge about the analyzed phenomena. the options for user-defined heuristics and user-driven learning can help refine the used models and components and optimize them to the users' intuition and domain understanding. to increase explainability and comparability, future work will allow the user to a) choose between different dl models for training, b) choose between hybrid models trained on different datasets, c) define their own rules for the hybrid classifier, and d) display the decision tree of the hybrid classifier for better exploration.