Neural network models have achieved state-of-the-art performances in a wide range of
natural language processing (NLP) tasks. However, a long-standing criticism against neural …

Abstract While Multiple Instance Learning (MIL) has shown promising results in digital
Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and …

We study the fact-checking problem, which aims to identify the veracity of a given claim.
Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its …

Self-rationalization models that predict task labels and generate free-text elaborations for
their predictions could enable more intuitive interaction with NLP systems. These models …

In interpretable NLP, we require faithful rationales that reflect the model's decision-making
process for an explained instance. While prior work focuses on extractive rationales (a …

Multi-view clustering (MVC) is a popular technique for improving clustering performance
using various data sources. However, existing methods primarily focus on acquiring …

Feature importance (FI) estimates are a popular form of explanation, and they are commonly
created and evaluated by computing the change in model confidence caused by removing …

A growing line of work has investigated the development of neural NLP models that can
produce rationales--subsets of input that can explain their model predictions. In this paper …

Many commonsense reasoning NLP tasks involve choosing between one or more possible
answers to a question or prompt based on knowledge that is often implicit. Large pretrained …

As an essential operation of legal retrieval, legal case matching plays a central role in
intelligent legal systems. This task has a high demand on the explainability of matching …