Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

TL;DR

This survey maps recent advances in natural language inference by examining benchmarks, knowledge resources, and learning/inference approaches. It synthesizes how benchmarks have evolved from linguistically driven tasks to those requiring external knowledge and commonsense reasoning, and it reviews the spectrum of knowledge resources and methods used to create and exploit them. The paper highlights trends, biases, and evaluation challenges, and emphasizes the need for integrating diverse knowledge sources and more robust, interpretable reasoning in models. It concludes by outlining future directions, including stronger external-knowledge integration, adversarial evaluation, and multidimensional assessment of model capabilities and generalization.

Abstract

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world. Many benchmark tasks and datasets have been created to support the development and evaluation of such natural language inference ability. As these benchmarks become instrumental and a driving force for the NLP research community, this paper aims to provide an overview of recent benchmarks, relevant knowledge resources, and state-of-the-art learning and inference approaches in order to support a better understanding of this growing field.

Figures (15)

• Figure 1: Main research efforts in natural language inference from the NLP community occur in three areas: benchmarks and tasks, knowledge resources, and learning and inference approaches.
• Figure 2: Since the early 2000s, there has been a surge of benchmark tasks geared toward natural language inference. In 2018, we saw the creation of more benchmarks of larger sizes than ever before.
• Figure 3: Examples from existing reference resolution benchmark tasks. Answers in bold.
• Figure 4: Examples from QA benchmarks which require inference through outside knowledge. Answers in bold.
• Figure 5: Examples from RTE benchmarks. Answers in bold.