Textual Entailment Recognition - Algorithms and Datasets: Analyzing algorithms and datasets for textual entailment recognition, which assesses the logical relationship between pairs of text fragments

Abstract

Textual entailment recognition is a fundamental task in natural language processing that involves determining whether a given text fragment (hypothesis) logically follows from another text fragment (premise). This paper provides an overview and analysis of algorithms and datasets used in textual entailment recognition. We discuss various approaches, including rule-based methods, machine learning models, and deep learning architectures, highlighting their strengths and limitations. Additionally, we examine popular datasets such as SNLI, MultiNLI, and SciTail, which are widely used for training and evaluating entailment models. Through this analysis, we aim to provide insights into the current state of textual entailment recognition research and suggest future directions for advancements in the field.