@inproceedings{b573d875671f46a2971a184ba37c6ba8,
title = "A Survey on Textual Entailment: Benchmarks, Approaches and Applications",
abstract = "Textual Entailment Recognition (TER), also known as natural language inference, is a crucial task in natural language processing that combines many fundamental aspects of language understanding. TER focuses on predicting the inference relationship between text fragments. Given two sentences (known as premise and hypothesis), the goal is to determine if the meaning of the hypothesis can be entailed/inferred from the premise. Understanding this relationship between two texts can be helpful in several tasks, such as information retrieval, semantic parsing, and common-sense reasoning. This survey paper provides an overview of TER and its variants and applications. We then highlighted TER benchmark datasets for various languages and the main approaches that have been proposed to tackle the problem for a better understanding of the progress this task has reached.",
keywords = "Survey, deep learning, machine learning, natural language inference, rule-based, textual entailment",
author = "Yara Alharahseheh and Rasha Obeidat and Mahmoud Al-Ayoub and Maram Gharaibeh",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on Information and Communication Systems, ICICS 2022 ; Conference date: 21-06-2022 Through 23-06-2022",
year = "2022",
doi = "10.1109/ICICS55353.2022.9811200",
language = "English",
series = "2022 13th International Conference on Information and Communication Systems, ICICS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "328--336",
editor = "Muhannad Quwaider",
booktitle = "2022 13th International Conference on Information and Communication Systems, ICICS 2022",
address = "United States",
}