Innatural language processing,textual entailment (TE), also known asnatural language inference (NLI), is a directional relation betweentext fragments. The relation holds whenever the truth of one text fragment follows from another text.
In the TE framework, the entailing and entailed texts are termedtext (t) andhypothesis (h), respectively. Textual entailment is not the same as purelogical entailment – it has a more relaxed definition: "t entailsh" (t ⇒h) if, typically, a human readingt would infer thath is most likely true.[1] (Alternatively:t ⇒h if and only if, typically, a human readingt would be justified in inferring the proposition expressed byh from the proposition expressed byt.[2]) The relation is directional because even if "t entailsh", the reverse "h entailst" is much less certain.[3][4]
Determining whether this relationship holds is an informal task, one which sometimes overlaps with the formal tasks offormal semantics (satisfying a strict condition will usually imply satisfaction of a less strict conditioned); additionally, textual entailment partially subsumesword entailment.
A characteristic of natural language is that there are many different ways to state what one wants to say: several meanings can be contained in a single text and the same meaning can be expressed by different texts. This variability of semantic expression can be seen as the dual problem of languageambiguity. Together, they result in amany-to-many mapping between language expressions and meanings. The task ofparaphrasing involves recognizing when two texts have the same meaning and creating a similar or shorter text that conveys almost the same information. Textual entailment is similar[6] but weakens the relationship to be unidirectional. Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarities of the texts involved.[4]
This section needs to beupdated. Please help update this article to reflect recent events or newly available information.(November 2023)
Textual entailment measuresnatural language understanding as it asks for a semantic interpretation of the text, and due to its generality remains an active area of research. Many approaches and refinements of approaches have been considered, such asword embedding, logical models, graphical models, rule systems, contextual focusing, and machine learning.[6] Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate.[3] As of 2005[update], state-of-the-art systems are far from human performance; a study found humans to agree on the dataset 95.25% of the time.[7] Algorithms from 2016 had not yet achieved 90%.[8]
Many natural language processing applications, likequestion answering,information extraction,summarization,multi-document summarization, andevaluation of machine translation systems, need to recognize that a particular target meaning can be inferred from different text variants. Typically entailment is used as part of a larger system, for example in a prediction system to filter out trivial or obvious predictions.[9] Textual entailment also has applications inadversarial stylometry, which has the objective of removing textual style without changing the overall meaning of communication.[10]
^Bos, Johan; Markert, Katja (6–8 October 2005). "Recognising textual entailment with logical inference". In Raymond Mooney; Joyce Chai; et al. (eds.).Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing – HLT '05. Vancouver: Association for Computational Linguistics. pp. 628–635.doi:10.3115/1220575.1220654.S2CID10202504.
^Zhao, Kai; Huang, Liang; Ma, Mingbo (4 January 2017). "Textual Entailment with Structured Attentions and Composition".arXiv:1701.01126 [cs.CL].
^Demszky, Dorottya; Guu, Kelvin; Liang, Percy (2018). "Transforming Question Answering Datasets Into Natural Language Inference Datasets".arXiv:1809.02922 [cs.CL].
^Conneau, Alexis; Rinott, Ruty; Lample, Guillaume; Williams, Adina; Bowman, Samuel R.; Schwenk, Holger; Stoyanov, Veselin (2018).XNLI: Evaluating Cross-lingual Sentence Representations(PDF).In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics. pp. 2475–2485.doi:10.18653/v1/D18-1269.
