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Natural language processing [PDF]
, 2003 Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information ...
Chowdhury, Gobinda G.
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Building Greibach Normal Form Grammars Using Genetic Algorithms
Signals, 2022 Grammatical inference of context-free grammars using positive and negative language examples is among the most challenging task in modern artificial and natural language technology.
Nikolaos Anastasopoulos +1 more
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Learning Natural Language Inference with LSTM [PDF]
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016 Natural language inference (NLI) is a fundamentally important task in natural language processing that has many applications. The recently released Stanford Natural Language Inference (SNLI) corpus has made it possible to develop and evaluate learning-centered methods such as deep neural networks for natural language inference (NLI).
WANG, Shuohang, JIANG, Jing
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FarsTail: a Persian natural language inference dataset
Soft Computing, 2023 Natural language inference (NLI) is known as one of the central tasks in natural language processing (NLP) which encapsulates many fundamental aspects of language understanding. With the considerable achievements of data-hungry deep learning methods in NLP tasks, a great amount of effort has been devoted to develop more diverse datasets for different ...
Hossein Amirkhani +5 more
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Knowledge-Aware Debiased Inference Model Integrating Intervention and Counter-factual [PDF]
Jisuanji kexue yu tansuoThe abductive natural language inference task (Abductive NLI) seeks to select more plausible hypothetical events based on given antecedent events and consequent events. However, inherent biases such as “logical defects” and “single-sentence label leakage”
SUN Shengjie, MA Tinghuai, HUANG Kai
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Identifying inherent disagreement in natural language inference [PDF]
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021 Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text. In this paper, we investigate how to tease systematic inferences (i.e., items for which people agree on the NLI label) apart from disagreement items (i.e., items which lead to different annotations ...
Xinliang Frederick Zhang +1 more
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Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models
IEEE Access, 2023 Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation.
Akiyoshi Tomihari, Hitomi Yanaka
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Natural Language Inference with Mixed Effects
CoRR, 2020 There is growing evidence that the prevalence of disagreement in the raw annotations used to construct natural language inference datasets makes the common practice of aggregating those annotations to a single label problematic. We propose a generic method that allows one to skip the aggregation step and train on the raw annotations directly without ...
William Gantt +2 more
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Porting Natural Language Interfaces between Domains: An Experimental User Study with the ORAKEL System [PDF]
, 2007 Cimiano P, Haase P, Heizmann J. Porting Natural Language Interfaces between Domains: An Experimental User Study with the ORAKEL System. In: Chin DN, Zhou MX, Lau TA, Puerta AR, eds.
Zhou, Michelle X. +6 more
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Disentangling Reasoning Factors for Natural Language Inference
Big Data Mining and AnalyticsNatural Language Inference (NLI) seeks to deduce the relations of two texts: a premise and a hypothesis. These two texts may share similar or different basic contexts, while three distinct reasoning factors emerge in the inference from premise to ...
Xixi Zhou +5 more
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