Results 41 to 50 of about 52,458 (341)

Explaining Simple Natural Language Inference [PDF]

Proceedings of the 13th Linguistic Annotation Workshop, 2019
The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today’s research, especially in the machine learning community. This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently ...
Aikaterini-Lida Kalouli, Annebeth Buis, Livy Real, Martha Palmer, Valeria de Paiva   +4 more
openaire   +1 more source

SICK-NL: A Dataset for Dutch Natural Language Inference [PDF]

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021
To appear at EACL ...
Gijs Wijnholds, Michael Moortgat
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Annotation Difficulties in Natural Language Inference

Anais do XIII Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL 2021), 2021
State-of-the-art models have obtained high accuracy on mainstream Natural Language Inference (NLI) datasets. However, recent research has suggested that the task is far from solved. Current models struggle to generalize and fail to consider the inherent human disagreements in tasks such as NLI.
Aikaterini-Lida Kalouli, Livy Real, Annebeth Buis, Martha Palmer, Valeria de Paiva   +4 more
openaire   +2 more sources

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
doaj   +1 more source

University Student Dropout Prediction Using Pretrained Language Models

Applied Sciences, 2023
Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic information (e.
Hyun-Sik Won, Min-Ji Kim, Dohyun Kim, Hee-Soo Kim, Kang-Min Kim   +4 more
doaj   +1 more source

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, Mohammad AzariJafari, Soroush Faridan-Jahromi, Zeinab Kouhkan, Zohreh Pourjafari, Azadeh Amirak   +5 more
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Knowledge-Aware Debiased Inference Model Integrating Intervention and Counter-factual [PDF]

Jisuanji kexue yu tansuo
The 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
doaj   +1 more source

Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora

, 2022
Within textual emotion classification, the set of relevant labels depends on the domain and application scenario and might not be known at the time of model development.
Plaza-del-Arco, Flor Miriam, Klinger, Roman, Martín-Valdivia, María-Teresa   +2 more
core  

Inherent Disagreements in Human Textual Inferences

Transactions of the Association for Computational Linguistics, 2019
We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context ...
Pavlick, Ellie, Kwiatkowski, Tom
doaj   +1 more source