In the NLP community, recent years have seen a surge of research activities
that address machines' ability to perform deep language understanding which
goes beyond what is explicitly stated in text, rather relying on reasoning and
knowledge of the world. Many benchmark tasks and datasets have been created to
support the development and evaluation of such natural language inference
ability. As these benchmarks become instrumental and a driving force for the
NLP research community, this paper aims to provide an overview of recent
benchmarks, relevant knowledge resources, and state-of-the-art learning and
inference approaches in order to support a better understanding of this growing
field.
Description
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
%0 Generic
%1 storks2019recent
%A Storks, Shane
%A Gao, Qiaozi
%A Chai, Joyce Y.
%D 2019
%K kg lm4kg nlp survey
%T Recent Advances in Natural Language Inference: A Survey of Benchmarks,
Resources, and Approaches
%U http://arxiv.org/abs/1904.01172
%X In the NLP community, recent years have seen a surge of research activities
that address machines' ability to perform deep language understanding which
goes beyond what is explicitly stated in text, rather relying on reasoning and
knowledge of the world. Many benchmark tasks and datasets have been created to
support the development and evaluation of such natural language inference
ability. As these benchmarks become instrumental and a driving force for the
NLP research community, this paper aims to provide an overview of recent
benchmarks, relevant knowledge resources, and state-of-the-art learning and
inference approaches in order to support a better understanding of this growing
field.
@misc{storks2019recent,
abstract = {In the NLP community, recent years have seen a surge of research activities
that address machines' ability to perform deep language understanding which
goes beyond what is explicitly stated in text, rather relying on reasoning and
knowledge of the world. Many benchmark tasks and datasets have been created to
support the development and evaluation of such natural language inference
ability. As these benchmarks become instrumental and a driving force for the
NLP research community, this paper aims to provide an overview of recent
benchmarks, relevant knowledge resources, and state-of-the-art learning and
inference approaches in order to support a better understanding of this growing
field.},
added-at = {2020-03-27T23:22:07.000+0100},
author = {Storks, Shane and Gao, Qiaozi and Chai, Joyce Y.},
biburl = {https://www.bibsonomy.org/bibtex/26dbfefd89df4a2776b5d37880c32b3c3/schwemmlein},
description = {Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches},
interhash = {4aa0fe6ee94c710e0486dc6cad159451},
intrahash = {6dbfefd89df4a2776b5d37880c32b3c3},
keywords = {kg lm4kg nlp survey},
note = {cite arxiv:1904.01172},
timestamp = {2020-03-27T23:22:33.000+0100},
title = {Recent Advances in Natural Language Inference: A Survey of Benchmarks,
Resources, and Approaches},
url = {http://arxiv.org/abs/1904.01172},
year = 2019
}