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1
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion ...
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Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem ...
Abstract: We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context. We argue that reasoning is crucial for understanding this broader class of offensive utterances and release SLIGHT, a dataset to support research on this task. Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only ${\sim} 11\%$ accuracy. In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement. We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains and show that even naive reasoning models can yield improved performance in most situations. Furthermore, ... : 18 pages, 4 figures, 10 tables, accepted in Findings of the Association for Computational Linguistics 2022 ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Human-Computer Interaction cs.HC
URL: https://dx.doi.org/10.48550/arxiv.2204.10521
https://arxiv.org/abs/2204.10521
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3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
BUSINESS MEETING ...
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7
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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8
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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9
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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10
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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11
Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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12
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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13
Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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14
Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Miyao, Yusuke; Poerner, Nina; Roth, Benjamin. - : Ludwig-Maximilians-Universität München, 2018
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15
Explicit retrofitting of distributional word vectors
Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018
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16
Ensuring annotation consistency and accuracy for Vietnamese treebank [<Journal>]
Nguyen, Quy T. [Verfasser]; Le, Ha T. T. [Sonstige]; Miyao, Yusuke [Sonstige].
DNB Subject Category Language
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17
Universal Dependencies 2.1
In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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18
Open SDP 1.2
Flickinger, Dan; Hajič, Jan; Ivanova, Angelina. - : Oslo University, 2017. : Charles University, 2017
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19
Universal Dependencies 2.0 alpha (obsolete)
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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20
Universal Dependencies 2.0
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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