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Incorporating Constituent Syntax for Coreference Resolution ...
Jiang, Fan; Cohn, Trevor. - : arXiv, 2022
Abstract: Syntax has been shown to benefit Coreference Resolution from incorporating long-range dependencies and structured information captured by syntax trees, either in traditional statistical machine learning based systems or recently proposed neural models. However, most leading systems use only dependency trees. We argue that constituent trees also encode important information, such as explicit span-boundary signals captured by nested multi-word phrases, extra linguistic labels and hierarchical structures useful for detecting anaphora. In this work, we propose a simple yet effective graph-based method to incorporate constituent syntactic structures. Moreover, we also explore to utilise higher-order neighbourhood information to encode rich structures in constituent trees. A novel message propagation mechanism is therefore proposed to enable information flow among elements in syntax trees. Experiments on the English and Chinese portions of OntoNotes 5.0 benchmark show that our proposed model either beats a strong ... : 9 pages, 2 figures, and 6 tables. In Proceedings of the 36th AAAI Conference on Artificial Intelligence. AAAI 2022 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2202.10710
https://arxiv.org/abs/2202.10710
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2
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation ...
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3
Fairness-aware Class Imbalanced Learning ...
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4
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation ...
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Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning ...
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It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data ...
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7
Generating Diverse Descriptions from Semantic Graphs ...
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8
Balancing out Bias: Achieving Fairness Through Training Reweighting ...
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9
ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents
In: Front Res Metr Anal (2021)
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10
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning ...
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11
Please Mind the Root: Decoding Arborescences for Dependency Parsing
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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12
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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13
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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14
Learning a Cost-Effective Annotation Policy for Question Answering
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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15
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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16
Speakers Fill Lexical Semantic Gaps with Context
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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17
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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18
Intrinsic Probing through Dimension Selection
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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19
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
In: Findings of the Association for Computational Linguistics: EMNLP 2020 (2020)
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20
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
Sutcliffe, Richard; Samothrakis, Spyridon; Quteineh, Husam. - : Association for Computational Linguistics, 2020
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