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1
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing ...
Gui, Tao; Wang, Xiao; Zhang, Qi. - : arXiv, 2021
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2
SpanNER: Named Entity Re-/Recognition as Span Prediction ...
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3
Align Voting Behavior with Public Statements for Legislator Representation Learning ...
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4
fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP ...
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5
{K-Adapter}: {I}nfusing {K}nowledge into {P}re-{T}rained {M}odels with {A}dapters ...
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6
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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7
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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8
Classifying Dyads for Militarized Conflict Analysis
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
Abstract: Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is to compare these two causes in terms of how they correlate with conflict between two entities. We do this by devising a set of textual and graph-based features which represent each of the causes. The features are extracted from Wikipedia and modeled as a large graph. Nodes in this graph represent entities connected by labeled edges representing ally or enemy-relationships. This allows casting the problem as an edge classification task, which we term dyad classification. We propose and evaluate classifiers to determine if a particular pair of entities are allies or enemies. Our results suggest that our systemic features might be slightly better correlates of conflict. Further, we find that Wikipedia articles of allies are semantically more similar than enemies.
URL: https://doi.org/10.3929/ethz-b-000518996
https://hdl.handle.net/20.500.11850/518996
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9
Efficient Sampling of Dependency Structure
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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10
Searching for More Efficient Dynamic Programs
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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11
“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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12
A Bayesian Framework for Information-Theoretic Probing
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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13
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
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14
Come hither or go away? Recognising pre-electoral coalition signals in the news
Rehbein, Ines; Ponzetto, Simone Paolo; Adendorf, Anna. - : Association for Computational Linguistics, 2021
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15
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters ...
Wang, Ruize; Tang, Duyu; Duan, Nan. - : arXiv, 2020
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16
FLAT: Chinese NER Using Flat-Lattice Transformer ...
Li, Xiaonan; Yan, Hang; Qiu, Xipeng. - : arXiv, 2020
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17
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 78-92 (2020) (2020)
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18
Chinese computational linguistics : 18th China National Conference, CCL 2019, Kunming, China, October 18-20, 2019 : proceedings
Liu, Zhiyuan (Herausgeber); Jiang, Heng (Herausgeber); Liu, Yang (Herausgeber). - Cham, Switzerland : Springer, 2019
BLLDB
UB Frankfurt Linguistik
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19
GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge ...
Huang, Luyao; Sun, Chi; Qiu, Xipeng. - : arXiv, 2019
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20
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning ...
Peng, Minlong; Xing, Xiaoyu; Zhang, Qi. - : arXiv, 2019
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