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TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing ...
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SpanNER: Named Entity Re-/Recognition as Span Prediction ...
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Align Voting Behavior with Public Statements for Legislator Representation Learning ...
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fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP ...
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{K-Adapter}: {I}nfusing {K}nowledge into {P}re-{T}rained {M}odels with {A}dapters ...
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Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble ...
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Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Classifying Dyads for Militarized Conflict Analysis
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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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.
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URL: https://doi.org/10.3929/ethz-b-000518996 https://hdl.handle.net/20.500.11850/518996
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Efficient Sampling of Dependency Structure
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Searching for More Efficient Dynamic Programs
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
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In: Findings of the Association for Computational Linguistics: EMNLP 2021 (2021)
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A Bayesian Framework for Information-Theoretic Probing
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation
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Come hither or go away? Recognising pre-electoral coalition signals in the news
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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters ...
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A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing
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In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 78-92 (2020) (2020)
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GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge ...
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Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning ...
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