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Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing ...
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Please Mind the Root: Decoding Arborescences for Dependency Parsing
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Learning a Cost-Effective Annotation Policy for Question Answering
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Intrinsic Probing through Dimension Selection
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
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LitCrit: exploring intentions as a basis for automated feedback on Related Work.
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Unsupervised stance detection for arguments from consequences
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Abstract:
Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.
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Keyword:
004 Informatik
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URL: https://madoc.bib.uni-mannheim.de/57482
https://madoc.bib.uni-mannheim.de/57482/
https://madoc.bib.uni-mannheim.de/57482/1/2020.emnlp-main.4.pdf
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| 15 |
XCOPA: A multilingual dataset for causal commonsense reasoning
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From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers
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Inference in an Approach to Discourse Anaphora
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In: North East Linguistics Society (2020)
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A survey of cross-lingual features for zero-shot cross-lingual semantic parsing ...
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