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USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation ...
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Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations ...
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Towards Explainable Evaluation Metrics for Natural Language Generation ...
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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset ...
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BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks ...
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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ...
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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ...
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Inducing Language-Agnostic Multilingual Representations ...
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Probing Multilingual BERT for Genetic and Typological Signals ...
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On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation ...
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Vec2Sent: Probing Sentence Embeddings With Natural Language Generation ...
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From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks ...
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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On aligning OpenIE extractions with Knowledge Bases: A case study
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Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry ...
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Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! ...
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What is the Essence of a Claim? Cross-Domain Claim Identification ...
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Abstract:
Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps. ... : Published at EMNLP 2017: http://www.aclweb.org/anthology/D/D17/D17-1217.pdf ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1704.07203 https://arxiv.org/abs/1704.07203
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