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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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Abstract:
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \url{http://github.com/UKPLab/coling2018-xling_argument_mining}. ... : Accepted at Coling 2018 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1807.08998 https://dx.doi.org/10.48550/arxiv.1807.08998
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What is the Essence of a Claim? Cross-Domain Claim Identification ...
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