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Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization ...
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Graph Algorithms for Multiparallel Word Alignment
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ; The 2021 Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03424044 ; The 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Nov 2021, Punta Cana, Dominica ; https://2021.emnlp.org/ (2021)
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Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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Measuring and Improving Consistency in Pretrained Language Models ...
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ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus ...
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Continuous Entailment Patterns for Lexical Inference in Context ...
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A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
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Measuring and Improving Consistency in Pretrained Language Models
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1012-1031 (2021) (2021)
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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP
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In: Transactions of the Association for Computational Linguistics, Vol 9, Pp 1408-1424 (2021) (2021)
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SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings
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In: EMNLP 2020 ; https://hal.archives-ouvertes.fr/hal-03013194 ; EMNLP 2020, Association for Computational Linguistics, Nov 2020, Online, United States. pp.1627 - 1643 (2020)
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Abstract:
International audience ; Word alignments are useful for tasks like statistical and neural machine translation (NMT) and cross-lingual annotation projection. Statistical word aligners perform well, as do methods that extract alignments jointly with translations in NMT. However, most approaches require parallel training data and quality decreases as less training data is available. We propose word alignment methods that require no parallel data. The key idea is to leverage multilingual word embeddings {--} both static and contextualized {--} for word alignment. Our multilingual embeddings are created from monolingual data only without relying on any parallel data or dictionaries. We find that alignments created from embeddings are superior for four and comparable for two language pairs compared to those produced by traditional statistical aligners {--} even with abundant parallel data; e.g., contextualized embeddings achieve a word alignment F1 for English-German that is 5 percentage points higher than eflomal, a high-quality statistical aligner, trained on 100k parallel sentences.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; contextualized embeddings; machine translation; word alignement
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URL: https://hal.archives-ouvertes.fr/hal-03013194/file/2020.findings-emnlp.147.pdf https://hal.archives-ouvertes.fr/hal-03013194/document https://hal.archives-ouvertes.fr/hal-03013194
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Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction ...
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Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
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BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance
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Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction
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Predicting the Growth of Morphological Families from Social and Linguistic Factors
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