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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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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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Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification
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Relational and Fine-Grained Argument Mining
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In: Datenbank-Spektrum (2020)
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
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In: Findings of ACL: EMNLP 2020 (2020)
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Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations
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Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention
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SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
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Identifying Necessary Elements for BERT's Multilinguality ...
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Identifying Elements Essential for BERT’s Multilinguality ...
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Identifying Necessary Elements for BERT’s Multilinguality ...
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Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations ...
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