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1
Graph Neural Networks for Multiparallel Word Alignment ...
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CaMEL: Case Marker Extraction without Labels ...
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Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments ...
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Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization ...
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5
Geographic Adaptation of Pretrained Language Models ...
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Distributed representations for multilingual language processing
Dufter, Philipp [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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7
Domain adaptation in Natural Language Processing
Sedinkina, Marina [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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8
Combining contextualized and non-contextualized embeddings for domain adaptation and beyond
Pörner, Nina Mareike [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021
DNB Subject Category Language
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9
Graph Algorithms for Multiparallel Word Alignment
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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10
Static Embeddings as Efficient Knowledge Bases? ...
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11
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models ...
Abstract: Recent progress in pretraining language models on large corpora has resulted in large performance gains on many NLP tasks. These large models acquire linguistic knowledge during pretraining, which helps to improve performance on downstream tasks via fine-tuning. To assess what kind of knowledge is acquired, language models are commonly probed by querying them with `fill in the blank' style cloze questions. Existing probing datasets mainly focus on knowledge about relations between words and entities. We introduce WDLMPro (Word Definition Language Model Probing) to evaluate word understanding directly using dictionary definitions of words. In our experiments, three popular pretrained language models struggle to match words and their definitions. This indicates that they understand many words poorly and that our new probing task is a difficult challenge that could help guide research on LMs in the future. ... : 5 pages, to appear in EACL 2021 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2102.03596
https://arxiv.org/abs/2102.03596
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12
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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13
Discrete and Soft Prompting for Multilingual Models ...
Zhao, Mengjie; Schütze, Hinrich. - : arXiv, 2021
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14
ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus ...
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15
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models ...
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16
Wine is Not v i n. -- On the Compatibility of Tokenizations Across Languages ...
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17
Graph Algorithms for Multiparallel Word Alignment ...
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18
Locating Language-Specific Information in Contextualized Embeddings ...
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19
Dynamic Contextualized Word Embeddings ...
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20
Measuring and Improving Consistency in Pretrained Language Models ...
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