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Hits 41 – 60 of 360
41
A Graph Auto-encoder Model of Derivational Morphology
Schütze, Hinrich
;
Pierrehumbert, Janet
;
Hofmann, Valentin
. - : Ludwig-Maximilians-Universität München, 2020
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42
Relational and Fine-Grained Argument Mining
Trautmann, Dietrich
;
Fromm, Michael
;
Tresp, Volker
...
In: Datenbank-Spektrum (2020)
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43
Identifying Elements Essential for BERT’s Multilinguality
Schütze, Hinrich
;
Dufter, Philipp
. - 2020
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44
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings
Sabet, Masoud Jalili
;
Dufter, Philipp
;
Schütze, Hinrich
In: Findings of ACL: EMNLP 2020 (2020)
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45
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations
Liang, Sheng
;
Dufter, Philipp
;
Schütze, Hinrich
. - 2020
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46
Increasing Learning Efficiency of Self-Attention Networks through Direct Position Interactions, Learnable Temperature, and Convoluted Attention
Schütze, Hinrich
;
Dufter, Philipp
;
Schmitt, Martin
. - 2020
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47
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
Sabet, Masoud Jalili
;
Dufter, Philipp
;
Yvon, François
;
Schütze, Hinrich
. - : arXiv, 2020
Abstract:
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 ... : EMNLP (Findings) 2020 ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/2004.08728
https://dx.doi.org/10.48550/arxiv.2004.08728
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48
Identifying Necessary Elements for BERT's Multilinguality ...
Dufter, Philipp
;
Schütze, Hinrich
. - : arXiv, 2020
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49
Identifying Elements Essential for BERT’s Multilinguality ...
Dufter, Philipp
;
Schütze, Hinrich
. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2020
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50
Identifying Necessary Elements for BERT’s Multilinguality ...
Dufter, Philipp
;
Schütze, Hinrich
. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2020
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51
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations ...
Liang, Sheng
;
Dufter, Philipp
;
Schütze, Hinrich
. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2020
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52
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings ...
Sabet, Masoud Jalili
;
Dufter, Philipp
;
Schütze, Hinrich
. - : Universitätsbibliothek der Ludwig-Maximilians-Universität München, 2020
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53
Dynamic Contextualized Word Embeddings ...
Hofmann, Valentin
;
Pierrehumbert, Janet B.
;
Schütze, Hinrich
. - : arXiv, 2020
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54
Predicting the Growth of Morphological Families from Social and Linguistic Factors ...
Hofmann, Valentin
;
Pierrehumbert, Janet
;
Schütze, Hinrich
. - : Association for Computational Linguistics, 2020
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55
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly ...
Kassner, Nora
;
Schütze, Hinrich
. - : Association for Computational Linguistics, 2020
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56
Quantifying the Contextualization of Word Representations with Semantic Class Probing ...
Zhao, Mengjie
;
Dufter, Philipp
;
Yaghoobzadeh, Yadollah
. - : Association for Computational Linguistics, 2020
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57
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity ...
Poerner, Nina
;
Waltinger, Ulli
;
Schütze, Hinrich
. - : Association for Computational Linguistics, 2020
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58
Monolingual and Multilingual Reduction of Gender Bias in Contextualized Representations ...
The 28th International Conference on Computational Linguistics 2020
;
., Hinrich
;
Dufter, Philipp
. - : Underline Science Inc., 2020
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59
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ...
Zhao, Mengjie
;
Zhu, Yi
;
Shareghi, Ehsan
. - : arXiv, 2020
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60
Unsupervised Embedding-based Detection of Lexical Semantic Changes ...
Asgari, Ehsaneddin
;
Ringlstetter, Christoph
;
Schütze, Hinrich
. - : arXiv, 2020
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