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Findings of the IWSLT 2020 Evaluation campaign
Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022
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2
MeetDot: Videoconferencing with Live Translation Captions ...
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MeetDot: Videoconferencing with Live Translation Captions ...
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4
Abstract Meaning Representation (AMR) Annotation Release 3.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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Parallel Corpus Filtering via Pre-trained Language Models ...
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Findings of the IWSLT 2020 Evaluation campaign ...
Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020
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Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020
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8
Learning to Pronounce Chinese Without a Pronunciation Dictionary ...
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9
Cross-lingual entity extraction and linking for 300 languages
Pan, Xiaoman. - 2020
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10
Multi-Source Attention for Unsupervised Domain Adaptation.
Bollegala, Danushka; Cui, Xia. - : Association for Computational Linguistics, 2020
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11
Neighbors helping the poor: improving low-resource machine translation using related languages [<Journal>]
Pourdamghani, Nima [Verfasser]; Knight, Kevin [Verfasser]
DNB Subject Category Language
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12
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling ...
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13
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding ...
Abstract: We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features than state-of-the-art multi-lingual embedding learning methods do. ... : 10 pages ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1804.07875
https://dx.doi.org/10.48550/arxiv.1804.07875
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14
Abstract Meaning Representation (AMR) Annotation Release 2.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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15
Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017
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16
Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features
Wiegand, Michael [Verfasser]; Schulder, Marc [Verfasser]; Ruppenhofer, Josef [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016
DNB Subject Category Language
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17
Opinion Holder and Target Extraction on Opinion Compounds – A Linguistic Approach
Wiegand, Michael [Verfasser]; Bocionek, Christine [Verfasser]; Ruppenhofer, Josef [Verfasser]. - Mannheim : Institut für Deutsche Sprache, Bibliothek, 2016
DNB Subject Category Language
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18
Leadership discourse as basis and means for developing L2 students into future leaders
Knight, Kevin; Candlin, Chris. - : Reading, UK : Garnet Publishing, 2015
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19
Analysing the discourses of leadership as a basis for developing leadership communication skills in a second or foreign language
Knight, Kevin. - : Sydney, Australia : Macquarie University, 2015
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20
Statistical Techniques for Translating to Morphologically Rich Languages (Dagstuhl Seminar 14061)
Koehn, Philipp; Uszkoreit, Hans; Schmid, Helmut. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2014. : Dagstuhl Reports. Dagstuhl Reports, Volume 4, Issue 2, 2014
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