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Hits 41 – 60 of 69

41
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences ...
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42
Can LSTM Learn to Capture Agreement? The Case of Basque ...
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43
Universal Dependencies 2.1
In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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44
Universal Dependencies 2.0 alpha (obsolete)
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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45
CoNLL 2017 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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46
Universal Dependencies 2.0
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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47
Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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48
Universal Dependencies 2.1
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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49
Exploring the Syntactic Abilities of RNNs with Multi-task Learning ...
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50
The Interplay of Semantics and Morphology in Word Embeddings ...
Avraham, Oded; Goldberg, Yoav. - : arXiv, 2017
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51
Controlling Linguistic Style Aspects in Neural Language Generation ...
Ficler, Jessica; Goldberg, Yoav. - : arXiv, 2017
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52
Towards String-to-Tree Neural Machine Translation ...
Aharoni, Roee; Goldberg, Yoav. - : arXiv, 2017
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53
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments ...
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54
Universal Dependencies 1.4
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2016
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55
Universal Dependencies 1.3
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2016
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56
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks ...
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57
Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss ...
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58
Semi Supervised Preposition-Sense Disambiguation using Multilingual Data ...
Gonen, Hila; Goldberg, Yoav. - : arXiv, 2016
Abstract: Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets. ... : 12 pages; COLING 2016 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1611.08813
https://dx.doi.org/10.48550/arxiv.1611.08813
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59
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies ...
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60
Universal Dependencies 1.1
Agić, Željko; Aranzabe, Maria Jesus; Atutxa, Aitziber. - : Universal Dependencies Consortium, 2015
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