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1
Learning to Generate Code Comments from Class Hierarchies ...
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Zero-shot Task Adaptation using Natural Language ...
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3
TellMeWhy: A Dataset for Answering Why-Questions in Narratives ...
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4
Supervised attention from natural language feedback for reinforcement learning
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5
Learning to Update Natural Language Comments Based on Code Changes ...
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6
Continually improving grounded natural language understanding through human-robot dialog
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7
STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE
Mooney,Raymond. - 2017
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8
Identifying lexical relationships and entailments with distributional semantics
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9
Advances in statistical script learning
Pichotta, Karl. - 2017
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10
Natural-language video description with deep recurrent neural networks
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11
Dialog for natural language to code
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12
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text ...
Abstract: This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality. ... : Accepted at EMNLP 2016. Project page: http://vsubhashini.github.io/language_fusion.html ...
Keyword: Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1604.01729
https://arxiv.org/abs/1604.01729
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13
Using Sentence-Level LSTM Language Models for Script Inference ...
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14
Natural language semantics using probabilistic logic
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15
Representing Meaning with a Combination of Logical and Distributional Models ...
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16
Inducing grammars from linguistic universals and realistic amounts of supervision
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17
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language ...
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18
Grounded language learning models for ambiguous supervision
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19
Learning from natural instructions
Goldwasser, Dan. - 2012
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Learning language from ambiguous perceptual context
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