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Is Information Density Uniform in Task-Oriented Dialogues? ...
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Analysing Human Strategies of Information Transmission as a Function of Discourse Context ...
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Syntactic Persistence in Language Models: Priming as a Window into Abstract Language Representations ...
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Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts ...
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Words are the Window to the Soul: Language-based User Representations for Fake News Detection ...
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Analysing Lexical Semantic Change with Contextualised Word Representations ...
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Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping
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In: Cathcart, Chundra; Rama, Taraka (2020). Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping. In: Fernández, Raquel; Linzen, Tal. Proceedings of the 24th Conference on Computational Natural Language Learning. Online: Association for Computational Linguistics, 620-630. (2020)
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Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network.
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Evaluating the Representational Hub of Language and Vision Models ...
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Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
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MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
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MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
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You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP ...
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Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ...
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La adquisición del lenguaje de tres a seis años y sus posibles trastornos
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Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ...
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Abstract:
We propose a grounded dialogue state encoder which addresses a foundational issue on how to integrate visual grounding with dialogue system components. As a test-bed, we focus on the GuessWhat?! game, a two-player game where the goal is to identify an object in a complex visual scene by asking a sequence of yes/no questions. Our visually-grounded encoder leverages synergies between guessing and asking questions, as it is trained jointly using multi-task learning. We further enrich our model via a cooperative learning regime. We show that the introduction of both the joint architecture and cooperative learning lead to accuracy improvements over the baseline system. We compare our approach to an alternative system which extends the baseline with reinforcement learning. Our in-depth analysis shows that the linguistic skills of the two models differ dramatically, despite approaching comparable performance levels. This points at the importance of analyzing the linguistic output of competing systems beyond numeric ... : Accepted to NAACL 2019 ...
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Keyword:
Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1809.03408 https://dx.doi.org/10.48550/arxiv.1809.03408
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