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Universal Dependencies 2.2
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In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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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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Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging ...
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The Best of Both Worlds: Lexical Resources To Improve Low-Resource Part-of-Speech Tagging ...
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Bleaching Text: Abstract Features for Cross-lingual Gender Prediction ...
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