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pro vyhledávání: '"Mueller, Erik T."'
Open domain neural dialogue models, despite their successes, are known to produce responses that lack relevance, diversity, and in many cases coherence. These shortcomings stem from the limited ability of common training objectives to directly expres
Externí odkaz:
http://arxiv.org/abs/1909.00925
Autor:
Olabiyi, Oluwatobi, Mueller, Erik T.
Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. These issues can attributed to reasons including (1) short-range model architectures that captu
Externí odkaz:
http://arxiv.org/abs/1908.01841
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture. To achieve this, we introduce an additional input modality into
Externí odkaz:
http://arxiv.org/abs/1905.01998
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such a
Externí odkaz:
http://arxiv.org/abs/1905.01992
We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encode
Externí odkaz:
http://arxiv.org/abs/1805.11752
Autor:
Mueller, Erik T., Minsky, Henry
We propose to use thought-provoking children's questions (TPCQs), namely Highlights BrainPlay questions, as a new method to drive artificial intelligence research and to evaluate the capabilities of general-purpose AI systems. These questions are des
Externí odkaz:
http://arxiv.org/abs/1508.06924
This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates doma
Externí odkaz:
http://arxiv.org/abs/1202.3728
Autor:
Mueller, Erik T.
Since scripts were proposed in the 1970's as an inferencing mechanism for AI and natural language processing programs, there have been few attempts to build a database of scripts. This paper describes a database and lexicon of scripts that has been a
Externí odkaz:
http://arxiv.org/abs/cs/0003004