Proceedings of the First Workshop on NLP for Conversational AI 2019
DOI: 10.18653/v1/w19-4103
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Augmenting Neural Response Generation with Context-Aware Topical Attention

Abstract: Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Se… Show more

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Cited by 50 publications
(41 citation statements)
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References 19 publications
(15 reference statements)
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“…Since we focus on image-grounded conversation, the personality information in the data is discarded. For the scenario of text-based conversation, we use the Reddit Conversation Corpus 1 published by Dziri et al (2018) which contains more than 15M dialogues and each dialogue has at least 3 utterances. We keep 30, 000 most frequent words in the two data as a vocabulary for the text encoder and the response decoder.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Since we focus on image-grounded conversation, the personality information in the data is discarded. For the scenario of text-based conversation, we use the Reddit Conversation Corpus 1 published by Dziri et al (2018) which contains more than 15M dialogues and each dialogue has at least 3 utterances. We keep 30, 000 most frequent words in the two data as a vocabulary for the text encoder and the response decoder.…”
Section: Methodsmentioning
confidence: 99%
“…For the first scenario, we exploit the image-chat data published in (Shuster et al, 2018), and check if the model learned using both multi-modal and single-modal data can improve upon the state-ofthe-art model learned solely from the multi-modal data, especially when the multi-modal data is small in scale. For the second scenario, we leverage the Reddit Conversation Corpus published by Dziri et al (2018), and examine if latent images can provide useful signals for response generation. Evaluation results indicate that the proposed model can significantly outperform state-of-the-art models in terms of response quality in Scenario I and response informativeness in Scenario II.…”
Section: Introductionmentioning
confidence: 99%
“…We used the Reddit conversation corpus to train our models. The Reddit conversation corpus, made available by Dziri et al (2018), consists of data extracted from 95 top-ranked subreddits that discuss various topics such as sports, news, education and politics. The corpus contains 9M training examples, 500K development dialogues and 400K dialogues as test data.…”
Section: Data and Modelsmentioning
confidence: 99%
“…• THRED: Topic Augmented Hierarchical Encoder-Decoder (Dziri et al, 2018) which uses topic words along with a hierarchical encoderdecoder to produce a response.…”
Section: Data and Modelsmentioning
confidence: 99%
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