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        <title>A Neural Network Approach for Knowledge-Driven Response Generation</title>
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        <namePart type="given">Pavlos</namePart>
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        <namePart type="given">Jonathon</namePart>
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    <name type="personal">
        <namePart type="given">Elena</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.</abstract>
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%0 Conference Proceedings
%T A Neural Network Approach for Knowledge-Driven Response Generation
%A Vougiouklis, Pavlos
%A Hare, Jonathon
%A Simperl, Elena
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F vougiouklis-etal-2016-neural
%X We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.
%U https://aclanthology.org/C16-1318
%P 3370-3380

Markdown (Informal)

[A Neural Network Approach for Knowledge-Driven Response Generation](https://aclanthology.org/C16-1318) (Vougiouklis et al., COLING 2016)

• A Neural Network Approach for Knowledge-Driven Response Generation (Vougiouklis et al., COLING 2016)

ACL

• Pavlos Vougiouklis, Jonathon Hare, and Elena Simperl. 2016. A Neural Network Approach for Knowledge-Driven Response Generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3370–3380, Osaka, Japan. The COLING 2016 Organizing Committee.