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    <titleInfo>
        <title>Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search</title>
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        <namePart type="given">Jingxuan</namePart>
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    <name type="personal">
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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        <name type="personal">
            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="given">Lalitha</namePart>
            <namePart type="family">Devi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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    <abstract>This paper concerns the problem of topic prediction in target-guided conversation, which requires the system to proactively and naturally guide the topic thread of the conversation, ending up with achieving a designated target subject. Existing studies usually resolve the task with a sequence of single-turn topic prediction. Greedy decision is made at each turn since it is impossible to explore the topics in future turns under the single-turn topic prediction mechanism. As a result, these methods often suffer from generating sub-optimal topic threads. In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP). To alleviate the problem of generating sub-optimal topic thread, Monte Carlo tree search (MCTS) is employed to improve the topic prediction by conducting long-term planning. At online topic prediction, given a target and a start utterance, our proposed MM-TP (MCTS-enhanced MDP for Topic Prediction) firstly performs MCTS to enhance the policy for predicting the topic for each turn. Then, two retrieval models are respectively used to generate the responses of the agent and the user. Quantitative evaluation and qualitative study showed that MM-TP significantly improved the state-of-the-art baselines.</abstract>
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            <start>324</start>
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%0 Conference Proceedings
%T Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search
%A Yang, Jingxuan
%A Li, Si
%A Guo, Jun
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F yang-etal-2021-multi
%X This paper concerns the problem of topic prediction in target-guided conversation, which requires the system to proactively and naturally guide the topic thread of the conversation, ending up with achieving a designated target subject. Existing studies usually resolve the task with a sequence of single-turn topic prediction. Greedy decision is made at each turn since it is impossible to explore the topics in future turns under the single-turn topic prediction mechanism. As a result, these methods often suffer from generating sub-optimal topic threads. In this paper, we formulate the target-guided conversation as a problem of multi-turn topic prediction and model it under the framework of Markov decision process (MDP). To alleviate the problem of generating sub-optimal topic thread, Monte Carlo tree search (MCTS) is employed to improve the topic prediction by conducting long-term planning. At online topic prediction, given a target and a start utterance, our proposed MM-TP (MCTS-enhanced MDP for Topic Prediction) firstly performs MCTS to enhance the policy for predicting the topic for each turn. Then, two retrieval models are respectively used to generate the responses of the agent and the user. Quantitative evaluation and qualitative study showed that MM-TP significantly improved the state-of-the-art baselines.
%U https://aclanthology.org/2021.icon-main.39
%P 324-334

Markdown (Informal)

[Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search](https://aclanthology.org/2021.icon-main.39) (Yang et al., ICON 2021)

• Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search (Yang et al., ICON 2021)

ACL

• Jingxuan Yang, Si Li, and Jun Guo. 2021. Multi-Turn Target-Guided Topic Prediction with Monte Carlo Tree Search. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 324–334, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).