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Juncen Li

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2 papers

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IJCAI Conference 2018 Conference Paper

Adaboost with Auto-Evaluation for Conversational Models

Juncen Li
Ping Luo
Ganbin Zhou
Fen Lin
Cheng Niu

We propose a boosting method for conversational models to encourage them to generate more human-like dialogs. In our method, we consider existing conversational models as weak generators and apply Adaboost to update those models. However, conventional Adaboost cannot be directly applied on conversational models. Because for conversational models, conventional Adaboost cannot adaptively adjust the weight on the instance for subsequent learning, result from the simple comparison between the true output y (to an input x) and its corresponding predicted output y' cannot directly evaluate the learning performance on x. To address this issue, we develop the Adaboost with Auto-Evaluation (called AwE). In AwE, an auto-evaluator is proposed to evaluate the predicted results, which makes it applicable to conversational models. Furthermore, we present the theoretical analysis that the training error drops exponentially fast only if certain assumption over the proposed auto-evaluator holds. Finally, we empirically show that AwE visibly boosts the performance of existing single conversational models and also outperforms the other ensemble methods for conversational models.

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AAAI Conference 2018 Conference Paper

Conversational Model Adaptation via KL Divergence Regularization

Juncen Li
Ping Luo
Fen Lin
Bo Chen

In this study we formulate the problem of conversational model adaptation, where we aim to build a generative conversational model for a target domain based on a limited amount of dialogue data from this target domain and some existing dialogue models from related source domains. This model facilitates the fast building of a chatbot platform, where a new vertical chatbot with only a small number of conversation data can be supported by other related mature chatbots. Previous studies on model adaptation and transfer learning mostly focus on classiﬁcation and recommendation problems, however, how these models work for conversation generation are still un-explored. To this end, we leverage a KL divergence (KLD) regularization to adapt the existing conversational models. Speciﬁcally, it employs the KLD to measure the distance between source and target domain. Adding KLD as a regularization to the objective function allows the proposed method to utilize the information from source domains effectively. We also evaluate the performance of this adaptation model for the online chatbots in Wechat platform of public accounts using both the BLEU metric and human judgement. The experiments empirically show that the proposed method visibly improves these evaluation metrics.

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