Personalizing Dialogue Agents via Meta-Learning

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5454–5459 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 5454 Personalizing Dialogue Agents via Meta-Learning Andrea Madotto†, Zhaojiang Lin†, Chien-Sheng Wu, Pascale Fung Center for Artificial Intelligence Research (CAiRE) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong [amadotto,zlinao,cwuak,pascale]@ust.hk Abstract Existing personalized dialogue models use hu- man designed persona descriptions to improve dialogue consistency. Collecting such descrip- tions from existing dialogues is expensive and requires hand-crafted feature designs. In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017) to personalized dialogue learning without using any persona descriptions. Our model learns to quickly adapt to new personas by lever- aging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions. Empirical results on Persona-chat dataset (Zhang et al., 2018) indi- cate that our solution outperforms non-meta- learning baselines using automatic evaluation metrics, and in terms of human-evaluated flu- ency and consistency. 1 Introduction There is a growing interest in learning personal- ized chit-chat dialogue agents for making chat- bots more consistent. Recently, a multi-turn conversational dataset called Persona-chat (Zhang et al., 2018) has been released, where two speak- ers are paired and a persona description (4-5 sen- tences) is randomly assigned to each of them. For example, “I am an old man” and “I like to play football” are one of the possible persona descrip- tions provided to the speaker. By conditioning the response generation on the persona descriptions, a chit-chat model is able to produce a more persona consistent dialogue (Zhang et al., 2018). However, it is difficult to capture a persona just by using few sentences, and collecting a non- synthetic set of persona descriptions from a real human-human conversation, e.g., Reddit, is chal- lenging as well since it requires hand-crafted fea- † These two authors contributed equally. ⋯ ⋯ Figure 1: The difference between finetuning from a) joint training on all personas and b) meta-learning per- sona. The solid line represents the optimization path of the initial parameters and dashed line the fine-tuning path. Meta-learned initial parameters can faster adapt to a new persona. ture designs (Mazare et al., 2018). In light of this, we propose to leverage a set of dialogues done by the same persona directly, instead of using its per- sona descriptions, to generate a more consistent response. We consider learning different personas as dif- ferent tasks via meta-learning algorithms, which is fundamentally different from optimizing the model to represent all the personas. A high-level intuition of the difference between these two ap- proaches is shown in Figure 1. We aim to learn a persona-independent model that is able to quickly adapt to a new persona given the dialogues. We formulate this task as a few-shot learning prob- lem, where K dialogues are used for training and the remaining for the …

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