Polite Dialogue Generation Without Parallel Data

Polite Dialogue Generation Without Parallel Data Tong Niu and Mohit Bansal UNC Chapel Hill {tongn, mbansal}@cs.unc.edu Abstract Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguis- tically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually un- available. We present three weakly-supervised models that can generate diverse, polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dia- logue model with a language model trained on stand-alone polite utterances. Our label-fine- tuning (LFT) model prepends to each source sequence a politeness-score scaled label (pre- dicted by our state-of-the-art politeness classi- fier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the cor- responding score. Our reinforcement learn- ing model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sam- pled response. We also present two retrieval- based, polite dialogue model baselines. Hu- man evaluation validates that while the Fu- sion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce sig- nificantly more polite responses without sacri- ficing dialogue quality. 1 Introduction Generating stylistic, personality-based language is crucial to developing engaging, convincing, and trustworthy conversational agents, for their effec- tive application in intelligent tutoring, home assis- tance, online reservations/purchasing, health care, etc. Most current chatbots and conversational mod- els lack any such style, which can be a social issue because human users might learn biased styles from such interactions, e.g., kids learning to be rude be- cause the dialogue system encourages short, curt re- sponses, and also does not itself use politeness to set an example.1 In this work, we focus on the impor- tant and diverse paralinguistic style axis of polite- ness vs. rudeness (Brown and Levinson, 1987). Generating stylistic dialogue responses is a sub- stantially challenging task because the generated re- sponse needs to be syntactically and semantically fluent, contextually-relevant to the conversation, as well as convey accurate paralinguistic features. This is further complicated by the fact that content and style are only available in separate unpaired datasets, as opposed to translation-type parallel datasets con- taining regular-to-stylistic text pairs. Hence, we need indirectly-supervised models that can incorpo- rate style into the generated response in absence of parallel data (i.e., where the training data for the conversation, versus style components, comes from two different datasets or domains), while still main- taining conversation relevance. In this work, we present three such weakly- supervised models2 that can generate diverse, nat- ural, and contextually-relevant polite (and rude) di- 1https://qz.com/701521/parents-are-worried-the-amazon- echo-is-conditioning-their-kids-to-be-rude/ 2The first version of this paper with the three Fusion, Discrete-LFT, and Polite-RL models was submitted on Oct 1, 2017. The two retrieval baselines and the continuous version 373 Transactions of the Association for Computational Linguistics, vol. 6, pp. 373–389, 2018. Action Editor: Colin Cherry. Submission batch: 10/2017; Revision batch: …

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