LEEET s-Dial: Linguistic Entrainment in End-to-End Task-oriented Dialogue systems (2024)

Abstract: Linguistic entrainment, or alignment, represents a phenomenon where linguistic patterns employed by conversational participants converge to one another. While entrainment has been shown to produce a more natural user experience, most dialogue systems do not have any provisions for it. In this work, we introduce methods for achieving dialogue entrainment in a GPT-2-based end-to-end task-oriented dialogue system through the utilization of shared vocabulary. We experiment with training instance weighting, entrainment-specific loss, and additional conditioning to generate responses that align with the user. We demonstrate that all three approaches produce significantly better entrainment than the base, non-entrainment-optimized model, as confirmed by both automated and manual evaluation metrics.

## introduction during a natural dialogue, speakers adapt (entrain, align) to the way of speaking of their conversational partners, thereby establishing a shared understanding. this was shown to correlate with dialogue success @xcite and it occurs at multiple linguistic levels: speakers synchronize their speech rate and phonetic patterns @xcite , adopt shared lexical terms @xcite and employ similar syntactic constructions @xcite . consequently, to facilitate successful and natural conversations, achieving entrainment is desirable in task-oriented dialogue systems (dss), where the aim is to assist users in accomplishing tasks such as reserving tickets or venues. in dss, entrainment can be useful for aligning on common lexical terms, increasing dialogue naturalness and possibly leading to higher efficiency. for instance, consider the two examples in figure 1 -using "reservation" vs. "booking" or "depart" vs. "leave". however, few prior works attempted at entrainment in dss, mostly with rule-"reservation" vs. "booking" (sng076) input: yes. i'll need a reservation for 3 nights starting sunday. there are 3 people in my party. dataset: i have made reservations for 3 starting on sunday for 3 nights. baseline: i'm sorry, but the booking was unsuccessful. would you like to try another hotel? ours: yes, your reservation is all set for 3 nights starting sunday. your reference number is 00000032. do you need anything else? input: hi, i'm looking to catch a train to norwich. are there any that depart at or around 17:00? dataset: there are departures most days from cambridge at 17:36. would you like any more information of train times or help booking a train? baseline: yes, there are 49 trains that leave norwich on that day. what day would you like to travel? ours: i have a train departing cambridge at 17:36 and arriving norwich at 18:55. would you like to book this train? based or modular dss only @xcite @xcite . recent years have seen significant advancements in task-oriented dss through end-to-end neural models, fully trainable from data @xcite @xcite . use of pretrained language models (lms) yielded more fluent responses while simultaneously ensuring the comprehension of user intents and achieving successful dialogues @xcite @xcite . however, the generated responses often suffer from low diversity compared to human-human dialogues @xcite , and the dss lack any dedicated support or mechanisms for entrainment, as their training relies on crossentropy or other objectives that focus on dialogue content rather than phrasing. using the gpt-2-based two-stage system augpt @xcite as our task-oriented end-to-end baseline ds, we propose the following three approaches to improve entrainment: we show that all our proposed approaches increase entrainment while minimally affecting other dialogue metrics; instance weighting and keyword conditioning also show improved human rankings. our experimental code is released on github. 1 ## proposed approaches as our baseline model, we choose augpt @xcite , a gpt-2 @xcite based task-oriented end-to-end ds, which models dialogue as a sequence-to-sequence task. same as other contemporary end-to-end systems, augpt works in two steps: (1) generating belief state (userpreferred slot values) from dialogue history and user input, and (2) generating response based on dialogue history, user input, generated belief state and database results (which are based on the belief state). we modify the response generation step. our modifications address primarily lexical entrainment and involve instance weighting (section 3.1), an additional loss based on user input tokens (section 3.2), and further conditioning on user keyword tokens on model input (section 3.3). ## conclusion although previous research showed that linguistic entrainment helps dialogue success, its application in end-to-end task-oriented dialogue systems has been largely overlooked. to address this gap, we introduced three techniques aimed at improving lexical entrainment of system responses to user inputs: (1) we show that prioritizing training instances with higher system-user word overlap improves entrainment, with comparable success rates. in the future, we plan to incorporate longer context and focus more on syntactical entrainment. we also plan to use retrieval-augmented generation @xcite .