Building a Conversational Agent Overnight with Dialogue Self-Play (1801.04871v1)

Abstract: We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.

Overview of "Building a Conversational Agent Overnight with Dialogue Self-Play"

The paper "Building a Conversational Agent Overnight with Dialogue Self-Play" introduces a novel framework, Machines Talking To Machines (M2M), which significantly reduces the time and effort required to develop robust goal-oriented dialogue agents. The key innovation of M2M lies in its ability to effectively combine automation with crowdsourcing, enabling the rapid generation of high-quality dialogue datasets. This approach contrasts the traditional Wizard-of-Oz (WoZ) data collection methods, which are often costly and inadvertently limited in dialogue diversity.

The M2M framework operates in two main stages. First, a simulated user bot and a domain-agnostic system bot engage in a self-play dialogue, generating comprehensive dialogue outlines characterized by sequences of template utterances and their accompanying semantic parses. Following this step, the outlines undergo a crowdsourcing process where human workers rewrite them into natural language, maintaining the semantic integrity of the original templates. The entire procedure can be completed in a short time frame, often under eight hours, making it a highly efficient method for dialogue dataset production.

Methodology

At the heart of the M2M approach is the dialogue self-play mechanism. The dialogue system developer provides a task schema and an API client, constituting the task-specific information necessary for dialogue interaction. The system bot and the user bot then use this information to simulate dialogue exchanges, resulting in outlines that represent possible interaction flows. These outlines ensure comprehensive coverage of dialogue scenarios anticipated in actual usage contexts, whilst automatically annotating the dialogues to streamline the dataset preparation process.

Significantly, the interaction between the user bot and the system bot is not constrained by predefined language complexity, thus encouraging the generation of diverse dialogue flows. This approach addresses the shortcomings of WoZ methods, which may result in simplistic or overly complex dialogues not suitable for effective training of dialogue agents.

Results

The framework's efficacy was demonstrated by developing a corpus of 3,000 dialogues across two domains—restaurant reservations and movie ticket bookings. A comparative analysis showed that datasets obtained through M2M exhibited greater linguistic diversity and dialogue flow coverage than traditionally compiled dialogue datasets like DSTC2. Specifically, metrics on unique token ratios and dialogue transitions confirmed M2M's advantage in fostering varied and high-fidelity data.

Moreover, M2M's reliance on the dialogue self-play paradigm ensures robust initial training of dialogue models, which can be further refined using reinforcement learning techniques once deployed. This capability aligns M2M with contemporary trends that emphasize adaptive learning and continuous improvement of AI systems through real user interactions.

Implications and Future Development

The M2M framework holds significant implications for the future of conversational AI development. By democratizing and accelerating the creation of dialogue datasets, M2M positions itself as a vital tool for developers seeking to deploy task-specific dialogue agents rapidly and efficiently. Practically, this advancement could lead to more personalized and responsive consumer-facing bots in wider-ranging applications beyond database querying.

Theoretically, M2M suggests a shift in focus towards leveraging controlled automation to manage dialogue complexity systematically. Future investigations might explore expanding M2M’s conceptual base to encompass more diverse dialogue types and other models of AI-human interaction. There is also potential for integrating more sophisticated user simulation strategies to further align simulated dialogues with real user behavior.

In conclusion, M2M presents a compelling case for rethinking existing procedural frameworks in dialogue system development. Its strategic integration of dialogue self-play and crowdsourcing offers a promising direction for creating scalable and adaptable AI-driven conversational agents.