Learning from Dialogue after Deployment: Feed Yourself, Chatbot! (1901.05415v4)

Abstract: The majority of conversations a dialogue agent sees over its lifetime occur after it has already been trained and deployed, leaving a vast store of potential training signal untapped. In this work, we propose the self-feeding chatbot, a dialogue agent with the ability to extract new training examples from the conversations it participates in. As our agent engages in conversation, it also estimates user satisfaction in its responses. When the conversation appears to be going well, the user's responses become new training examples to imitate. When the agent believes it has made a mistake, it asks for feedback; learning to predict the feedback that will be given improves the chatbot's dialogue abilities further. On the PersonaChat chit-chat dataset with over 131k training examples, we find that learning from dialogue with a self-feeding chatbot significantly improves performance, regardless of the amount of traditional supervision.

Learning from Dialogue after Deployment: The Self-Feeding Chatbot Approach

The paper, "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!", presents a novel framework for enhancing the performance of dialogue agents through continuous learning from post-deployment interactions. In traditional settings, chatbots are trained on large datasets, often derived from human-human interactions, before being deployed. These datasets, while extensive, do not always encompass the dynamic and task-specific nature of dialogues faced during real-world deployment. The authors address this limitation by introducing the concept of a self-feeding chatbot, capable of extracting and utilizing new training examples from its own interactions.

Key Concepts and Methodology

The primary innovation presented in this work is the self-feeding capability of the chatbot. This mechanism allows the dialogue agent to autonomously gather training data without additional manual intervention or annotation, thus reducing costs and enhancing adaptability to specific deployment environments. The approach is grounded in two auxiliary tasks: Satisfaction classification and Feedback prediction.

1. Satisfaction Classification: The chatbot uses a classifier to estimate user satisfaction with its responses. When a user's satisfaction score is high, their responses are harvested as new Human-Bot (HB) Dialogue examples. These examples are added to the training dataset alongside the pre-deployment Human-Human (HH) Dialogue examples.
2. Feedback Prediction: In cases where satisfaction is low, the chatbot solicits feedback from the user, asking them to suggest an appropriate response. This feedback is then used to create new training examples for the Feedback prediction task. These tasks are interwoven into the chatbot's dialogue model, allowing for continuous improvement through multitask learning.

The framework was validated using the PersonaChat dataset, providing empirical evidence that the self-feeding approach enhances dialogue performance irrespective of the initial number of HH training examples. By dividing the deployment conversation logs into HB Dialogue and Feedback datasets, the authors demonstrate improved dialogue quality and offer new potential for active learning in dialogue systems.

Numerical Results and Findings

The experiments reveal significant improvements in dialogue accuracy metrics. The incorporation of 60,000 HB Dialogue examples, alongside Feedback examples, facilitated substantial enhancements in the chatbot's conversational abilities, as measured by hits@1/20 metrics. Concretely, improvements were most pronounced when initial supervised data was limited, increasing accuracy by up to 31%. Even with large supervised datasets, the addition of real-time deployment data yielded gains, highlighting the value of dynamically sourced training for effective conversational engagement.

Moreover, the results underscore the complementarity of HB Dialogue and Feedback data, each addressing distinct facets of conversation improvement — HB Dialogue examples ensure coherent conversation flow, while Feedback examples target model errors, enabling focused correction.

Implications and Future Directions

The self-feeding chatbot framework opens new avenues for dialogue systems to emulate human-like learning and adaptability. The theoretical implications suggest a shift towards models capable of continuous self-improvement without exhaustive pre-labeled data, increasing the feasibility of deploying dialogue agents in diverse and evolving domains. Practically, this approach reduces the reliance on costly data curation and annotator interventions, paving the way for more dynamic and cost-effective conversational AI systems.

Future developments in this domain may explore the integration of meta-learning strategies, refining question formulations to optimize feedback utility. Additionally, expanding the framework's applicability to asymmetric dialogue settings could further enhance versatility.

In summary, the self-feeding approach presents a promising advancement in the autonomous learning capabilities of dialogue agents, fostering improved human-computer interaction quality and adaptability in real-world deployment environments. The release of new datasets accompanying this research provides a valuable resource for continuing exploration and innovation in dialogue system methodologies.