Feedback-Based Self-Learning in Large-Scale Conversational AI Agents (1911.02557v1)

Abstract: Today, most large-scale conversational AI agents (e.g. Alexa, Siri, or Google Assistant) are built using manually annotated data to train the different components of the system. Typically, the accuracy of the ML models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages user-system interaction feedback signals to automate learning without any manual annotation. Users here tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win/loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.

Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

The paper "Feedback-Based Self-Learning in Large-Scale Conversational AI Agents" by Pragaash Ponnusamy et al. proposes an innovative methodology for enhancing the performance of conversational AI systems, such as Amazon Alexa, by leveraging user interaction feedback to facilitate autonomous learning. In contrast to traditional methods relying on manual data annotation for training system components such as ASR, NLU, and ER, this research introduces a scalable, feedback-based system capable of minimizing the dependency on manual intervention.

System Design and Methodology

The presented self-learning system capitalizes on user-generated reformulations—adjustments users make to their queries following unsatisfactory responses from the AI. By identifying and analyzing these reformulations across a vast dataset, the system can detect recurring patterns indicative of errors in ASR, NLU, or ER, and rectify them. A fundamental aspect of the proposed method is using an absorbing Markov Chain model as a collaborative filtering mechanism. This model ingests comprehensive logs of user interactions and structures them into sessions of reformulated queries, facilitating the identification of successful reformulations.

The architecture facilitates reformulation at runtime, intercepting user utterances before they are processed by the NLU system and rewriting them, if necessary, into more coherent forms that align with the NLU's expected structure. The determination of rewrite candidates is carried out by mining patterns of user interactions stored in a distributed and anonymized log database.

Results and Implications

The utilization of an absorbing Markov Chain empowers the system to achieve a substantial reduction in user-experienced defects, as evidenced by a 30% reduction in defect rates and a win/loss ratio of 11.8, in reported A/B tests with millions of Alexa users. The analysis of interactions and the conversion of historical user sessions into actionable insights represent a formidable advancement in dealing with vast datasets typical of large-scale systems.

Moreover, the implications of this self-learning mechanism are significant. By not requiring annotated data, the presented system fosters a reduction in resource dependency while maintaining high precision and scalability. This approach significantly improves user satisfaction through continuous enhancement of response accuracy without necessitating user confirmation for every reformulated query.

Future Prospects and Speculation

The research demonstrates a pioneering application of absorbing Markov Chains in vocal virtual assistants, setting a precedent for similar AI-driven self-improvement systems. The capacity to autonomously mitigate conversational friction suggests potential expansion into other domains of AI, where feedback loops can further adapt system responses without human oversight.

Looking forward, future developments could focus on further optimizing the accuracy of reformulations by integrating dynamic real-time learning algorithms that adapt more swiftly to unique user interaction styles and the broader conversational context. Additionally, enhancing the robustness of the system to reduce false positives in rewrite recommendations can further conserve the integrity of user intent during interactions.

In conclusion, this paper presents a robust model for integrating feedback-based learning into conversational AI systems. By effectively bridging the gap between user intent and system understanding, it enhances the adaptability and scalability of AI agents, positioning them to meet the evolving demands of a growing user base efficiently. As AI technologies continue to expand, the methodologies delineated in this paper could profoundly influence the design of future systems oriented towards autonomous refinement and user-centric optimization.