Local Knowledge Powered Conversational Agents
This work addresses the issue of context-aware conversational AI for users needing more engaging and relevant interactions, representing an incremental improvement by integrating local knowledge into existing models.
The paper tackles the problem of conversational agents lacking informative and coherent responses by proposing a dialog framework that incorporates local knowledge and past dialogues, demonstrating that this approach outperforms state-of-the-art models on Reddit data across informativeness, coherency, and realisticness measures.
State-of-the-art conversational agents have advanced significantly in conjunction with the use of large transformer-based language models. However, even with these advancements, conversational agents still lack the ability to produce responses that are informative and coherent with the local context. In this work, we propose a dialog framework that incorporates both local knowledge as well as users' past dialogues to generate high quality conversations. We introduce an approach to build a dataset based on Reddit conversations, where outbound URL links are widely available in the conversations and the hyperlinked documents can be naturally included as local external knowledge. Using our framework and dataset, we demonstrate that incorporating local knowledge can largely improve informativeness, coherency and realisticness measures using human evaluations. In particular, our approach consistently outperforms the state-of-the-art conversational model on the Reddit dataset across all three measures. We also find that scaling the size of our models from 117M to 8.3B parameters yields consistent improvement of validation perplexity as well as human evaluated metrics. Our model with 8.3B parameters can generate human-like responses as rated by various human evaluations in a single-turn dialog setting.