Augmenting Memory Networks for Rich and Efficient Retrieval in Grounded Dialogue
Grounded dialogue consists of conditioning a conversation on additional latent inputs ("factoids") beyond the dialogue context, such as Wikipedia articles, IMDB reviews, persona, and images. Due to a scarcity of <context, factoid> labels, it is common practice to jointly learn the knowledge-selection and grounded response generation tasks end-to-end. When conditioning the response on these factoids, previous work has either treated the factoids as a weighed average vector, or separately computed probabilities for each <context, factoid> pair. However, the former creates a bottleneck whilst the latter prevents factoids from being considered jointly. Our new method, PolyMemNet, learns a matrix representation of the context and factoids, allowing for multiple factoids to be jointly considered in response selection, without imposing a bottleneck. We show how this achieves up to a $17\%$ boost in knowledge-selection accuracy and $13\%$ in response-selection accuracy versus memory networks.
Code (1)
Tasks
ArticlesResponse GenerationRetrievalSimilar Papers 제목 키워드 기반
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challenge by retrieving the relevant sentences …
Response GenerationRetrievalTrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable,…
Answer GenerationTurning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (e.g., vehicle, laptop), have been gaining research interest in rece…
Data AugmentationResponse GenerationRetrievalSynthetic Data GenerationHexa: Self-Improving for Knowledge-Grounded Dialogue System
A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible co…
Dialogue GenerationDiversityRetrievalAn Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model that stores knowledge in its parameters, or…
Computational EfficiencyQuestion AnsweringRetrieval