Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog

TL;DR

This paper presents efficient methods for retrieving and utilizing unstructured knowledge to generate accurate responses in task-oriented dialogue systems, balancing computational cost and retrieval accuracy.

Contribution

It introduces hierarchical classification and Dense Knowledge Retrieval methods for efficient document selection in knowledge-grounded dialogue generation.

Findings

Hierarchical classification improves efficiency with high accuracy.

Dense Knowledge Retrieval reduces computation time by over 100x.

Retrieval Augmented Generation effectively generates responses from multiple snippets.

Abstract

This paper summarizes our work on the first track of the ninth Dialog System Technology Challenge (DSTC 9), "Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access". The goal of the task is to generate responses to user turns in a task-oriented dialog that require knowledge from unstructured documents. The task is divided into three subtasks: detection, selection and generation. In order to be compute efficient, we formulate the selection problem in terms of hierarchical classification steps. We achieve our best results with this model. Alternatively, we employ siamese sequence embedding models, referred to as Dense Knowledge Retrieval, to retrieve relevant documents. This method further reduces the computation time by a factor of more than 100x at the cost of degradation in R@1 of 5-6% compared to the first model. Then for either approach, we use Retrieval Augmented Generation to generate responses based on multiple selected snippets and we show how the method can be used to fine-tune trained embeddings.