A retrieval-based dialogue system utilizing utterance and context embeddings

TL;DR

This paper explores a retrieval-based dialogue system that uses utterance and context embeddings with LSH Forest for answer retrieval, outperforming generative models on Ubuntu and customer service datasets.

Contribution

It introduces a retrieval approach utilizing embeddings and LSH Forest for dialogue response selection, demonstrating superior performance over generative models.

Findings

Retrieval-based approach outperforms generative models on benchmark datasets.

Embedding-based retrieval effectively captures conversation semantics.

Promising results suggest future research directions in retrieval-based dialogue systems.

Abstract

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.