A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

TL;DR

This paper introduces a neural network-based system for generating context-aware conversational responses trained on Twitter data, improving response quality over traditional models by effectively incorporating dialogue context.

Contribution

It proposes a novel neural architecture that handles context in response generation, addressing sparsity issues and enabling end-to-end training on large-scale unstructured conversations.

Findings

Consistent improvements over baseline models in response relevance.

Effective handling of contextual information in dialogue generation.

Demonstrated scalability with large Twitter datasets.

Abstract

We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations. A neural network architecture is used to address sparsity issues that arise when integrating contextual information into classic statistical models, allowing the system to take into account previous dialog utterances. Our dynamic-context generative models show consistent gains over both context-sensitive and non-context-sensitive Machine Translation and Information Retrieval baselines.