Importance of a Search Strategy in Neural Dialogue Modelling
Year: 2018
Type: preprint
Abstract: Search strategies for generating a response from a neural dialogue model have received relatively little attention compared to improving network architectures and learning algorithms in recent years. In this paper, we consider a standard neural dialogue model based on recurrent networks with an attention mechanism, and focus on evaluating the impact of the search strategy. We compare four search strategies: greedy search, beam search, iterative beam search and iterative beam search followed by selection scoring. We evaluate these strategies using human evaluation of full conversations and compare them using automatic metrics including log-probabilities, scores and diversity metrics. We observe a significant gap between greedy search and the proposed iterative beam search augmented with selection scoring, demonstrating the importance of the search algorithm in neural dialogue generation.
Source: arXiv (Cornell University)
Authors: Ilya Kulikov, Alexander Miller, Kyunghyun Cho, Jason Weston
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2898658996