Hierarchical reinforcement learning for situated natural language generation

Dethlefs, Nina; Cuayáhuitl, Heriberto

Abstract

Natural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and interaction history. This is particularly true in interactive and situated settings. In this paper we present a novel approach for situated Natural Language Generation in dialogue that is based on hierarchical reinforcement learning and learns the best utterance for a context by optimisation through trial and error. The model is trained from human–human corpus data and learns particularly to balance the trade-off between efficiency and detail in giving instructions: the user needs to be given sufficient information to execute their task, but without exceeding their cognitive load. We present results from simulation and a task-based human evaluation study comparing two different versions of hierarchical reinforcement learning: One operates using a hierarchy of policies with a large state space and local knowledge, and the other additionally shares knowledge across generation subtasks to enhance performance. Results show that sharing knowledge across subtasks achieves better performance than learning in isolation, leading to smoother and more successful interactions that are better perceived by human users.

Citation

Dethlefs, N., & Cuayáhuitl, H. (2015). Hierarchical reinforcement learning for situated natural language generation. Natural language engineering, 21(3), 391-435. https://doi.org/10.1017/S1351324913000375

• Acceptance Date: Nov 18, 2013
• Online Publication Date: Jan 10, 2014
• Publication Date: 2015-05
• Deposit Date: Jan 19, 2016
• Publicly Available Date: Nov 23, 2017
• Journal: Natural language engineering
• Print ISSN: 1351-3249
• Publisher: Cambridge University Press
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Issue: 3
• Pages: 391-435
• DOI: https://doi.org/10.1017/S1351324913000375
• Keywords: Natural language generation
• Public URL: https://hull-repository.worktribe.com/output/384273
• Publisher URL: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=9719879&fulltextType=RA&fileId=S1351324913000375
• Additional Information: Author's accepted manuscript of article published in: Natural language engineering, 2015, v.21, issue 3.
• Contract Date: Nov 23, 2017