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Calibrate your listeners! Robust communication-based training for pragmatic speakers
- Publication Year :
- 2021
-
Abstract
- To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners.<br />Comment: Findings of EMNLP 2021 Code: https://github.com/rosewang2008/calibrate_your_listeners
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2110.05422
- Document Type :
- Working Paper