Calibrate your listeners! Robust communication-based training for pragmatic speakers

• URL: http://arxiv.org/abs/2110.05422v1
• Date: Mon, 11 Oct 2021 17:07:38 GMT
• Title: Calibrate your listeners! Robust communication-based training for pragmatic speakers
• Authors: Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman
• Abstract summary: We propose a method that uses a population of neural listeners to regularize speaker training. We show that language drift originates from the poor uncertainty calibration of a neural listener. 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.
• Score: 30.731870275051957
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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