Benchmarking Compositionality with Formal Languages

June 21, 2023

Abstract

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties contribute to learnability of a compositional relation by a neural network. We find that the models either learn the relations completely or not at all. The key is transition coverage, setting a soft learnability limit at 400 examples per transition.

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AUTHORS

Written by

Josef Valvoda

Naomi Saphra

Jonathan Rawski

Adina Williams

Ryan Cotterell

Publisher

ACL

Research Topics

Human & Machine Intelligence

Natural Language Processing (NLP)

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