Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Ben Bogin; Sanjay Subramanian; Matt Gardner; Jonathan Berant

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arXiv · 2007.00266

Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Ben Bogin·Sanjay Subramanian·Matt Gardner·Jonathan Berant

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Abstract

Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art models in grounded question answering often do not explicitly perform decomposition, leading to difficulties in generalization to out-of-distribution examples. In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser. Our model induces latent trees, driven by end-to-end (the answer) supervision only. We show that this inductive bias towards tree structures dramatically improves systematic generalization to out-of-distribution examples, compared to strong baselines on an arithmetic expressions benchmark as well as on CLOSURE, a dataset that focuses on systematic generalization for grounded question answering. On this challenging dataset, our model reaches an accuracy of 96.1%, significantly higher than prior models that almost perfectly solve the task on a random, in-distribution split.

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Ben Bogin, Sanjay Subramanian, Matt Gardner, Jonathan Berant. 2020-07-01. Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering. https://arxiv.org/abs/2007.00266

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