Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts

• URL: http://arxiv.org/abs/2210.16865v1
• Date: Sun, 30 Oct 2022 15:38:03 GMT
• Title: Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
• Authors: Ben Zhou and Kyle Richardson and Xiaodong Yu and Dan Roth
• Abstract summary: We look at large-scale intermediate pre-training of decomposition-based transformers using distant supervision from comparable texts. We show that with such intermediate pre-training, developing robust decomposition-based models for a diverse range of tasks becomes more feasible.
• Score: 65.84370471189676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets and resources built as part of this effort, the majority have small-scale annotations and limited scope, which is insufficient to solve general decomposition tasks. In this paper, we look at large-scale intermediate pre-training of decomposition-based transformers using distant supervision from comparable texts, particularly large-scale parallel news. We show that with such intermediate pre-training, developing robust decomposition-based models for a diverse range of tasks becomes more feasible. For example, on semantic parsing, our model, DecompT5, improves 20% to 30% on two datasets, Overnight and TORQUE, over the baseline language model. We further use DecompT5 to build a novel decomposition-based QA system named DecompEntail, improving over state-of-the-art models, including GPT-3, on both HotpotQA and StrategyQA by 8% and 4%, respectively.

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