# Logic-Guided Data Augmentation and Regularization for Consistent Question Answering

Source: [Logic-Guided Data Augmentation and Regularization for Consistent Question Answering](<https://www.sota2.com/research/paper/68fa67991e209131cea17578>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:02:46.151Z

Authors: Akari Asai, Hannaneh Hajishirzi

Published: 2020-04-21T17:03:08.000Z

## Abstract

Many natural language questions require qualitative, quantitative or logical comparisons between two entities or events. This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models. Our method leverages logical and linguistic knowledge to augment labeled training data and then uses a consistency-based regularizer to train the model. Improving the global consistency of predictions, our approach achieves large improvements over previous methods in a variety of question answering (QA) tasks including multiple-choice qualitative reasoning, cause-effect reasoning, and extractive machine reading comprehension. In particular, our method significantly improves the performance of RoBERTa-based models by 1-5% across datasets. We advance the state of the art by around 5-8% on WIQA and QuaRel and reduce consistency violations by 58% on HotpotQA. We further demonstrate that our approach can learn effectively from limited data.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Knowledge Graph Hypothesis Generation on DBpedia50 (specific-relation)](<https://www.sota2.com/research/sota/knowledge-graph-hypothesis-generation-on-dbpedia50-specific-relation.md>): Knowledge Graph Hypothesis Generation; DBpedia50 (specific-relation); Jaccard Index: 70.4.

- [Logical Hypothesis Generation on DBpedia50 (pattern)](<https://www.sota2.com/research/sota/logical-hypothesis-generation-on-dbpedia50-pattern.md>): Logical Hypothesis Generation; DBpedia50 (pattern); Jaccard: 69.5.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.10157>)

[PDF](<https://arxiv.org/pdf/2004.10157.pdf>)