RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding

TL;DR

The paper introduces the RDR method, which enhances language understanding by combining paraphrasing, external graph encoding, and classification to improve robustness and semantic capture in neural models.

Contribution

It proposes a novel RDR paradigm integrating three objectives to prevent benchmark gaming and improve semantic understanding in neural language models.

Findings

Up to 2% performance improvement on GLUE tasks

Enhanced robustness against benchmark manipulation

Better semantic pattern capture in models

Abstract

Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data. Through Prior research, it has been evident that NLU benchmarks are susceptible to manipulation by neural models, wherein these models exploit statistical artifacts within the encoded external knowledge to artificially inflate performance metrics for downstream tasks. Our proposed approach, known as the Recap, Deliberate, and Respond (RDR) paradigm, addresses this issue by incorporating three distinct objectives within the neural network pipeline. Firstly, the Recap objective involves paraphrasing the input text using a paraphrasing model in order to summarize and encapsulate its essence. Secondly, the Deliberation objective entails encoding external graph information related to entities mentioned in the input text, utilizing a graph embedding model. Finally, the Respond objective employs a classification head model that utilizes representations from the Recap and Deliberation modules to generate the final prediction. By cascading these three models and minimizing a combined loss, we mitigate the potential for gaming the benchmark and establish a robust method for capturing the underlying semantic patterns, thus enabling accurate predictions. To evaluate the effectiveness of the RDR method, we conduct tests on multiple GLUE benchmark tasks. Our results demonstrate improved performance compared to competitive baselines, with an enhancement of up to 2\% on standard metrics. Furthermore, we analyze the observed evidence for semantic understanding exhibited by RDR models, emphasizing their ability to avoid gaming the benchmark and instead accurately capture the true underlying semantic patterns.