  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.
