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

TL;DR

The proposed approach, known as the Recap, Deliberate, and Respond (RDR) paradigm, addresses natural language understanding issues by incorporating three distinct objectives within the neural network pipeline, and demonstrates improved performance compared to competitive baselines.

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.

Figures (2)

• Figure 1: (a) A traditional neural network pipeline which is enhanced with external knowledge to handle GLUE tasks such as entailment, similarity, and other types of natural language inference tasks. Initially, the tokenized text undergoes encoding by a language model, which outputs an embedding. Following that, a method based on graph embedding is employed to extract and embed a subgraph that is relevant to the input text. This involves extracting entities within a certain distance threshold from the entities present in the text. Subsequently, the two embeddings - the language model embedding and the graph embedding, are merged and passed through a classification head to obtain the predicted logits. To train this model, the cross-entropy loss between the predicted logits and the actual output is minimized. (b) The RDR paradigm. The tokenized input goes through a paraphrasing model, and a paraphrasing loss is calculated. Additionally, the graph-embedding-based subgraph extraction method is compared against a ground truth subgraph, then a graph embedding loss is computed. The total loss is the sum of the losses from the paraphrasing loss, graph embedding loss, and classification head loss.
• Figure 2: Illustration of the process of extracting subgraphs from the knowledge graph given an input instance. For the pre-trained Graph Encoder Network, we use ConceptNet's Numberbatch embeddings and a span length of three in our experiments.