In this article, we propose a novel approach called the Recap, Deliberate, and Respond (RDR) paradigm to address limitations in natural language understanding (NLU) using neural networks. The RDR approach incorporates three distinct objectives within the neural network pipeline: Recap, which involves paraphrasing the input text; Deliberation, which focuses on encoding external graph information related to entities mentioned in the input; and Respond, which employs a classification head model to classify the input. By integrating these objectives, the RDR paradigm enhances the neural network’s ability to comprehend language and extract relevant information for downstream tasks.
Title: What is RDR?
The RDR paradigm is a novel approach that addresses limitations in NLU using neural networks. It incorporates three distinct objectives within the neural network pipeline: Recap, Deliberation, and Respond.
Title: How does RDR work?
The RDR approach starts by paraphrasing the input text using a dedicated model, which captures the essential information and essence of the input. Then, it encodes external graph information related to entities mentioned in the input text using a graph embedding model. Finally, a classification head model is used to classify the input based on the integrated context from the paraphrasing and graph embedding models.
Title: What are the benefits of RDR?
The RDR paradigm enhances the neural network’s ability to comprehend language and extract relevant information for downstream tasks. By incorporating external context into the neural network pipeline, RDR improves the model’s performance in NLU tasks.
Title: How is RDR trained?
To train the RDR model, a cross-entropy loss between the predicted logits and the actual output is minimized. Additionally, a paraphrasing loss is calculated between the paraphrased input and the original input. A graph embedding loss is also computed based on the comparison of the extracted subgraph with a ground truth subgraph. The total loss is the sum of these losses.
Title: Conclusion
In conclusion, RDR offers an innovative approach to enhanced language understanding using neural networks. By integrating external context into the neural network pipeline, RDR improves the model’s performance in NLU tasks and enhances its ability to comprehend language.
