Dual Supervised Learning for Natural Language Understanding and Generation

TL;DR

This paper introduces a dual supervised learning framework that leverages the inherent duality between natural language understanding and generation to improve performance in both tasks.

Contribution

It proposes a novel dual supervised learning framework specifically designed for joint NLU and NLG tasks, exploiting their dual relationship.

Findings

Boosts performance for both NLU and NLG tasks

Demonstrates the effectiveness of dual learning in NLP

Provides a new perspective for joint language modeling

Abstract

Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in the literature. This paper proposes a new learning framework for language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks.