Transforming Multi-Conditioned Generation from Meaning Representation
Joosung Lee

TL;DR
This paper introduces a one-stage GPT2-based framework for multi-conditioned language generation from meaning representations, simplifying the process and achieving competitive results with less data and zero-shot capabilities.
Contribution
Proposes a novel single-step generation model directly from meaning representations, eliminating the need for sentence planning and surface realization stages.
Findings
Comparable performance to previous systems on E2E dataset
Effective zero-shot generation with only 10% of training data
Demonstrates the simplicity and efficiency of the proposed approach
Abstract
In task-oriented conversation systems, natural language generation systems that generate sentences with specific information related to conversation flow are useful. Our study focuses on language generation by considering various information representing the meaning of utterances as multiple conditions of generation. NLG from meaning representations, the conditions for sentence meaning, generally goes through two steps: sentence planning and surface realization. However, we propose a simple one-stage framework to generate utterances directly from MR (Meaning Representation). Our model is based on GPT2 and generates utterances with flat conditions on slot and value pairs, which does not need to determine the structure of the sentence. We evaluate several systems in the E2E dataset with 6 automatic metrics. Our system is a simple method, but it demonstrates comparable performance to…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
