Control-dag: Constrained Decoding For Non-autoregressive Directed Acyclic T5 Using Weighted Finite State Automata

Chen Jinghong, Lin Weizhe, Mei Jingbiao, Byrne Bill. Arxiv 2024

[Paper]    
Applications GPT Model Architecture Pretraining Methods Transformer

The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation. Two issues prevent its application to general Natural Language Generation (NLG) tasks: frequent Out-Of-Vocabulary (OOV) errors and the inability to faithfully generate entity names. We introduce Control-DAG, a constrained decoding algorithm for our Directed Acyclic T5 (DA-T5) model which offers lexical, vocabulary and length control. We show that Control-DAG significantly enhances DA-T5 on the Schema Guided Dialogue and the DART datasets, establishing strong NAR results for Task-Oriented Dialogue and Data-to-Text NLG.

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