Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata
<|reference_start|>Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata: 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.<|reference_end|>
@article{chen2024control-dag:,
    title={Control-DAG: Constrained Decoding for Non-Autoregressive Directed
  Acyclic T5 using Weighted Finite State Automata},
    author={Jinghong Chen, Weizhe Lin, Jingbiao Mei, Bill Byrne},
    journal={arXiv preprint arXiv:2404.06854},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06854},
    primaryClass={cs.CL}
}