What Makes Data-to-Text Generation Hard for Pretrained Language Models?

Moniba Keymanesh, Adrian Benton, Mark Dredze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Expressing natural language descriptions of structured facts or relations - data-to-text generation (D2T) - increases the accessibility of structured knowledge repositories. Previous work (Nan et al., 2020) shows that pre-trained language models (PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other hand, while auto-regressive PLMs can generalize from a few task examples, their efficacy at D2T is largely unexplored. Furthermore, we have an incomplete understanding of the limits of PLMs on D2T. In this work, we conduct an empirical study of both fine-tuned and auto-regressive PLMs on the DART multi-domain D2T dataset. We consider their performance as a function of the amount of task-specific data and how the data is incorporated into the models: zero and few-shot learning, and fine-tuning of model weights. In addition, we probe the limits of PLMs by measuring performance on subsets of the evaluation data: novel predicates and abstractive test examples. To improve the performance on these subsets, we investigate two techniques: providing predicate descriptions in the context and re-ranking generated candidates by information reflected in the source. Finally, we conduct a human evaluation of model errors and show that D2T generation tasks would benefit from datasets with more careful manual curation.

Original languageEnglish (US)
Title of host publicationGEM 2022 - 2nd Workshop on Natural Language Generation, Evaluation, and Metrics, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages539-554
Number of pages16
ISBN (Electronic)9781959429128
DOIs
StatePublished - 2022
Externally publishedYes
Event2nd Workshop on Natural Language Generation, Evaluation, and Metrics, GEM 2022, as part of EMNLP 2022 - Abu Dhabi, United Arab Emirates
Duration: Dec 7 2022 → …

Publication series

NameGEM 2022 - 2nd Workshop on Natural Language Generation, Evaluation, and Metrics, Proceedings of the Workshop

Conference

Conference2nd Workshop on Natural Language Generation, Evaluation, and Metrics, GEM 2022, as part of EMNLP 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period12/7/22 → …

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Fingerprint

Dive into the research topics of 'What Makes Data-to-Text Generation Hard for Pretrained Language Models?'. Together they form a unique fingerprint.

Cite this