Revisiting Challenges in Data-to-Text Generation with Fact Grounding

By Hongmin Wang. 2020

56 citations recorded by the source corpus.

Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source. To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box- and line-score tables. However, limited attempts have been made in this direction and the challenges remain. We observe a prominent bottleneck in the corpus where only about 60% of the summary contents can be grounded to the boxscore records. Such information deficiency tends to misguide a conditioned language model to produce unconditioned random facts and thus leads to factual hallucinations. In this work, we restore the information balance and revamp this task to focus on fact-grounded data-to-text generation. We introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding), with 50% more data from the year 2017-19 and enriched input tables, and hope to attract research focuses in this direction. Moreover, we achieve improved data fidelity over the state-of-the-art models by integrating a new form of table reconstruction as an auxiliary task to boost the generation quality.

Subfields: Artificial Intelligence, Natural Language Processing, Natural Language Generation

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “” is not mentioned in the source table [194].”
DART: Open-Domain Structured Data Record to Text Generation (2020, background): “Applications include generating sports commentaries (Tanaka-Ishii et al., 1998; Chen and Mooney, 2008; Wiseman et al., 2017; Wang, 2019; Taniguchi et al., 2019), weather forecasts (Goldberg et al.”
Controlling hallucinations at word level in data-to-text generation (2021, unknown): “Similarly to the coverage constraints, a reconstruction loss has proven only marginally eﬃcient in these settings, as it contradicts the content selection task [68], and needs to be well calibrated using expert insight in order to bring improvements.”
A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems (2020, background): “Wang (2019) aims to prevent generation of sentences not grounded in the data, which does bring the data and generated text into closer alignment, although at the cost of limiting the types of sentence the system is capable of generating.”