@inproceedings{kasner-etal-2021-text,
    title = "Text-in-Context: Token-Level Error Detection for Table-to-Text Generation",
    author = "Kasner, Zden{\v{e}}k  and
      Mille, Simon  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    editor = "Belz, Anya  and
      Fan, Angela  and
      Reiter, Ehud  and
      Sripada, Yaji",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
    month = aug,
    year = "2021",
    address = "Aberdeen, Scotland, UK",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.inlg-1.25/",
    doi = "10.18653/v1/2021.inlg-1.25",
    pages = "259--265",
    abstract = "We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in Generated Texts at INLG 2021. Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs). We first utilize a rule-based NLG system to generate sentences with facts that can be derived from the input. For each sentence we evaluate, we select a subset of facts which are relevant by measuring semantic similarity to the sentence in question. Finally, we finetune a pretrained language model on annotated data along with the relevant facts for fine-grained error detection. On the test set, we achieve 69{\%} recall and 75{\%} precision with a model trained on a mixture of human-annotated and synthetic data."
}