Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
| Approach: | They propose a controlled task where annotators directly revise existing Wikipedia sentences to generate a one-sentence description. |
| Outcome: | The proposed task produces a one-sentence description from a Wikipedia table and highlighted cells. |
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| Challenge: | Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users. |
| Approach: | They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models. |
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TWT: Table with Written Text for Controlled Data-to-Text Generation (2021.findings-emnlp)
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| Challenge: | Existing methods output hallucinated text that is not faithful on TWT. |
| Approach: | They propose to generate text conditioned on the structured data and a prefix by leveraging pre-trained neural models. |
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TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
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| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
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OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)
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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan
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| Challenge: | Controlled table-to-text generation is a new approach to generate textual descriptions for highlighted subparts of a table. |
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| Challenge: | In experiments, models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. |
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Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
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| Challenge: | Existing datasets for data-to-text generation focus on single-sentence generation or long-form generation. |
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Text-to-Table: A New Way of Information Extraction (2022.acl-long)
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| Challenge: | Existing methods for information extraction are not well understood . text-to-table is a problem that aims to extract information from text data . |
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A Sequence-to-Sequence&Set Model for Text-to-Table Generation (2023.findings-acl)
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| Challenge: | Existing models for text-to-table generation are order-insensitive, but suffer from errors . a novel sequence-tosequence&set model generates table body rows in parallel . |
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