Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation (P19-1)
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| Challenge: | Existing methods for table-to-text generation use encoder-decoder framework, but lack of large parallel data is a problem for many domains. |
| Approach: | They propose a model to separate table-to-text generation into two stages: key fact prediction and surface realization. |
| Outcome: | The proposed model achieves 27.34 BLEU score with only 1,000 parallel data, while the baseline model only achieves 9.71 BLUE score. |
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| Challenge: | Existing methods for paraphrase generation rely on language as the pivot . however, there is no evidence that parallel data of paraphrases is needed for paraphrasing. |
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
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| Challenge: | Existing knowledge base population systems require a machine translation task to generate multiple facts, but the fact order is not considered. |
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Knowledge Graph Generation From Text (2022.findings-emnlp)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
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Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
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Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)
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| Challenge: | Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. |
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Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)
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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang
| Challenge: | Existing methods for data-to-text generation focus on specific types of structured data. |
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Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation (2021.findings-acl)
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| Challenge: | Existing methods for table-to-text generation suffer from poor faithfulness and low coverage. |
| Approach: | They propose a method that combines Autoregressive and Non-Autoregressive generation to generate a table-to-text from a key-value table using a skeleton and an edit-based non-autoregressively generation model. |
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PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (2022.emnlp-main)
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| Challenge: | Logical table-to-text generation requires models to derive logical-level facts from table records via logical inference. |
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