Papers with data-to-text model
AggGen: Ordering and Aggregating while Generating (2021.acl-long)
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| Challenge: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
| Approach: | AggGen re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. |
| Outcome: | AggGen is a data-to-text model which re-introduces two explicit sentence planning stages into neural data- to-text systems: input ordering and input aggregation. |
Generating Weather Comments from Meteorological Simulations (2021.eacl-main)
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Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | generating weather-forecast comments from meteorological simulations is labor intensive and requires a solid knowledge of meteorology. |
| Approach: | They propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. |
| Outcome: | The proposed model performs best against baselines in terms of informativeness . it is available online and the results are available to the general public . |
EMGLLM: Data-to-Text Alignment for Electromyogram Diagnosis Generation with Medical Numerical Data Encoding (2025.findings-acl)
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| Challenge: | Existing Large Language Models struggle to interpret EMG tables . EMGLLM is a data-to-text model for medical examination tables based on electrical signals . |
| Approach: | They propose a data-to-text model that aligns EMG data into word embeddings that reflect health degree. |
| Outcome: | The proposed model outperforms baseline models in understanding EMG tables and generating high-quality diagnoses. |