Improving User Controlled Table-To-Text Generation Robustness (2023.findings-eacl)
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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. |
| Approach: | They propose a user controlled table-to-text generation task where users explore the content in a table by selecting cells and reading a natural language description thereof. |
| Outcome: | The proposed model gains 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases. |
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