Papers with Data-to-text generation
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to data-to-text generation require limited training examples . a data-based approach is based on a set of pre-trained language models with optional finetuning. |
| Approach: | They propose a data-to-text generation task that makes use of any given (or no) examples. |
| Outcome: | The proposed approach improves on baselines on a dataset with zero/few/full-shot settings. |
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation (N19-1)
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| Challenge: | Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. |
| Approach: | They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. |
| Outcome: | The proposed method improves reliability and adequacy while maintaining fluent output. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |
Compositional Generalization for Data-to-Text Generation (2023.findings-emnlp)
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| Challenge: | Data-to-text generation models can be used to generate textual descriptions from structured data . despite advances, systems struggle when confronted with unseen combinations of predicates . |
| Approach: | They propose a data-to-text generation model that addresses compositional generalization by clustering predicates into groups. |
| Outcome: | The proposed model outperforms T5-baselines in all evaluation metrics. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |