# E2E Refined Dataset

arXiv: https://arxiv.org/abs/2211.00513 · HTML: https://mlsanity.com/paper/2211.00513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge](https://mlsanity.com/paper/1901.07931/) (2019)
- [Semantic Noise Matters for Neural Natural Language Generation](https://mlsanity.com/paper/1911.03905/) (2019)
- [Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings](https://mlsanity.com/paper/1606.05491/) (2016)
- [A Survey of Natural Language Generation](https://mlsanity.com/paper/2112.11739/) (2021)

## Abstract (arXiv)

Although the well-known MR-to-text E2E dataset has been used by many researchers, its MR-text pairs include many deletion/insertion/substitution errors. Since such errors affect the quality of MR-to-text systems, they must be fixed as much as possible. Therefore, we developed a refined dataset and some python programs that convert the original E2E dataset into a refined dataset.