# Char2char Generation with Reranking for the E2E NLG Challenge

arXiv: https://arxiv.org/abs/1811.05826 · HTML: https://mlsanity.com/paper/1811.05826/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Neural Data-to-Text Generation with LM-based Text Augmentation](https://mlsanity.com/paper/2102.03556/) (2021)
- [Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG](https://mlsanity.com/paper/2109.07043/) (2021)
- [Incremental Beam Manipulation for Natural Language Generation](https://mlsanity.com/paper/2102.02574/) (2021)
- [Chitranuvad: Adapting Multi-Lingual LLMs for Multimodal Translation](https://mlsanity.com/paper/2502.20420/) (2025)
- [Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy](https://mlsanity.com/paper/2109.05312/) (2021)

## Abstract (arXiv)

This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing delexicalization (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation.