Char2char Generation with Reranking for the E2E NLG Challenge
Shubham Agarwal, Marc Dymetman, Éric Gaussier · 2018
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.