Split and Rephrase: Better Evaluation and a Stronger Baseline
Table 2: BLEU scores, simple sentences per complex sentence (#S/C) and tokens per simple sentence (#T/S), as computed over the test set. Source are the complex sentences and Reference are the reference rephrasings from the test set. Models marked with * use the semantic RDF triples.
['Model', 'BLEU', '#S/C', '#T/S']
then the Seq2Seq baseline, with up to 28.35 BLEU improvement, despite being single-layer models vs. the 3-layer models used in \newciteshashi2017. A possible explanation for this discrepancy is the Seq2Seq baseline using a dropout rate of 0.8, while we use 0.3 and only apply it on the LSTM outputs. Our results are also better than the MultiSeq2Seq and Split-MultiSeq2Seq models, which use explicit RDF information. To better suit the split-and-rephrase task, we augment the Seq2Seq models with a copy mechanism. Such mechanisms have proven to be beneficial in similar tasks like abstractive summarization Gu et al.