Multilingual Unsupervised Sentence Simplification
Table 3: Unsupervised Sentence Simplification for English. We display SARI, BLEU and FKGL on TurkCorpus and ASSET English evaluation datasets.
['[EMPTY]', '[BOLD] Data', '[BOLD] ASSET SARI', '[BOLD] ASSET BLEU', '[BOLD] ASSET FKGL', '[BOLD] TurkCorpus SARI', '[BOLD] TurkCorpus BLEU', '[BOLD] TurkCorpus FKGL']
Using Seq2Seq Models on Mined Data It is surprising that a model trained solely on a paraphrase corpus might achieve such good results on simplification benchmarks. Multiple works have shown that simplification models suffered from not making enough modifications to the source sentence and showed that forcing models to rewrite the input was beneficial Wubben et al. We speculate that this might explain why our sequence-to-sequence model trained on paraphrases achieves these results. Furthermore, when generating a paraphrase, our model might assign higher probabilities to frequent words which might naturally operate lexical simplifications to some extent. Using a monolingual version of BART trained for French or Spanish would perform even better.