Name
Title | ||
|---|---|---|
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity |
Abstract | ||
|---|---|---|
Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language; however, attempting to generate paraphrases from the model using beam search produces trivial copies or near copies. We introduce a simple paraphrase generation algorithm which discourages the production of n-grams that are present in the input. Our approach enables paraphrase generation in many languages from a single multilingual NMT model. Furthermore, the trade-off between semantic similarity and lexical/syntactic diversity between the input and output can be controlled at generation time. We conduct human evaluation to compare our method to a paraphraser trained on a large English synthetic paraphrase database and find that our model produces paraphrases that better preserve semantic meaning and grammatically, for the same level of lexical/syntactic diversity. Additional smaller human assessments demonstrate our approach also works in non-English languages. |
Year | Venue | DocType |
|---|---|---|
2020 | WMT@EMNLP | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian Thompson | 1 | 8 | 8.60 |
| Matt Post | 2 | 414 | 35.72 |