Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
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 (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard 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 amount of lexical diversity between the input and output can be controlled at generation time. We conduct a human evaluation to compare our method to a paraphraser trained on the large English synthetic paraphrase database ParaBank 2 (Hu et al., 2019c) and find that our method produces paraphrases that better preserve meaning and are more gramatical, for the same level of lexical diversity. Additional smaller human assessments demonstrate our approach also works in two non-English languages.
Code (1)
Tasks
DiversityMachine TranslationNMTParaphrase GenerationSemantic SimilaritySemantic Textual SimilaritySentenceTranslationSimilar Papers 제목 키워드 기반
Multilingual Lexical Simplification via Paraphrase Generation
Lexical simplification (LS) methods based on pretrained language models have made remarkable progress, generating potential substitutes for a complex word through analysis of its contextual surroundings. However, these m…
DiversityLexical SimplificationMachine TranslationParaphrase Generation+2Zero-Shot Paraphrase Generation with Multilingual Language Models
Leveraging multilingual parallel texts to automatically generate paraphrases has drawn much attention as size of high-quality paraphrase corpus is limited. Round-trip translation, also known as the pivoting method, is a …
DenoisingDiversityMachine TranslationParaphrase Generation+2Visual Information Guided Zero-Shot Paraphrase Generation
Zero-shot paraphrase generation has drawn much attention as the large-scale high-quality paraphrase corpus is limited. Back-translation, also known as the pivot-based method, is typical to this end. Several works leverag…
DiversityImage CaptioningParaphrase GenerationTranslationAutomatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing
We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multili…
Machine TranslationNMTTranslationImproving the Diversity of Unsupervised Paraphrasing with Embedding Outputs
We present a novel technique for zero-shot paraphrase generation. The key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora to generate paraphrases into "mean…
DiversityParaphrase GenerationWord Embeddings