Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

• URL: http://arxiv.org/abs/2008.04935v2
• Date: Wed, 28 Oct 2020 02:54:13 GMT
• Title: Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
• Authors: Brian Thompson and Matt Post
• Abstract summary: 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.
• Score: 11.564158965143418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (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.

Related papers

• Generative Spoken Language Model based on continuous word-sized audio tokens [52.081868603603844]
  We introduce a Generative Spoken Language Model based on word-size continuous-valued audio embeddings. The resulting model is the first generative language model based on word-size continuous embeddings.
  arXiv  Detail & Related papers  (2023-10-08T16:46:14Z)
• Multilingual Lexical Simplification via Paraphrase Generation [19.275642346073557]
  We propose a novel multilingual LS method via paraphrase generation. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation. Our approach surpasses BERT-based methods and zero-shot GPT3-based method significantly on English, Spanish, and Portuguese.
  arXiv  Detail & Related papers  (2023-07-28T03:47:44Z)
• ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation [59.91139600152296]
  ParaAMR is a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation back-translation. We show that ParaAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning.
  arXiv  Detail & Related papers  (2023-05-26T02:27:33Z)
• Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models [55.35106713257871]
  We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
  arXiv  Detail & Related papers  (2023-05-22T14:52:47Z)
• VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning [56.47303426167584]
  We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments. Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs. token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
  arXiv  Detail & Related papers  (2023-04-17T12:23:41Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation [15.32063273544696]
  We discretize the latent space of multilingual models by assigning encoder states to entries in a codebook. We validate our approach on large-scale experiments with realistic data volumes and domains. We also use the learned artificial language to analyze model behavior, and discover that using a similar bridge language increases knowledge-sharing among the remaining languages.
  arXiv  Detail & Related papers  (2022-11-02T17:14:42Z)
• Improving the Diversity of Unsupervised Paraphrasing with Embedding Outputs [28.16894664889912]
  We present a novel technique for zero-shot paraphrase generation. Key contribution is an end-to-end multilingual paraphrasing model that is trained using translated parallel corpora.
  arXiv  Detail & Related papers  (2021-10-25T19:33:38Z)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)
• Informed Sampling for Diversity in Concept-to-Text NLG [8.883733362171034]
  We propose an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output.
  arXiv  Detail & Related papers  (2020-04-29T17:43:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.