ReDecode Framework for Iterative Improvement in Paraphrase Generation
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP. Automatically generating paraphrases has its utility in many NLP tasks like question answering, information retrieval, conversational systems to name a few. In this paper, we introduce iterative refinement of generated paraphrases within VAE based generation framework. Current sequence generation models lack the capability to (1) make improvements once the sentence is generated; (2) rectify errors made while decoding. We propose a technique to iteratively refine the output using multiple decoders, each one attending on the output sentence generated by the previous decoder. We improve current state of the art results significantly - with over 9% and 28% absolute increase in METEOR scores on Quora question pairs and MSCOCO datasets respectively. We also show qualitatively through examples that our re-decoding approach generates better paraphrases compared to a single decoder by rectifying errors and making improvements in paraphrase structure, inducing variations and introducing new but semantically coherent information.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderInformation RetrievalParaphrase GenerationQuestion AnsweringRetrievalSentenceSimilar Papers 제목 키워드 기반
SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension
Satire, a form of artistic expression combining humor with implicit critique, holds significant social value by illuminating societal issues. Despite its cultural and societal significance, satire comprehension, particul…
A Deep Generative Framework for Paraphrase Generation
Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generat…
DecoderInformation RetrievalParaphrase GenerationQuestion Answering+2Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though…
Language ModelingLanguage ModellingMeta-LearningParaphrase Generation+1Paraphrase Types for Generation and Detection
Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this s…
Binary ClassificationParaphrase GenerationNeural Clinical Paraphrase Generation with Attention
Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical para…
Document SummarizationInformation RetrievalMachine TranslationNMT+4