Targeted Paraphrasing on Deep Syntactic Layer for MT Evaluation
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationSimilar Papers 제목 키워드 기반
Syntax-Infused Variational Autoencoder for Text Generation
We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SI…
SentenceText GenerationDynamic Multi-Level Multi-Task Learning for Sentence Simplification
Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed…
Multi-Task LearningParaphrase GenerationSentenceText Simplification+1Neural Syntactic Preordering for Controlled Paraphrase Generation
Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passiviz…
DecoderDiversityMachine TranslationParaphrase Generation+2SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
Targeted syntactic evaluations have yielded insights into the generalizations learned by neural network language models. However, this line of research requires an uncommon confluence of skills: both the theoretical know…
Experimental DesignLanguage ModelingLanguage ModellingCoherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
In this paper, we propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online ped…
DenoisingDiversityMemorizationParaphrase Generation