Papers Concept-To-Text Generation
“Concept-To-Text Generation” 태그가 달린 논문 17편 · 필터 해제
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context. On the other hand, when generating text, human writers are gifted at creative visualization, which enhanc…
Concept-To-Text GenerationImage GenerationStory GenerationText GenerationRetrieve, Caption, Generate: Visual Grounding for Enhancing Commonsense in Text Generation Models
We investigate the use of multimodal information contained in images as an effective method for enhancing the commonsense of Transformer models for text generation. We perform experiments using BART and T5 on concept-to-…
Concept-To-Text GenerationSpecificityText GenerationVisual GroundingSAPPHIRE: Approaches for Enhanced Concept-to-Text Generation
We motivate and propose a suite of simple but effective improvements for concept-to-text generation called SAPPHIRE: Set Augmentation and Post-hoc PHrase Infilling and REcombination. We demonstrate their effectiveness on…
Concept-To-Text GenerationSpecificityText GenerationTUDA-Reproducibility @ ReproGen: Replicability of Human Evaluation of Text-to-Text and Concept-to-Text Generation
This paper describes our contribution to the Shared Task ReproGen by Belz et al. (2021), which investigates the reproducibility of human evaluations in the context of Natural Language Generation. We selected the paper “G…
Concept-To-Text GenerationPaper generationText GenerationInformed Sampling for Diversity in Concept-to-Text NLG
Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived flu…
Concept-To-Text GenerationDiversityImitation LearningText GenerationEnhancing Topic-to-Essay Generation with External Commonsense Knowledge
Automatic topic-to-essay generation is a challenging task since it requires generating novel, diverse, and topic-consistent paragraph-level text with a set of topics as input. Previous work tends to perform essay generat…
Concept-To-Text GenerationExtracting Linguistic Resources from the Web for Concept-to-Text Generation
Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly. Focusing on NaturalOWL, a publicly …
Concept-To-Text GenerationSentenceText GenerationGenerating Texts with Integer Linear Programming
Concept-to-text generation typically employs a pipeline architecture, which often leads to suboptimal texts. Content selection, for example, may greedily select the most important facts, which may require, however, too m…
Concept-To-Text GenerationReferring ExpressionReferring expression generationSentence+1Deep Attentive Sentence Ordering Network
In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture globa…
Concept-To-Text GenerationDocument SummarizationFeature EngineeringMulti-Document Summarization+4Neural Text Generation from Structured Data with Application to the Biography Domain
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magnitude larger than existing re…
Concept-To-Text GenerationLanguage ModelingLanguage ModellingSentence+2