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Found 6 papers, 4 papers with code
Train Hard, Finetune Easy: Multilingual Denoising for RDF-to-Text Generation
We describe our system for the RDF-to-text generation task of the WebNLG Challenge 2020.
Text-in-Context: Token-Level Error Detection for Table-to-Text Generation
Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs).
Neural Pipeline for Zero-Shot Data-to-Text Generation
In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise.
Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference
A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i. e. checking if the output text contains all and only facts supported by the input data.
Data-to-Text Generation with Iterative Text Editing
Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LaserTagger) and language modeling (GPT-2) to improve the text fluency.
Improving Fluency of Non-Autoregressive Machine Translation
Non-autoregressive (nAR) models for machine translation (MT) manifest superior decoding speed when compared to autoregressive (AR) models, at the expense of impaired fluency of their outputs.
