Search Results for author:
Found 12 papers, 9 papers with code
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).
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.
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
We propose the problem of conversational web navigation, where a digital agent controls a web browser and follows user instructions to solve real-world tasks in a multi-turn dialogue fashion.
Ranked #1 on
Conversational Web Navigation
on WebLINX
Beyond Reference-Based Metrics: Analyzing Behaviors of Open LLMs on Data-to-Text Generation
We analyze the behaviors of open large language models (LLMs) on the task of data-to-text (D2T) generation, i. e., generating coherent and relevant text from structured data.
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising
Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content.
TabGenie: A Toolkit for Table-to-Text Generation
We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models
Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples.
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.
