15 citations · 21 across the 7 of their papers we have counts for
7 papers
Learning to Imagine: Visually-Augmented Natural Language Generation
Tianyi Tang, Yushuo Chen, Yifan Du +3
People often imagine relevant scenes to aid in the writing process. In this work, we aim to utilize visual information for composition in the same manner as humans. We propose a me…
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
Junyi Li, Tianyi Tang, Wayne Xin Zhao +2
We study the text generation task under the approach of pre-trained language models (PLMs). Typically, an auto-regressive (AR) method is adopted for generating texts in a token-by-…
Learning to Transfer Prompts for Text Generation
Junyi Li, Tianyi Tang, Jian-Yun Nie +2
Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in a data-scarce situation. The…
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
Junyi Li, Tianyi Tang, Zheng Gong +6
Nowadays, pretrained language models (PLMs) have dominated the majority of NLP tasks. While, little research has been conducted on systematically evaluating the language abilities…
Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models
Junyi Li, Tianyi Tang, Wayne Xin Zhao +3
This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG). Considering the few-shot setting, we leverage the excelle…
Pretrained Language Models for Text Generation: A Survey
Junyi Li, Tianyi Tang, Wayne Xin Zhao +1
Text generation has become one of the most important yet challenging tasks in natural language processing (NLP). The resurgence of deep learning has greatly advanced this field by…