687 citations · 4k across the 58 of their papers we have counts for
29 papers · 1 filter
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Weizhen Qi, Yeyun Gong, Jian Jiao +9
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniforml…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Shusheng Xu, Xingxing Zhang, Yi Wu +2
Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph…
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Daya Guo, Duyu Tang, Nan Duan +3
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context th…
MoBoAligner: a Neural Alignment Model for Non-autoregressive TTS with Monotonic Boundary Search
Naihan Li, Shujie Liu, Yanqing Liu +3
To speed up the inference of neural speech synthesis, non-autoregressive models receive increasing attention recently. In non-autoregressive models, additional durations of text to…
DocBank: A Benchmark Dataset for Document Layout Analysis
Minghao Li, Yiheng Xu, Lei Cui +4
Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality label…