Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Prompting approaches have been recently explored in text style transfer, where a textual
prompt is used to query a pretrained language model to generate style-transferred texts …

The goal of compositional generalization benchmarks is to evaluate how well models
generalize to new complex linguistic expressions. Existing benchmarks often focus on …

We present Referee, a novel framework for sentence summarization that can be trained
reference-free (ie, requiring no gold summaries for supervision), while allowing direct control …

Lexically constrained text generation (CTG) is to generate text that contains given
constrained keywords. However, the text diversity of existing models is still unsatisfactory. In …

In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and
chunking, has mostly relied on manual annotations of syntactic structures. This paper …

Long sentences have been a persistent issue in written communication for many years since
they make it challenging for readers to grasp the main points or follow the initial intention of …

Sentence summarization shortens given texts while maintaining core contents of the texts.
Unsupervised approaches have been studied to summarize texts without human-written …

Sentence summarization aims at compressing a long sentence into a short one that keeps
the main gist, and has extensive real-world applications such as headline generation. In …

Non-autoregressive neural machine translation (NAT) models are proposed to accelerate
the inference process while maintaining relatively high performance. However, existing NAT …