Progress in sentence simplification has been hindered by a lack of labeled parallel
simplification data, particularly in languages other than English. We introduce MUSS, a …

Paraphrase generation has been widely used in various downstream tasks. Most tasks
benefit mainly from high quality paraphrases, namely those that are semantically similar to …

In this work, we propose TGLS, a novel framework for unsupervised Text Generation by
Learning from Search. We start by applying a strong search algorithm (in particular …

Sequence generation applications require satisfying semantic constraints, such as ensuring
that programs are correct, using certain keywords, or avoiding undesirable content …

We present a novel iterative, edit-based approach to unsupervised sentence simplification.
Our model is guided by a scoring function involving fluency, simplicity, and meaning …

In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings
that disobey conventional wisdom:(1) Reference-free metrics achieve better performance …

Paraphrase generation is a fundamental and long-standing task in natural language
processing. In this paper, we concentrate on two contributions to the task:(1) we propose …

Reinforcement learning (RL) has been widely used in text generation to alleviate the
exposure bias issue or to utilize non-parallel datasets. The reward function plays an …

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 …

Lexically constrained generation requires the target sentence to satisfy some lexical
constraints, such as containing some specific words or being the paraphrase to a given …