The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

This paper focuses on paraphrase generation, which is a widely studied natural language
generation task in NLP. With the development of neural models, paraphrase generation …

The rise in malicious usage of large language models, such as fake content creation and
academic plagiarism, has motivated the development of approaches that identify AI …

Recently, retrieval-augmented text generation attracted increasing attention of the
computational linguistics community. Compared with conventional generation models …

Many natural language processing (NLP) tasks appear in dual forms, which are generally
solved by dual learning technique that models the dualities between the coupled tasks. In …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Modern NLP defines the task of style transfer as modifying the style of a given sentence
without appreciably changing its semantics, which implies that the outputs of style transfer …

We study a formalization of the grammar induction problem that models sentences as being
generated by a compound probabilistic context-free grammar. In contrast to traditional …

The emergence of generative large language models (LLMs) raises the question: what will
be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring …

Controlled text perturbation is useful for evaluating and improving model generalizability.
However, current techniques rely on training a model for every target perturbation, which is …