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 …

Prior work on controllable text generation usually assumes that the controlled attribute can
take on one of a small set of values known a priori. In this work, we propose a novel task …

Paraphrasing natural language sentences is a multifaceted process: it might involve
replacing individual words or short phrases, local rearrangement of content, or high-level …

Given a sentence (eg,“I like mangoes”) and a constraint (eg, sentiment flip), the goal of
controlled text generation is to produce a sentence that adapts the input sentence to meet …

Paraphrase generation plays an essential role in natural language process (NLP), and it has
many downstream applications. However, training supervised paraphrase models requires …

We propose to control paraphrase generation through carefully chosen target syntactic
structures to generate more proper and higher quality paraphrases. Our model, AESOP …

While recent studies have looked into the abilities of large language models in various
benchmark tasks, including question generation, reading comprehension, multilingual and …

We propose a generative model of paraphrase generation, that encourages syntactic
diversity by conditioning on an explicit syntactic sketch. We introduce Hierarchical …

Pre-trained Transformers have enabled impressive breakthroughs in generating long and
fluent text, yet their outputs are often" rambling" without coherently arranged content. In this …

Recent advancements in Large Language Models empower them to follow freeform
instructions, including imitating generic or specific demographic personas in conversations …