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 …

Recently retrieval-augmented text generation has achieved state-of-the-art performance in
many NLP tasks and has attracted increasing attention of the NLP and IR community, this …

GPT-$3 $ has attracted lots of attention due to its superior performance across a wide range
of NLP tasks, especially with its powerful and versatile in-context few-shot learning ability …

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 present ToTTo, an open-domain English table-to-text dataset with over 120,000 training
examples that proposes a controlled generation task: given a Wikipedia table and a set of …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

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

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 …

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 …

We present a study on leveraging multilingual pre-trained generative language models for
zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language …