This article surveys and organizes research works in a new paradigm in natural language
processing, which we dub “prompt-based learning.” Unlike traditional supervised learning …

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 presents a new Unified pre-trained Language Model (UniLM) that can be fine-
tuned for both natural language understanding and generation tasks. The model is pre …

This paper presents a new sequence-to-sequence pre-training model called ProphetNet,
which introduces a novel self-supervised objective named future n-gram prediction and the …

Neural abstractive summarization models are flexible and can produce coherent summaries,
but they are sometimes unfaithful and can be difficult to control. While previous studies …

Recent advances in Named Entity Recognition (NER) show that document-level contexts
can significantly improve model performance. In many application scenarios, however, such …

Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

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

Generating long and coherent reports to describe medical images poses challenges to
bridging visual patterns with informative human linguistic descriptions. We propose a novel …

Generating long and semantic-coherent reports to describe medical images poses great
challenges towards bridging visual and linguistic modalities, incorporating medical domain …