Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

There are two main paradigms for Named Entity Recognition (NER): sequence labelling and
span classification. Sequence labelling aims to assign a label to each word in an input text …

Large language models (LLMs) have recently shown significant potential in various
biological tasks such as protein engineering and molecule design. These tasks typically …

摘要基于知识库的问答(QuestionAnsweringoverKnowledgeBase, KBQA)
是问答系统的重要组成部分, 要求计算机**确理解自然语言问题的语义, 并从知识库中提取问题 …

Abstract Knowledge graph-based question answering (QA) is a critical domain within natural
language processing, aimed at delivering precise and efficient responses to user queries …

The constantly updating big data in the ocean engineering domain has challenged the
traditional manner of manually extracting knowledge, thereby underscoring the current …

Image-based virtual try-on aims to transfer a target clothes onto a person has attracted
increased attention. However, the existing methods are heavily based on accurate parsing …

Semantic parsing (SP) is a core component of modern virtual assistants like Google
Assistant and Amazon Alexa. While sequence-to-sequence-based auto-regressive (AR) …

The dominant paradigm for semantic parsing in recent years is to formulate parsing as a
sequence-to-sequence task, generating predictions with auto-regressive sequence …

Many transformer-based encoder-decoder models have made significant progress on
summary generating tasks. And the availability of pre-trained models further improves its …