63 citations · 95 across the 20 of their papers we have counts for
20 papers
Event Extraction in Large Language Model
Bobo Li, Xudong Han, Jiang Liu +11
Large language models (LLMs) and multimodal LLMs are changing event extraction (EE): prompting and generation can often produce structured outputs in zero shot or few shot settings…
LLMs Can Also Do Well! Breaking Barriers in Semantic Role Labeling via Large Language Models
Xinxin Li, Huiyao Chen, Chengjun Liu +4
Semantic role labeling (SRL) is a crucial task of natural language processing (NLP). Although generative decoder-based large language models (LLMs) have achieved remarkable success…
Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing
Peiming Guo, Meishan Zhang, Jianling Li +2
Cross-domain constituency parsing is still an unsolved challenge in computational linguistics since the available multi-domain constituency treebank is limited. We investigate auto…
Towards Text-Image Interleaved Retrieval
Xin Zhang, Ziqi Dai, Yongqi Li +7
Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved cont…
A Systematic Survey of Semantic Role Labeling in the Era of Pretrained Language Models
Huiyao Chen, Meishan Zhang, Jing Li +4
Semantic role labeling (SRL) is a central natural language processing task for understanding predicate-argument structures within texts and enabling downstream applications. Despit…
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Xin Zhang, Yanzhao Zhang, Wen Xie +7
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a comb…
Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image
Yu Zhao, Hao Fei, Xiangtai Li +6
In the visual spatial understanding (VSU) area, spatial image-to-text (SI2T) and spatial text-to-image (ST2I) are two fundamental tasks that appear in dual form. Existing methods f…
Grammar Induction from Visual, Speech and Text
Yu Zhao, Hao Fei, Shengqiong Wu +3
Grammar Induction could benefit from rich heterogeneous signals, such as text, vision, and acoustics. In the process, features from distinct modalities essentially serve complement…
SpeechEE: A Novel Benchmark for Speech Event Extraction
Bin Wang, Meishan Zhang, Hao Fei +5
Event extraction (EE) is a critical direction in the field of information extraction, laying an important foundation for the construction of structured knowledge bases. EE from tex…
An End-to-End Model for Photo-Sharing Multi-modal Dialogue Generation
Peiming Guo, Sinuo Liu, Yanzhao Zhang +4
Photo-Sharing Multi-modal dialogue generation requires a dialogue agent not only to generate text responses but also to share photos at the proper moment. Using image text caption…
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Xin Zhang, Yanzhao Zhang, Dingkun Long +10
We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder…
LLM-Driven Multimodal Opinion Expression Identification
Bonian Jia, Huiyao Chen, Yueheng Sun +2
Opinion Expression Identification (OEI) is essential in NLP for applications ranging from voice assistants to depression diagnosis. This study extends OEI to encompass multimodal i…
Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment
Hao Fei, Shengqiong Wu, Meishan Zhang +3
While pre-training large-scale video-language models (VLMs) has shown remarkable potential for various downstream video-language tasks, existing VLMs can still suffer from certain…
AutoSurvey: Large Language Models Can Automatically Write Surveys
Yidong Wang, Qi Guo, Wenjin Yao +10
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial…
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction
Meishan Zhang, Hao Fei, Bin Wang +4
In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply re…
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training
Longhui Zhang, Dingkun Long, Meishan Zhang +3
Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on th…
In-Context Learning for Few-Shot Nested Named Entity Recognition
Meishan Zhang, Bin Wang, Hao Fei +1
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested…
LLM-enhanced Self-training for Cross-domain Constituency Parsing
Jianling Li, Meishan Zhang, Peiming Guo +2
Self-training has proven to be an effective approach for cross-domain tasks, and in this study, we explore its application to cross-domain constituency parsing. Traditional self-tr…
A Two-Stage Adaptation of Large Language Models for Text Ranking
Longhui Zhang, Yanzhao Zhang, Dingkun Long +3
Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities…
Language Models are Universal Embedders
Xin Zhang, Zehan Li, Yanzhao Zhang +4
In the large language model (LLM) revolution, embedding is a key component of various systems, such as retrieving knowledge or memories for LLMs or building content moderation filt…