1 code implementation • ACL 2022 • Xu Han, Yuqi Luo, Weize Chen, Zhiyuan Liu, Maosong Sun, Zhou Botong, Hao Fei, Suncong Zheng
In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages.
no code implementations • 23 Mar 2024 • Minghui Xu, Hao Fei, Fei Li, Shengqiong Wu, Rui Sun, Chong Teng, Donghong Ji
To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph.
1 code implementation • 18 Feb 2024 • Long Qian, Juncheng Li, Yu Wu, Yaobo Ye, Hao Fei, Tat-Seng Chua, Yueting Zhuang, Siliang Tang
Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks.
no code implementations • 2 Feb 2024 • Meishan Zhang, Bin Wang, Hao Fei, Min Zhang
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address.
no code implementations • 28 Jan 2024 • Kangkang Lu, Yanhua Yu, Hao Fei, Xuan Li, Zixuan Yang, Zirui Guo, Meiyu Liang, Mengran Yin, Tat-Seng Chua
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
1 code implementation • 23 Dec 2023 • Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions.
1 code implementation • 30 Nov 2023 • Sijin Chen, Xin Chen, Chi Zhang, Mingsheng Li, Gang Yu, Hao Fei, Hongyuan Zhu, Jiayuan Fan, Tao Chen
However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud 3D representations of the 3D scene.
no code implementations • 21 Nov 2023 • Minghe Gao, Juncheng Li, Hao Fei, Liang Pang, Wei Ji, Guoming Wang, Wenqiao Zhang, Siliang Tang, Yueting Zhuang
Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks.
1 code implementation • 19 Oct 2023 • Zhiyuan Liu, Sihang Li, Yanchen Luo, Hao Fei, Yixin Cao, Kenji Kawaguchi, Xiang Wang, Tat-Seng Chua
MolCA enables an LM (e. g., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector.
Ranked #4 on Molecule Captioning on ChEBI-20
no code implementations • 29 Sep 2023 • Wei Ji, Li Li, Hao Fei, Xiangyan Liu, Xun Yang, Juncheng Li, Roger Zimmermann
Referring Image Understanding (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms.
1 code implementation • 11 Sep 2023 • Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, Tat-Seng Chua
While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities.
no code implementations • 26 Aug 2023 • Hao Fei, Shengqiong Wu, Wei Ji, Hanwang Zhang, Tat-Seng Chua
In this work, we investigate strengthening the awareness of video dynamics for DMs, for high-quality T2V generation.
no code implementations • 19 Aug 2023 • Kaihang Pan, Juncheng Li, Wenjie Wang, Hao Fei, Hongye Song, Wei Ji, Jun Lin, Xiaozhong Liu, Tat-Seng Chua, Siliang Tang
Recent studies indicate that dense retrieval models struggle to perform well on a wide variety of retrieval tasks that lack dedicated training data, as different retrieval tasks often entail distinct search intents.
no code implementations • 9 Aug 2023 • Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure.
no code implementations • 9 Aug 2023 • Leigang Qu, Shengqiong Wu, Hao Fei, Liqiang Nie, Tat-Seng Chua
Afterward, we propose a fine-grained object-interaction diffusion method to synthesize high-faithfulness images conditioned on the prompt and the automatically generated layout.
no code implementations • 8 Aug 2023 • Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li
On the other hand, during the feature fusion stage, we propose a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism (CRM) for multimodal and context integration, respectively.
Ranked #5 on Emotion Recognition in Conversation on IEMOCAP
no code implementations • 8 Aug 2023 • Yiyun Xiong, Mengwei Dai, Fei Li, Hao Fei, Bobo Li, Shengqiong Wu, Donghong Ji, Chong Teng
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference.
no code implementations • 3 Aug 2023 • Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua
Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications.
no code implementations • 6 Jun 2023 • Jiang Liu, Hao Fei, Fei Li, Jingye Li, Bobo Li, Liang Zhao, Chong Teng, Donghong Ji
Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions.
no code implementations • 6 Jun 2023 • Li Zheng, Donghong Ji, Fei Li, Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Chong Teng
The existing emotion-cause pair extraction (ECPE) task, unfortunately, ignores extracting the emotion type and cause type, while these fine-grained meta-information can be practically useful in real-world applications, i. e., chat robots and empathic dialog generation.
no code implementations • 6 Jun 2023 • Bobo Li, Hao Fei, Fei Li, Shengqiong Wu, Lizi Liao, Yinwei Wei, Tat-Seng Chua, Donghong Ji
Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute.
no code implementations • 20 May 2023 • Shengqiong Wu, Hao Fei, Wei Ji, Tat-Seng Chua
Unpaired cross-lingual image captioning has long suffered from irrelevancy and disfluency issues, due to the inconsistencies of the semantic scene and syntax attributes during transfer.
no code implementations • 20 May 2023 • Hao Fei, Meishan Zhang, Min Zhang, Tat-Seng Chua
Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e. g., either target-biased or source-biased) due to the inevitable linguistic disparity between languages.
1 code implementation • 20 May 2023 • Hao Fei, Qian Liu, Meishan Zhang, Min Zhang, Tat-Seng Chua
In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup, inference-time image-free UMMT, where the model is trained with source-text image pairs, and tested with only source-text inputs.
1 code implementation • 19 May 2023 • Yu Zhao, Hao Fei, Wei Ji, Jianguo Wei, Meishan Zhang, Min Zhang, Tat-Seng Chua
With an external 3D scene extractor, we obtain the 3D objects and scene features for input images, based on which we construct a target object-centered 3D spatial scene graph (Go3D-S2G), such that we model the spatial semantics of target objects within the holistic 3D scenes.
1 code implementation • 19 May 2023 • Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua
First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG).
2 code implementations • 18 May 2023 • Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua
While sentiment analysis systems try to determine the sentiment polarities of given targets based on the key opinion expressions in input texts, in implicit sentiment analysis (ISA) the opinion cues come in an implicit and obscure manner.
1 code implementation • NeurIPS 2023 • Ao Zhang, Hao Fei, Yuan YAO, Wei Ji, Li Li, Zhiyuan Liu, Tat-Seng Chua
While developing a new multimodal LLM (MLLM) by pre-training on tremendous image-text pairs from scratch can be exceedingly resource-consuming, connecting an existing LLM with a comparatively lightweight visual prompt generator (VPG) becomes a feasible paradigm.
no code implementations • 19 Apr 2023 • Hao Fei, Tat-Seng Chua, Chenliang Li, Donghong Ji, Meishan Zhang, Yafeng Ren
In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • 13 Apr 2023 • Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua
Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM.
1 code implementation • 10 Nov 2022 • Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, Donghong Ji
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • 29 Oct 2022 • Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai
First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths.
no code implementations • 27 Oct 2022 • Chengyu Huang, Zheng Zhang, Hao Fei, Lizi Liao
Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations.
no code implementations • 6 Oct 2022 • Hao Fei, Shengqiong Wu, Meishan Zhang, Yafeng Ren, Donghong Ji
In this work, we investigate the integration of a latent graph for CSRL.
1 code implementation • COLING 2022 • Shunjie Chen, Xiaochuan Shi, Jingye Li, Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji
We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature.
1 code implementation • COLING 2022 • Hu Cao, Jingye Li, Fangfang Su, Fei Li, Hao Fei, Shengqiong Wu, Bobo Li, Liang Zhao, Donghong Ji
Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text.
1 code implementation • Conference 2022 • Hao Fei, Fei Li, Chenliang Li, Shengqiong Wu, Jingye Li, Donghong Ji
So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4
1 code implementation • Conference 2022 • Hao Fei, Jingye Li, Shengqiong Wu, Chenliang Li, Donghong Ji, Fei Li
In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.
Ranked #3 on Dialog Relation Extraction on DialogRE
1 code implementation • ACL 2022 • Wenxuan Shi, Fei Li, Jingye Li, Hao Fei, Donghong Ji
The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer.
1 code implementation • 19 Dec 2021 • Jingye Li, Hao Fei, Jiang Liu, Shengqiong Wu, Meishan Zhang, Chong Teng, Donghong Ji, Fei Li
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka.
Ranked #2 on Chinese Named Entity Recognition on OntoNotes 4
no code implementations • IEEE 2021 • Hao Fei, Yafeng Ren, Yue Zhang, Donghong Ji
Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i. e., aspect terms, opinion expressions, and correlated sentiment polarities.
Ranked #3 on Aspect Sentiment Triplet Extraction on ASTE-Data-V2
1 code implementation • 5 Oct 2021 • Shengqiong Wu, Hao Fei, Fei Li, Donghong Ji, Meishan Zhang, Yijiang Liu, Chong Teng
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text.
Ranked #1 on Fine-Grained Opinion Analysis on MPQA (F1 (Opinion) metric)
1 code implementation • Conference 2021 • Hao Fei, Fei Li, Bobo Li, Yijiang Liu, Yafeng Ren, Donghong Ji
A majority of research interests in irregular (eg, nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention.
1 code implementation • Conference 2021 • Hao Fei, Fei Li, Bobo Li, Donghong Ji
Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests.
1 code implementation • 6 May 2021 • Shengqiong Wu, Hao Fei, Yafeng Ren, Donghong Ji, Jingye Li
In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge.
1 code implementation • 2 Jan 2021 • Hao Fei, Meishan Zhang, Bobo Li, Donghong Ji
It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly.
Ranked #1 on Semantic Role Labeling on CoNLL-2009
no code implementations • COLING 2020 • Jingye Li, Hao Fei, Donghong Ji
In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Hao Fei, Yafeng Ren, Donghong Ji
Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics.
no code implementations • 19 Sep 2020 • Shengqiong Wu, Hao Fei, Donghong Ji
Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community.
no code implementations • 19 Sep 2020 • Bobo Li, Hao Fei, Yafeng Ren, Donghong Ji
Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Hao Fei, Yafeng Ren, Donghong Ji
Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network.
no code implementations • EMNLP 2020 • Hao Fei, Yafeng Ren, Donghong Ji
We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model.
no code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Hao Fei, Yafeng Ren, Donghong Ji
Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models.
no code implementations • 24 Aug 2020 • Hao Fei, Meishan Zhang, Fei Li, Donghong Ji
In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods.
1 code implementation • ACL 2020 • Hao Fei, Meishan Zhang, Donghong Ji
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding.