no code implementations • ArgMining (ACL) 2022 • Ameer Saadat-Yazdi, Xue Li, Sandrine Chausson, Vaishak Belle, Björn Ross, Jeff Z. Pan, Nadin Kökciyan
The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair.
Ranked #2 on ValNov on ValNov Subtask A
no code implementations • 19 Apr 2024 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
Multi-modal entity alignment (MMEA) aims to identify equivalent entity pairs across different multi-modal knowledge graphs (MMKGs).
no code implementations • 15 Apr 2024 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity.
1 code implementation • 1 Apr 2024 • Yijun Yang, Jie He, Pinzhen Chen, Víctor Gutiérrez-Basulto, Jeff Z. Pan
We hypothesize that simultaneously debiasing these objectives can be the key to generalisation over unseen prompts.
no code implementations • 22 Feb 2024 • Wendi Zhou, Tianyi Li, Pavlos Vougiouklis, Mark Steedman, Jeff Z. Pan
Identifying and understanding user intents is a pivotal task for E-Commerce.
no code implementations • 19 Feb 2024 • Danna Zheng, Mirella Lapata, Jeff Z. Pan
We present Archer, a challenging bilingual text-to-SQL dataset specific to complex reasoning, including arithmetic, commonsense and hypothetical reasoning.
1 code implementation • 19 Feb 2024 • Danna Zheng, Danyang Liu, Mirella Lapata, Jeff Z. Pan
Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications.
6 code implementations • 8 Feb 2024 • Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng, Lingbing Guo, Xiang Chen, Qian Li, Wen Zhang, Jiaoyan Chen, Yushan Zhu, Jiaqi Li, Xiaoze Liu, Jeff Z. Pan, Ningyu Zhang, Huajun Chen
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
no code implementations • 29 Jan 2024 • Yong Guan, Freddy Lecue, Jiaoyan Chen, Ru Li, Jeff Z. Pan
Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.
no code implementations • 26 Jan 2024 • Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Sheng Bi, Tongtong Wu, Jeff Z. Pan
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
no code implementations • 24 Jan 2024 • Hongru Wang, WenYu Huang, Yang Deng, Rui Wang, Zezhong Wang, YuFei Wang, Fei Mi, Jeff Z. Pan, Kam-Fai Wong
To better plan and incorporate the use of multiple sources in generating personalized response, we firstly decompose it into three sub-tasks: Knowledge Source Selection, Knowledge Retrieval, and Response Generation.
1 code implementation • 4 Dec 2023 • Yuxia Geng, Jiaoyan Chen, Yuhang Zeng, Zhuo Chen, Wen Zhang, Jeff Z. Pan, Yuxiang Wang, Xiaoliang Xu
Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information.
no code implementations • 21 Oct 2023 • Vivek Iyer, Edoardo Barba, Alexandra Birch, Jeff Z. Pan, Roberto Navigli
Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022).
1 code implementation • 18 Oct 2023 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs.
1 code implementation • 8 Oct 2023 • Simon Chi Lok U, Jie He, Víctor Gutiérrez-Basulto, Jeff Z. Pan
Hierarchical multi-label text classification (HMTC) aims at utilizing a label hierarchy in multi-label classification.
1 code implementation • 12 Aug 2023 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers.
no code implementations • 11 Aug 2023 • Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, Damien Graux
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
1 code implementation • 30 Jul 2023 • Zhuo Chen, Lingbing Guo, Yin Fang, Yichi Zhang, Jiaoyan Chen, Jeff Z. Pan, Yangning Li, Huajun Chen, Wen Zhang
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on Multi-modal Entity Alignment on UMVM-oea-d-w-v2 (using extra training data)
no code implementations • 25 May 2023 • Jie He, Simon Chi Lok U, Víctor Gutiérrez-Basulto, Jeff Z. Pan
Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in their scope.
3 code implementations • 19 May 2023 • Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye, Mengshu Sun, Lei Liang, Jeff Z. Pan, Huajun Chen, Ningyu Zhang
Large language models can perform well on general natural language tasks, but their effectiveness is still not optimal for information extraction.
1 code implementation • 18 Mar 2023 • Nan Hu, Yike Wu, Guilin Qi, Dehai Min, Jiaoyan Chen, Jeff Z. Pan, Zafar Ali
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).
1 code implementation • 3 Feb 2023 • Mingyang Chen, Wen Zhang, Zhen Yao, Yushan Zhu, Yang Gao, Jeff Z. Pan, Huajun Chen
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
no code implementations • 3 Feb 2023 • Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen
In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation.
1 code implementation • 29 Dec 2022 • Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on Entity Alignment on FBYG15k (using extra training data)
1 code implementation • 20 Oct 2022 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, Ru Li, Jeff Z. Pan
We investigate the knowledge graph entity typing task which aims at inferring plausible entity types.
1 code implementation • 8 Oct 2022 • Yuxia Geng, Jiaoyan Chen, Jeff Z. Pan, Mingyang Chen, Song Jiang, Wen Zhang, Huajun Chen
Subgraph reasoning with message passing is a promising and popular solution.
1 code implementation • 30 Sep 2022 • Shumin Deng, Chengming Wang, Zhoubo Li, Ningyu Zhang, Zelin Dai, Hehong Chen, Feiyu Xiong, Ming Yan, Qiang Chen, Mosha Chen, Jiaoyan Chen, Jeff Z. Pan, Bryan Hooi, Huajun Chen
We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks.
1 code implementation • 26 Aug 2022 • Tianyi Li, WenYu Huang, Nikos Papasarantopoulos, Pavlos Vougiouklis, Jeff Z. Pan
Our system is the winner of track 1 of the LM-KBC challenge, based on BERT LM; it achieves 55. 0% F-1 score on the hidden test set of the challenge.
no code implementations • 19 Aug 2022 • Hiba Arnaout, Simon Razniewski, Gerhard Weikum, Jeff Z. Pan
This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates.
1 code implementation • 19 Aug 2022 • Yufeng Huang, Zhuo Chen, Jiaoyan Chen, Jeff Z. Pan, Zhen Yao, Wen Zhang
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
2 code implementations • 4 Jul 2022 • Zhuo Chen, Yufeng Huang, Jiaoyan Chen, Yuxia Geng, Wen Zhang, Yin Fang, Jeff Z. Pan, Huajun Chen
Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.
Ranked #1 on Zero-Shot Learning on CUB-200-2011
1 code implementation • 8 Jun 2022 • Yuxia Geng, Jiaoyan Chen, Wen Zhang, Yajing Xu, Zhuo Chen, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
1 code implementation • 2 May 2022 • Zhiwei Hu, Víctor Gutiérrez-Basulto, Zhiliang Xiang, XiaoLi Li, Ru Li, Jeff Z. Pan
Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information.
no code implementations • 15 Feb 2022 • Wen Zhang, Jiaoyan Chen, Juan Li, Zezhong Xu, Jeff Z. Pan, Huajun Chen
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.
no code implementations • 18 Dec 2021 • Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Jeff Z. Pan, Yuan He, Wen Zhang, Ian Horrocks, Huajun Chen
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
no code implementations • 27 Aug 2021 • Camille Bourgaux, Ana Ozaki, Jeff Z. Pan
It has been shown that convex geometric regions capture the so-called quasi-chained rules.
no code implementations • ACL 2021 • Xuefeng Su, Ru Li, XiaoLi Li, Jeff Z. Pan, Hu Zhang, Qinghua Chai, Xiaoqi Han
In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings.
2 code implementations • 12 Jul 2021 • Zhuo Chen, Jiaoyan Chen, Yuxia Geng, Jeff Z. Pan, Zonggang Yuan, Huajun Chen
Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need.
Ranked #1 on Visual Question Answering (VQA) on F-VQA
1 code implementation • 29 Jun 2021 • Yuxia Geng, Jiaoyan Chen, Xiang Zhuang, Zhuo Chen, Jeff Z. Pan, Juan Li, Zonggang Yuan, Huajun Chen
different ZSL methods.
1 code implementation • 26 Feb 2021 • Jiaoyan Chen, Yuxia Geng, Zhuo Chen, Ian Horrocks, Jeff Z. Pan, Huajun Chen
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.
1 code implementation • 15 Feb 2021 • Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, Huajun Chen
The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e. g., features) from training classes (i. e., seen classes) to unseen classes.
1 code implementation • 30 Jun 2020 • Jiaoyan Chen, Freddy Lecue, Yuxia Geng, Jeff Z. Pan, Huajun Chen
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information.
no code implementations • 13 Jan 2020 • Hiba Arnaout, Simon Razniewski, Gerhard Weikum, Jeff Z. Pan
Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities.
no code implementations • 2 Jul 2019 • Xiaxia Wang, Jinchi Chen, Shuxin Li, Gong Cheng, Jeff Z. Pan, Evgeny Kharlamov, Yuzhong Qu
Reusing existing datasets is of considerable significance to researchers and developers.
no code implementations • 31 May 2019 • Freddy Lecue, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings.
2 code implementations • 27 May 2019 • Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, Gerhard Weikum
Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications.
1 code implementation • 22 Jul 2018 • Jiaoyan Chen, Freddy Lecue, Jeff Z. Pan, Ian Horrocks, Huajun Chen
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i. e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.