Search Results for author: Zaiqiao Meng

Found 34 papers, 22 papers with code

Integrating Transformers and Knowledge Graphs for Twitter Stance Detection

no code implementations WNUT (ACL) 2021 Thomas Clark, Costanza Conforti, Fangyu Liu, Zaiqiao Meng, Ehsan Shareghi, Nigel Collier

Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.

Knowledge Graphs Knowledge Probing +3

I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction

no code implementations19 Jul 2024 Zaiqiao Meng, Hao Zhou, Yifang Chen

Visual Language Models (VLMs) are essential for various tasks, particularly visual reasoning tasks, due to their robust multi-modal information integration, visual reasoning capabilities, and contextual awareness.

3D Reconstruction Visual Reasoning

TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation

2 code implementations17 Jun 2024 Jinyuan Fang, Zaiqiao Meng, Craig Macdonald

Retrieval-augmented generation (RAG) offers an effective approach for addressing question answering (QA) tasks.

Question Answering RAG +1

FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction

1 code implementation3 Jun 2024 Zhaohan Meng, Zaiqiao Meng, Iadh Ounis

Despite remarkable advances in recent DTI models through the integration of representations from diverse drug and target encoders, such models often struggle to capture the fine-grained interactions between drugs and protein, i. e. the binding of specific drug atoms (or substructures) and key amino acids of proteins, which is crucial for understanding the binding mechanisms and optimising drug design.

Drug Discovery

CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion

no code implementations22 Feb 2024 Zijun Long, George Killick, Lipeng Zhuang, Gerardo Aragon-Camarasa, Zaiqiao Meng, Richard McCreadie

State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE).

Contrastive Learning Few-Shot Learning +2

CLEX: Continuous Length Extrapolation for Large Language Models

1 code implementation25 Oct 2023 Guanzheng Chen, Xin Li, Zaiqiao Meng, Shangsong Liang, Lidong Bing

We generalise the PE scaling approaches to model the continuous dynamics by ordinary differential equations over the length scaling factor, thereby overcoming the constraints of current PE scaling methods designed for specific lengths.

4k Position

GenKIE: Robust Generative Multimodal Document Key Information Extraction

1 code implementation24 Oct 2023 Panfeng Cao, Ye Wang, Qiang Zhang, Zaiqiao Meng

Key information extraction (KIE) from scanned documents has gained increasing attention because of its applications in various domains.

Decoder Key Information Extraction +2

Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models

1 code implementation31 Aug 2023 Yupan Huang, Zaiqiao Meng, Fangyu Liu, Yixuan Su, Nigel Collier, Yutong Lu

Our experiments validate the effectiveness of SparklesChat in understanding and reasoning across multiple images and dialogue turns.

Instruction Following Visual Reasoning

When hard negative sampling meets supervised contrastive learning

no code implementations28 Aug 2023 Zijun Long, George Killick, Richard McCreadie, Gerardo Aragon Camarasa, Zaiqiao Meng

State-of-the-art image models predominantly follow a two-stage strategy: pre-training on large datasets and fine-tuning with cross-entropy loss.

Contrastive Learning Few-Shot Learning

Label Denoising through Cross-Model Agreement

no code implementations27 Aug 2023 Yu Wang, Xin Xin, Zaiqiao Meng, Joemon Jose, Fuli Feng

We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.

Denoising Image Classification

BAND: Biomedical Alert News Dataset

1 code implementation23 May 2023 Zihao Fu, Meiru Zhang, Zaiqiao Meng, Yannan Shen, David Buckeridge, Nigel Collier

Infectious disease outbreaks continue to pose a significant threat to human health and well-being.

Epidemiology named-entity-recognition +3

Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization

1 code implementation22 May 2023 Zihao Fu, Yixuan Su, Zaiqiao Meng, Nigel Collier

To alleviate the need of human effort, dictionary-based approaches have been proposed to extract named entities simply based on a given dictionary.

named-entity-recognition Named Entity Recognition

LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision Transformers

1 code implementation31 Mar 2023 Zijun Long, Zaiqiao Meng, Gerardo Aragon Camarasa, Richard McCreadie

Vision Transformers (ViTs) have emerged as popular models in computer vision, demonstrating state-of-the-art performance across various tasks.

Benchmarking Image Classification +1

COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

1 code implementation25 Mar 2023 Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu, Nigel Collier

In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word.

Event Extraction

Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications

1 code implementation1 Feb 2023 Muhammad Arslan Manzoor, Sarah Albarri, Ziting Xian, Zaiqiao Meng, Preslav Nakov, Shangsong Liang

This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks.

Question Answering Representation Learning +3

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

no code implementations7 Nov 2022 Jiahang Cao, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang

Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective.

Knowledge Graph Embedding Knowledge Graphs +1

Can Pretrained Language Models (Yet) Reason Deductively?

1 code implementation12 Oct 2022 Zhangdie Yuan, Songbo Hu, Ivan Vulić, Anna Korhonen, Zaiqiao Meng

Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks.

Revisiting Parameter-Efficient Tuning: Are We Really There Yet?

1 code implementation16 Feb 2022 Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, Shangsong Liang

Parameter-Efficient Tuning (PETuning) methods have been deemed by many as the new paradigm for using pretrained language models (PLMs).

Structure-Aware Random Fourier Kernel for Graphs

no code implementations NeurIPS 2021 Jinyuan Fang, Qiang Zhang, Zaiqiao Meng, Shangsong Liang

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels.

Gaussian Processes Graph Learning +1

Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models

1 code implementation ACL 2022 Zaiqiao Meng, Fangyu Liu, Ehsan Shareghi, Yixuan Su, Charlotte Collins, Nigel Collier

To catalyse the research in this direction, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, which is constructed based on the Unified Medical Language System (UMLS) Metathesaurus.

Knowledge Probing Transfer Learning

Graph Neural Pre-training for Enhancing Recommendations using Side Information

no code implementations8 Jul 2021 Zaiqiao Meng, Siwei Liu, Craig Macdonald, Iadh Ounis

For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks.

Entity Embeddings Recommendation Systems

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 May 2021 Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng

A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.

Denoising Recommendation Systems

Self-Alignment Pretraining for Biomedical Entity Representations

1 code implementation NAACL 2021 Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, Nigel Collier

Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.

Benchmarking Entity Linking +2

Exploring Data Splitting Strategies for the Evaluation of Recommendation Models

no code implementations26 Jul 2020 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

In this paper, we both show that there is no standard splitting strategy and that the selection of splitting strategy can have a strong impact on the ranking of recommender systems.

Recommendation Systems

Variational Bayesian Context-aware Representation for Grocery Recommendation

1 code implementation17 Sep 2019 Zaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis

We train our VBCAR model based on the Bayesian Skip-gram framework coupled with the amortized variational inference so that it can learn more expressive latent representations that integrate both the non-linearity and Bayesian behaviour.

Variational Inference

Neural Variational Hybrid Collaborative Filtering

no code implementations12 Oct 2018 Teng Xiao, Shangsong Liang, Hong Shen, Zaiqiao Meng

Specifically, we consider both the generative processes of users and items, and the prior of latent factors of users and items to be side informationspecific, which enables our model to alleviate matrix sparsity and learn better latent representations of users and items.

Collaborative Filtering Recommendation Systems

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