Search Results for author: Yuan-Fang Li

Found 67 papers, 22 papers with code

Modelling Political Coalition Negotiations Using LLM-based Agents

no code implementations18 Feb 2024 Farhad Moghimifar, Yuan-Fang Li, Robert Thomson, Gholamreza Haffari

Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties.

Language Modelling Large Language Model

Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs

no code implementations17 Feb 2024 Minh-Vuong Nguyen, Linhao Luo, Fatemeh Shiri, Dinh Phung, Yuan-Fang Li, Thuy-Trang Vu, Gholamreza Haffari

Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.

Knowledge Graphs Multi-hop Question Answering +1

Continual Learning for Large Language Models: A Survey

no code implementations2 Feb 2024 Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, Gholamreza Haffari

Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.

Continual Learning Continual Pretraining +2

Towards Event Extraction from Speech with Contextual Clues

no code implementations27 Jan 2024 Jingqi Kang, Tongtong Wu, Jinming Zhao, Guitao Wang, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari

While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-explored problem.

Event Extraction speech-recognition +1

Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning

no code implementations26 Jan 2024 Tao He, Tongtong Wu, Dongyang Zhang, Guiduo Duan, Ke Qin, Yuan-Fang Li

Besides, extensive experiments on the two mainstream benchmark datasets, VG and Open-Image(v6), show the superiority of our proposed model to a number of competitive SGG models in terms of continuous learning and conventional settings.

Graph Generation In-Context Learning +1

Transformer Multivariate Forecasting: Less is More?

no code implementations30 Dec 2023 Jingjing Xu, Caesar Wu, Yuan-Fang Li, Pascal Bouvry

From the model perspective, one of the PCA-enhanced models: PCA+Crossformer, reduces mean square errors (MSE) by 33. 3% and decreases runtime by 49. 2% on average.

Temporal Sequences Time Series +1

Hypergraph Node Representation Learning with One-Stage Message Passing

no code implementations1 Dec 2023 Shilin Qu, Weiqing Wang, Yuan-Fang Li, Xin Zhou, Fajie Yuan

HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian.

Representation Learning

Trustworthy AI: Deciding What to Decide

no code implementations21 Nov 2023 Caesar Wu, Yuan-Fang Li, Jian Li, Jingjing Xu, Bouvry Pascal

We aim to use this framework to conduct the TAI experiments by quantitive and qualitative research methods to satisfy TAI properties for the decision-making context.

Decision Making

DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding

no code implementations24 Oct 2023 Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

One representative benchmark for its study is Social Intelligence Queries (Social-IQ), a dataset of multiple-choice questions on videos of complex social interactions.

Language Modelling Multiple-choice

Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

1 code implementation2 Oct 2023 Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan

In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning.

Knowledge Graphs Language Modelling +3

ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning

1 code implementation4 Sep 2023 Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan

Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).

Knowledge Graphs

Generating Faithful Text From a Knowledge Graph with Noisy Reference Text

no code implementations12 Aug 2023 Tahsina Hashem, Weiqing Wang, Derry Tanti Wijaya, Mohammed Eunus Ali, Yuan-Fang Li

Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph.

Contrastive Learning Hallucination +2

Informative Scene Graph Generation via Debiasing

no code implementations10 Aug 2023 Lianli Gao, Xinyu Lyu, Yuyu Guo, Yuxuan Hu, Yuan-Fang Li, Lu Xu, Heng Tao Shen, Jingkuan Song

It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances.

Blocking Graph Generation +4

NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

no code implementations26 May 2023 Farhad Moghimifar, Shilin Qu, Tongtong Wu, Yuan-Fang Li, Gholamreza Haffari

Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context.

Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain

no code implementations4 May 2023 Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Reza Haffari, Van Nguyen, Shuang Yu

In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i. e., in the form of probabilistic knowledge graphs).

Knowledge Graphs

Few-shot Domain-Adaptive Visually-fused Event Detection from Text

no code implementations4 May 2023 Farhad Moghimifar, Fatemeh Shiri, Van Nguyen, Reza Haffari, Yuan-Fang Li

In this paper, we present a novel domain-adaptive visually-fused event detection approach that can be trained on a few labelled image-text paired data points.

Event Detection

Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

1 code implementation17 Apr 2023 Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan

In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).

Meta-Learning Metric Learning

Syntax-Aware On-the-Fly Code Completion

1 code implementation9 Nov 2022 Wannita Takerngsaksiri, Chakkrit Tantithamthavorn, Yuan-Fang Li

However, existing syntax-aware code completion approaches are not on-the-fly, as we found that for every two-thirds of characters that developers type, AST fails to be extracted because it requires the syntactically correct source code, limiting its practicality in real-world scenarios.

Code Completion

Complex Reading Comprehension Through Question Decomposition

no code implementations7 Nov 2022 Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence.

Language Modelling Multi-Hop Reading Comprehension

Towards Relation Extraction From Speech

1 code implementation17 Oct 2022 Tongtong Wu, Guitao Wang, Jinming Zhao, Zhaoran Liu, Guilin Qi, Yuan-Fang Li, Gholamreza Haffari

We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Teaching Neural Module Networks to Do Arithmetic

no code implementations COLING 2022 Jiayi Chen, Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning.

Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning

no code implementations17 Aug 2022 Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li

In this paper, we introduce open-vocabulary scene graph generation, a novel, realistic and challenging setting in which a model is trained on a set of base object classes but is required to infer relations for unseen target object classes.

Graph Generation Object +1

Structured Two-stream Attention Network for Video Question Answering

no code implementations2 Jun 2022 Lianli Gao, Pengpeng Zeng, Jingkuan Song, Yuan-Fang Li, Wu Liu, Tao Mei, Heng Tao Shen

To date, visual question answering (VQA) (i. e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA.

Question Answering Video Question Answering +2

Paraphrasing Techniques for Maritime QA system

no code implementations21 Mar 2022 Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Van Nguyen, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li

In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.

Neural Topic Modeling with Deep Mutual Information Estimation

no code implementations12 Mar 2022 Kang Xu, Xiaoqiu Lu, Yuan-Fang Li, Tongtong Wu, Guilin Qi, Ning Ye, Dong Wang, Zheng Zhou

NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation.

Mutual Information Estimation Text Clustering +1

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

1 code implementation17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Time Series +1

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

no code implementations20 Nov 2021 Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.

Contrastive Learning Graph Representation Learning +1

An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers

no code implementations9 Nov 2021 Yong-Bin Kang, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Natalie Wieland, Elizabeth Kollias, Hung Du, Steven Thomson, Yuan-Fang Li

There has been a recent and rapid shift to digital learning hastened by the pandemic but also influenced by ubiquitous availability of digital tools and platforms now, making digital learning ever more accessible.

Multiple-choice Question Generation +1

Pretrained Language Model in Continual Learning: A Comparative Study

no code implementations ICLR 2022 Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, Gholamreza Haffari

In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.

Continual Learning Language Modelling

Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs

no code implementations29 Sep 2021 Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan

Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.

Graph structure learning Representation Learning +2

Semi-supervised Network Embedding with Differentiable Deep Quantisation

no code implementations20 Aug 2021 Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks.

Link Prediction Network Embedding +2

Unsupervised Domain-adaptive Hash for Networks

no code implementations20 Aug 2021 Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li

Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms.

Link Prediction Node Classification +1

Exploiting Scene Graphs for Human-Object Interaction Detection

1 code implementation ICCV 2021 Tao He, Lianli Gao, Jingkuan Song, Yuan-Fang Li

Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects.

Human-Object Interaction Detection Object

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

1 code implementation Findings (ACL) 2021 Tahmid Hasan, Abhik Bhattacharjee, Md Saiful Islam, Kazi Samin, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar

XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training.

Abstractive Text Summarization

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1 code implementation12 May 2021 Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.

Contrastive Learning Graph Representation Learning

Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction

no code implementations4 Feb 2021 Bhagya Hettige, Weiqing Wang, Yuan-Fang Li, Suong Le, Wray Buntine

Although a point process (e. g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption.

Decision Making Point Processes +1

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

1 code implementation EMNLP 2020 Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu

Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.

Knowledge Base Question Answering Meta Reinforcement Learning +3

Less is More: Data-Efficient Complex Question Answering over Knowledge Bases

1 code implementation29 Oct 2020 Yuncheng Hua, Yuan-Fang Li, Guilin Qi, Wei Wu, Jingyao Zhang, Daiqing Qi

Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer.

Multi-hop Question Answering Question Answering

Understanding Unnatural Questions Improves Reasoning over Text

no code implementations COLING 2020 Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions which is then executed on the raw text by the interpreter.

Natural Questions Question Answering

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

no code implementations COLING 2020 Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.

Question Generation Question-Generation

Boosting House Price Predictions using Geo-Spatial Network Embedding

1 code implementation1 Sep 2020 Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali, Yuan-Fang Li, Yong-Bin Kang, Timos Sellis

Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.

Network Embedding regression

Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation

no code implementations13 Jun 2020 Tao He, Lianli Gao, Jingkuan Song, Jianfei Cai, Yuan-Fang Li

Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue.

Graph Generation Object +2

Generating Question Titles for Stack Overflow from Mined Code Snippets

1 code implementation20 May 2020 Zhipeng Gao, Xin Xia, John Grundy, David Lo, Yuan-Fang Li

Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet.

Software Engineering

$\mathtt{MedGraph:}$ Structural and Temporal Representation Learning of Electronic Medical Records

1 code implementation8 Dec 2019 Bhagya Hettige, Yuan-Fang Li, Weiqing Wang, Suong Le, Wray Buntine

To address these limitations, we present $\mathtt{MedGraph}$, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the temporal sequencing of visits through a point process.

Attribute Point Processes +1

Gaussian Embedding of Large-scale Attributed Graphs

1 code implementation2 Dec 2019 Bhagya Hettige, Yuan-Fang Li, Weiqing Wang, Wray Buntine

Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations.

Graph Embedding Link Prediction +1

Question Generation from Paragraphs: A Tale of Two Hierarchical Models

no code implementations8 Nov 2019 Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, Gholamreza Haffari

Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.

Question Generation Question-Generation +2

Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text

no code implementations CONLL 2019 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.

Question Generation Question-Generation

One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network

1 code implementation1 Jul 2019 Tao He, Yuan-Fang Li, Lianli Gao, Dongxiang Zhang, Jingkuan Song

We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.

Generative Adversarial Network Image Retrieval +2

Vector and Line Quantization for Billion-scale Similarity Search on GPUs

1 code implementation2 Jan 2019 Wei Chen, Jincai Chen, Fuhao Zou, Yuan-Fang Li, Ping Lu, Qiang Wang, Wei Zhao

The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs.


Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text

no code implementations15 Aug 2018 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.

Question Generation Question-Generation

CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks

no code implementations1 Jun 2017 Yuan-Fang Li, Ardavan Pedram

Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations.

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