Search Results for author: Yuan-Fang Li

Found 45 papers, 16 papers with code

Syntax-Aware On-the-Fly Code Completion

no code implementations9 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 Relation Extraction +1

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 Scene Graph Generation

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 +1

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 Traffic Prediction

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 +1

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

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 Scene Graph Generation +1

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.

Point Processes Representation Learning

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

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.

Image Retrieval Object Recognition +1

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.

Quantization

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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