Search Results for author:
Found 71 papers, 25 papers with code
MMGRec: Multimodal Generative Recommendation with Transformer Model
Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information.
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter
In this study, we address this gap by developing a two-step hierarchical model, the Augmented CARDS model, specifically designed for detecting contrarian climate claims on Twitter.
Double Mixture: Towards Continual Event Detection from Speech
To address the challenges of catastrophic forgetting and effective disentanglement, we propose a novel method, 'Double Mixture.'
HGT: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding
Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HGT, a framework with a heterogeneous graph (HG)-enhanced large language model (LLM) to tackle few-shot TU tasks. It leverages the LLM by aligning the table semantics with the LLM's parametric knowledge through soft prompts and instruction turning and deals with complex tables by a multi-task pre-training scheme involving three novel multi-granularity self-supervised HG pre-training objectives. We empirically demonstrate the effectiveness of HGT, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks.
Modelling Political Coalition Negotiations Using LLM-based Agents
Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties.
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs
Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought (CoT) explanations alongside answers.
Continual Learning for Large Language Models: A Survey
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
Towards Event Extraction from Speech with Contextual Clues
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.
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning
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.
Latte: Latent Diffusion Transformer for Video Generation
We propose a novel Latent Diffusion Transformer, namely Latte, for video generation.
Transformer Multivariate Forecasting: Less is More?
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.
Hypergraph Node Representation Learning with One-Stage Message Passing
HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian.
Trustworthy AI: Deciding What to Decide
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.
Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection
In light of this, we introduce SG2HOI+, a unified one-step model based on the Transformer architecture.
DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding
One representative benchmark for its study is Social Intelligence Queries (Social-IQ), a dataset of multiple-choice questions on videos of complex social interactions.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning.
ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph.
Informative Scene Graph Generation via Debiasing
It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances.
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery
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.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
Few-shot Domain-Adaptive Visually-fused Event Detection from Text
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.
Toward the Automated Construction of Probabilistic Knowledge Graphs for the Maritime Domain
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).
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex
Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question.
Syntax-Aware On-the-Fly Code Completion
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.
Complex Reading Comprehension Through Question Decomposition
Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence.
Towards Relation Extraction From Speech
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
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
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.
Structured Two-stream Attention Network for Video Question Answering
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.
Paraphrasing Techniques for Maritime QA system
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
NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation.
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs
LaSE is strongly correlated with ROUGE and, unlike ROUGE, can be reliably measured even in the absence of references in the target language.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+1
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
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.
An AI-based Solution for Enhancing Delivery of Digital Learning for Future Teachers
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.
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
Pretrained Language Model in Continual Learning: A Comparative Study
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.
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text
Numerical reasoning skills are essential for complex question answering (CQA) over text.
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks.
Unsupervised Domain-adaptive Hash for Networks
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms.
Exploiting Scene Graphs for Human-Object Interaction Detection
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects.
Semantic Compositional Learning for Low-shot Scene Graph Generation
Scene graphs provide valuable information to many downstream tasks.
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
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.
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
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.
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
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.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation
Question generation (QG) has recently attracted considerable attention.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
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
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.
Understanding Unnatural Questions Improves Reasoning over Text
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.
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
Boosting House Price Predictions using Geo-Spatial Network Embedding
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.
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue.
Generating Question Titles for Stack Overflow from Mined Code Snippets
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
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.
Gaussian Embedding of Large-scale Attributed Graphs
Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations.
Ranked #1 on
Link Prediction
on Pubmed (nonstandard variant)
Question Generation from Paragraphs: A Tale of Two Hierarchical Models
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.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text
The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.
ParaQG: A System for Generating Questions and Answers from Paragraphs
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.
OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies
Ontologies are formal representations of concepts and complex relationships among them.
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network
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.
Vector and Line Quantization for Billion-scale Similarity Search on GPUs
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
The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text.
CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations.
