Search Results for author: See-Kiong Ng

Found 28 papers, 13 papers with code

On Generating Fact-Infused Question Variations

1 code implementation RANLP 2021 Arthur Deschamps, Sujatha Das Gollapalli, See-Kiong Ng

We study a double encoder-decoder model, Fact-Infused Question Generator (FIQG), for learning to generate fact-infused questions from a given question.

Question Generation Question-Generation

Causal Augmentation for Causal Sentence Classification

1 code implementation EMNLP (CINLP) 2021 Fiona Anting Tan, Devamanyu Hazarika, See-Kiong Ng, Soujanya Poria, Roger Zimmermann

Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.

Classification Sentence Classification

QSTS: A Question-Sensitive Text Similarity Measure for Question Generation

no code implementations COLING 2022 Sujatha Das Gollapalli, See-Kiong Ng

Indeed, QG models continue to be evaluated using traditional measures such as BLEU, METEOR, and ROUGE scores which were designed for other text generation problems.

Question Generation Question-Generation +5

NUS-IDS at CASE 2021 Task 1: Improving Multilingual Event Sentence Coreference Identification With Linguistic Information

1 code implementation ACL (CASE) 2021 Fiona Anting Tan, Sujatha Das Gollapalli, See-Kiong Ng

Event Sentence Coreference Identification (ESCI) aims to cluster event sentences that refer to the same event together for information extraction.


Beyond Words: A Comprehensive Survey of Sentence Representations

no code implementations22 May 2023 Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria

In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques.

Question Answering Representation Learning +4

Constructing and Interpreting Causal Knowledge Graphs from News

no code implementations16 May 2023 Fiona Anting Tan, Debdeep Paul, Sahim Yamaura, Miura Koji, See-Kiong Ng

In this work, we propose a methodology to construct causal knowledge graphs (KGs) from news using two steps: (1) Extraction of Causal Relations, and (2) Argument Clustering and Representation into KG.

Knowledge Graphs

EasySpider: A No-Code Visual System for Crawling the Web

1 code implementation ACM The Web Conference 2023 Naibo Wang, Wenjie Feng, Jianwei Yin, See-Kiong Ng

As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research.


Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market

1 code implementation23 Feb 2023 Naibo Wang, Wenjie Feng, Fusheng Liu, Moming Duan, See-Kiong Ng

The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence of multiple well-trained models to improve the performance of the resultant model through one-shot federated learning and ensemble learning in a data-free manner.

Ensemble Learning Federated Learning

GPTScore: Evaluate as You Desire

1 code implementation8 Feb 2023 Jinlan Fu, See-Kiong Ng, Zhengbao Jiang, PengFei Liu

Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the utilization of large pre-trained models.

Text Generation

Fourier Sensitivity and Regularization of Computer Vision Models

no code implementations31 Jan 2023 Kiran Krishnamachari, See-Kiong Ng, Chuan-Sheng Foo

Using this result, we propose a general measure of any differentiable model's Fourier-sensitivity using the unitary Fourier-transform of its input-gradient.

CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations

no code implementations COLING 2022 Lin Xu, Qixian Zhou, Jinlan Fu, Min-Yen Kan, See-Kiong Ng

Knowledge-grounded dialog systems need to incorporate smooth transitions among knowledge selected for generating responses, to ensure that dialog flows naturally.


Joint Triplet Loss Learning for Next New POI Recommendation

no code implementations25 Sep 2022 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh

Sparsity of the User-POI matrix is a well established problem for next POI recommendation, which hinders effective learning of user preferences.

UniCausal: Unified Benchmark and Repository for Causal Text Mining

1 code implementation19 Aug 2022 Fiona Anting Tan, Xinyu Zuo, See-Kiong Ng

Current causal text mining datasets vary in objectives, data coverage, and annotation schemes.

Classification Relation Extraction

Polyglot Prompt: Multilingual Multitask PrompTraining

1 code implementation29 Apr 2022 Jinlan Fu, See-Kiong Ng, PengFei Liu

This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i. e. without any task/language-specific module?

named-entity-recognition Named Entity Recognition +7

Syntax-informed Question Answering with Heterogeneous Graph Transformer

no code implementations1 Apr 2022 Fangyi Zhu, Lok You Tan, See-Kiong Ng, Stéphane Bressan

Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks.

Language Modelling Question Answering

Trusted Media Challenge Dataset and User Study

no code implementations13 Jan 2022 Weiling Chen, Sheng Lun Benjamin Chua, Stefan Winkler, See-Kiong Ng

To tackle the issue, we have organized the Trusted Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies could be leveraged to combat fake media.

NUS-IDS at FinCausal 2021: Dependency Tree in Graph Neural Network for Better Cause-Effect Span Detection

1 code implementation FNP 2021 Fiona Anting Tan, See-Kiong Ng

Automatic identification of cause-effect spans in financial documents is important for causality modelling and understanding reasons that lead to financial events.

Spatial Frequency Sensitivity Regularization for Robustness

no code implementations29 Sep 2021 Kiran Chari, Chuan-Sheng Foo, See-Kiong Ng

The ability to generalize to out-of-distribution data is a major challenge for modern deep neural networks.

Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

no code implementations19 Jan 2021 Dacheng Chen, Dan Li, Xiuqin Xu, Ruizhi Yang, See-Kiong Ng

We trained our model using the publicly available dataset from 2017 PhysioNet Computing in Cardiology(CinC) Challenge containing 8528 single-lead ECG recordings of short-term heart rhythms (9-61s).

Classification Feature Engineering +1

Origin-Aware Next Destination Recommendation with Personalized Preference Attention

1 code implementation3 Dec 2020 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Rui Tan

Next destination recommendation is an important task in the transportation domain of taxi and ride-hailing services, where users are recommended with personalized destinations given their current origin location.

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation

no code implementations6 Oct 2020 Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, Jagannadan Varadarajan

Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation.

Graph Attention

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

no code implementations18 Feb 2019 Baihong Jin, Dan Li, Seshadhri Srinivasan, See-Kiong Ng, Kameshwar Poolla, Alberto~Sangiovanni-Vincentelli

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings.

Fault Detection

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

1 code implementation15 Jan 2019 Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, See-Kiong Ng

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.

Anomaly Detection BIG-bench Machine Learning +1

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

2 code implementations13 Sep 2018 Dan Li, Dacheng Chen, Jonathan Goh, See-Kiong Ng

We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS.

Anomaly Detection Time Series Analysis

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