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Found 28 papers, 13 papers with code
On Generating Fact-Infused Question Variations
We study a double encoder-decoder model, Fact-Infused Question Generator (FIQG), for learning to generate fact-infused questions from a given question.
Causal Augmentation for Causal Sentence Classification
Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification.
QSTS: A Question-Sensitive Text Similarity Measure for Question Generation
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.
NUS-IDS at CASE 2021 Task 1: Improving Multilingual Event Sentence Coreference Identification With Linguistic Information
Event Sentence Coreference Identification (ESCI) aims to cluster event sentences that refer to the same event together for information extraction.
Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS-IDS at the CLPsych 2021 Shared Task
We describe our system for identifying users at-risk for suicide based on their tweets developed for the CLPsych 2021 Shared Task.
Beyond Words: A Comprehensive Survey of Sentence Representations
In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques.
Constructing and Interpreting Causal Knowledge Graphs from News
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.
EasySpider: A No-Code Visual System for Crawling the Web
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
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.
GPTScore: Evaluate as You Desire
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.
Fourier Sensitivity and Regularization of Computer Vision Models
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
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
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
Current causal text mining datasets vary in objectives, data coverage, and annotation schemes.
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary.
Polyglot Prompt: Multilingual Multitask PrompTraining
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?
Syntax-informed Question Answering with Heterogeneous Graph Transformer
Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks.
COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks
We present an outlook attention mechanism, COOL, for natural language processing.
Trusted Media Challenge Dataset and User Study
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
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
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
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).
Origin-Aware Next Destination Recommendation with Personalized Preference Attention
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.
ESTeR: Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection
Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management.
STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation
Next Point-of-Interest (POI) recommendation is a longstanding problem across the domains of Location-Based Social Networks (LBSN) and transportation.
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
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 with Generative Adversarial Networks for Multivariate Time Series
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.
