67 Result(s)
within
Conference Paper
Yi**ang Chen
Artificial Intelligence
Data Mining and Knowledge Discovery
Database Management
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Chapter and Conference Paper
Artificial Intelligences on Automated Context-Brain Recognition with Mobile Detection Devices
In the past few decades, lots of studies were proposed on investigations of brain circuits for physical health, mental health, educational learning, controlling system and so on. However, very few studies conc...
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Chapter and Conference Paper
Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification
Dialogue Acts (DAs) can be used to explain what expert tutors do and what students know during the tutoring process. Most empirical studies adopt the random sampling method to obtain sentence samples for manua...
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Chapter and Conference Paper
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design...
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Chapter and Conference Paper
NTDA: Noise-Tolerant Data Augmentation for Document-Level Event Argument Extraction
Event argument extraction (EAE), aiming at identifying event arguments over multiple sentences, mainly faces data sparsity problem. Cross-domain data augmentation can leverage annotated data to augment trainin...
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Chapter and Conference Paper
The Road Not Taken: Preempting Dropout in MOOCs
Massive Open Online Courses (MOOCs) are often plagued by a low level of student engagement and retention, with many students drop** out before completing the course. In an effort to improve student retention...
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Chapter and Conference Paper
Generalizable Automatic Short Answer Scoring via Prototypical Neural Network
We investigated the challenging task of generalizable automatic short answer scoring (ASAS), where a scoring model is tasked with generalizing to target domains (provided only with limited labeled data) that h...
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Chapter and Conference Paper
Measuring Inconsistency in Written Feedback: A Case Study in Politeness
Feedback, indisputably, has been widely recognized as one of the most important forms of communication between teachers and students and a significant lever to enhance learning experience and success. However,...
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Chapter and Conference Paper
Popularity Prediction in MOOCs: A Case Study on Udemy
Massive Open Online Courses (MOOCs) have dramatically changed how people access education. Though substantial research works have been carried out to improve students’ learning experiences, very little attenti...
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Chapter and Conference Paper
Towards the Automated Evaluation of Legal Casenote Essays
A legal casenote essay is a commonly assigned writing task to first-year law students aiming to promote their understanding of legal reasoning and help them acquire writing skills in a legal domain. To ensure ...
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Chapter and Conference Paper
Effects of Fairness and Explanation on Trust in Ethical AI
AI ethics has been a much discussed topic in recent years. Fairness and explainability are two important ethical principles for trustworthy AI. In this paper, the impact of AI explainability and fairness on us...
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Chapter and Conference Paper
Aligning Internal Regularity and External Influence of Multi-granularity for Temporal Knowledge Graph Embedding
Representation learning for the Temporal Knowledge Graphs (TKGs) is an emerging topic in the knowledge reasoning community. Existing methods consider the internal and external influence at either element level...
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Chapter and Conference Paper
Incorporating Ranking Context for End-to-End BERT Re-ranking
Ranking context has been shown crucial for the performance of learning to rank. Its use for the BERT-based re-rankers, however, has not been fully explored. In this work, an end-to-end BERT-based ranking model...
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Chapter and Conference Paper
Multimodal Named Entity Recognition with Image Attributes and Image Knowledge
Multimodal named entity extraction is an emerging task which uses both textual and visual information to detect named entities and identify their entity types. The existing efforts are often flawed in two aspe...
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Chapter and Conference Paper
Learning to Label with Active Learning and Reinforcement Learning
Training data labelling is financially expensive in domain-specific learning applications, which heavily relies on the intelligence from domain experts. Thus, with budget constraint, it is important to judicio...
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Chapter and Conference Paper
On Robustness and Bias Analysis of BERT-Based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on standard natural language processing benchmarks. However, the resultant model generalizability remains poorly understood. We do not know, ...
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Chapter and Conference Paper
Towards Nested and Fine-Grained Open Information Extraction
Open Information Extraction is a crucial task in natural language processing with wide applications. Existing efforts only work on extracting simple flat triplets that are not minimized, which neglect triplets...
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Chapter and Conference Paper
GNE: Generic Heterogeneous Information Network Embedding
As an effective approach to solve graph mining problems, network embedding aims to learn low-dimensional latent representation of nodes in a network. We develop a representation learning method called GNE for ...
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Chapter and Conference Paper
An Advanced Q-Learning Model for Multi-agent Negotiation in Real-Time Bidding
This work develops a reinforcement learning method for multi-agent negotiation. While existing works have developed various learning methods for multi-agent negotiation, they have primarily focus on the Tempor...
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Chapter and Conference Paper
Distributed Differential Evolution for Anonymity-Driven Vertical Fragmentation in Outsourced Data Storage
Vertical fragmentation is a promising technique for outsourced data storage. It can protect data privacy while conserving original data without any transformation. Previous vertical fragmentation approaches ne...
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Chapter and Conference Paper
CIFEF: Combining Implicit and Explicit Features for Friendship Inference in Location-Based Social Networks
With the increasing popularity of location-based social networks (LBSNs), users can share their check-in location information more easily. One of the most active problems in LBSNs is friendship inference based...
