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Full-Text Articles in Entire DC Network
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi
Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi
Research Collection School Of Computing and Information Systems
Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, while graphs in real-world scenarios are gradually generated with different-typed temporal events, containing abundant semantics and dynamics. Limited work has been done for embedding dynamic heterogeneous graphs since it is very challenging to model the complete formation process of heterogeneous events. In this paper, we propose a novel Heterogeneous Hawkes Process based dynamic Graph Embedding (HPGE) to handle this problem. HPGE effectively integrates the Hawkes process into …
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Research Collection School Of Computing and Information Systems
Pre-trained language models have been widely adopted as backbones in various natural language processing tasks. However, existing pre-trained language models ignore the descriptive meta-information in the text such as the distinction between the title and the mainbody, leading to over-weighted attention to insignificant text. In this paper, we propose a hypernetwork-based architecture to model the descriptive meta-information and integrate it into pre-trained language models. Evaluations on three natural language processing tasks show that our method notably improves the performance of pre-trained language models and achieves the state-of-the-art results on keyphrase extraction.
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann
Research Collection School Of Computing and Information Systems
An empathetic car that is capable of reading the driver’s emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., …
A Lagrangian Column Generation Approach For The Probabilistic Crowdsourced Logistics Planning, Chung-Kyun Han, Shih-Fen Cheng
A Lagrangian Column Generation Approach For The Probabilistic Crowdsourced Logistics Planning, Chung-Kyun Han, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
In recent years we have increasingly seen the movement for the retail industry to move their operations online. Along the process, it has created brand new patterns for the fulfillment service, and the logistics service providers serving these retailers have no choice but to adapt. The most challenging issues faced by all logistics service providers are the highly fluctuating demands and the shortening response times. All these challenges imply that maintaining a fixed fleet will either be too costly or insufficient. One potential solution is to tap into the crowdsourced workforce. However, existing industry practices of relying on human planners …
An Empirical Study Of The Discreteness Prior In Low-Rank Matrix Completion, Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius And Kloft
An Empirical Study Of The Discreteness Prior In Low-Rank Matrix Completion, Rodrigo Alves, Antoine Ledent, Renato Assunção, Marius And Kloft
Research Collection School Of Computing and Information Systems
A reasonable assumption in recommender systems is that the rows (users) and columns (items) of the rating matrix can be split into groups (communities) with the following property: each entry of the matrix is the sum of components corresponding to community behavior and a purely low-rank component corresponding to individual behavior. We investigate (1) whether such a structure is present in real-world datasets, (2) whether the knowledge of the existence of such structure alone can improve performance, without explicit information about the community memberships. To these ends, we formulate a joint optimization problem over all (completed matrix, set of communities) …
A Survey On Ml4vis: Applying Machine Learning Advances To Data Visualization, Qianwen Wang, Zhutian Chen, Yong Wang, Huamin Qu
A Survey On Ml4vis: Applying Machine Learning Advances To Data Visualization, Qianwen Wang, Zhutian Chen, Yong Wang, Huamin Qu
Research Collection School Of Computing and Information Systems
Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS is needed. In this article, we systematically survey 88 ML4VIS studies, aiming to answer two motivating questions: “what visualization processes can be assisted by ML?” and “how ML techniques can be used to solve visualization problems? ” This survey reveals seven main processes where …
Learning To Assign: Towards Fair Task Assignment In Large-Scale Ride Hailing, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, Qiang Yang
Learning To Assign: Towards Fair Task Assignment In Large-Scale Ride Hailing, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, Qiang Yang
Research Collection School Of Computing and Information Systems
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives. Despite extensive research on task assignment in ride hailing, the fairness of earnings among drivers is largely neglected. Pioneer studies on fair task assignment in ride hailing are ineffective and inefficient due to their myopic optimization perspective and timeconsuming assignment techniques. In this work, we propose LAF, an effective and efficient task assignment scheme that optimizes both utility and fairness. We adopt reinforcement learning to make assignments in a holistic manner and propose a set of acceleration techniques …
Boundary Detection With Bert For Span-Level Emotion Cause Analysis, Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang
Boundary Detection With Bert For Span-Level Emotion Cause Analysis, Xiangju Li, Wei Gao, Shi Feng, Yifei Zhang, Daling Wang
Research Collection School Of Computing and Information Systems
Emotion cause analysis (ECA) has been anemerging topic in natural language processing,which aims to identify the reasons behind acertain emotion expressed in the text. MostECA methods intend to identify the clausewhich contains the cause of a given emotion,but such clause-level ECA (CECA) can be ambiguous and imprecise. In this paper, we aimat span-level ECA (SECA) by detecting theprecise boundaries of text spans conveying accurate emotion causes from the given context.We formulate this task as sequence labelingand position identification problems and design two neural methods to solve them. Experiments on two benchmark ECA datasets showthat the proposed methods substantially outperform the …
Modeling Transitions Of Focal Entities For Conversational Knowledge Base Question Answering, Yunshi Lan, Jing Jiang
Modeling Transitions Of Focal Entities For Conversational Knowledge Base Question Answering, Yunshi Lan, Jing Jiang
Research Collection School Of Computing and Information Systems
Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the …
Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele
Pruning-Aware Merging For Efficient Multitask Inference, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Lothar Thiele
Research Collection School Of Computing and Information Systems
Many mobile applications demand selective execution of multiple correlated deep learning inference tasks on resource-constrained platforms. Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost. Pruning each network separately yields suboptimal computation cost due to task relatedness. A promising remedy is to merge the networks into a multitask network to eliminate redundancy across tasks before network pruning. However, pruning a multitask network combined by existing network merging schemes cannot minimise the computation cost of every task combination because they do not consider such …
Crossasr++: A Modular Differential Testing Framework For Automatic Speech Recognition, Muhammad Hilmi Asyrofi, Zhou Yang, David Lo
Crossasr++: A Modular Differential Testing Framework For Automatic Speech Recognition, Muhammad Hilmi Asyrofi, Zhou Yang, David Lo
Research Collection School Of Computing and Information Systems
Developers need to perform adequate testing to ensure the quality of Automatic Speech Recognition (ASR) systems. However, manually collecting required test cases is tedious and time-consuming. Our recent work proposes CrossASR, a differential testing method for ASR systems. This method first utilizes Text-to-Speech (TTS) to generate audios from texts automatically and then feed these audios into different ASR systems for cross-referencing to uncover failed test cases. It also leverages a failure estimator to find failing test cases more efficiently. Such a method is inherently self-improvable: the performance can increase by leveraging more advanced TTS and ASR systems. So, in this …
Toward Explainable Deep Anomaly Detection, Guansong Pang, Charu Aggarwal
Toward Explainable Deep Anomaly Detection, Guansong Pang, Charu Aggarwal
Research Collection School Of Computing and Information Systems
Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many realworld applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability …
Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen
Independent Reinforcement Learning For Weakly Cooperative Multiagent Traffic Control Problem, Chengwei Zhang, Shan Jin, Wanli Xue, Xiaofei Xie, Shengyong Chen, Rong Chen
Research Collection School Of Computing and Information Systems
The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We …
Anomaly And Novelty Detection, Explanation, And Accommodation (Andea), Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbing Cao, Thomas G. Dietterich
Anomaly And Novelty Detection, Explanation, And Accommodation (Andea), Guansong Pang, Jundong Li, Anton Van Den Hengel, Longbing Cao, Thomas G. Dietterich
Research Collection School Of Computing and Information Systems
The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, …
An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao
An Empirical Study Of Gui Widget Detection For Industrial Mobile Games, Jiaming Ye, Ke Chen, Xiaofei Xie, Lei Ma, Ruochen Huang, Yingfeng Chen, Yinxing Xue, Jianjun Zhao
Research Collection School Of Computing and Information Systems
With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges …
Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu
Ava: Adversarial Vignetting Attack Against Visual Recognition, Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu
Research Collection School Of Computing and Information Systems
Vignetting is an inherent imaging phenomenon within almost all optical systems, showing as a radial intensity darkening toward the corners of an image. Since it is a common effect for photography and usually appears as a slight intensity variation, people usually regard it as a part of a photo and would not even want to post-process it. Due to this natural advantage, in this work, we study the vignetting from a new viewpoint, i.e., adversarial vignetting attack (AVA), which aims to embed intentionally misleading information into the vignetting and produce a natural adversarial example without noise patterns. This example can …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li
Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li
Research Collection School Of Computing and Information Systems
As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient inmemory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic …
Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li
Forecasting Interaction Order On Temporal Graphs, Wenwen Xia, Yuchen Li, Jianwei Tian, Shenghong Li
Research Collection School Of Computing and Information Systems
Link prediction is a fundamental task for graph analysis and the topic has been studied extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a binary classification problem about two nodes. However, for temporal graphs, links (or interactions) among node sets appear in sequential orders. And the orders may lead to interesting applications. While a binary link prediction formulation fails to handle such an order-sensitive case. In this paper, we focus on such an interaction order prediction (IOP) problem among a given node set on temporal graphs. For the technical aspect, we develop a graph neural …
Inter-Retailer Channel Competition: Empirical Analyses Of Store Entry Effects On Online Purchases, Qian Tang, Mei Lin, Youngsoo Kim
Inter-Retailer Channel Competition: Empirical Analyses Of Store Entry Effects On Online Purchases, Qian Tang, Mei Lin, Youngsoo Kim
Research Collection School Of Computing and Information Systems
This study empirically examines the effect of offline store entry on a competing online retailer in the footwear industry and investigates how this effect depends on the relative product assortment and price between the offline store and the online retailer. Using transaction data from a large online footwear retailer and offline store entry data from 19 major shoe retail chains and 3 department store chains, we quantify the entry effect of offline stores. Categorizing offline stores by assortment and price, we find that the entry of regular-price narrow-assortment stores generates a complementary effect that increases online purchases, while the entry …
Information Systems Business Analytics Curriculum: Competencies From National Infocomm Skills Model And Job Listings, Swapna Gottipati, Venky Shankaranan, Kyong Jin Shim
Information Systems Business Analytics Curriculum: Competencies From National Infocomm Skills Model And Job Listings, Swapna Gottipati, Venky Shankaranan, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
To the best of our knowledge, no research has examined how to integrate the national skills model with the industry demand for designing business analytics curricula. To answer this question, we took the initial steps of identifying the job roles and skills required by the nation in “Data” domain. We then collected the skills that are required by business analytics job positions listed by industry. Further, we extracted the top tools, technologies, and programming languages (TTP) demanded by the industry for various categories of technical skills. We then combined the analyses to design the curriculum, courses, and content for various …
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Research Collection School Of Computing and Information Systems
The Vehicle Routing Problem (VRP) was formally presented to the scientific literature in 1959 by Dantzig and Ramser (DOI:10.1287/mnsc.6.1.80). Sixty years on, the problem is still heavily researched, with hundreds of papers having been published addressing this problem and the many variants that now exist. Many datasets have been proposed to enable researchers to compare their algorithms using the same problem instances where either the best known solution is known or, in some cases, the optimal solution is known. In this survey paper, we provide a list of Vehicle Routing Problem datasets, categorized to enable researchers to have easy access …
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …
Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung
Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung
Research Collection School Of Computing and Information Systems
An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by …
Graph-Based Seed Object Synthesis For Search-Based Unit Testing, Yun Lin, You Seng Ong, Jun Sun, Gordon Fraser, Jin Song Dong
Graph-Based Seed Object Synthesis For Search-Based Unit Testing, Yun Lin, You Seng Ong, Jun Sun, Gordon Fraser, Jin Song Dong
Research Collection School Of Computing and Information Systems
Search-based software testing (SBST) generates tests using search algorithms guided by measurements gauging how far a test case is away from exercising a coverage goal. The effectiveness of SBST largely depends on the continuity and monotonicity of the fitness landscape decided by these measurements and the search operators. Unfortunately, the fitness landscape is challenging when the function under test takes object inputs, as classical measurements hardly provide guidance for constructing legitimate object inputs. To overcome this problem, we propose test seeds, i.e., test code skeletons of legitimate objects which enable the use of classical measurements. Given a target branch in …
Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy
Estimating Homophily In Social Networks Using Dyadic Predictions, George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy
Research Collection School Of Computing and Information Systems
Predictions of node categories are commonly used to estimate homophily and other relational properties in networks. However, little is known about the validity of using predictions for this task. We show that estimating homophily in a network is a problem of predicting categories of dyads (edges) in the graph. Homophily estimates are unbiased when predictions of dyad categories are unbiased. Node-level prediction models, such as the use of names to classify ethnicity or gender, do not generally produce unbiased predictions of dyad categories and therefore produce biased homophily estimates. Bias comes from three sources: sampling bias, correlation between model errors …
Effective Digital Learning Practices For Is Design Courses During Covid-19, Eng Lieh Ouh, Benjamin Gan
Effective Digital Learning Practices For Is Design Courses During Covid-19, Eng Lieh Ouh, Benjamin Gan
Research Collection School Of Computing and Information Systems
The COVID-19 pandemic has pushed educational institutions to adopt digital learning for an extended period. This research studies the effectiveness of digital learning practices based on student feedback data collected for two Information Systems design courses: human interaction design and solution architecture design. This paper leverages the data to analyze the effectiveness of a set of digital learning practices: ZOOM lectures, polling or Kahoot questions, self-reflection, virtual exercises and virtual mentorship. Our research questions are on the effectiveness of these learning practices to keep the student’s interest and learn the course materials. The research compares each learning practice and the …
Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang
Learning-Based Extraction Of First-Order Logic Representations Of Api Directives, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Xuefang Bai, Gang Lyu, Jiazhen Xie, Xiaoxin Zhang
Research Collection School Of Computing and Information Systems
Developers often rely on API documentation to learn API directives, i.e., constraints and guidelines related to API usage. Failing to follow API directives may cause defects or improper implementations. Since there are no industry-wide standards on how to document API directives, they take many forms and are often hard to understand by developers or challenging to parse with tools. In this paper, we propose a learning based approach for extracting first-order logic representations of API directives (FOL directives for short). The approach, called LeadFOL, uses a joint learning method to extract atomic formulas by identifying the predicates and arguments involved …
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Research Collection School Of Computing and Information Systems
A graph game proceeds as follows: two players move a token through a graph to produce a finite or infinite path, which determines the payoff of the game. We study bidding games in which in each turn, an auction determines which player moves the token. Bidding games were largely studied in combination with two variants of first-price auctions called “Richman” and “poorman” bidding. We study taxman bidding, which span the spectrum between the two. The game is parameterized by a constant τ∈[0,1]: portion τ of the winning bid is paid to the other player, and portion 1−τ to the bank. …
Towards Generative Aspect-Based Sentiment Analysis, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam
Towards Generative Aspect-Based Sentiment Analysis, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam
Research Collection School Of Computing and Information Systems
Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative …