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Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew Jan 2022

Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew

Research Collection School Of Computing and Information Systems

Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of …

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The (De)Territorialised Appeal Of International Schools In China: Forging Brands, Boundaries And Inter-Belonging In Segregated Urban Space, Lily Kong, Orlando Woods, Hong Zhu Jan 2022

The (De)Territorialised Appeal Of International Schools In China: Forging Brands, Boundaries And Inter-Belonging In Segregated Urban Space, Lily Kong, Orlando Woods, Hong Zhu

Research Collection School of Social Sciences

This paper considers how the (de)territorialised appeal of international schools in China can reflect, enforce and expand pre-existing patterns of urban segregation. Whilst exploration of the effects of educational marketplaces on urban environments has become a focus of scholarly research, the recent expansion in the supply of, and demand for, international education has caused these effects to become more nuanced. As (de)territorialised entities, international schools can cause multiple forms of spatial and psycho-social distinction and (dis)association to become intertwined, the effects of which start from the school and radiate out from there. International schools can therefore cause segregation to become …

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Media And Peer Culture: Young People Sharing Norms And Collective Identities With And Through Media, Sun Sun Lim Jan 2022

Media And Peer Culture: Young People Sharing Norms And Collective Identities With And Through Media, Sun Sun Lim

Research Collection College of Integrative Studies

When young people interact, they absorb the peer culture that underpins and sustains their relationships with each other. Peer culture encompasses norms and conventions, shared interests and activities, and the unique modes of communication deployed in the afore-mentioned elements. The ways in which young people integrate their media consumption into their peer culture is the focus of this chapter. Specifically, it examines how young people incorporate media content into their peer interactions and appropriate a variety of communication platforms to socialize with their peers, thus generating distinctive traits, norms, practices, codes and shared identities that make up their unique peer …

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Impact Investing Via Affiliation With Academia, Ngos, Or Other Collaborations?, Theodor Florian Cojoianu, Andreas G. F. Hoepnner, Yanan. Lin, Fabiola Schneider Jan 2022

Impact Investing Via Affiliation With Academia, Ngos, Or Other Collaborations?, Theodor Florian Cojoianu, Andreas G. F. Hoepnner, Yanan. Lin, Fabiola Schneider

Research Collection College of Integrative Studies

While ESG investing has become mainstream, impact investment has yet to be institutionalised and legitimized. We investigate how impact investment firms claim legitimacy compared to their ESG peers. Impact investment is still a nascent field and suffers from a heavier burden of proof. Legitimacy has been recognised as a strategy to overcome the liability of newness. Combining private market data with statements made by investment firms on their websites we investigate how they claim legitimacy. We find that impact investment firms are more likely to claim to be involved in partnerships, specifically with academia and corporations than their ESG peers. …

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Russian Logics And The Culture Of Impossible: Part Ii: Reinterpreting Algorithmic Rationality, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol Jan 2022

Russian Logics And The Culture Of Impossible: Part Ii: Reinterpreting Algorithmic Rationality, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol

Research Collection School of Social Sciences

This article reinterprets algorithmic rationality by looking at the interaction between mathematical logic, mechanized reasoning, and, later, computing in the Russian Imperial and Soviet contexts to offer a history of the algorithm as a mathematical object bridging the inner and outer worlds, a humanistic vision that we, following logician Vladimir Uspensky, call the “culture of the impossible.” We unfold the deep roots of this vision as embodied in scientific intelligentsia. In Part I, we examine continuities between the turn-of-the-twentieth-century discussions of poznaniye—an epistemic orientation towards the process of knowledge acquisition—and the postwar rise of the Soviet school of mathematical logic. …

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Does A District Mandate Matter For The Behavior Of Politicians? An Analysis Of Roll-Call Votes And Parliamentary Speeches, Andreas Born, Aljoscha Janssen Jan 2022

Does A District Mandate Matter For The Behavior Of Politicians? An Analysis Of Roll-Call Votes And Parliamentary Speeches, Andreas Born, Aljoscha Janssen

Research Collection School Of Economics

In most democracies, members of parliament (MPs) are elected either through a party list or by a district. We use a discontinuity in the German electoral system to investigate the causal effect of a district election on an MP’s conformity with the party line. A district election does not affect roll-call voting behavior causally, possibly due to overall high adherence to party-line voting. Analyzing the parliamentary speeches of each MP allows us to overcome the high party-line discipline with regard to parliamentary voting. Using textual analysis and machine learning techniques, we create two measures of closeness of an MP’s speeches …

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Does A District Mandate Matter For The Behavior Of Politicians? An Analysis Of Roll-Call Votes And Parliamentary Speeches, Andreas Born, Aljoscha Janssen Jan 2022

Does A District Mandate Matter For The Behavior Of Politicians? An Analysis Of Roll-Call Votes And Parliamentary Speeches, Andreas Born, Aljoscha Janssen

Research Collection School Of Economics

In most democracies, members of parliament (MPs) are elected either through a party list or by a district. We use a discontinuity in the German electoral system to investigate the causal effect of a district election on an MP’s conformity with the party line. A district election does not affect roll-call voting behavior causally, possibly due to overall high adherence to party-line voting. Analyzing the parliamentary speeches of each MP allows us to overcome the high party-line discipline with regard to parliamentary voting. Using textual analysis and machine learning techniques, we create two measures of closeness of an MP’s speeches …

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Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan Jan 2022

Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Cross-modal recipe retrieval has recently been explored for food recognition and understanding. Text-rich recipe provides not only visual content information (e.g., ingredients, dish presentation) but also procedure of food preparation (cutting and cooking styles). The paired data is leveraged to train deep models to retrieve recipes for food images. Most recipes on the Web include sample pictures as the references. The paired multimedia data is not noise-free, due to errors such as pairing of images containing partially prepared dishes with recipes. The content of recipes and food images are not always consistent due to free-style writing and preparation of food …

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Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu Jan 2022

Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu

Research Collection School Of Computing and Information Systems

User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …

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Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng Dec 2021

Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap …

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Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang Dec 2021

Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of “good” representation from a group-theoretic view using Higgins’ definition of disentangled representation [38], and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. …

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Setiya On Consequentialism And Constraints, Ryan Cox, Matthew Hammerton Dec 2021

Setiya On Consequentialism And Constraints, Ryan Cox, Matthew Hammerton

Research Collection School of Social Sciences

It is widely held that agent-neutral consequentialism is incompatible with deontic constraints. Recently, Kieran Setiya has challenged this orthodoxy by presenting a form of agent-neutral consequentialism that he claims can capture deontic constraints. In this reply, we argue against Setiya's proposal by pointing to features of deontic constraints that his account fails to capture.

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Russian Logics And The Culture Of Impossible: Part 1. Recovering Intelligentsia Logics, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol Dec 2021

Russian Logics And The Culture Of Impossible: Part 1. Recovering Intelligentsia Logics, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol

Research Collection College of Integrative Studies

This article reinterprets algorithmic rationality by looking at the interaction between mathematical logic, mechanized reasoning, and, later, computing in the Russian Imperial and Soviet contexts to offer a history of the algorithm as a mathematical object bridging the inner and outer worlds, a humanistic vision that we, following logician Vladimir Uspensky, call the “culture of the impossible.” We unfold the deep roots of this vision as embodied in scientific intelligentsia. In Part I, we examine continuities between the turn-of-the-twentieth-century discussions of poznaniye—an epistemic orientation towards the process of knowledge acquisition—and the postwar rise of the Soviet school of mathematical logic. …

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Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang Dec 2021

Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Research Collection School Of Computing and Information Systems

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry …

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Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin Dec 2021

Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin

Research Collection School Of Computing and Information Systems

Objective-space decomposition algorithms (ODAs) are widely studied for solvingmulti-objective integer programs. However, they often encounter difficulties inhandling scalarized problems, which could cause infeasibility or repetitive nondominatedpoints and thus induce redundant runtime. To mitigate the issue, we presenta graph neural network (GNN) based method to learn the reduction rule in the ODA.We formulate the algorithmic procedure of generic ODAs as a Markov decisionprocess, and parameterize the policy (reduction rule) with a novel two-stage GNNto fuse information from variables, constraints and especially objectives for betterstate representation. We train our model with imitation learning and deploy it ona state-of-the-art ODA. Results show that …

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Etherlearn: Decentralizing Learning Via Blockchain, Nguyen Binh Duong Ta, Tian Jun Joel Yang Dec 2021

Etherlearn: Decentralizing Learning Via Blockchain, Nguyen Binh Duong Ta, Tian Jun Joel Yang

Research Collection School Of Computing and Information Systems

In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and …

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Towards Understanding Why Lookahead Generalizes Better Than Sgd And Beyond, Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan Dec 2021

Towards Understanding Why Lookahead Generalizes Better Than Sgd And Beyond, Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan

Research Collection School Of Computing and Information Systems

To train networks, lookahead algorithm [1] updates its fast weights k times via an inner-loop optimizer before updating its slow weights once by using the latest fast weights. Any optimizer, e.g. SGD, can serve as the inner-loop optimizer, and the derived lookahead generally enjoys remarkable test performance improvement over the vanilla optimizer. But theoretical understandings on the test performance improvement of lookahead remain absent yet. To solve this issue, we theoretically justify the advantages of lookahead in terms of the excess risk error which measures the test performance. Specifically, we prove that lookahead using SGD as its inner-loop optimizer can …

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Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu Nov 2021

Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu

Research Collection Lee Kong Chian School Of Business

Stock return prediction has been a hot topic in both research and industry given its potential for large financial gain. The return signal, apart from its inherent volatility and complexity, is often accompanied by a multitude of noises, such as other stocks’ performance, macroeconomic factors and financial news, etc. To better characterize these factors, we propose a new model that consists of two levels of sequence: an NLP-based module to capture the sequential nature of words and sentences in the financial news, and a time-series-based module to exploit the sequential nature of adjacent observations in the stock price. In this …

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Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw Nov 2021

Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct …

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Adaptive Posterior Knowledge Selection For Improving Knowledge-Grounded Dialogue Generation, Weichao Wang, Wei Gao, Shi Feng, Ling Chen, Daling Wang Nov 2021

Adaptive Posterior Knowledge Selection For Improving Knowledge-Grounded Dialogue Generation, Weichao Wang, Wei Gao, Shi Feng, Ling Chen, Daling Wang

Research Collection School Of Computing and Information Systems

In open-domain dialogue systems, knowledge information such as unstructured persona profiles, text descriptions and structured knowledge graph can help incorporate abundant background facts for delivering more engaging and informative responses. Existing studies attempted to model a general posterior distribution over candidate knowledge by considering the entire response utterance as a whole at the beginning of decoding process for knowledge selection. However, a single smooth distribution could fail to model the variability of knowledge selection patterns over different decoding steps, and make the knowledge expression less consistent. To remedy this issue, we propose an adaptive posterior knowledge selection framework, which sequentially …

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Span-Level Emotion Cause Analysis With Neural Sequence Tagging, Xiangju Li, Wei Gao, Shi Feng, Daling Wang, Shafiq Joty Nov 2021

Span-Level Emotion Cause Analysis With Neural Sequence Tagging, Xiangju Li, Wei Gao, Shi Feng, Daling Wang, Shafiq Joty

Research Collection School Of Computing and Information Systems

This paper addresses the task of span-level emotion cause analysis (SECA). It is a finer-grained emotion cause analysis (ECA) task, which aims to identify the specific emotion cause span(s) behind certain emotions in text. In this paper, we formalize SECA as a sequence tagging task for which several variants of neural network-based sequence tagging models to extract specific emotion cause span(s) in the given context. These models combine different types of encoding and decoding approaches. Furthermore, to make our models more "emotionally sensitive'', we utilize the multi-head attention mechanism to enhance the representation of context. Experimental evaluations conducted on two …

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Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan Nov 2021

Stock Market Trend Forecasting Based On Multiple Textual Features: A Deep Learning Method, Zhenda Hu, Zhaoxia Wang, Seng-Beng Ho, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Stock market trend forecasting is a valuable and challenging research task for both industry and academia. In order to explore the influence of stock news information on the stock market trend, a textual embedding construction method is proposed to encode multiple textual features, including topic features, sentiment features, and semantic features extracted from stock news textual content. In addition, a deep learning method is designed by using financial data and multiple textual features obtained from multiple news textual embeddings for short-term stock market trend prediction. For evaluation, extensive experiments on real stock market data are conducted. The experimental results illustrate …

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Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability, Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai Nov 2021

Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability, Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai

Research Collection School Of Computing and Information Systems

Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are actually unreasonable, while little work has been done on interpretability evaluation for them. In this paper, we propose a unified framework to quantitatively evaluate the interpretability of multi-hop reasoning models so as to advance their development. In specific, we define three metrics, including path recall, local interpretability, and global interpretability for evaluation, and design an approximate strategy to calculate these metrics using the interpretability scores of rules. We manually annotate all possible …

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Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam Nov 2021

Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question …

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Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall Nov 2021

Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall

Research Collection Lee Kong Chian School Of Business

There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have substance, but there is a need is to articulate the mechanisms by which AI may actually do both, and in the process, transform work and business organizations alike. We use economic studies showing past transformations automation wrought on the structure of employment and skills (such as the favouring of nonroutine skills) to articulate a ground for discussion. We then use case evidence of AI and automation to show how AI is …

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Wav-Bert: Cooperative Acoustic And Linguistic Representation Learning For Low-Resource Speech Recognition, Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin Nov 2021

Wav-Bert: Cooperative Acoustic And Linguistic Representation Learning For Low-Resource Speech Recognition, Guolin Zheng, Yubei Xiao, Ke Gong, Pan Zhou, Xiaodan Liang, Liang Lin

Research Collection School Of Computing and Information Systems

Unifying acoustic and linguistic representation learning has become increasingly crucial to transfer the knowledge learned on the abundance of high-resource language data for low-resource speech recognition. Existing approaches simply cascade pre-trained acoustic and language models to learn the transfer from speech to text. However, how to solve the representation discrepancy of speech and text is unexplored, which hinders the utilization of acoustic and linguistic information. Moreover, previous works simply replace the embedding layer of the pre-trained language model with the acoustic features, which may cause the catastrophic forgetting problem. In this work, we introduce Wav-BERT, a cooperative acoustic and linguistic …

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A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai Nov 2021

A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai

Research Collection School Of Computing and Information Systems

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on …

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Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li Nov 2021

Automating Developer Chat Mining, Shengyi Pan, Lingfeng Bao, Xiaoxue Ren, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Online chatrooms are gaining popularity as a communication channel between widely distributed developers of Open Source Software (OSS) projects. Most discussion threads in chatrooms follow a Q&A format, with some developers (askers) raising an initial question and others (respondents) joining in to provide answers. These discussion threads are embedded with rich information that can satisfy the diverse needs of various OSS stakeholders. However, retrieving information from threads is challenging as it requires a thread-level analysis to understand the context. Moreover, the chat data is transient and unstructured, consisting of entangled informal conversations. In this paper, we address this challenge by …

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Code-Switching Patterns Differentially Shape Cognitive Control: Testing The Predictions Of The Adaptive Control Hypothesis, Giliaine Ng, Hwajin Yang Nov 2021

Code-Switching Patterns Differentially Shape Cognitive Control: Testing The Predictions Of The Adaptive Control Hypothesis, Giliaine Ng, Hwajin Yang

Research Collection School of Social Sciences

Bilinguals engage in qualitatively different code-switching patterns (alternation, insertion, and congruent lexicalization) to different degrees, according to their engagement in different types of interactional contexts (single-language context, dual-language context, and dense code-switching context). Drawing on the adaptive control hypothesis, we examined whether bilinguals’ code-switching patterns would differentially shape multiple aspects of cognitive control (interference control, salient cue detection, and opportunistic planning). We found that a dense code-switching context, which predominantly involves insertion and congruent lexicalization, was positively associated with verbal opportunistic planning but negatively associated with interference control and salient cue detection. In contrast, a dual-language context, which predominantly involves …

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Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding Nov 2021

Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

Research Collection School Of Computing and Information Systems

Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship …

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