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49 results on '"Leong, Chee Wee"'

1. Investigating the Effect of Linguistic Features on Personality and Job Performance Predictions

Author

Le, Hung, Li, Sixia, Mawalim, Candy Olivia, Huang, Hung-Hsuan, Leong, Chee Wee, Okada, Shogo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Coman, Adela, editor, and Vasilache, Simona, editor

Published
2023
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2. Automated Scoring of Nonnative Speech Using the 'SpeechRater'? v. 5.0 Engine. Research Report. ETS RR-18-10

Author

Chen, Lei, Zechner, Klaus, Yoon, Su-Youn, Evanini, Keelan, Wang, Xinhao, Loukina, Anatassia, Tap, Jidong, Davis, Lawrence, Lee, Chong Min, Ma, Min, Mundowsky, Robert, Lu, Chi, Leong, Chee Wee, and Gyawali, Binod

Abstract

This research report provides an overview of the R&D efforts at Educational Testing Service related to its capability for automated scoring of nonnative spontaneous speech with the "SpeechRater"? automated scoring service since its initial version was deployed in 2006. While most aspects of this R&D work have been published in various venues in recent years, no comprehensive account of the current state of SpeechRater has been provided since the initial publications following its first operational use in 2006. After a brief review of recent related work by other institutions, we summarize the main features and feature classes that have been developed and introduced into SpeechRater in the past 10 years, including features measuring aspects of pronunciation, prosody, vocabulary, grammar, content, and discourse. Furthermore, new types of filtering models for flagging nonscorable spoken responses are described, as is our new hybrid way of building linear regression scoring models with improved feature selection. Finally, empirical results for SpeechRater 5.0 (operationally deployed in 2016) are provided.

Published
2018

3. A Robust Microservice Architecture for Scaling Automated Scoring Applications. Research Report. ETS RR-18-14

Author

Madnani, Nitin, Cahill, Aoife, Blanchard, Daniel, Andreyev, Slava, Napolitano, Diane, Gyawali, Binod, Heilman, Michael, Lee, Chong Min, Leong, Chee Wee, Mulholland, Matthew, and Riordan, Brian

Abstract

We present a microservice architecture for large-scale automated scoring applications. Our architecture builds on the open-source Apache Storm framework and facilitates the development of robust, scalable automated scoring applications that can easily be extended and customized. We demonstrate our architecture with an application for automated content scoring.

Published
2018

4. To Trust, or Not to Trust? A Study of Human Bias in Automated Video Interview Assessments

Author

Leong, Chee Wee, Roohr, Katrina, Ramanarayanan, Vikram, Martin-Raugh, Michelle P., Kell, Harrison, Ubale, Rutuja, Qian, Yao, Mladineo, Zydrune, and McCulla, Laura

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, I.2.0, H.1.2
Abstract

Supervised systems require human labels for training. But, are humans themselves always impartial during the annotation process? We examine this question in the context of automated assessment of human behavioral tasks. Specifically, we investigate whether human ratings themselves can be trusted at their face value when scoring video-based structured interviews, and whether such ratings can impact machine learning models that use them as training data. We present preliminary empirical evidence that indicates there might be biases in such annotations, most of which are visual in nature., Comment: ICCV Workshop on Interpreting and Explaining Visual Artificial Intelligence Models, Seoul, South Korea, 2019

Published
2019

6. Designing an Automated Assessment of Public Speaking Skills Using Multimodal Cues

Author

Chen, Lei, Feng, Gary, Leong, Chee Wee, Joe, Jilliam, Kitchen, Christopher, and Lee, Chong Min

Abstract

Traditional assessments of public speaking skills rely on human scoring. We report an initial study on the development of an automated scoring model for public speaking performances using multimodal technologies. Task design, rubric development, and human rating were conducted according to standards in educational assessment. An initial corpus of 17 speakers with four speaking tasks was collected using audio, video, and 3-D motion capturing devices. A scoring model based on basic features in speech content, speech delivery, and hand, body, and head movements significantly predicts human rating, suggesting the feasibility of using multimodal technologies in the assessment of public speaking skills.

Published
2016

7. Analysis of Modality-Based Presentation Skills Using Sequential Models

Author

Shwe Yi Tun, Su, Okada, Shogo, Huang, Hung-Hsuan, Leong, Chee Wee, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Meiselwitz, Gabriele, editor

Published
2021
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11. Improving Query Expansion for Image Retrieval via Saliency and Picturability

Author

Leong, Chee Wee, Hassan, Samer, Ruiz, Miguel Enrique, Mihalcea, Rada, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Forner, Pamela, editor, Gonzalo, Julio, editor, Kekäläinen, Jaana, editor, Lalmas, Mounia, editor, and de Rijke, Marteen, editor

Published
2011
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21. Are Humans Biased in Assessment of Video Interviews?

Author

Leong, Chee Wee, primary, Roohr, Katrina, additional, Ramanarayanan, Vikram, additional, Martin-Raugh, Michelle P., additional, Kell, Harrison, additional, Ubale, Rutuja, additional, Qian, Yao, additional, Mladineo, Zydrune, additional, and McCulla, Laura, additional

Published
2019
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23. Automated Scoring of Nonnative Speech Using the SpeechRaterSMv. 5.0 Engine

Author

Chen, Lei, primary, Zechner, Klaus, additional, Yoon, Su‐Youn, additional, Evanini, Keelan, additional, Wang, Xinhao, additional, Loukina, Anastassia, additional, Tao, Jidong, additional, Davis, Lawrence, additional, Lee, Chong Min, additional, Ma, Min, additional, Mundkowsky, Robert, additional, Lu, Chi, additional, Leong, Chee Wee, additional, and Gyawali, Binod, additional

Published
2018
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33. Automated Scoring of Nonnative Speech Using the SpeechRaterSM v. 5.0 Engine.

Author

Chen, Lei, Zechner, Klaus, Yoon, Su‐Youn, Evanini, Keelan, Wang, Xinhao, Loukina, Anastassia, Tao, Jidong, Davis, Lawrence, Lee, Chong Min, Ma, Min, Mundkowsky, Robert, Lu, Chi, Leong, Chee Wee, and Gyawali, Binod

Subjects
TEMPO (Phonetics), TEST scoring, EDUCATIONAL testing services
Abstract

This research report provides an overview of the R&D efforts at Educational Testing Service related to its capability for automated scoring of nonnative spontaneous speech with the SpeechRaterSM automated scoring service since its initial version was deployed in 2006. While most aspects of this R&D work have been published in various venues in recent years, no comprehensive account of the current state of SpeechRater has been provided since the initial publications following its first operational use in 2006. After a brief review of recent related work by other institutions, we summarize the main features and feature classes that have been developed and introduced into SpeechRater in the past 10 years, including features measuring aspects of pronunciation, prosody, vocabulary, grammar, content, and discourse. Furthermore, new types of filtering models for flagging nonscorable spoken responses are described, as is our new hybrid way of building linear regression scoring models with improved feature selection. Finally, empirical results for SpeechRater 5.0 (operationally deployed in 2016) are provided. Report Number: ETS RR–18‐10 [ABSTRACT FROM AUTHOR]

Published
2018
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40. Exploiting Wikipedia for Directional Inferential Text Similarity

Author

Samer Hassan and Leong Chee Wee

Subjects
Information retrieval, Computer science, business.industry, Inference, computer.software_genre, Semantic similarity, Explicit semantic analysis, Similarity (psychology), Metric (mathematics), Encyclopedia, Question answering, Artificial intelligence, business, computer, Natural language processing, Natural language
Abstract

In natural languages, variability of semantic expression refers to the situation where the same meaning can be inferred from different words or texts. Given that many natural language processing tasks nowadays (e.g. question answering, information retrieval, document summarization) often model this variability by requiring a specific target meaning to be inferred from different text variants, it is helpful to capture text similarity in a directional manner to serve such inference needs. In this paper, we show how Wikipedia can be used as a semantic resource to build a directional inferential similarity metric between words, and subsequently, texts. Through experiments, we show that our Wikipedia-based metric performs significantly better when applied to a standard evaluation dataset, with a reduction in error rate of 16.1% over the random metric baseline.

Published
2008
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48. Modeling Synergistic Relationships Between Words and Images

Author

Leong, Chee Wee

Subjects
Natural language processing, image, word, sematic, multimodal semantic spaces, cross-modal relatedness
Abstract

Texts and images provide alternative, yet orthogonal views of the same underlying cognitive concept. By uncovering synergistic, semantic relationships that exist between words and images, I am working to develop novel techniques that can help improve tasks in natural language processing, as well as effective models for text-to-image synthesis, image retrieval, and automatic image annotation. Specifically, in my dissertation, I will explore the interoperability of features between language and vision tasks. In the first part, I will show how it is possible to apply features generated using evidence gathered from text corpora to solve the image annotation problem in computer vision, without the use of any visual information. In the second part, I will address research in the reverse direction, and show how visual cues can be used to improve tasks in natural language processing. Importantly, I propose a novel metric to estimate the similarity of words by comparing the visual similarity of concepts invoked by these words, and show that it can be used further to advance the state-of-the-art methods that employ corpus-based and knowledge-based semantic similarity measures. Finally, I attempt to construct a joint semantic space connecting words with images, and synthesize an evaluation framework to quantify cross-modal semantic relationships that exist between arbitrary pairs of words and images. I study the effectiveness of unsupervised, corpus-based approaches to automatically derive the semantic relatedness between words and images, and perform empirical evaluations by measuring its correlation with human annotators.

Published
2012

49. Towards Communicating Simple Sentence using Pictorial Representations

Author

Leong, Chee Wee

Subjects
Visual communication., pictorial translation, pictures, communication, machine translation, translation paradigm, language translation
Abstract

Language can sometimes be an impediment in communication. Whether we are talking about people who speak different languages, students who are learning a new language, or people with language disorders, the understanding of linguistic representations in a given language requires a certain amount of knowledge that not everybody has. In this thesis, we propose "translation through pictures" as a means for conveying simple pieces of information across language barriers, and describe a system that can automatically generate pictorial representations for simple sentences. Comparative experiments conducted on visual and linguistic representations of information show that a considerable amount of understanding can be achieved through pictorial descriptions, with results within a comparable range of those obtained with current machine translation techniques. Moreover, a user study conducted around the pictorial translation system reveals that users found the system to generally produce correct word/image associations, and rate the system as interactive and intelligent.

Published
2006

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