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A long short-term memory framework for predicting humor in dialogues 15

A long short-term memory framework for predicting humor in d...

15th Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2016

作者： Bertero, Dario Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Hong Kong

ISBN: (纸本)9781941643914

We propose a first-ever attempt to employ a Long Short-Term memory based framework to predict humor in dialogues. We analyze data from a popular TV-sitcom, whose canned laughters give an indication of when the audience would react. We model the setup-punchline relation of conversational humor with a Long Short-Term Memory, with utterance encodings obtained from a Convolutional Neural Network. Out neural network framework is able to improve the F-score of 8% over a Conditional Random Field baseline. We show how the LSTM effectively models the setup-punchline relation reducing the number of false positives and increasing the recall. We aim to employ our humor prediction model to build effective empathetic machine able to understand jokes. ©2016 Association for Computational Linguistics.

关键词： Long short-term memory

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How blue can you get? Learning structural relationships for microtones via continuous stochastic transduction grammars 7

How blue can you get? Learning structural relationships for ...

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7th International Conference on Computational Creativity, ICCC 2016

作者： Wu, Dekai HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782746691551

We describe a new approach to probabilistic modeling of structural inter-part relationships between continuous-valued musical events such as microtones, through a novel class of continuous stochastic transduction grammars. Linguistic and grammar oriented models for music commonly approximate features like pitch using discrete symbols to represent ‘clean’ notes on scales. In many musical genres, however, contextual relationships between continuous values are essential to improvisational and accompaniment decisions—as with the ‘bent notes’ that blues rely heavily upon. In this paper, we study how stochastic transduction grammars or STGs, which have until now only been able to handle discrete symbols, can be generalized to model continuous valued features for such applications. STGs are interesting for modeling the learning of musical improvisation and accompaniment where parallel musical sequences interact hierarchically (compositionally) at many overlapping levels of granularity. Each part influences decisions made by other parts while at the same time satisfying contextual preferences across multiple dimensions;applications to flamenco and hip hop have recently been shown using discrete STGs. We propose to use a formulation of continuous STGs in which musical signals are finely represented as continuous values without crude quantization into discrete symbols, yet still retaining the ability to model probabilistic structural relations between multiple musical languages. We instantiate this approach for the specific class of stochastic inversion transduction grammars (SITGs), which has proven useful in many applications, via a polynomial time algorithm for expectation-maximization training of continuous SITGs. © 2016 Proceedings of the 7th International Conference on Computational Creativity, ICCC 2016. All rights reserved.

关键词： Stochastic systems

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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A Teacher-Student framework for zero-resource neural machine translation

arXiv

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arXiv 2017年

作者： Cheny, Yun Liuz, Yang Cheng, Yong Li, Victor O.K. Department of Electrical and Electronic Engineering University of Hong Kong State Key Laboratory of Intelligent Technology Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing Jiangsu Collaborative Innovation Center for Language Competence Jiangsu Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China

While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on this assumption, our method is able to train a source-to-target NMT model ("student") without parallel corpora available, guided by an existing pivot-to-target NMT model ("teacher") on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs. Copyright © 2017, The Authors. All rights reserved.

关键词： Neural machine translation

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An expectation-maximization eigenvector clustering approach to direction of arrival estimation of multiple speech sources 41

An expectation-maximization eigenvector clustering approach ...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

作者： Xiao, Xiong Zhao, Shengkui Nguyen, Thi Ngoc Tho Jones, Douglas L. Chng, Eng Siong Li, Haizhou Temasek Lab at NTU Nanyang Technological University Singapore Advanced Digital Sciences Center Singapore School of Computer Engineering Nanyang Technological University Singapore Department of Human Language Technology Institute for Infocomm Research Singapore

ISBN: (纸本)9781479999880

This paper presents an eigenvector clustering approach for estimating the direction of arrival (DOA) of multiple speech signals using a microphone array. Existing clustering approaches usually only use low frequencies to avoid spatial aliasing. In this study, we propose a probabilistic eigenvector clustering approach to use all frequencies. In our work, time-frequency (TF) bins dominated by only one source are first detected using a combination of noise-floor tracking, onset detection and coherence test. For each selected TF bin, the largest eigenvector of its spatial covariance matrix is extracted for clustering. A mixture density model is introduced to model the distribution of the eigenvectors, where each component distribution corresponds to one source and is parameterized by the source DOA. To use eigenvectors of all frequencies, the steering vectors of all frequencies of the sources are used in the distribution function. The DOAs of the sources can be estimated by maximizing the likelihood of the eigenvectors using an expectation-maximization (EM) algorithm. Simulation and experimental results show that the proposed approach significantly improves the root-mean-square error (RMSE) for DOA estimation of multiple speech sources compared to the MUSIC algorithm implemented on the single-source dominated TF bins and our previous clustering approach. © 2016 IEEE.

关键词： direction of arrival eigenvector clustering expectation-maximization microphone arrays spatial covariance

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Context-dependent point process models for keyword search and detection-based ASR

Context-dependent point process models for keyword search an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chunxi Liu Aren Jansen Sanjeev Khudanpur Department of Electrical and Computer Engineering Center for Language and Speech Processing Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479999897

The point process model (PPM) for keyword search (KWS) is a whole-word parametric approach that characterizes each query type by the timing of phonetic events observed during its production. In this paper, we first extend the PPM modeling framework to operate on context-dependent phonetic event patterns instead of monophone patterns considered in the past, which provides significant KWS improvements. Second, we use the context-dependent PPMs to drive a detection-based speech recognition architecture thats runs parallel word detectors covering the whole vocabulary and uses the independent detections to construct lattices that can be used for both KWS indexing and LVCSR decoding. This strategy produces significant improvements over the original PPM KWS framework and provides an encouraging first attempt at PPM-based LVCSR.

关键词： Hidden Markov models Lattices Acoustics Indexes Context modeling Vocabulary Computational modeling

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Origin of Microglia in Brain: A Study in Zebrafish Model

Origin of Microglia in Brain: A Study in Zebrafish Model

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Optics and the Brain, BRAIN 2016

作者： He, Sicong Xu, Jin Wen, Zilong Qu, Jianan Y. Department of Electronic and Computer Engineering Division of Life Science State Key Laboratory of Molecular Neuroscience Center of Systems Biology and Human Health Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

We developed an infrared laser-evoked gene operator microscope heating system to achieve high temporal-spatial resolution cell labeling in zebrafish. Using the unique gene induction technique, we identified two distin... 详细信息

ISBN: (纸本)9781943580101

关键词： Genes

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Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification

Dominant and Complementary Multi-Emotional Facial Expression...

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International Conference on Automatic Face and Gesture Recognition

作者： Christer Loob Pejman Rasti Iiris Lüsi Julio C.S. Jacques Xavier Baró Sergio Escalera Tomasz Sapinski Dorota Kaminska Gholamreza Anbarjafari iCV Research Group University of Tartu Tartu Estonia Universitat de Barcelona Barcelona Catalunya ES Computer Vision Center and Universitat Oberta de Catalunya Barcelona Spain Human Pose Recovery and Behavior Analysis University of Barcelona Barcelona Spain Institute of Mechatronics and Information Systems Lodz University of Technology Lodz Poland Department of Electrical and Electronic Engineering Hasan Kalyoncu University Gaziantep Turkey

We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.

关键词： Face Feature extraction Face recognition Emotion recognition Databases Support vector machines Kernel

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Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases

Joint Challenge on Dominant and Complementary Emotion Recogn...

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International Conference on Automatic Face and Gesture Recognition

作者： Iiris Lüsi Julio C. S. Jacques Junior Jelena Gorbova Xavier Baró Sergio Escalera Hasan Demirel Juri Allik Cagri Ozcinar Gholamreza Anbarjafari ICV Research Group Institute of Technology University of Tartu Tartu Estonia Universitat de Barcelona Barcelona Catalunya ES Computer Vision Center and Universitat Oberta de Catalunya Barcelona Spain Human Pose Recovery and Behavior Analysis University of Barcelona Barcelona Spain Department of Electrical and Electronic Engineering Eastern Mediterranean University Famagusta TRNC Turkey The Estonian Center of Behavioral and Health Sciences Tartu Estonia Institute of Psychology University of Tartu Tartu Estonia Trinity College Dublin School of Computer Science and Statistics Ireland

In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.

关键词： Databases Emotion recognition Face Psychology Three-dimensional displays Nose

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HLTC-HKUST: A Neural Network Paraphrase Classifier using Translation Metrics, Semantic Roles and Lexical Similarity Features 9

HLTC-HKUST: A Neural Network Paraphrase Classifier using Tra...

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Bertero, Dario Fung, Pascale Human Language Technology Center Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781941643402

This paper describes the system developed by our team (HLTC-HKUST) for task 1 of SemEval 2015 workshop about paraphrase classification and semantic similarity in Twitter. We trained a neural network classifier over a range of features that includes translation metrics, lexical and syntactic similarity score and semantic features based on semantic roles. The neural network was trained taking into consideration in the objective function the six different similarity levels provided in the corpus, in order to give as output a more fine-grained estimation of the similarity level of the two sentences, as required by subtask 2. With an F-score of 0.651 in the binary paraphrase classification subtask 1, and a Pearson coefficient of 0.697 for the sentence similarity subtask 2, we achieved respectively the 6th place and the 3rd place, above the average of what obtained by the other contestants. © 2015 Association for Computational Linguistics

关键词： Classification (of information)

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