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2nd International Workshop on Spoken language Translation, IWSLT 2005

作者： Zens, Richard Bender, Oliver Hasan, Saša Khadivi, Shahram Matusov, Evgeny Xu, Jia Zhang, Yuqi Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University AachenD-52056 Germany

We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approach. In the first pass, we generate a list of the N best translation candidates. The second pass consists of rescoring and reranking this N-best list. We will give a description of the search algorithm as well as the models that are used in each pass. We participated in the supplied data tracks for manual transcriptions for the following translation directions: Arabic-English, Chinese-English, English-Chinese and Japanese-English. For Japanese-English, we also participated in the C-Star track. In addition, we performed translations of automatic speech recognition output for Chinese-English and Japanese-English. For both language pairs, we translated the single-best ASR hypotheses. Additionally, we translated Chinese ASR lattices. © 2005 International Workshop on Spoken language Translation, IWSLT 2005.

关键词： Speech recognition

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EMPIRICAL MEASUREMENTS ON A SESOTHO TONE LABELING ALGORITHM 3

EMPIRICAL MEASUREMENTS ON A SESOTHO TONE LABELING ALGORITHM

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3rd Workshop on Spoken language Technologies for Under-Resourced languages, SLTU 2012

作者： Raborife, Mpho Zerbian, Sabine Ewert, Sigrid Meraka Institute Human Language Technology Group South Africa Potsdam University Department of Linguistics Germany University of the Witwatersrand School of Computer Science South Africa

This article discusses the empirical assessments employed on two versions of a Sesotho tone labeling algorithm. This algorithm uses linguistically-defined Sesotho tonal rules to predict the tone labels on the syllables of Sesotho words. The two versions differed in the number of tonal rules that they employ as well the lexical categories that the tone rules apply to. Both versions were tested on the same input text and we employed t-tests to prove that one version is an improvement on the other. © SLTU 2012. All Rights Reserved.

关键词： Algorithm Sesotho Tone

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The RWTH Machine Translation System for IWSLT 2007 4

The RWTH Machine Translation System for IWSLT 2007

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4th International Workshop on Spoken language Translation, IWSLT 2007

作者： Mauser, Arne Vilar, David Leusch, Gregor Zhang, Yuqi Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

The RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase model. We describe the individual systems and the method that was used for combining the system outputs. Compared to our 2006 system, we newly introduce a hierarchical phrase-based translation model and show improvements in system combination for Machine Translation. RWTH participated in the Italian-to-English and Chinese-to-English translation directions. © 2007 International Workshop on Spoken language Translation, IWSLT 2007. All rights reserved.

关键词： Machine translation

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Towards a corpus of speech emotion for interactive dialog systems 19

Towards a corpus of speech emotion for interactive dialog sy...

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19th Annual Conference of the Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2016

作者： Bertero, Dario Siddique, Farhad Bin 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)9781509035168

We present and discuss an ongoing data collection and annotation effort to build a large corpus on speech emotion detection. We collected 207 hours of public speech data from TED talks. We highlight the expected relation between the API output emotion distribution and the common features of a TED talk. We then employed manual annotators to improve the quality of the annotations, building a two-level annotation process where to a main emotion label we add multiple secondary emotion descriptors. Additional annotations were also obtained through crowdsourcing, and we provide an analysis of the agreement between multiple annotators. We conducted a comparison between the automatic and manual labeling, obtaining an average accuracy of 28.4% of the automatic multiclass annotation API, as well as automatic classification with a CNN, reaching more than 60% average accuracy in various experimental settings. We also discuss various active learning methods we are using to select the samples to be annotated in order to obtain more relevant data at a faster pace. A large speech emotion detection corpus will enable more accurate emotion detection systems, which can then be integrated into dialog systems to recognize and react to the user emotion. © 2016 IEEE.

关键词： Speech recognition

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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...

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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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Raising the bar: Stacked conservative error correction beyond boosting 4

Raising the bar: Stacked conservative error correction beyon...

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4th International Conference on language Resources and Evaluation, LREC 2004

作者： Wu, Dekai Ngai, Grace Carpuat, Marine Human Language Technology Center HKUST Department of Computer Science University of Science and Technology Clear Water Bay Hong Kong Hong Kong Hong Kong Polytechnic University Department of Computing Kowloon Hong Kong

ISBN: (纸本)2951740816

We introduce a conservative error correcting model, Stacked TBL, that is designed to improve the performance of even high-performing models like boosting, with little risk of accidentally degrading performance. Stacked TBL is particularly well suited for corpus-based natural language applications involving high-dimensional feature spaces, since it leverages the characteristics of the TBL paradigm that we appropriate. We consider here the task of automatically annonating named entities in text corpora. The task does pose a number of challenges for TBL, to which there are some simple yet effective solutions. We discuss the empirical behavior of Stacked T BL, and consider evidence that despite its simplicity, more complex and time-consuming variants are not generally required.

关键词： Error correction

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Quantum Particle Swarm Optimization with Deep Learning-Based Arabic Tweets Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 2575-2591页

作者： Badriyya BAl-onazi Abdulkhaleq Q.A.Hassan Mohamed K.Nour Mesfer Al Duhayyim Abdullah Mohamed Amgad Atta Abdelmageed Ishfaq Yaseen Gouse Pasha Mohammed Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24231Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

Sentiment Analysis(SA),a Machine Learning(ML)technique,is often applied in the *** SA technique is specifically applied to the data collected from social media *** research studies conducted earlier upon the SA of the tweets were mostly aimed at automating the feature extraction *** this background,the current study introduces a novel method called Quantum Particle Swarm Optimization with Deep Learning-Based Sentiment Analysis on Arabic Tweets(QPSODL-SAAT).The presented QPSODL-SAAT model determines and classifies the sentiments of the tweets written in ***,the data pre-processing is performed to convert the raw tweets into a useful ***,the word2vec model is applied to generate the feature *** Bidirectional Gated Recurrent Unit(BiGRU)classifier is utilized to identify and classify the ***,the QPSO algorithm is exploited for the optimal finetuning of the hyperparameters involved in the BiGRU *** proposed QPSODL-SAAT model was experimentally validated using the standard *** extensive comparative analysis was conducted,and the proposed model achieved a maximum accuracy of 98.35%.The outcomes confirmed the supremacy of the proposed QPSODL-SAAT model over the rest of the approaches,such as the Surface Features(SF),Generic Embeddings(GE),Arabic Sentiment Embeddings constructed using the Hybrid(ASEH)model and the Bidirectional Encoder Representations from Transformers(BERT)model.

关键词： Sentiment analysis Arabic tweets quantum particle swarm optimization deep learning word embedding

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Semantic cohesion model for phrase-based SMT

Semantic cohesion model for phrase-based SMT

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24th International Conference on Computational Linguistics, COLING 2012

作者： Feng, Minwei Sun, Weiwei Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany MOE Key Laboratory of Computational Linguistics Institute of Computer Science and Technology Peking University Beijing China

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicateargument structures as soft constraints and plays the role as a reordering model in the phrasebased statistical machine translation system. We build a translation system with GALE data. Experimental results on the NIST02, NIST03, NIST04, NIST05 and NIST08 Chinese-English tasks show that our model improves the baseline system by 0.93 BLEU 0.98 TER on average. We also compare our method with a syntax-augmented model (Cherry, 2008), and demonstrate the importance of predicate-argument semantics in machine translation. © 2012 The COLING.

关键词： Semantics

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Situation-centered goal reinforcement of activities of daily living in smart home environments

Situation-centered goal reinforcement of activities of daily...

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作者： Oyeleke, Richard O. Chang, Carl K. Margrett, Jennifer Department of Computer Science Iowa State University Ames IA United States Department of Human Development and Family Studies Iowa State University Ames IA United States

Older adults with early-stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety-critical and often risky situations. A safety-critical risky situation is one that constitutes a hazard. To support independent living, a sensor-laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation-centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short-Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ-learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open-source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1-score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly. © 2019 John Wiley & Sons, Ltd

关键词： Hazards

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Ubiquitous Computing Environment: Literature review 1

Ubiquitous Computing Environment: Literature review

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1st International Conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Alshqaqi, Sarah Abdalrahman Zahary, Ammar T. Zayed, Mohammed Mohammed Sanaa University Department of Information Technology Sanaa Yemen Faculty of Computer and IT Sanaa University Faculty of Engineering and IT Azal University for Human Development Sanaa Yemen Sanaa University Department of Computer Science Sanaa Yemen

ISBN: (纸本)9781728144870

During the fast improvement of computing technology worldwide, the new intelligent concept of Ubiquitous Computing (Ubicomp) has appeared as a promising field that cover different areas such as sensors network, artificial intelligence and computer science. Ubicomp is a way to improve the quality of users life by making many computers available and effectively invisible to the user throughout the physical environment. There have been number of studies, research developments and technologies that have emerged in Ubicomp such as the components and fundamental properties of Ubicomp system, context aware systems, wearable systems, Internet of Things technology(IoT) and Things That Think(TTT). However, there is still a lack of understanding of Ubicomp environments and the comprehensive studies in this part are limited. Therefore, this paper aims to show understanding literature review that classify the Ubicomp environments into Pervasive computing, Context-Aware system, Internet of things (IoT), Wearable system and Things that think (TTT). © 2019 IEEE.

关键词： Ubiquitous computing

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