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Using comparable corpora to adapt mt models to new domains 9

Using comparable corpora to adapt mt models to new domains

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Irvine, Ann Burch, Chris Callison Center for Language and Speech Processing Johns Hopkins University United States Computer and Information Science Dept University of Pennsylvania United States

ISBN: (纸本)9781941643174

In previous work we showed that when using an SMT model trained on old-domain data to translate text in a new-domain, most errors are due to unseen source words, unseen target translations, and inaccurate translation model scores (Irvine et al., 2013a). In this work, we target errors due to inaccurate translation model scores using new-domain comparable corpora, which we mine from Wikipedia. We assume that we have access to a large olddomain parallel training corpus but only enough new-domain parallel data to tune model parameters and do evaluation. We use the new-domain comparable corpora to estimate additional feature scores over the phrase pairs in our baseline models. Augmenting models with the new features improves the quality of machine translations in the medical and science domains by up to 1.3 BLEU points over very strong baselines trained on the 150 million word Canadian Hansard dataset. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Computational linguistics

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Low-resource open vocabulary keyword search using point process models 15

Low-resource open vocabulary keyword search using point proc...

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15th Annual Conference of the International speech Communication Association: Celebrating the Diversity of Spoken languages, INTERspeech 2014

作者： Liu, Chunxi Jansen, Aren Chen, Guoguo Kintzley, Keith Trmal, Jan Khudanpur, Sanjeev Center for Language and Speech Processing Department of Electrical and Computer Engineering United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States

The point process model (PPM) for keyword search is a whole-word parametric modeling framework based on the timing of phonetic events rather than the evolution of frame-level phonetic likelihoods. Recent progress in PPM training and decoding algorithms has yielded state-of-the-art phonetic search performance in high-resource settings, both in terms of accuracy and computational efficiency. In this paper, we consider PPM application to low-resource settings where the amount of transcribed speech is severely limited and the pronunciation dictionary is incomplete. By using (i) state-of-the-art deep neural network acoustic models to generate phonetic events and (ii) grapheme-to-phoneme conversion to generate pronunciations for out-of-vocabulary (OOV) keywords, we find the PPM system reaches state-of-the-art OOV search performance at a small computational cost. Moreover, due to their complementary methodologies, combining PPM outputs with the LVCSR baseline produces average relative ATWV improvements of 7% and 50% for in-vocabulary and OOV keywords, respectively (16% overall). Copyright © 2014 ISCA.

关键词： Deep neural networks

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Windowed DMD as a Microtexture Descriptor for Finger Vein Counter-spoofing in Biometrics

Windowed DMD as a Microtexture Descriptor for Finger Vein Co...

引用

IEEE International Workshop on Information Forensics and Security

作者： Santosh Tirunagari Norman Poh Miroslaw Bober David Windridge Department of Computer Science University of Surrey Center for Vision Speech and Signal Processing University of Surrey

ISBN: (纸本)9781467368032

Recent studies have shown that it is possible to attack a finger vein (FV) based biometric system using printed materials. In this study, we propose a novel method to detect spoofing of static finger vein images using Windowed Dynamic mode decomposition (W-DMD). This is an atemporal variant of the recently proposed Dynamic Mode Decomposition for image sequences. The proposed method achieves better results when compared to established methods such as local binary patterns (LBP), discrete wavelet transforms (DWT), histogram of gradients (HoG), and filter methods such as range-filters, standard deviation filters (STD) and entropy filters, when using SVM with a minimum intersection kernel. The overall pipeline which consists of W-DMD and SVM, proves to be efficient, and convenient to use, given the absence of additional parameter tuning requirements. The effectiveness of our methodology is demonstrated using FV-Spoofing-Attack database which is publicly available. Our test results show that W-DMD can successfully detect printed finger vein images because they contain micro-level artefacts that not only differ in quality but also in light reflection properties compared to valid/live finger vein images.

关键词： Biometrics DMD gene Veins Fingers Support Vector Network Printed materials LIGHT REFLECTION pipeline images Sexually Transmitted Diseases MICROTEXTURE

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Hallucinating phrase translations for low resource MT 18

Hallucinating phrase translations for low resource MT

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18th Conference on Computational Natural language Learning, CoNLL 2014

作者： Irvine, Ann Callison-Burch, Chris Center for Language and Speech Processing Johns Hopkins University United States Computer and Information Science Dept. University of Pennsylvania United States

ISBN: (纸本)9781941643020

We demonstrate that "hallucinating" phrasal translations can significantly improve the quality of machine translation in low resource conditions. Our hallucinated phrase tables consist of entries composed from multiple unigram translations drawn from the baseline phrase table and from translations that are induced from monolingual corpora. The hallucinated phrase table is very noisy. Its translations are low precision but high recall. We counter this by introducing 30 new feature functions (including a variety of monolingually-estimated features) and by aggressively pruning the phrase table. Our analysis evaluates the intrinsic quality of our hallucinated phrase pairs as well as their impact in end-to-end Spanish-English and Hindi-English MT. © 2014 Association for Computational Linguistics.

关键词： Quality control

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SemEval-2014 Task 4: Aspect Based Sentiment Analysis 8

SemEval-2014 Task 4: Aspect Based Sentiment Analysis

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8th International Workshop on Semantic Evaluation, SemEval 2014

作者： Pontiki, Maria D., Galanis J., Pavlopoulos H., Papageorgiou I., Androutsopoulos S., Manandhar Institute for Language and Speech Processing "Athena" Research Center Greece Dept. of Informatics Athens University of Economics and Business Greece Dept. of Computer Science University of York United Kingdom

ISBN: (纸本)9781941643242

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, irrespective of the entities mentioned (e.g., laptops) and their aspects (e.g., battery, screen). SemEval-2014 Task 4 aimed to foster research in the field of aspect-based sentiment analysis, where the goal is to identify the aspects of given target entities and the sentiment expressed for each aspect. The task provided datasets containing manually annotated reviews of restaurants and laptops, as well as a common evaluation procedure. It attracted 163 submissions from 32 teams. © 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings. All rights reserved.

关键词： Sentiment analysis

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The Tutorbot corpus - A corpus for studying tutoring behaviour in multiparty face-to-face spoken dialogue 9

The Tutorbot corpus - A corpus for studying tutoring behavio...

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9th International Conference on language Resources and Evaluation, LREC 2014

作者： Koutsombogera, Maria Al Moubayed, Samer Bollepalli, Bajibabu Abdelaziz, Ahmed Hussen Johansson, Martin Lopes, José David Aguas Novikova, Jekaterina Oertel, Catharine Stefanov, Kalin Varol, Gül KTH Speech Music and Hearing Sweden Institute for Language and Speech Processing - Athena R.C. Greece Institute of Communication Acoustics Ruhr-Universität Bochum Germany Spoken Language Systems Laboratory INESC ID Portugal Department of Computer Science University of Bath United Kingdom Department of Computer Engineering Bogazici University Turkey

ISBN: (纸本)9782951740884

This paper describes a novel experimental setup exploiting state-of-the - art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutor's behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants' temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.

关键词： Visual communication

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Tutoring robots multiparty multimodal social dialogue with an embodied tutor 9

Tutoring robots multiparty multimodal social dialogue with a...

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9th IFIPWG 5.5 International Summer Workshop on Multimodal Interfaces, eNTERFACE 2013

作者： Moubayed, Samer Al Beskow, Jonas Bollepalli, Bajibabu Hussen-Abdelaziz, Ahmed Johansson, Martin Koutsombogera, Maria Lopes, José David Novikova, Jekaterina Oertel, Catharine Skantze, Gabriel Stefanov, Kalin Varol, Gül KTH Speech Music and Hearing Sweden Institute for Language and Speech Processing AthenaRC Greece Spoken Language Systems Laboratory INESC ID Lisboa Portugal Department of Computer Science University of Bath United Kingdom Institute of Communication Acoustics Ruhr-Universität Bochum Germany Department of Computer Engineering Bo ğaziçi University Turkey

ISBN: (纸本)9783642551420

This project explores a novel experimental setup towards building spoken, multi-modally rich, and human-like multiparty tutoring agent. A setup is developed and a corpus is collected that targets the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions with embodied agents. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. With the participants sits a tutor that helps the participants perform the task and organizes and balances their interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies were coupled with manual annotations to build a situated model of the interaction based on the participants personalities, their temporally-changing state of attention, their conversational engagement and verbal dominance, and the way these are correlated with the verbal and visual feedback, turn-management, and conversation regulatory actions generated by the tutor. At the end of this chapter we discuss the potential areas of research and developments this work opens and some of the challenges that lie in the road ahead. © IFIP International Federation for Information processing 2014

关键词： Behavioral research

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Elastic and Efficient Virtual Network Provisioning for Cloud-Based Multi-tier Applications

Elastic and Efficient Virtual Network Provisioning for Cloud...

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International Conference on Parallel processing (ICPP)

作者： Meng Shen Ke Xu Fan Li Kun Yang Liehuang Zhu Lei Guan Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Department of Computer Science Tsinghua University P. R. China School of Computer Science & Electronic Engineering University of Essex UK The ISN National Key Lab XiDian University P. R. China School of Management & Economics Beijing Institute of Technology P. R. China

ISBN: (纸本)9781467375887

The multi-tier architecture is prevalently adopted by cloud applications, such as the three-tier web application. It is highly desirable for both tenants and providers to provide virtual networks in an efficient and elastic way, where tenant applications can automatically scale in or out with varying workloads and providers can accommodate as many requests as possible in the underlying network. However, due to potential conflicts between efficiency and elasticity, it is challenging to achieve these two goals simultaneously in abstracting tenant requirements and designing corresponding provisioning algorithms. In this paper, we propose an efficient and elastic virtual network provisioning solution called Easy Alloc, which is comprised of an elasticity-aware abstraction model and a virtual network provisioning algorithm. To accurately capture the tenant requirement and maintain the provisioning simplicity for providers, the elasticity-aware model enables two types of decoupling, i.e., Always-on VMs for normal load and on-demand VMs for dynamic scaling, and the bandwidth requirement of each VM for intra- and inter-tier communications. Then we formulate the virtual network provisioning as an overhead minimization problem, where the objective simultaneously considers the bandwidth and elasticity overhead. Due to the NP-completeness of this problem, we leverage two heuristics, slot reservation and tier iteration, to obtain an efficient algorithm. Extensive simulation results show that compared with a typical elasticity-agnostic method under a heavy load, Easy Alloc enables a 9% increase of request acceptance rate and a 16.8% improvement of the successful extension rate. To the best of our knowledge, this is the first work targeting at the elastic virtual network provisioning.

关键词： Bandwidth Elasticity Cloud computing Algorithm design and analysis Load modeling Resource management

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Discriminative confidence measure using linear combination of duration-based features and acoustic-based scores in keyword spotting

Discriminative confidence measure using linear combination o...

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International Symposium on Telecommunications, IST

作者： Mohammad Mohsen Goodarzi Yasser Shekofteh Iman Sarraf Rezaei Jahanshah Kabudian Speech and Natural Language Processing Group Research Center of Intelligent Signal Processing (RCISP) Tehran Iran Department of Computer Engineering and Information Technology Razi University Kermanshah Iran

One of the most important steps in a keyword spotting (KWS) system is a post-processing procedure to compute a confidence measure (CM) for each hypothesized keyword. The CM is commonly estimated by likelihood-based acoustic scores. However durations of the detected keyword, which include useful information, has not been studied directly in the KWS systems. In this paper, three duration-based features are proposed for such system. Also, using linear discriminant analysis (LDA), the proposed duration-based features are discriminatively combined with the acoustic scores and used as the final discriminative CM (DCM) in the KWS system. The proposed DCM results up to 11.3% improvement in FOM against to the conventional likelihood-based CM over a Persian conversational telephone speech database.

关键词： speech speech recognition Hidden Markov models Acoustics Acoustic measurements Databases Computational modeling

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Speaker adaptation of hybrid NN/HMM model for speech recognition based on singular value decomposition

Speaker adaptation of hybrid NN/HMM model for speech recogni...

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International Symposium on Chinese Spoken language processing

作者： Shaofei Xue Hui Jiang Lirong Dai National Engineering Laboratory of Speech and Language Information Processing University of Science and Technology of China Hefei Department of Electrical Engineering and Computer Science York University Toronto Canada

Recently several speaker adaptation methods have been proposed for deep neural network (DNN) in many large vocabulary continuous speech recognition (LVCSR) tasks. However, only a few methods rely on tuning the weight matrices in trained DNNs to optimize system performance since it is very prone to over-fitting especially when some class labels are missing in the adaptation data. In this paper, we propose a new speaker adaptation method for the hybrid NN/HMM speech recognition model based on singular value decomposition (SVD). We apply SVD on the weight matrices in trained DNNs, and then tune diagonal matrices with the adaptation data. This solves the over-fitting problem since we can change the weight matrices slightly by only modifying the singular values. We evaluate the proposed adaptation method in two standard speech recognition tasks, namely TIMIT phone recognition and large vocabulary speech recognition in the Switchboard task. Experimental results have shown that it is effective to adapt large DNN models using only a small amount of adaptation data. For example, the Switchboard results have shown that the proposed SVD-based adaptation method may achieve up to 3-6% relative error reduction using only a few dozens of adaptation utterances per speaker.

关键词： Hidden Markov models Adaptation models speech recognition Neural networks speech Matrix decomposition Signal processing

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