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Dirt cheap web-scale parallel text from the Common Crawl

Dirt cheap web-scale parallel text from the Common Crawl

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Smith, Jason R. Koehn, Philipp Saint-Amand, Herve Callison-Burch, Chris Plamada, Magdalena Lopez, Adam Department of Computer Science Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States School of Informatics University of Edinburgh United Kingdom Institute of Computational Linguistics University of Zurich Switzerland Computer and Information Science Department University of Pennsylvania United States

ISBN: (纸本)9781937284503

Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon's Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about $500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data resource.1 © 2013 Association for Computational Linguistics.

关键词： Digital storage

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A Co-Robotic Assistant Capable of Object Selection and Search via a Brain Machine Interface

A Co-Robotic Assistant Capable of Object Selection and Searc...

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International IEEE/EMBS Conference on Neural Engineering

作者： Varsha Shankar Lena Sherbakov Byron Galbraith Aisha Sohail Gennady Livitz Anatoli Gorchetchnikov Heather Ames Frank H. Guenther Massimiliano Versace GRASP Laboratory University of Pennsylvania Boston University and CELEST Center of Excellence for Learning in Education Science and Technology Departments of Speech Language & Hearing Sciences and Biomedical Engineering Boston University

ISBN: (纸本)9781467319676

Co-robotic assistants, or Cobots can improve the quality of life for individuals with locked-in syndrome (LIS) by allowing augmented control over their surroundings. Implemented in collaboration between the Boston University Neuromorphics Lab and Neural Prosthesis Lab, this work provides a proof of concept of an autonomous robot coupled with a non-invasive brain machine interface (BMI). The system uses Steady State Visually Evoked Potential (SSVEP) for target selection and a massively parallel neural network that models functionality of the primate "where" and "what" visual path-ways. The simulated visual processes perform object recognition on images streamed from the Cobot equipped with a pan-and-tilt camera. In this paper, we describe a subcomponent of the system designed to allow the neural network to learn the identity of objects, the user to select a target object and the Cobot to perform autonomous visual investigation.

关键词： main machine interface human machine interaction machine Autonomous robots target selection Neural network Visualization parallel neural network object recognition Primates Cerebrum Locked-In Syndrome Neural Prostheses

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Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings

Fixed-dimensional acoustic embeddings of variable-length seg...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Keith Levin Katharine Henry Aren Jansen Karen Livescu Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA University of Chicago Chicago IL USA Toyota Technological Institute at Chicago Chicago IL USA

ISBN: (纸本)9781479927579

Measures of acoustic similarity between words or other units are critical for segmental exemplar-based acoustic models, spoken term discovery, and query-by-example search. Dynamic time warping (DTW) alignment cost has been the most commonly used measure, but it has well-known inadequacies. Some recently proposed alternatives require large amounts of training data. In the interest of finding more efficient, accurate, and low-resource alternatives, we consider the problem of embedding speech segments of arbitrary length into fixed-dimensional spaces in which simple distances (such as cosine or Euclidean) serve as a proxy for linguistically meaningful (phonetic, lexical, etc.) dissimilarities. Such embeddings would enable efficient audio indexing and permit application of standard distance learning techniques to segmental acoustic modeling. In this paper, we explore several supervised and unsupervised approaches to this problem and evaluate them on an acoustic word discrimination task. We identify several embedding algorithms that match or improve upon the DTW baseline in low-resource settings.

关键词： Vectors Acoustics Laplace equations Principal component analysis Time series analysis speech Training

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Zero resource graph-based confidence estimation for open vocabulary spoken term detection

Zero resource graph-based confidence estimation for open voc...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Atta Norouzian Richard Rose Sina Hamidi Ghalehjegh Aren Jansen Department of Electrical and Computer Engineering McGill University Montreal Canada Human Language Technology Center of Excellence Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479903573

In this paper the use of acoustic similarity of speech intervals for generating improved confidence scores for spoken term detection (STD) is investigated. A procedure based on acoustic dotplots which requires no training data is deployed for discovering similar speech intervals. A graph based random walk algorithm incorporates acoustic similarity of hypothesized term occurrences for improving the corresponding confidence scores. The proposed approach is evaluated in an open vocabulary STD task defined on a lecture domain corpus. It is shown that updating the confidence scores in this fashion results in a significant increase in term detection performance of out of vocabulary search terms. A relative improvement of 12.9% in figure of merit was gained relative to that obtained from a baseline lattice based STD system.

关键词： Acoustics speech Lattices Acoustic measurements Vectors Vocabulary Feature extraction

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Adaptation transforms of auto-associative neural networks as features for speaker verification

Adaptation transforms of auto-associative neural networks as...

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Speaker and language Recognition Workshop, Odyssey 2012

作者： Thomas, Samuel Mallidi, Sri Harish Ganapathy, Sriram Hermansky, Hynek Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States

ISBN: (纸本)9789810730932

We present a new approach of using Auto-Associative Neural Networks (AANNs) in the conventional GMM speaker verification framework with i-vector feature extraction and PLDA modeling. In this technique, an i-vector feature extractor is trained using adaptation parameters from a mixture of AANNs. In order to model parts of each speaker's acoustic space, a training objective function based on posterior probabilities of broad phonetic classes is used. The AANN based i-vectors are fused with GMM based i-vectors and a joint PLDA model is trained. The proposed approach provides promising results and significant gains when combined with baseline systems on the telephone conditions of NIST SRE 2010 and the recently concluded IARPA BEST 2011 speaker evaluations. © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： speech recognition

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Emotional adaptive training for speaker verification

Emotional adaptive training for speaker verification

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Fanhu Bie Dong Wang Thomas Fang Zheng Javier Tejedor Ruxin Chen Center for Speech and Language Technologies Tsinghua University China Human Computer Technology Laboratory Universidad Autónoma de Madrid Spain R&D Sony Computer Entertainment America Foster City CA USA

Speaker verification suffers from significant performance degradation with emotion variation. In a previous study, we have demonstrated that an adaptation approach based on MLLR/CMLLR can provide a significant performance improvement for verification on emotional speech. This paper follows this direction and presents an emotional adaptive training (EAT) approach. This approach iteratively estimates the emotion-dependent CMLLR transformations and re-trains the speaker models with the transformed speech, which therefore can make use of emotional enrollment speech to train a stronger speaker model. This is similar to the speaker adaptive training (SAT) in speech recognition. The experiments are conducted on an emotional speech database which involves speech recordings of 30 speakers in 5 emotions. The results demonstrate that the EAT approach provides significant performance improvements over the baseline system where the neutral enrollment data are used to train the speaker models and the emotional test utterances are verified directly. The EAT also outperforms another two emotionadaptation approaches in a significant way: (1) the CMLLR-based approach where the speaker models are trained with the neutral enrollment speech and the emotional test utterances are transformed by CMLLR in verification; (2) the MAP-based approach where the emotional enrollment data are used to train emotion-dependent speaker models and the emotional utterances are verified based on the emotion-matched models.

关键词： speech Training Adaptation models Data models Hidden Markov models Computers Spectrogram

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Joshua 4.0: Packing, PRO, and Paraphrases 12

Joshua 4.0: Packing, PRO, and Paraphrases

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Workshop on Statistical Machine Translation

作者： Juri Ganitkevitch Yuan Cao Jonathan Weese Matt Post Chris Callison-Burch Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781622765928

We present Joshua 4.0, the newest version of our open-source decoder for parsing-based statistical machine translation. The main contributions in this release are the introduction of a compact grammar representation based on packed tries, and the integration of our implementation of pairwise ranking optimization, J-PRO. We further present the extension of the Thrax SCFG grammar extractor to pivot-based extraction of syntactically informed sentential paraphrases.

关键词： machine translation syntax DNA Integration integration Release PACKINGS open source extraction device trees (mathematics) decoders

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Using Categorial Grammar to Label Translation Rules 7

Using Categorial Grammar to Label Translation Rules

引用

7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Weese, Jonathan Callison-Burch, Chris Lopez, Adam Center Of Excellence Johns Hopkins University Human Language Technology United States

ISBN: (纸本)9781937284206

Adding syntactic labels to synchronous context-free translation rules can improve performance, but labeling with phrase structure constituents, as in GHKM (Galley et al., 2004), excludes potentially useful translation rules. SAMT (Zollmann and Venugopal, 2006) introduces heuristics to create new non-constituent labels, but these heuristics introduce many complex labels and tend to add rarely-applicable rules to the translation grammar. We introduce a labeling scheme based on categorial grammar, which allows syntactic labeling of many rules with a minimal, well-motivated label set. We show that our labeling scheme performs comparably to SAMT on an Urdu-English translation task, yet the label set is an order of magnitude smaller, and translation is twice as fast. ©2012 Association for Computational Linguistics.

关键词： Syntactics

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Putting human Assessments of Machine Translation Systems in Order 7

Putting Human Assessments of Machine Translation Systems in ...

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作者： Lopez, Adam Human Language Technology Center Of Excellence Johns Hopkins University United States

ISBN: (纸本)9781937284206

human assessment is often considered the gold standard in evaluation of translation systems. But in order for the evaluation to be meaningful, the rankings obtained from human assessment must be consistent and repeatable. Recent analysis by Bojar et al. (2011) raised several concerns about the rankings derived from human assessments of English-Czech translation systems in the 2010 Workshop on Machine Translation. We extend their analysis to all of the ranking tasks from 2010 and 2011, and show through an extension of their reasoning that the ranking is naturally cast as an instance of finding the minimum feedback arc set in a tournament, a wellknown NP-complete problem. All instances of this problem in the workshop data are efficiently solvable, but in some cases the rankings it produces are surprisingly different from the ones previously published. This leads to strong caveats and recommendations for both producers and consumers of these rankings. ©2012 Association for Computational Linguistics.

关键词： Machine translation

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Annotated Gigaword

Annotated Gigaword

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2021 Joint Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction, AKBC-WEKEX 2012 at the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2012

作者： Napoles, Courtney Gormley, Matthew Van Durme, Benjamin Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)1937284204

We have created layers of annotation on the English Gigaword v.5 corpus to render it useful as a standardized corpus for knowledge extraction and distributional semantics. Most existing large-scale work is based on inconsistent corpora which often have needed to be re-annotated by research teams independently, each time introducing biases that manifest as results that are only comparable at a high level. We provide to the community a public reference set based on current state-of-the-art syntactic analysis and coreference resolution, along with an interface for programmatic access. Our goal is to enable broader involvement in large-scale knowledge-acquisition efforts by researchers that otherwise may not have had the ability to produce such a resource on their own. © 2012 Association for Computational Linguistics

关键词： Syntactics

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