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arXiv 2017年

作者： Peris, Álvaro Cebrián, Luis Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

Neural machine translation has meant a revolution of the field. Nevertheless, post-editing the outputs of the system is mandatory for tasks requiring high translation quality. Post-editing offers a unique opportunity for improving neural machine translation systems, using online learning techniques and treating the post-edited translations as new, fresh training data. We review classical learning methods and propose a new optimization algorithm. We thoroughly compare online learning algorithms in a post-editing scenario. Results show significant improvements in translation quality and effort reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： Learning algorithms

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Interactive-predictive neural multimodal systems?

arXiv

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arXiv 2019年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Demonstration of Joshua: An open source toolkit for parsing-based machine translation

Demonstration of Joshua: An open source toolkit for parsing-...

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N.G. Weese, Jonathan Zaidan, Omar F. Center for Language and Speech Processing Johns Hopkins University United States Computational Linguistics and Information Processing Lab. University of Maryland United States Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab. University of Minnesota United States

ISBN: (纸本)9781617382581

We describe Joshua (Li et al., 2009a)1, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for translation via synchronous context free grammars (SCFGs): chart-parsing, n-gram language model integration, beam- and cubepruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We also provide a demonstration outline for illustrating the toolkit's features to potential users, whether they be newcomers to the field or power users interested in extending the toolkit. © 2009 ACL and AFNLP.

关键词： Distributed computer systems

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Data Mapping by Restricted Boltzmann Machines for Social Circles Detection

Data Mapping by Restricted Boltzmann Machines for Social Cir...

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International Joint Conference on Neural Networks

作者： Jesús Alonso Roberto Paredes Paolo Rosso Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València

ISBN: (纸本)9781479919611

Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. In this paper, we propose a two-step technique, making emphasis on the mapping of the data by Restricted Boltzmann Machines (RBMs). Social circles are subsequently inferred by k-means over the preprocessed data. We define different vectorial representations from both structural egonet information and user profile features, and perform a set of tests to adjust the optimal parameters of the RBMs. We study and compare the performance on the ego-Facebook dataset of social circles from Facebook from the Stanford Large Network Dataset Collection. We compare our results with several different baselines.

关键词： Data Mapping Restricted Boltzmann Machines Social Circles Detection Boltzmann machines Circles Social Media MAPPING(MATHEMATICAL) Interest research Facebook optimal parameter Social Networks

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Incorporating On-demand Stereo for Real Time recognition

Incorporating On-demand Stereo for Real Time Recognition

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Conference on Computer Vision and pattern recognition (CVPR)

作者： T. Deselaers A. Criminisi J. Winn A. Agarwal Microsoft Research Limited Cambridge UK Human Language Technology and Pattern Recognition RWTH Aachen University of Technology Germany

A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. This minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. This leads to improved efficiency. The proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.

关键词： Object detection pattern recognition Shape Cameras humans Event detection Costs Visual databases Hardware Object recognition

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Joshua: An Open Source Toolkit for Parsing-based Machine Translation 4

Joshua: An Open Source Toolkit for Parsing-based Machine Tra...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N. G. Weese, Jonathan Zaidan, Omar F. Center For Language And Speech Processing Johns Hopkins University BaltimoreMD United States Computational Linguistics And Information Processing Lab University of Maryland College ParkMD United States Human Language Technology And Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab University of Minnesota MinneapolisMN United States

We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, ngram language model integration, beamand cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We demonstrate that the toolkit achieves state of the art translation performance on the WMT09 French-English translation task. ©2009 Association for Computational Linguistics.

关键词： Distributed computer systems

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MORPHEME-BASED FEATURE-RICH language MODELS USING DEEP NEURAL NETWORKS FOR LVCSR OF EGYPTIAN ARABIC

MORPHEME-BASED FEATURE-RICH LANGUAGE MODELS USING DEEP NEURA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Amr El-Desoky Mousa Hong-Kwang Jeff Kuo Lidia Mangu Hagen Soltau Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University IBM T. J. Watson Research Center

ISBN: (纸本)9781479903573

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs.

关键词： language model Morpheme Feature-rich Deep neural network Egyptian Arabic modelling languages Neural network Arabic language additional features Word Religious Missions Continuous speech recognition

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A comparative large scale study of MLP features for mandarin ASR

A comparative large scale study of MLP features for mandarin...

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作者： Valente, Fabio Doss, Mathew Magimai Plahl, Christian Ravuri, Suman Wang, Wen IDIAP Research Institute CH-1920 Martigny Switzerland Human Language Technology and Pattern Recognition RWTH Aachen University Germany International Computer Science Institute 1947 Center Street Berkeley CA 94704 United States Speech Technology and Research Laboratory SRI International Menlo Park CA United States

MLP based front-ends have shown significant complementary properties to conventional spectral features. As part of the DARPA GALE program, different MLP features were developed for Mandarin ASR. In this paper, all the proposed frontends are compared in systematic manner and we extensively investigate the scalability of these features in terms of the amount of training data (from 100 hours to 1600 hours) and system complexity (maximum likelihood training, SAT, lattice level combination, and discriminative training). Results on 5 hours of evaluation data from the GALE project reveal that the MLP features consistently produce relative improvements in the range of 15% - 23% at the different steps of a multipass system when compared to the conventional short-term spectral based features like MFCC and PLP. The largest improvement is obtained using a hierarchical MLP approach. © 2010 ISCA.

关键词： Maximum likelihood

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Bag-of-visual-words models for adult image classification and filtering

Bag-of-visual-words models for adult image classification an...

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International Conference on pattern recognition

作者： Thomas Deselaers Lexi Pimenidis Hermann Ney Human Language Technology and Pattern Recognition Security and Privacy Research Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424421749

We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system.

关键词： Image classification Filters Vocabulary Filtering Skin Telecommunication traffic Image databases Histograms Feature extraction Iterative algorithms

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NMT-Keras: A very flexible toolkit with a focus on interactive NMT and online learning

arXiv

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arXiv 2018年

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particularemphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning. NMT-Keras is based on an extended version of the popular Keras library, and it runs on Theano and TensorFlow. State-of-the-art neural machine translation models are deployed and used following the high-level framework provided by Keras. Given its high modularity and flexibility, it also has been extended to tackle different problems, such as image and video captioning, sentence classification and visual question answering. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural machine translation

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