Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System

[EN] Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on n-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and N-gram-based systems, showing that the integrated approach seems more promising for N-gram-based systems, even with nonfull-quality NNLMs.This work was partially supported by the Spanish MINECO and FEDER found under project TIN2017-85854-C4-2-R.Zamora Martínez, FJ.; Castro-Bleda, MJ. (2018). Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System. International Journal of Neural Systems. 28(9). https://doi.org/10.1142/S0129065718500077S28

The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing

[EN] This paper presents the `NoisyOffice¿ database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems.This research was undertaken as part of the project TIN2017-85854-C4-2-R, jointly funded by the Spanish MINECO and FEDER founds.Castro-Bleda, MJ.; España Boquera, S.; Pastor Pellicer, J.; Zamora Martínez, FJ. (2020). The NoisyOffice Database: A Corpus To Train Supervised Machine Learning Filters For Image Processing. The Computer Journal. 63(11):1658-1667. https://doi.org/10.1093/comjnl/bxz098S165816676311Bozinovic, R. M., & Srihari, S. N. (1989). Off-line cursive script word recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(1), 68-83. doi:10.1109/34.23114Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 63-84. doi:10.1109/34.824821Vinciarelli, A. (2002). A survey on off-line Cursive Word Recognition. Pattern Recognition, 35(7), 1433-1446. doi:10.1016/s0031-3203(01)00129-7Impedovo, S. (2014). More than twenty years of advancements on Frontiers in handwriting recognition. Pattern Recognition, 47(3), 916-928. doi:10.1016/j.patcog.2013.05.027Baird, H. S. (2007). The State of the Art of Document Image Degradation Modelling. Advances in Pattern Recognition, 261-279. doi:10.1007/978-1-84628-726-8_12Egmont-Petersen, M., de Ridder, D., & Handels, H. (2002). Image processing with neural networks—a review. Pattern Recognition, 35(10), 2279-2301. doi:10.1016/s0031-3203(01)00178-9Marinai, S., Gori, M., & Soda, G. (2005). Artificial neural networks for document analysis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 23-35. doi:10.1109/tpami.2005.4Rehman, A., & Saba, T. (2012). Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review, 42(2), 253-273. doi:10.1007/s10462-012-9337-zLazzara, G., & Géraud, T. (2013). Efficient multiscale Sauvola’s binarization. International Journal on Document Analysis and Recognition (IJDAR), 17(2), 105-123. doi:10.1007/s10032-013-0209-0Fischer, A., Indermühle, E., Bunke, H., Viehhauser, G., & Stolz, M. (2010). Ground truth creation for handwriting recognition in historical documents. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems - DAS ’10. doi:10.1145/1815330.1815331Belhedi, A., & Marcotegui, B. (2016). Adaptive scene‐text binarisation on images captured by smartphones. IET Image Processing, 10(7), 515-523. doi:10.1049/iet-ipr.2015.0695Kieu, V. C., Visani, M., Journet, N., Mullot, R., & Domenger, J. P. (2013). An efficient parametrization of character degradation model for semi-synthetic image generation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501127Fischer, A., Visani, M., Kieu, V. C., & Suen, C. Y. (2013). Generation of learning samples for historical handwriting recognition using image degradation. Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing - HIP ’13. doi:10.1145/2501115.2501123Journet, N., Visani, M., Mansencal, B., Van-Cuong, K., & Billy, A. (2017). DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images. Journal of Imaging, 3(4), 62. doi:10.3390/jimaging3040062Walker, D., Lund, W., & Ringger, E. (2012). A synthetic document image dataset for developing and evaluating historical document processing methods. Document Recognition and Retrieval XIX. doi:10.1117/12.912203Dong, C., Loy, C. C., He, K., & Tang, X. (2016). Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307. doi:10.1109/tpami.2015.2439281Suzuki, K., Horiba, I., & Sugie, N. (2003). Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1582-1596. doi:10.1109/tpami.2003.1251151Hidalgo, J. L., España, S., Castro, M. J., & Pérez, J. A. (2005). Enhancement and Cleaning of Handwritten Data by Using Neural Networks. Lecture Notes in Computer Science, 376-383. doi:10.1007/11492429_46Pastor-Pellicer, J., España-Boquera, S., Zamora-Martínez, F., Afzal, M. Z., & Castro-Bleda, M. J. (2015). Insights on the Use of Convolutional Neural Networks for Document Image Binarization. Lecture Notes in Computer Science, 115-126. doi:10.1007/978-3-319-19222-2_10España-Boquera, S., Zamora-Martínez, F., Castro-Bleda, M. J., & Gorbe-Moya, J. (s. f.). Efficient BP Algorithms for General Feedforward Neural Networks. Lecture Notes in Computer Science, 327-336. doi:10.1007/978-3-540-73053-8_33Zamora-Martínez, F., España-Boquera, S., & Castro-Bleda, M. J. (s. f.). Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement. Lecture Notes in Computer Science, 144-151. doi:10.1007/978-3-540-73007-1_18Graves, A., Fernández, S., & Schmidhuber, J. (2007). Multi-dimensional Recurrent Neural Networks. Artificial Neural Networks – ICANN 2007, 549-558. doi:10.1007/978-3-540-74690-4_56Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2Pastor-Pellicer, J., Castro-Bleda, M. J., & Adelantado-Torres, J. L. (2015). esCam: A Mobile Application to Capture and Enhance Text Images. Lecture Notes in Computer Science, 601-604. doi:10.1007/978-3-319-19222-2_5

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training

[EN] This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in order to ease the use of a fallback mechanism together with the precomputation of softmax normalization constants for these inputs. The proposed approach is empirically validated, showing their capability to emulate lower order N-grams with a single Neural Network. A machine translation task shows that the proposed model constitutes a good solution to the normalization cost of the output softmax layer of Neural Networks, for some practical cases, without a significant impact in performance while improving the system speed.This work was partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R (to MJCB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Zamora Martínez, FJ.; España Boquera, S.; Castro-Bleda, MJ.; Palacios Corella (2018). Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training. PLoS ONE. 13(7). https://doi.org/10.1371/journal.pone.0200884S13

Transcripción humana o asistencia a la transcripción automática interactiva: reconocimiento automático del texto, anotación y edición erudita en el siglo XXI

[EN] Computer assisted transcription tools can speed up the initial process of reading and transcribing texts. At the same time, new annotation tools open new ways of accessing the text in its graphical form. The balance and value of each method still needs to be explored. STATE, a complete assisted transcription system for ancient documents, was presented to the audience of the 2013 International Medieval Congress at Leeds. The system offers a multimodal interaction environment to assist humans in transcribing ancient documents: the user can type, write on the screen with a stylus, or utter a word. When one of these actions is used to correct an erroneous word, the system uses this new information to look for other mistakes in the rest of the line. The system is modular, composed of different parts: one part creates projects from a set of images of documents, another part controls an automatic transcription system, and the third part allows the user to interact with the transcriptions and easily correct them as needed. This division of labour allows great flexibility for organising the work in a team of transcribers.[ES] Las herramientas de ayuda a la transcripción automática pueden acelerar el proceso inicial de la lectura y transcripción de textos. Al mismo tiempo, las nuevas herramientas de anotación aportan nuevas formas de acceder al texto en su forma original gráfica. Sin embargo, todavía es necesario evaluar las bondades y capacidades de los distintos métodos. STATE, un completo sistema de asistencia a la transcripción de documentos antiguos, se presentó a la audiencia del International Medieval Congress de 2013 celebrado en Leeds. El sistema ofrece un entorno de interacción multimodal para ayudar a las personas en la transcripción de documentos antiguos: el usuario puede teclear, escribir en la pantalla con un lápiz óptico o corregir usando la voz. Cada vez que el usuario cambia de esta forma una palabra, el sistema utiliza la corrección para buscar errores en el resto de la línea. El sistema está dividido en diferentes módulos: uno crea proyectos a partir de un conjunto de imágenes de documentos, otro módulo controla el sistema de transcripción automática, y un tercer módulo permite al usuario interactuar con las transcripciones y corregirlas fácilmente cuando sea necesario. Esta división de las tareas permite una gran flexibilidad para organizar el trabajo de los transcriptores en equipo.Work supported by the Spanish Government (TIN2010-18958) and the Generalitat Valenciana (Prometeo/2010/028)Castro-Bleda, MJ.; Vilar Torres, JM.; España Boquera, S.; Llorens, D.; Marzal Varó, A.; Prat, F.; Zamora Martínez, FJ. (2014). Human or computer assisted interactive transcription: automated text recognition, text annotation, and scholarly edition in the twenty-first century. Mirabilia Journal. 18(1):247-253. http://hdl.handle.net/10251/61398S24725318

31 visiones actuales de la transparencia

Treinta y una aportaciones de autores nacionales y extranjeros sobre la transparencia de las instituciones públicas y de los sujetos obligados por la Ley 19/2013 de 9 de diciembre y el derecho de acceso a la información pública

Detection of kinase domain mutations in BCR::ABL1 leukemia by ultra-deep sequencing of genomic DNA

The screening of the BCR::ABL1 kinase domain (KD) mutation has become a routine analysis in case of warning/failure for chronic myeloid leukemia (CML) and B-cell precursor acute lymphoblastic leukemia (ALL) Philadelphia (Ph)-positive patients. In this study, we present a novel DNA-based next-generation sequencing (NGS) methodology for KD ABL1 mutation detection and monitoring with a 1.0E-4 sensitivity. This approach was validated with a well-stablished RNA-based nested NGS method. The correlation of both techniques for the quantification of ABL1 mutations was high (Pearson r = 0.858, p < 0.001), offering DNA-DeepNGS a sensitivity of 92% and specificity of 82%. The clinical impact was studied in a cohort of 129 patients (n = 67 for CML and n = 62 for B-ALL patients). A total of 162 samples (n = 86 CML and n = 76 B-ALL) were studied. Of them, 27 out of 86 harbored mutations (6 in warning and 21 in failure) for CML, and 13 out of 76 (2 diagnostic and 11 relapse samples) did in B-ALL patients. In addition, in four cases were detected mutation despite BCR::ABL1 < 1%. In conclusion, we were able to detect KD ABL1 mutations with a 1.0E-4 sensitivity by NGS using DNA as starting material even in patients with low levels of disease

Aportaciones al modelado conexionista de lenguaje y su aplicación al reconocimiento de secuencias y traducción automática

El procesamiento del lenguaje natural es un área de aplicación de la inteligencia artificial, en particular, del reconocimiento de formas que estudia, entre otras cosas, incorporar información sintáctica (modelo de lenguaje) sobre cómo deben juntarse las palabras de una determinada lengua, para así permitir a los sistemas de reconocimiento/traducción decidir cual es la mejor hipótesis �con sentido común�. Es un área muy amplia, y este trabajo se centra únicamente en la parte relacionada con el modelado de lenguaje y su aplicación a diversas tareas: reconocimiento de secuencias mediante modelos ocultos de Markov y traducción automática estadística. Concretamente, esta tesis tiene su foco central en los denominados modelos conexionistas de lenguaje, esto es, modelos de lenguaje basados en redes neuronales. Los buenos resultados de estos modelos en diversas áreas del procesamiento del lenguaje natural han motivado el desarrollo de este estudio. Debido a determinados problemas computacionales que adolecen los modelos conexionistas de lenguaje, los sistemas que aparecen en la literatura se construyen en dos etapas totalmente desacopladas. En la primera fase se encuentra, a través de un modelo de lenguaje estándar, un conjunto de hipótesis factibles, asumiendo que dicho conjunto es representativo del espacio de búsqueda en el cual se encuentra la mejor hipótesis. En segundo lugar, sobre dicho conjunto, se aplica el modelo conexionista de lenguaje y se extrae la hipótesis con mejor puntuación. A este procedimiento se le denomina �rescoring�. Este escenario motiva los objetivos principales de esta tesis: � Proponer alguna técnica que pueda reducir drásticamente dicho coste computacional degradando lo mínimo posible la calidad de la solución encontrada. � Estudiar el efecto que tiene la integración de los modelos conexionistas de lenguaje en el proceso de búsqueda de las tareas propuestas. � Proponer algunas modificaciones del modelo original que permitan mejorar su calidadZamora Martínez, FJ. (2012). Aportaciones al modelado conexionista de lenguaje y su aplicación al reconocimiento de secuencias y traducción automática [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18066Palanci

Modelos Conexionistas para el Procesado del Lenguaje Natural

El Procesado del Lenguaje Natural es un campo muy importante en tareas del Reconocimiento de Formas. En este trabajo se exponen diferentes modelos de redes neuronales y su aplicación a tareas de traducción estadística, reconocimiento del habla, y etiquetado Part-of-Speech. Se muestran resultados prometedores en las tres tareas, a falta de una investigación más profunda.Zamora Martínez, FJ. (2008). Modelos Conexionistas para el Procesado del Lenguaje Natural. http://hdl.handle.net/10251/12188Archivo delegad

F-Measure as the error function to train neural networks

Imbalance datasets impose serious problems in machine learning. For many tasks characterized by imbalanced data, the F-Measure seems more appropiate than the Mean Square Error or other error measures. This paper studies the use of F-Measure as the training criterion for Neural Networks by integrating it in the Error-Backpropagation algorithm. This novel training criterion has been validated empirically on a real task for which F-Measure is typically applied to evaluate the quality. The task consists in cleaning and enhancing ancient document images which is performed, in this work, by means of neural filters.This work has been partially supported by MICINN project HITITA (TIN2010-18958) and by the FPI-MICINN (BES-2011-046167) scholarship from Ministerio de Ciencia e Innovación, Gobierno de España.Pastor Pellicer, J.; Zamora Martínez, FJ.; España Boquera, S.; Castro-Bleda, MJ. (2013). F-Measure as the error function to train neural networks. En Advances in Computational Intelligence. Springer Verlag (Germany). 376-384. https://doi.org/10.1007/978-3-642-38679-4_37S376384Dembczyński, K., Waegeman, W., Cheng, W., Hüllermeier, E.: An exact algorithm for f-measure maximization. Advances in Neural Information Processing Systems 24, 223–230 (2011)Al-Haddad, L., Morris, C.W., Boddy, L.: Training radial basis function neural networks: effects of training set size and imbalanced training sets. J. of Microbiological Methods 43(1), 33–44 (2000)Bilmes, J., Asanovic, K., Chin, C.W., Demmel, J.: Using PHiPAC to speed error back-propagation learning. In: Proc. of ICASSP, vol. 5, pp. 4153–4156 (1997)Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley (2001)Gatos, B., Ntirogiannis, K., Pratikakis, I.: ICDAR 2009 document image binarization contest (DIBCO 2009). In: Proc. of ICDAR, pp. 1375–1382 (2009)Gatos, B., Ntirogiannis, K., Pratikakis, I.: DIBCO 2009: document image binarization contest. Int. J. on Document Analysis and Recognition 14(1), 35–44 (2011)Hidalgo, J.L., España, S., Castro, M.J., Pérez, J.A.: Enhancement and cleaning of handwritten data by using neural networks. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 376–383. Springer, Heidelberg (2005)Jansche, M.: Maximum expected f-measure training of logistic regression models. In: Proc. of HLT & EMNLP, pp. 692–699 (2005)Musicant, D.R., Kumar, V., Ozgur, A.: Optimizing f-measure with support vector machines. In: Proc. of Int. Florida AI Research Society Conference, pp. 356–360 (2003)Ntirogiannis, K., Gatos, B., Pratikakis, I.: A Performance Evaluation Methodology for Historical Document Image Binarization (2012)Pratikakis, I., Gatos, B., Ntirogiannis, K.: ICFHR 2012 Competition on Handwritten Document Image Binarization (H-DIBCO 2012) (2012)Pratikakis, I., Gatos, B., Ntirogiannis, K.: H-DIBCO 2010-handwritten document image binarization competition. In: Proc. of ICFHR, pp. 727–732 (2010)van Rijsbergen, C.J.: A theoretical basis for the use of co-occurrence data in information retrieval. J. of Documentation 33(2), 106–119 (1977)Wolf, C.: Document Ink Bleed-Through Removal with Two Hidden Markov Random Fields and a Single Observation Field. IEEE PAMI 32(3), 431–447 (2010)Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. on Knowledge and Data Engineering 18(1), 63–77 (2006

Improving offline handwritten text recognition with hybrid HMM/ANN models

This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task. © 2006 IEEE.The authors acknowledge the valuable help provided by Moises Pastor, Juan Miguel Vilar, Alex Graves, and Marcus Liwicki. Thanks are also due to the reviewers and the Editor-in-Chief for their many valuable comments and suggestions. This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia (TIN2006-12767) and by the BPFI 06/250 Scholarship from the Conselleria d'Empresa, Universitat i Ciencia, Generalitat Valenciana.España Boquera, S.; Castro-Bleda, MJ.; Gorbe Moya, J.; Zamora Martínez, FJ. (2011). Improving offline handwritten text recognition with hybrid HMM/ANN models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(4):767-779. https://doi.org/10.1109/TPAMI.2010.141S76777933