Un nuevo modelo para la estimación de bi-gramas en reconocimiento del habla

Se presenta un nuevo método para el suavizado de N-gramas utilizando regularización en un modelo de máxima entropía. Dicha regularización se efectúa introduciendo un término en la función objetivo al estilo de las máquinas de soporte vectorial. Relacionado con dicho término se incluye una variable que actúa como descuento de probabilidades en el estimador, similar al usado en otros métodos de suavizado de modelos de lenguaje, pero considerando dicho descuento como otra variable a optimizar. El modelo fue evaluado en una tarea de reconocimiento de habla usando modelos de lenguaje de bi-gramas. Los resultados se testaron usando la base de datos Latino-40 midiendo perplejidad y porcentaje de palabras reconocidas. Los resultados fueron significativamente superiores a un modelo que es estado del arte.Sociedad Argentina de Informática e Investigación Operativ

Modeling of learning curves with applications to POS tagging

An algorithm to estimate the evolution of learning curves on the whole of a training data base, based on the results obtained from a portion and using a functional strategy, is introduced. We approximate iteratively the sought value at the desired time, independently of the learning technique used and once a point in the process, called prediction level, has been passed. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition. This allows the user to fix a convergence threshold with respect to the accuracy finally achievable, which extends the concept of stopping criterion and seems to be effective even in the presence of distorting observations. Our aim is to evaluate the training effort, supporting decision making in order to reduce the need for both human and computational resources during the learning process. The proposal is of interest in at least three operational procedures. The first is the anticipation of accuracy gain, with the purpose of measuring how much work is needed to achieve a certain degree of performance. The second relates the comparison of efficiency between systems at training time, with the objective of completing this task only for the one that best suits our requirements. The prediction of accuracy is also a valuable item of information for customizing systems, since we can estimate in advance the impact of settings on both the performance and the development costs. Using the generation of part-of-speech taggers as an example application, the experimental results are consistent with our expectations.Ministerio de Economía y Competitividad | Ref. FFI2014-51978-C2-1-