Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.

This paper presents the submissions of the PRHLT group for the evaluation campaign of the International Workshop on Spoken Language Translation. We focus on the development of reliable translation systems between syntactically different languages (DIALOG task) and on the efficient training of SMT models in resource-rich scenarios (TALK task).

In this paper, we describe the machine translation system developed at the Polytechnic University of Valencia, which was used in our participation in the International Workshop on Spoken Language Translation (IWSLT) 2009. We have taken part only in the Chinese-English BTEC Task. In the evaluation campaign, we focused on the use of our hybrid translation system over the provided corpus and less effort was devoted to the use of preand post-processing techniques that could have improved the results. Our decoder is a hybrid machine translation system that combines phrase-based models together with syntax-based translation models. The syntactic formalism that underlies the whole decoding process is a Chomsky Normal Form Stochastic Inversion Transduction Grammar (SITG) with phrasal productions and a log-linear combination of probability models. The decoding algorithm is a CYK-like algorithm that combines the translated phrases inversely or directly in order to get a complete translation of the input sentence.

An important problem when using Stochastic Inversion Transduction Grammars is their computational cost. More specifically, when dealing with corpora such as Europarl. only one iteration of the estimation algorithm becomes prohibitive. In this work, we apply a reduction of the cost by taking profit of the bracketing information in parsed corpora and show machine translation results obtained with a bracketed Europarl corpus, yielding interresting improvements when increasing the number of non-terminal symbols.