Cristina España-Bonet

Variable cosmological constant as a Planck scale effect

Abstract We construct a semiclassical Friedmann–Lemaitre–Robertson–Walker (FLRW) cosmological model assuming a running cosmological constant (CC). It turns out that the CC becomes variable at arbitrarily low energies due to the remnant quantum effects of the heaviest particles, e.g., the Planck scale physics. These effects are universal in the sense that they lead to a low-energy structure common to a large class of high-energy theories. Remarkably, the uncertainty concerning the unknown high-energy dynamics is accumulated into a single parameter ν , such that the model has an essential predictive power. Future Type Ia supernovae experiments (like SNAP) can verify whether this framework is correct. For the flat FLRW case and a moderate value ν ∼10 −2 , we predict an increase of 10–20% in the value of Ω Λ at redshifts z =1–1.5 perfectly reachable by SNAP.

A hybrid machine translation architecture guided by syntax

This article presents a hybrid architecture which combines rule-based machine translation (RBMT) with phrase-based statistical machine translation (SMT). The hybrid translation system is guided by the rule-based engine. Before the transfer step, a varied set of partial candidate translations is calculated with the SMT system and used to enrich the tree-based representation with more translation alternatives. The final translation is constructed by choosing the most probable combination among the available fragments using monotone statistical decoding following the order provided by the rule-based system. We apply the hybrid model to a pair of distantly related languages, Spanish and Basque, and perform extensive experimentation on two different corpora. According to our empirical evaluation, the hybrid approach outperforms the best individual system across a varied set of automatic translation evaluation metrics. Following some output analysis to better understand the behaviour of the hybrid system, we explore the possibility of adding alternative parse trees and extra features to the hybrid decoder. Finally, we present a twofold manual evaluation of the translation systems studied in this paper, consisting of (i) a pairwise output comparison and (ii) a individual task-oriented evaluation using HTER. Interestingly, the manual evaluation shows some contradictory results with respect to the automatic evaluation; humans tend to prefer the translations from the RBMT system over the statistical and hybrid translations.

Discriminative Phrase-Based Models for Arabic Machine Translation

A design for an Arabic-to-English translation system is presented. The core of the system implements a standard phrase-based statistical machine translation architecture, but it is extended by incorporating a local discriminative phrase selection model to address the semantic ambiguity of Arabic. Local classifiers are trained using linguistic information and context to translate a phrase, and this significantly increases the accuracy in phrase selection with respect to the most frequent translation traditionally considered. These classifiers are integrated into the translation system so that the global task gets benefits from the discriminative learning. As a result, we obtain significant improvements in the full translation task at the lexical, syntactic, and semantic levels as measured by an heterogeneous set of automatic evaluation metrics.

TYPE Ia SNe ALONG REDSHIFT: THE R(Si ii) RATIO AND THE EXPANSION VELOCITIES IN INTERMEDIATE-z SUPERNOVAE

We present a study of intermediate-z Type Ia supernovae (SNe Ia) using empirical physical diagrams which permit the investigation of those SNe explosions. This information can be very useful to reduce systematic uncertainties of the Hubble diagram of SNe Ia up to high z. The study of the expansion velocities and the measurement of the ratio \mathcal {R}(Si II) allow subtyping of SNe Ia as done in nearby samples. The evolution of this ratio as seen in the diagram \mathcal {R}(Si II)-(t) together with \mathcal {R}(Si II)max versus (B - V)0 indicates consistency of the properties at intermediate-z compared with the nearby SNe Ia. At intermediate-z, expansion velocities of Ca II and Si II are found similar to those of the nearby sample. This is found in a sample of six SNe Ia in the range 0.033 <=z<= 0.329 discovered within the International Time Programme of SNe Ia for Cosmology and Physics in the spring run of 2002.7The program run under Omega and Lambda from Supernovae and the Physics of Supernova Explosions within the International Time Programme at the telescopes of the European Northern Observatory (ENO) at La Palma (Canary Islands, Spain). Two SNe Ia at intermediate-z were of the cool FAINT type, one being an SN1986G-like object highly reddened. The \mathcal {R}(Si II) ratio as well as subclassification of the SNe Ia beyond templates help to place SNe Ia in their sequence of brightness and to distinguish between reddened and intrinsically red supernovae. This test can be done with very high z SNe Ia and it will help to reduce systematic uncertainties due to extinction by dust. It should allow to map the high-z sample into the nearby one.

The patents retrieval prototype in the MOLTO project

This paper describes the patents retrieval prototype developed within the MOLTO project. The prototype aims to provide a multilingual natural language interface for querying the content of patent documents. The developed system is focused on the biomedical and pharmaceutical domain and includes the translation of the patent claims and abstracts into English, French and German. Aiming at the best retrieval results of the patent information and text content, patent documents are preprocessed and semantically annotated. Then, the annotations are stored and indexed in an OWLIM semantic repository, which contains a patent specific ontology and others from different domains. The prototype, accessible online at http://molto-patents.ontotext.com, presents a multilingual natural language interface to query the retrieval system. In MOLTO, the multilingualism of the queries is addressed by means of the GF Tool, which provides an easy way to build and maintain controlled language grammars for interlingual translation in limited domains. The abstract representation obtained from the GF is used to retrieve both the matched RDF instances and the list of patents semantically related to the users search criteria. The online interface allows to browse the retrieved patents and shows on the text the semantic annotations that explain the reason why any particular patent has matched the users criteria.

A Factory of Comparable Corpora from Wikipedia

Multiple approaches to grab comparable data from the Web have been developed up to date. Nevertheless, coming out with a high-quality comparable corpus of a specific topic is not straightforward. We present a model for the automatic extraction of comparable texts in multiple languages and on specific topics from Wikipedia. In order to prove the value of the model, we automatically extract parallel sentences from the comparable collections and use them to train statistical machine translation engines for specific domains. Our experiments on the English‐ Spanish pair in the domains of Computer Science, Science, and Sports show that our in-domain translator performs significantly better than a generic one when translating in-domain Wikipedia articles. Moreover, we show that these corpora can help when translating out-of-domain texts.

Automatic speech recognition with deep neural networks for impaired speech

Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13 % for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.

Tracing the equation of state and the density of the cosmological constant along z

We investigate the equation of state w(z) in a non-parametric form using the latest compilations of the luminosity distance from SNe Ia at high z. We combine the inverse problem approach with a Monte Carlo method to scan the space of priors. In the light of the latest high redshift supernova data sets, we reconstruct w(z). A comparison between a sample including the latest results at z>1 and a sample without those results shows the improvement achieved through observations of very high z supernovae. We present the prospects for measuring the variation of dark energy density along z by this method.

Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation.

We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation. Our model uses word embeddings trained on significantly large unlabelled monolingual corpora and learns over a fairly small, word-to-word bilingual dictionary. Given an out-of-vocabulary source word, the model generates a probabilistic list of possible translations in the target language using the trained bilingual embeddings. We integrate these translation options into a standard phrase-based statistical machine translation system and obtain consistent improvements in translation quality on the English–Spanish language pair. When tested over an out-of-domain testset, we get a significant improvement of 3.9 BLEU points.

The TALP-UPC Spanish-English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System.

This paper describes the TALP–UPC system in the Spanish–English WMT 2016 biomedical shared task. Our system is a standard phrase-based system enhanced with vocabulary expansion using bilingual word embeddings and a characterbased neural language model with rescoring. The former focuses on resolving outof- vocabulary words, while the latter enhances the fluency of the system. The two modules progressively improve the final translation as measured by a combination of several lexical metrics.