There are several MT metrics used to evaluate translation into Spanish, although most of them use partial or little linguistic information. In this paper we present the multilingual capability of VERTa, an automatic MT metric that combines linguistic information at lexical, morphological, syntactic and semantic level. In the experiments conducted we aim at identifying those linguistic features that prove the most effective to evaluate adequacy in Spanish segments. This linguistic information is tested both as independent modules (to observe what each type of feature provides) and in a combinatory fastion (where different kinds of information interact with each other). This allows us to extract the optimal combination. In addition we compare these linguistic features to those used in previous versions of VERTa aimed at evaluating adequacy for English segments. Finally, experiments show that VERTa can be easily adapted to other languages than English and that its collaborative approach correlates better with human judgements on adequacy than other well-known metrics.

In the last decades, a wide range of automatic metrics that use linguistic knowledge has been developed. Some of them are based on lexical information, such as METEOR; others rely on the use of syntax, either using constituent or dependency analysis; and others use semantic information, such as Named Entities and semantic roles. All these metrics work at a specific linguistic level, but some researchers have tried to combine linguistic information, either by combining several metrics following a machine-learning approach or focusing on the combination of a wide variety of metrics in a simple and straightforward way. However, little research has been conducted on how to combine linguistic features from a linguistic point of view. In this paper we present VERTa, a metric which aims at using and combining a wide variety of linguistic features at lexical, morphological, syntactic and semantic level. We provide a description of the metric and report some preliminary experiments which will help us to discuss the use and combination of certain linguistic features in order to improve the metric performance

This paper describes version 1.3 of the FreeLing suite of NLP tools. FreeLing was first released in February 2004 providing morphological analysis and PoS tagging for Catalan, Spanish, and English. From then on, the package has been improved and enlarged to cover more languages (i.e. Italian and Galician) and offer more services: Named entity recognition and classification, chunking, dependency parsing, and WordNet based semantic annotation. FreeLing is not conceived as end-user oriented tool, but as library on top of which powerful NLP applications can be developed. Nevertheless, sample interface programs are provided, which can be straightforwardly used as fast, flexible, and efficient corpus processing tools. A remarkable feature of FreeLing is that it is distributed under a free-software LGPL license, thus enabling any developer to adapt the package to his needs in order to get the most suitable behaviour for the application being developed.

This paper describes the evaluation of the FAME interlingua-based speech-to-speech translation system for Catalan, English and Spanish. This system is an extension of the already existing NESPOLE! that translates between English, French, German and Italian. This article begins with a brief introduction followed by a description of the system architecture and the components of the translation module including the Speech Recognizer, the analysis chain, the generation chain and the Speech Synthesizer. Then we explain the interlingua formalism used, called Interchange Format (IF). We show the results obtained from the evaluation of the system and we describe the three types of evaluation done. We also compare the results of our system with those obtained by a stochastic translator which has been independently developed over the course of the FAME project. Finally, we conclude with future work.

In this paper we describe the FAME interlingual speech-to- speech translation System for Spanish, Catalan and English which is intended to assist users in the reservation of a hotel room when calling or visiting abroad. The System has been developed as an extension of the existing NESPOLE! translation system [4] which translates between English, German, Italian and French. After a brief introduction we describe the Spanish and Catalan System components including speech recognition, transcription to IF mapping, IF to text generation and speech synthesis. We also present a task-oriented evaluation method used to inform about system development and some preliminary results.