This paper presents an extensive evaluation of five different alignments and investigates their impact on the corresponding MT system output. We introduce new measures for intrinsic evaluations and examine the distribution of phrases and untranslated words during decoding to identify which characteristics of different alignments affect translation. We show that precision-oriented alignments yield better MT output (translating more words and using longer phrases) than recalloriented alignments. 1

The continuously growing MT market faces the challenge of translating new languages, diverse genres, and di#erent domains using a variety of available linguistic resources.

This paper presents a new approach to combining outputs of existing word alignment systems. Each alignment link is represented with a set of feature functions extracted from linguistic features and input alignments. These features are used as the basis of alignment decisions made by a maximum entropy approach. The learning method has been evaluated on three language pairs, yielding significant improvements over input alignments and three heuristic combination methods. The impact of word alignment on MT quality is investigated, using a phrase-based MT system. 1

In this paper, we present the Thot toolkit, a set of tools to train phrase-based models for statistical machine translation, which is publicly available as open source software. The toolkit obtains phrase-based models from word-based alignment models; to our knowledge, this functionality has not been offered by any publicly available toolkit. The Thot toolkit also implements a new way for estimating phrase models, this allows to obtain more complete phrase models than the methods described in the literature, including a segmentation length submodel. The toolkit output can be given in different formats in order to be used by other statistical machine translation tools like Pharaoh, which is a beam search decoder for phrase-based alignment models which was used in order to perform translation experiments with the generated models. Additionally, the Thot toolkit can be used to obtain the best alignment between a sentence pair at phrase level. 1

Constraint satisfaction inference is presented as a generic, theory-neutral inference engine for machine translation. The approach enables the integration of many different solutions to aspects of the output space, including classification-based translation models that take source-side context into account, as well as stochastic components such as target language models. The approach is contrasted with a word-based SMT system using the same decoding algorithm, but optimising a different objective function. The incorporation of source-side context models in our model filters out many irrelevant candidate translations, leading to superior translation scores.

In order to simultaneously translate speech into multiple languages an extension of stochastic finite-state transducers is proposed. In this approach the speech translation model consists of a single network where acoustic models (in the input) and the multilingual model (in the output) are embedded. The multi-target model has been evaluated in a practical situation, and the results have been compared with those obtained using several mono-target models. Experimental results show that the multi-target one requires less amount of memory. In addition, a single decoding is enough to get the speech translated into multiple languages. 1

Creating a reusable

for participation in two SemEval-2 tasks:

In this paper, we present a hybrid approach to align single words, compound words and idiomatic expressions from bilingual parallel corpora. The objective is to develop, improve and maintain automatically translation lexicons. This approach combines linguistic and statistical information in order to improve word alignment results. The linguistic improvements taken into account refer to the use of an existing bilingual lexicon, named entities recognition, grammatical tags matching and detection of syntactic dependency relations between words. Statistical information refer to the number of occurrences of repeated words, their positions in the parallel corpus and their lengths in terms of number of characters. Single-word alignment uses an existing bilingual lexicon, named entities and cognates detection and grammatical tags matching. Compound-word alignment consists in establishing correspondences between the compound words of the source sentence and the compound words of the target sentences. A syntactic analysis is applied on the source and target sentences in order to extract dependency relations between words and to recognize compound words. Idiomatic expressions alignment starts with a monolingual term extraction