We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al., 2018) and attention-based Transformer (Vaswani et al., 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

This paper presents the particular use of "Jibiki" (Papillon's web server development platform) for the LexALP 1 project. LexALP's goal is to harmonise the terminology on spatial planning and sustainable development used within the Alpine Convention 2 , so that the member states are able to cooperate and communicate efficiently in the four official languages (French, German, Italian and Slovene). To this purpose, LexALP uses the Jibiki platform to build a term bank for the contrastive analysis of the specialised terminology used in six different national legal systems and four different languages. In this paper we present how a generic platform like Jibiki can cope with a new kind of dictionary.

Standard techniques used in multilingual terminology management fail to describe legal terminologies as they are bound to different legal systems and terms do not share a common meaning. In the LexALP project, we use a technique defined for general lexical databases to achieve cross language interoperability between languages of the Alpine Convention. In this paper we present the methodology and tools developed for the collection, description and harmonisation of the legal terminology of spatial planning and sustainable development in the four languages of the countries of the Alpine Space.

The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a dependency parser that has larger coverage and is more robust than its component parsers. In the future, it should be possible to "compile" the knowledge extracted from several analyzers into an autonomous dependency parser.

The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a dependency parser that has larger coverage and is more robust than its component parsers. In the future, it should be possible to "compile" the knowledge extracted from several analyzers into an autonomous dependency parser.