173 research outputs found
The InFile project: a crosslingual filtering systems evaluation campaign
International audienceThe InFile project (INformation, FILtering, Evaluation) is a cross-language adaptive filtering evaluation campaign, sponsored by the French National Research Agency. The campaign is organized by the CEA LIST, ELDA and the University of Lille3-GERiiCO. It has an international scope as it is a pilot track of the CLEF 2008 campaigns. The corpus is built from a collection of about 1,4 millions newswires (10 GB) in three languages, Arabic, English and French provided by Agence France Press (AFP) and selected from a 3 years period. The profiles corpus is made of 50 profiles from which 30 concern general news and events (national and international affairs, politics, sports...) and 20 concern scientific and technical subject
A road map for interoperable language resource metadata
LRs remain expensive to create and thus rare relative to demand across languages and technology types. The accidental re-creation of an LR that already exists is a nearly unforgiveable waste of scarce resources that is unfortunately not so easy to avoid. The number of catalogs the HLT researcher must search, with their different formats, make it possible to overlook an existing resource. This paper sketches the sources of this problem and outlines a proposal to rectify along with a new vision of LR cataloging that will to facilitates the documentation and exploitation of a much wider range of LRs than previously considered
Software Defined Networking (SDN): Etat de L'art
International audienceInternet a connu un Ă©norme succĂšs, Il est devenu un outil universel indispensable pour les entreprises et la plupart dâindividus. Cependant, malgrĂ© leur adoption, les rĂ©seaux classiques sont complexes et difficiles Ă gĂ©rer. Une des raisons de cette difficultĂ© rĂ©side dans lâarchitecture des rĂ©seaux actuels oĂč le plan de contrĂŽle et le plan de donnĂ©es sont intĂ©grĂ©s verticalement dans chaque Ă©quipement rĂ©seau. SDN est un nouveau paradigme rĂ©seau, qui permet de simplifier la gestion et lâinnovation dans le rĂ©seau, en sĂ©parant la logique de contrĂŽle du rĂ©seau des Ă©quipements dâinterconnexions ,en promouvant la centralisation du contrĂŽle et la capacitĂ© de programmer le rĂ©seau. Dans cet article, nous prĂ©sentons une vue gĂ©nĂ©rale sur SDN. Nous commençons par prĂ©senter SDN, son architecture, et ses interfaces de communications. Nous dĂ©crivons par la suite le protocole Openflow, son fonctionnement, et les principaux contrĂŽleurs SDN. Nous examinons Ă©galement les problĂšmes confrontĂ©es par SDN, en nous concentrant sur les principaux dĂ©fis de plan de contrĂŽle tels que la performance, la scalabilitĂ©, la sĂ©curitĂ©, et la fiabilitĂ©, nous discutons ainsi, les solutions existantes afin de surmonter ces dĂ©fis
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding
Voice communication between air traffic controllers (ATCos) and pilots is
critical for ensuring safe and efficient air traffic control (ATC). This task
requires high levels of awareness from ATCos and can be tedious and
error-prone. Recent attempts have been made to integrate artificial
intelligence (AI) into ATC in order to reduce the workload of ATCos. However,
the development of data-driven AI systems for ATC demands large-scale annotated
datasets, which are currently lacking in the field. This paper explores the
lessons learned from the ATCO2 project, a project that aimed to develop a
unique platform to collect and preprocess large amounts of ATC data from
airspace in real time. Audio and surveillance data were collected from publicly
accessible radio frequency channels with VHF receivers owned by a community of
volunteers and later uploaded to Opensky Network servers, which can be
considered an "unlimited source" of data. In addition, this paper reviews
previous work from ATCO2 partners, including (i) robust automatic speech
recognition, (ii) natural language processing, (iii) English language
identification of ATC communications, and (iv) the integration of surveillance
data such as ADS-B. We believe that the pipeline developed during the ATCO2
project, along with the open-sourcing of its data, will encourage research in
the ATC field. A sample of the ATCO2 corpus is available on the following
website: https://www.atco2.org/data, while the full corpus can be purchased
through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We
demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when
little or near to no ATC in-domain data is available. For instance, with the
CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9%
WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but
supervised CNN-TDNNf model.Comment: Manuscript under revie
Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources
Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations
The European Language Resources and Technologies Forum: Shaping the Future of the Multilingual Digital Europe
Proceedings of the 1st FLaReNet Forum on the European Language Resources and Technologies, held in Vienna, at the Austrian Academy of Science, on 12-13 February 2009
The European language technology landscape in 2020: Language-centric and human-centric AI for cross-cultural communication in multilingual Europe
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europeâs specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI â including many opportunities, synergies but also misconceptions â has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.publishedVersio
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications
Personal assistants, automatic speech recognizers and dialogue understanding
systems are becoming more critical in our interconnected digital world. A clear
example is air traffic control (ATC) communications. ATC aims at guiding
aircraft and controlling the airspace in a safe and optimal manner. These
voice-based dialogues are carried between an air traffic controller (ATCO) and
pilots via very-high frequency radio channels. In order to incorporate these
novel technologies into ATC (low-resource domain), large-scale annotated
datasets are required to develop the data-driven AI systems. Two examples are
automatic speech recognition (ASR) and natural language understanding (NLU). In
this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering
research on the challenging ATC field, which has lagged behind due to lack of
annotated data. The ATCO2 corpus covers 1) data collection and pre-processing,
2) pseudo-annotations of speech data, and 3) extraction of ATC-related named
entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set
corpus contains 4 hours of ATC speech with manual transcripts and a subset with
gold annotations for named-entity recognition (callsign, command, value). 2)
The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched
with automatic transcripts from an in-domain speech recognizer, contextual
information, speaker turn information, signal-to-noise ratio estimate and
English language detection score per sample. Both available for purchase
through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3)
The ATCO2-test-set-1h corpus is a one-hour subset from the original test set
corpus, that we are offering for free at https://www.atco2.org/data. We expect
the ATCO2 corpus will foster research on robust ASR and NLU not only in the
field of ATC communications but also in the general research community.Comment: Manuscript under review; The code will be available at
https://github.com/idiap/atco2-corpu
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