Combining multiple translation systems for spoken language understanding portability

[EN] We are interested in the problem of learning Spoken Language Understanding (SLU) models for multiple target languages. Learning such models requires annotated corpora, and porting to different languages would require corpora with parallel text translation and semantic annotations. In this paper we investigate how to learn a SLU model in a target language starting from no target text and no semantic annotation. Our proposed algorithm is based on the idea of exploiting the diversity (with regard to performance and coverage) of multiple translation systems to transfer statistically stable word-to-concept mappings in the case of the romance language pair, French and Spanish. Each translation system performs differently at the lexical level (wrt BLEU). The best translation system performances for the semantic task are gained from their combination at different stages of the portability methodology. We have evaluated the portability algorithms on the French MEDIA corpus, using French as the source language and Spanish as the target language. The experiments show the effectiveness of the proposed methods with respect to the source language SLU baseline.This work is partially supported by the Spanish MICINN under contract TIN2011-28169-C05-01, and by the Vic. d'Investigacio of the UPV under contracts PAID-00-09 and PAID-06-10 The author work was partially funded by FP7 PORTDIAL project n.296170García-Granada, F.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E.; Riccardi, G. (2012). Combining multiple translation systems for spoken language understanding portability. IEEE. 194-198. https://doi.org/10.1109/SLT.2012.642422119419

Coordinating Pluggable Transceiver Control in SONiC-based Disaggregated Packet-Optical Networks

Effective control of pluggable transceivers in SONiC-based packet-optical nodes is demonstrated. A workflow for multi-layer recovery upon soft failure detection is validated, showing no traffic disruption and fast node-driven coordination between packet and optical operations

A techno-economic study of optical network disaggregation employing Open-Source Software business models for Metropolitan Area Networks

This work provides a techno-economic evaluation of optical disaggregation architectures in the context of metropolitan area networks. The study compares two optical disaggregation options (partial vs. total) against the legacy benchmark where optical equipment is subject to vendor lock-in, as it is deployed in most networks today. We show that emerging open source software projects within the software-defined networking ecosystem can potentially yield significant cost savings for medium- and large-size network operators, while they can introduce extra flexibility and agility to network operations and service deployments.This work has been supported by EU H2020 project Metro-Haul, grant no. 761727 (https://metro-haul.eu)

Photonic Provisioning Using a Packaged SOI Hybrid All-Optical Wavelength Converter in a Meshed Optical Network Testbed

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Complex-Valued Neural Network Design for Mitigation of Signal Distortions in Optical Links

Nonlinearity compensation is considered as a key enabler to increase channel transmission rates in the installed optical communication systems. Recently, data-driven approaches - motivated by modern machine learning techniques - have been proposed for optical communications in place of traditional model-based counterparts. In particular, the application of neural networks (NN) allows improving the performance of complex modern fiber-optic systems without relying on any a priori knowledge of their specific parameters. In this work, we introduce a novel design of complex-valued NN for optical systems and examine its performance in standard single mode fiber (SSMF) and large effective-area fiber (LEAF) links operating in relatively high nonlinear regime. First, we present a methodology to design a new type of NN based on the assumption that the channel model is more accurate in the nonlinear regime. Second, we implement a Bayesian optimizer to jointly adapt the size of the NN and its number of input taps depending on the different fiber properties and total length. Finally, the proposed NN is numerically and experimentally validated showing an improvement of 1.7 dB in the linear regime, 2.04 dB at the optimal optical power and 2.61 at the max available power on Q-factor when transmitting a WDM 30 × 200G DP-16QAM signal over a 612 km SSMF legacy link. The results highlight that the NN is able to mitigate not only part of the nonlinear impairments caused by optical fiber propagation but also imperfections resulting from using low-cost legacy transceiver components, such as digital-to-analog converter (DAC) and Mach-Zehnder modulator

On the Filter Narrowing Issues in Elastic Optical Networks

This paper describes the problematic filter narrowing effect in the context of next-generation elastic optical networks. First, three possible scenarios are introduced: the transition from an actual fixed-grid to a flexigrid network, the generic full flexi-grid network, and a proposal for a filterless optical network. Next, we investigate different transmission techniques and evaluate the penalty introduced by the filtering effect when considering Nyquist wavelength division multiplexing, single side-band direct-detection orthogonal frequency division multiplexing, and symbol-rate variable dual polarization quadrature amplitude modulation. Also, different approaches to compensate for the filter narrowing effect are discussed. Results show that the specific needs per each scenario can be fulfilled by the aforementioned technologies and techniques or a combination of them, when balancing performance, network reach, and cost