Luis Fernando D’Haro

Also published as: Luis F. d’Haro


2018

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Attention-based Semantic Priming for Slot-filling
Jiewen Wu | Rafael E. Banchs | Luis Fernando D’Haro | Pavitra Krishnaswamy | Nancy Chen
Proceedings of the Seventh Named Entities Workshop

The problem of sequence labelling in language understanding would benefit from approaches inspired by semantic priming phenomena. We propose that an attention-based RNN architecture can be used to simulate semantic priming for sequence labelling. Specifically, we employ pre-trained word embeddings to characterize the semantic relationship between utterances and labels. We validate the approach using varying sizes of the ATIS and MEDIA datasets, and show up to 1.4-1.9% improvement in F1 score. The developed framework can enable more explainable and generalizable spoken language understanding systems.

2015

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RevUP: Automatic Gap-Fill Question Generation from Educational Texts
Girish Kumar | Rafael Banchs | Luis Fernando D’Haro
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

2009

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Speeding Up the Design of Dialogue Applications by Using Database Contents and Structure Information
Luis Fernando D’Haro | Ricardo de Cordoba | Juan Manuel Lucas | Roberto Barra-Chicote | Ruben San-Segundo
Proceedings of the SIGDIAL 2009 Conference

2007

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A Multimodal Interface for Access to Content in the Home
Michael Johnston | Luis Fernando D’Haro | Michelle Levine | Bernard Renger
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Error Analysis of Statistical Machine Translation Output
David Vilar | Jia Xu | Luis Fernando D’Haro | Hermann Ney
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Evaluation of automatic translation output is a difficult task. Several performance measures like Word Error Rate, Position Independent Word Error Rate and the BLEU and NIST scores are widely use and provide a useful tool for comparing different systems and to evaluate improvements within a system. However the interpretation of all of these measures is not at all clear, and the identification of the most prominent source of errors in a given system using these measures alone is not possible. Therefore some analysis of the generated translations is needed in order to identify the main problems and to focus the research efforts. This area is however mostly unexplored and few works have dealt with it until now. In this paper we will present a framework for classification of the errors of a machine translation system and we will carry out an error analysis of the system used by the RWTH in the first TC-STAR evaluation.

2004

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Semi-Automatic Generation of Dialogue Applications in the GEMINI Project
Stefan Hamerich | Volker Schubert | Volker Schless | Ricardo de Córdoba | José M. Pardo | Luis F. d’Haro | Basilis Kladis | Otilia Kocsis | Stefan Igel
Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue at HLT-NAACL 2004