Adapting SimpleNLG to Galician language

Cascallar-Fuentes, Andrea; Ramos-Soto, Alejandro; Diz, Alberto Bugarín

doi:10.18653/v1/w18-6507

Cited by 15 publications

(10 citation statements)

References 10 publications

(8 reference statements)

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“…There have been a few approaches using handcrafted grammars (Bateman, 1997;Allman et al, 2012). The procedural Sim-pleNLG realizer (Gatt and Reiter, 2009) has also been ported into multiple languages (Bollmann, 2011;Vaudry and Lapalme, 2013;de Oliveira and Sripada, 2014;Mazzei et al, 2016;Ramos-Soto et al, 2017;Cascallar-Fuentes et al, 2018;Chen et al, 2018;de Jong and Theune, 2018). Further works using multilingual rule-based surface realiza-tion pipelines were developed in the context of machine translation (Aikawa et al, 2001;Žabokrtský et al, 2008;Dušek et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Neural Generation for Czech: Data and Baselines

Dušek

Jurčíček

2019

Proceedings of the 12th International Conference on Natural Language Generation

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We present the first dataset targeted at end-toend NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-ofthe-box and lexicalizing the generated outputs is non-trivial.In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.• Using both automatic and manual evaluation in Section 4, we show that our extensions improve

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Section: Related Workmentioning

confidence: 99%

Neural Generation for Czech: Data and Baselines

Dušek

Jurčíček

2019

Proceedings of the 12th International Conference on Natural Language Generation

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“…It is implemented in Java and its current Version (4.4.8) is available under the Mozilla Public License (MPL). 1 Since it was published in 2009, SimpleNLG was adapted to seven other languages, these are (in chronological order): German (Bollmann, 2011), French (Vaudry and Lapalme, 2013), Italian (Mazzei et al, 2016), Spanish (Ramos-Soto et al, 2017), Dutch (de Jong and Theune, 2018), Mandarin (Chen et al, 2018), and Galician (Cascallar-Fuentes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

SimpleNLG-DE: Adapting SimpleNLG 4 to German

Braun

Klimt

Schneider³

et al. 2019

Proceedings of the 12th International Conference on Natural Language Generation

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SimpleNLG is a popular open source surface realiser for the English language. For German, however, the availability of open source and non-domain specific realisers is sparse, partly due to the complexity of the German language. In this paper, we present SimpleNLG-DE, an adaption of SimpleNLG to German. We discuss which parts of the German language have been implemented and how we evaluated our implementation using the TIGER Corpus and newly created data-sets.

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“…SimpleNLG[29] github: https://github.com/simplenlg/simplenlg13 SimpleNLG-ES[30] github: https://github.com/citiususc/SimpleNLG-ES14 SimpleNLG-GL[31] github: https://github.com/citiususc/SimpleNLG-GL…”

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confidence: 99%

Teaching Explainable Artificial Intelligence to High School Students

Alonso¹

2020

IJCIS

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Artificial Intelligence (AI) is part of our everyday life and has become one of the most outstanding and strategic technologies. Explainable AI (XAI) is expected to endow intelligent systems with fairness, accountability, transparency and explanation ability when interacting with humans. This paper describes how to teach fundamentals of XAI to high school students who take part in interactive workshop activities at CiTIUS-USC. These workshop activities are carried out in the context of a strategic plan for promoting careers on Science, Technology, Engineering and Mathematics. Students learn (1) how to build datasets free of bias, (2) how to build interpretable classifiers and (3) how to build multi-modal explanations.

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Adapting SimpleNLG to Galician language

Cited by 15 publications

References 10 publications

Neural Generation for Czech: Data and Baselines

Neural Generation for Czech: Data and Baselines

SimpleNLG-DE: Adapting SimpleNLG 4 to German

Teaching Explainable Artificial Intelligence to High School Students

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