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KI - Künstliche Intelligenz
Published in: KI - Künstliche Intelligenz 4/2018

10-09-2018 | Dissertation and Habilitation Abstracts

Multitask and Multilingual Modelling for Lexical Analysis

Author: Johannes Bjerva

Published in: KI - Künstliche Intelligenz | Issue 4/2018

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Abstract

In Natural Language Processing (NLP), one traditionally considers a single task (e.g. part-of-speech tagging) for a single language (e.g. English) at a time. However, recent work has shown that it can be beneficial to take advantage of relatedness between tasks, as well as between languages. In this work I examine the concept of relatedness and explore how it can be utilised to build NLP models that require less manually annotated data. A large selection of NLP tasks is investigated for a substantial language sample comprising 60 languages. The results show potential for joint multitask and multilingual modelling, and hints at linguistic insights which can be gained from such models.
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The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society - with constributions from troughout the field of artificial intelligence.

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Footnotes
1
In NLP words are commonly represented by embedding them in a vector space, typically with 64–256 dimensions. These representations are learnt by predicting contexts in large text corpora, such that words occurring in similar contexts are close to one another, which is useful since such words tend to have similar meanings (i.e. distributional semantics).
 
2
SemTags: [1, 9]. POS: UD1.3 (universaldependencies.org).
 
3
This can be done by learning multilingual word embeddings, in which, e.g., the words dialects and Dialekten are close to one another.
 
4
Bi-directional RNNs are frequently used in NLP. One advantage of this is that one can use both the preceding and succeeding contexts of a word when predicting its tag.
 
5
Evaluation of a model trained on one language on a test instance for an unobserved language.
 
Literature
1.
Abzianidze L, Bjerva J, Evang K, Haagsma H, van Noord R, Ludmann P, Nguyen DD, Bos J (2017) The parallel meaning bank: towards a multilingual corpus of translations annotated with compositional meaning representations. In: EACL, pp 242–247
2.
Bjerva J (2016) Byte-based language identification with deep convolutional networks. In: VarDial3, pp 119–125
3.
4.
Bjerva J (2017) Will my auxiliary tagging task help? Estimating auxiliary tasks effectivity in multi-task learning. In: NoDaLiDa, pp 216–220
5.
Bjerva J, Augenstein I (2018) From phonology to syntax: unsupervised linguistic typology at different levels with language embeddings. In: NAACL-HLT
6.
Bjerva J, Augenstein I (2018) Tracking typological features of uralic languages in distributed language representations. In: IWCLUL
7.
Bjerva J, Bos J, Van der Goot R, Nissim M (2014) The meaning factory: Formal semantics for recognizing textual entailment and determining semantic similarity. In: SemEval 2014, pp 642–646
8.
Bjerva J, Östling R (2017) Cross-lingual learning of semantic textual similarity with multilingual word representations. In: NoDaLiDa, pp 211–215
9.
Bjerva J, Plank B, Bos J (2016) Semantic tagging with deep residual networks. In: COLING, pp 3531–3541
10.
Bos J, Basile V, Evang K, Venhuizen NJ, Bjerva J (2017) The Groningen meaning bank. Springer, Dordrecht, pp 463–496
11.
12.
de Lhoneux M, Bjerva J, Augenstein I, Søgaard A (2018) Parameter sharing between dependency parsers for related languages. In: EMNLP
Metadata
Title
Multitask and Multilingual Modelling for Lexical Analysis
Author
Johannes Bjerva
Publication date
10-09-2018
Publisher
Springer Berlin Heidelberg
Published in
KI - Künstliche Intelligenz / Issue 4/2018
Print ISSN: 0933-1875
Electronic ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-018-0557-5

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