Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell

Seddah, Djamé; Essaidi, Farah; Fethi, Amal; Futeral, Matthieu; Müller, Benjamin; Suárez, Pedro Javier Ortiz; Sagot, Benoît; Srivastava, Abhishek

doi:10.18653/v1/2020.acl-main.107

Cited by 26 publications

(36 citation statements)

References 17 publications

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“…The development of such models is a matter of high importance for the inclusion of communities, the preservation of endangered languages and more generally to support the rise of tailored NLP ecosystems for such languages (Schmidt and Wiegand, 2017;Stecklow, 2018;Seddah et al, 2020). In that regard, the advent of the Universal Dependencies project (Nivre et al, 2016) and the WikiAnn dataset (Pan et al, 2017) have greatly increased the number of covered languages by providing annotated datasets for more than 90 languages for dependency parsing and 282 languages for NER.…”

Section: Background and Motivationmentioning

confidence: 99%

“…OSCAR is a corpus extracted from a Common Crawl Web snapshot. 2 It provides a significant amount of data for all the unseen languages we work with, except for Buryat, Meadow Mari, Erzya and Livvi for which we use Wikipedia dumps and for Narabizi, Naija and Faroese, for which we use data collected by Seddah et al (2020), Caron et al (2019) and Biemann et al (2007) respectively.…”

Section: Raw Datamentioning

confidence: 99%

“…We consider Narabizi (Seddah et al, 2020), an Arabic dialect spoken in North-Africa written in the Latin script and code-mixed with French, to fall in the same Intermediate category, because it follows the same pattern. For both POS tagging and parsing, the multilingual models outperform the monolingual NarabiziBERT.…”

Section: Intermediatementioning

confidence: 99%

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When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

Müller¹,

Anastasopoulos²,

Sagot³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-theart performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. We show that transliterating those languages significantly improves the potential of large-scale multilingual language models on downstream tasks. This result provides a promising direction towards making these massively multilingual models useful for a new set of unseen languages. 1

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Section: Background and Motivationmentioning

confidence: 99%

Section: Raw Datamentioning

confidence: 99%

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When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

Müller¹,

Anastasopoulos²,

Sagot³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…We obtain the data for the part-of-speech tagging task from the Universal Dependencies project (Zeman et al, 2020), which currently gathers data annotated with universal POS tags for more than 90 languages, although there are differences in size and domain. For Algerian we use the annotations from Seddah et al (2020). We found no training sets available for Thai and Cantonese, hence we use them for testing only.…”

Section: Part-of-speechmentioning

confidence: 99%

Evaluating morphological typology in zero-shot cross-lingual transfer

Martinez-Garcia¹,

Badía²,

Barnes³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Cross-lingual transfer has improved greatly through multi-lingual language model pretraining, reducing the need for parallel data and increasing absolute performance. However, this progress has also brought to light the differences in performance across languages. Specifically, certain language families and typologies seem to consistently perform worse in these models. In this paper, we address what effects morphological typology has on zero-shot cross-lingual transfer for two tasks: Part-of-speech tagging and sentiment analysis. We perform experiments on 19 languages from four language typologies (fusional, isolating, agglutinative, and introflexive) and find that transfer to another morphological type generally implies a higher loss than transfer to another language with the same morphological typology. Furthermore, POS tagging is more sensitive to morphological typology than sentiment analysis and, on this task, models perform much better on fusional languages than on the other typologies.

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“…There is also the NArabizi Treebank(Seddah et al, 2020) which includes partial morphological annotation where the total number of unique annotations is 46 in contrast to the SAGT Treebank which has 795 unique morphological annotations. Hence, we did not use this treebank in our study.…”

mentioning

confidence: 99%

Benchmark Dataset for Propaganda Detection in Czech Newspaper Texts

Horák

Baisa

Herman

2019

Proceedings - Natural Language Processing in a Deep Learning World

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Propaganda of various pressure groups ranging from big economies to ideological blocks is often presented in a form of objective newspaper texts. However, the real objectivity is here shaded with the support of imbalanced views and distorted attitudes by means of various manipulative stylistic techniques.In the project of Manipulative Propaganda Techniques in the Age of Internet, a new resource for automatic analysis of stylistic mechanisms for influencing the readers' opinion is developed. In its current version, the resource consists of 7,494 newspaper articles from four selected Czech digital news servers annotated for the presence of specific manipulative techniques.In this paper, we present the current state of the annotations and describe the structure of the dataset in detail. We also offer an evaluation of bag-of-words classification algorithms for the annotated manipulative techniques.

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Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell

Cited by 26 publications

References 17 publications

When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

Evaluating morphological typology in zero-shot cross-lingual transfer

Benchmark Dataset for Propaganda Detection in Czech Newspaper Texts

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