Toward Universal Dependencies for Shipibo-Konibo

Vásquez, Alonso; Aguirre, Renzo Ego; Candy, Angulo,; Miller, J. J.; Villanueva, Claudia; Agić, Željko; Zariquiey, Roberto; Oncevay, Arturo

doi:10.18653/v1/w18-6018

Cited by 7 publications

(9 citation statements)

References 8 publications

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“…However, clitics are also used, and are a widespread element in Panoan literature (Valenzuela, 2003). Shipibo-Konibo uses an SOV word order (Faust, 1973) and postpositions (Vasquez et al, 2018). The translations in AmericasNLI make use of the official alphabet and standard writing supported by the Ministry of Education in Peru.…”

Section: Languagesmentioning

confidence: 99%

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

Ebrahimi¹,

Mager²,

Oncevay³

et al. 2021

Preprint

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Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.62%. Continued pretraining offers improvements, with an average accuracy of 44.05%. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 48.72%.

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Section: Languagesmentioning

confidence: 99%

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

Ebrahimi¹,

Mager²,

Oncevay³

et al. 2021

Preprint

Self Cite

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“…Shipibo-Konibo is an indigenous language spoken by around 35,000 native speakers in the Amazon region of Peru (Vasquez et al, 2018), and is "definitely endangered" according to the UNESCO's Atlas of the World's Languages in Danger (Moseley, 2010). There is no large monolingual corpus for the language, 11 but for cross-lingual resources there are two parallel corpora aligned with Spanish, which are extracted from the Bible and educational books (Galarreta et al, 2017).…”

Section: Shipibo-konibomentioning

confidence: 99%

“…There is no large monolingual corpus for the language, 11 but for cross-lingual resources there are two parallel corpora aligned with Spanish, which are extracted from the Bible and educational books (Galarreta et al, 2017). Similar to Na, Shipibo-Konibo is an SOV language with very rich morphology (Valenzuela, 1997;Vasquez et al, 2018), whereas Spanish is an SVO language.…”

Section: Shipibo-konibomentioning

confidence: 99%

Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

Wada¹,

Iwata²,

Matsumoto³

et al. 2021

Proceedings of the 1st Workshop on Multilingual Representation Learning

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We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning crosslingual representations even in extremely lowresource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task. 1

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“…As the number of South American languages represented in the Universal Dependencies project has grown rapidly in the last years (see i.e. Vasquez et al, 2018;Thomas, 2019), the descriptions of individual treebanks are thereby also a very valuable 1 https://github.com/UniversalDependencies/UD_Apurina-UFPA resource that helps to maintain consistency in the treebanks of this complex linguistic regions.…”

Section: Introductionmentioning

confidence: 99%

Apurinã Universal Dependencies Treebank

Rueter¹,

Freitas²,

Facundes³

et al. 2021

Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

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This paper presents and discusses the first Universal Dependencies treebank for the Apurinã language. The treebank contains 76 fully annotated sentences, applies 14 parts-of-speech, as well as seven augmented or new features -some of which are unique to Apurinã. The construction of the treebank has also served as an opportunity to develop finite-state description of the language and facilitate the transfer of open-source infrastructure possibilities to an endangered language of the Amazon. The source materials used in the initial treebank represent fieldwork practices where not all tokens of all sentences are equally annotated. For this reason, establishing regular annotation practices for the entire Apurinã treebank is an ongoing project.

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Toward Universal Dependencies for Shipibo-Konibo

Cited by 7 publications

References 8 publications

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora

Apurinã Universal Dependencies Treebank

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