Arturo Oncevay

Also published as: Arturo Oncevay-Marcos


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Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks
Kervy Rivas Rojas | Gina Bustamante | Arturo Oncevay | Marco Antonio Sobrevilla Cabezudo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy’s layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.

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No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru
Gina Bustamante | Arturo Oncevay | Roberto Zariquiey
Proceedings of the 12th Language Resources and Evaluation Conference

We introduce new monolingual corpora for four indigenous and endangered languages from Peru: Shipibo-konibo, Ashaninka, Yanesha and Yine. Given the total absence of these languages in the web, the extraction and processing of texts from PDF files is relevant in a truly low-resource language scenario. Our procedure for monolingual corpus creation considers language-specific and language-agnostic steps, and focuses on educational PDF files with multilingual sentences, noisy pages and low-structured content. Through an evaluation based on language modelling and character-level perplexity on a subset of manually extracted sentences, we determine that our method allows the creation of clean corpora for the four languages, a key resource for natural language processing tasks nowadays.

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Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations
Arturo Oncevay | Barry Haddow | Alexandra Birch
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sparse language vectors from linguistic typology databases and learned embeddings from tasks like multilingual machine translation have been investigated in isolation, without analysing how they could benefit from each other’s language characterisation. We propose to fuse both views using singular vector canonical correlation analysis and study what kind of information is induced from each source. By inferring typological features and language phylogenies, we observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy in tasks that require information about language similarities, such as language clustering and ranking candidates for multilingual transfer. With our method, we can easily project and assess new languages without expensive retraining of massive multilingual or ranking models, which are major disadvantages of related approaches.


CSI Peru News: finding the culprit, victim and location in news articles
Gina Bustamante | Arturo Oncevay
Proceedings of the 2019 Workshop on Widening NLP

We introduce a shift on the DS method over the domain of crime-related news from Peru, attempting to find the culprit, victim and location of a crime description from a RE perspective. Obtained results are highly promising and show that proposed modifications are effective in non-traditional domains.

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Assessing Back-Translation as a Corpus Generation Strategy for non-English Tasks: A Study in Reading Comprehension and Word Sense Disambiguation
Fabricio Monsalve | Kervy Rivas Rojas | Marco Antonio Sobrevilla Cabezudo | Arturo Oncevay
Proceedings of the 13th Linguistic Annotation Workshop

Corpora curated by experts have sustained Natural Language Processing mainly in English, but the expensiveness of corpora creation is a barrier for the development in further languages. Thus, we propose a corpus generation strategy that only requires a machine translation system between English and the target language in both directions, where we filter the best translations by computing automatic translation metrics and the task performance score. By studying Reading Comprehension in Spanish and Word Sense Disambiguation in Portuguese, we identified that a more quality-oriented metric has high potential in the corpora selection without degrading the task performance. We conclude that it is possible to systematise the building of quality corpora using machine translation and automatic metrics, besides some prior effort to clean and process the data.

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A Continuous Improvement Framework of Machine Translation for Shipibo-Konibo
Héctor Erasmo Gómez Montoya | Kervy Dante Rivas Rojas | Arturo Oncevay
Proceedings of the 2nd Workshop on Technologies for MT of Low Resource Languages


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ChAnot: An Intelligent Annotation Tool for Indigenous and Highly Agglutinative Languages in Peru
Rodolfo Mercado-Gonzales | José Pereira-Noriega | Marco Sobrevilla | Arturo Oncevay
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Corpus Building and Evaluation of Aspect-based Opinion Summaries from Tweets in Spanish
Daniel Peñaloza | Rodrigo López | Juanjosé Tenorio | Héctor Gómez | Arturo Oncevay-Marcos | Marco A. Sobrevilla Cabezudo
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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WordNet-Shp: Towards the Building of a Lexical Database for a Peruvian Minority Language
Diego Maguiño-Valencia | Arturo Oncevay-Marcos | Marco A. Sobrevilla Cabezudo
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Toward Universal Dependencies for Shipibo-Konibo
Alonso Vasquez | Renzo Ego Aguirre | Candy Angulo | John Miller | Claudia Villanueva | Željko Agić | Roberto Zariquiey | Arturo Oncevay
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD. We describe the linguistic aspects of how the tagset was defined and the treebank was annotated; in addition we present our specific treatment of linguistic units called clitics. Although the treebank is still under development, it allowed us to perform a typological comparison against Spanish, the predominant language in Peru, and dependency syntax parsing experiments in both monolingual and cross-lingual approaches.


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Corpus Creation and Initial SMT Experiments between Spanish and Shipibo-konibo
Ana-Paula Galarreta | Andrés Melgar | Arturo Oncevay
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

In this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp). There are very few digital texts written in Shipibo-konibo and even less bilingual texts that can be aligned, hence we had to create a parallel corpus using both bilingual and monolingual texts. We will describe how this corpus was made, as well as the process we followed to improve the quality of the sentences used to build a statistical MT model or SMT. The results obtained surpassed the baseline proposed (dictionary based) and made a promising result for further development considering the size of corpus used. Finally, it is expected that this MT system can be reinforced with the use of additional linguistic rules and automatic language processing functions that are being implemented.

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Spell-Checking based on Syllabification and Character-level Graphs for a Peruvian Agglutinative Language
Carlo Alva | Arturo Oncevay
Proceedings of the First Workshop on Subword and Character Level Models in NLP

There are several native languages in Peru which are mostly agglutinative. These languages are transmitted from generation to generation mainly in oral form, causing different forms of writing across different communities. For this reason, there are recent efforts to standardize the spelling in the written texts, and it would be beneficial to support these tasks with an automatic tool such as an spell-checker. In this way, this spelling corrector is being developed based on two steps: an automatic rule-based syllabification method and a character-level graph to detect the degree of error in a misspelled word. The experiments were realized on Shipibo-konibo, a highly agglutinative and amazonian language, and the results obtained have been promising in a dataset built for the purpose.

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Exploratory Analysis for Ontology Learning from Social Events on Social Media Streaming in Spanish
Enrique Valeriano | Arturo Oncevay-Marcos
Proceedings of Language, Ontology, Terminology and Knowledge Structures Workshop (LOTKS 2017)