MOROCO: The Moldavian and Romanian Dialectal Corpus

39891

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Abstract

AbstractIn this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.

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Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 688–698 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 688 MOROCO: The Moldavian and Romanian Dialectal Corpus Andrei M. Butnaru and Radu Tudor Ionescu Department of Computer Science, University of Bucharest 14 Academiei, Bucharest, Romania butnaruandreimadalin@gmail.com raducu.ionescu@gmail.com Abstract In this work, we introduce the Moldavian and Romanian Dialectal Corpus (MOROCO), which is freely available for download at https://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discrim- inative features of our best performing model, before and after named entity removal. 1 Introduction The high number of evaluation campaigns on spo- ken or written dialect identification conducted in recent years (Ali et al., 2017; Malmasi et al., 2016; Rangel et al., 2017; Zampieri et al., 2017, 2018) prove that dialect identification is an inter- esting and challenging natural language process- ing (NLP) task, actively studied by researchers in nowadays. Due to the recent interest in dialect identification, we introduce the Moldavian and Romanian Dialectal Corpus (MOROCO), which is composed of 33564 samples of text collected from the news domain. Figure 1: Map of Romania and the Republic of Moldova. Romanian is part of the Balkan-Romance group that evolved from several dialects of Vulgar Latin, which separated from the Western Ro- mance branch of languages from the fifth cen- tury (Coteanu et al., 1969). In order to dis- tinguish Romanian within the Balkan-Romance group in comparative linguistics, it is referred to as Daco-Romanian. Along with Daco-Romanian, which is currently spoken in Romania, there are three other dialects in the Balkan-Romance branch, namely Aromanian, Istro-Romanian, and Megleno-Romanian. Moldavian is a subdialect of Daco-Romanian, that is spoken in the Republic of Moldova and in northeastern Romania. The delim- itation of the Moldavian dialect, as with all other Romanian dialects, is made primarily by analyz- ing its phonetic features and only marginally by morphological, syntactical, and lexical character- istics. Although the spoken dialects in Romania and the Republic of Moldova are different, the two countries share the same literary standard (Mina- han, 2013). Some linguists (Pavel, 2008) consider that the border between Romania and the Repub- lic of Moldova (see Figure 1) does not correspond to any significant isoglosses to justify a …

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AbstractIn this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.

Publication Details

Paper ID:
39891
Year:
2019
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan