pwc-archive/papers-with-abstracts huggingface dataset:
An Empirical Evaluation of Annotation Practices in Corpora from Language Documentation
Authors: Kilu von Prince, Sebastian Nordhoff
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Translation

For most of the world{'}s languages, no primary data are available, even as many languages are disappearing. Throughout the last two decades, however, language documentation projects have produced substantial amounts of primary data from a wide variety of endangered languages. These resources are still in the early days of their exploration. One of the factors that makes them hard to use is a relative lack of standardized annotation conventions. In this paper, we will describe common practices in existing corpora in order to facilitate their future processing. After a brief introduction of the main formats used for annotation files, we will focus on commonly used tiers in the widespread ELAN and Toolbox formats. Minimally, corpora from language documentation contain a transcription tier and an aligned translation tier, which means they constitute parallel corpora. Additional common annotations include named references, morpheme separation, morpheme-by-morpheme glosses, part-of-speech tags and notes.
Annotated Corpus for Sentiment Analysis in Odia Language
Authors: Gaurav Mohanty, Pruthwik Mishra, Radhika Mamidi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence, Sentiment Analysis

Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available to promote research in the field. Secondly, in order to test the usability of currently available Odia sentiment lexicon, we experimented with various classifiers by training and testing on the sentiment annotated corpus while using identified affective words from the same as features. Annotation and classification are done at sentence level as the usage of sentiment lexicon is best suited to sentiment analysis at this level. The created corpus contains 2045 Odia sentences from news domain annotated with sentiment labels using a well-defined annotation scheme. An inter-annotator agreement score of 0.79 is reported for the corpus.
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque
Authors: Maddalen L{\'o}pez de Lacalle, Xabier Saralegi, I{\~n}aki San Vicente
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Classification, General Classification, intent-classification, Intent Classification, Intent Classification and Slot Filling, Machine Translation, NMT, slot-filling, Slot Filling, Translation

This paper presents an approach for developing a task-oriented dialog system for less-resourced languages in scenarios where training data is not available. Both intent classification and slot filling are tackled. We project the existing annotations in rich-resource languages by means of Neural Machine Translation (NMT) and posterior word alignments. We then compare training on the projected monolingual data with direct model transfer alternatives. Intent Classifiers and slot filling sequence taggers are implemented using a BiLSTM architecture or by fine-tuning BERT transformer models. Models learnt exclusively from Basque projected data provide better accuracies for slot filling. Combining Basque projected train data with rich-resource languages data outperforms consistently models trained solely on projected data for intent classification. At any rate, we achieve competitive performance in both tasks, with accuracies of 81{\%} for intent classification and 77{\%} for slot filling.
SENCORPUS: A French-Wolof Parallel Corpus
Authors: Elhadji Mamadou Nguer, Alla Lo, Cheikh M. Bamba Dione, Sileye O. Ba, Moussa Lo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, Translation

In this paper, we report efforts towards the acquisition and construction of a bilingual parallel corpus between French and Wolof, a Niger-Congo language belonging to the Northern branch of the Atlantic group. The corpus is constructed as part of the SYSNET3LOc project. It currently contains about 70,000 French-Wolof parallel sentences drawn on various sources from different domains. The paper discusses the data collection procedure, conversion, and alignment of the corpus as well as it{'}s application as training data for neural machine translation. In fact, using this corpus, we were able to create word embedding models for Wolof with relatively good results. Currently, the corpus is being used to develop a neural machine translation model to translate French sentences into Wolof.
A Major Wordnet for a Minority Language: Scottish Gaelic
Authors: G{\'a}bor Bella, Fiona McNeill, Rody Gorman, Caoimhin O Donnaile, Kirsty MacDonald, Ch, Yamini rashekar, Abed Alhakim Freihat, Fausto Giunchiglia
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60,000 speakers, most of whom live in Northwestern Scotland. The wordnet contains over 15 thousand word senses and was constructed by merging ten thousand new, high-quality translations, provided and validated by language experts, with an existing wordnet derived from Wiktionary. This new, considerably extended wordnet{---}currently among the 30 largest in the world{---}targets multiple communities: language speakers and learners; linguists; computer scientists solving problems related to natural language processing. By publishing it as a freely downloadable resource, we hope to contribute to the long-term preservation of Scottish Gaelic as a living language, both offline and on the Web.
Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers
Authors: Basil Abraham, Danish Goel, Divya Siddarth, Kalika Bali, Manu Chopra, Monojit Choudhury, Pratik Joshi, Preethi Jyoti, Sunayana Sitaram, Vivek Seshadri
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity

Voice-based technologies are essential to cater to the hundreds of millions of new smartphone users. However, most of the languages spoken by these new users have little to no labelled speech data. Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task. Moreover, existing platforms typically collect speech data only from urban speakers familiar with digital technology whose dialects are often very different from low-income users. In this paper, we explore the possibility of collecting labelled speech data directly from low-income workers. In addition to providing diversity to the speech dataset, we believe this approach can also provide valuable supplemental earning opportunities to these communities. To this end, we conducted a study where we collected labelled speech data in the Marathi language from three different user groups: low-income rural users, low-income urban users, and university students. Overall, we collected 109 hours of data from 36 participants. Our results show that the data collected from low-income participants is of comparable quality to the data collected from university students (who are typically employed to do this work) and that crowdsourcing speech data from low-income rural and urban workers is a viable method of gathering speech data.
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction
Authors: Preni Golazizian, Behnam Sabeti, Seyed Arad Ashrafi Asli, Zahra Majdabadi, Omid Momenzadeh, reza fahmi
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis, Transfer Learning

Irony is a linguistic device used to intend an idea while articulating an opposing expression. Many text analytic algorithms used for emotion extraction or sentiment analysis, produce invalid results due to the use of irony. Persian speakers use this device more often due to the language{'}s nature and some cultural reasons. This phenomenon also appears in social media platforms such as Twitter where users express their opinions using ironic or sarcastic posts. In the current research, which is the first attempt at irony detection in Persian language, emoji prediction is used to build a pretrained model. The model is finetuned utilizing a set of hand labeled tweets with irony tags. A bidirectional LSTM (BiLSTM) network is employed as the basis of our model which is improved by attention mechanism. Additionally, a Persian corpus for irony detection containing 4339 manually-labeled tweets is introduced. Experiments show the proposed approach outperforms the adapted state-of-the-art method tested on Persian dataset with an accuracy of 83.1{\%}, and offers a strong baseline for further research in Persian language.
Towards Computational Resource Grammars for Runyankore and Rukiga
Authors: David Bamutura, Peter Ljungl{\"o}f, Peter Nebende
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive

In this paper, we present computational resource grammars of Runyankore and Rukiga (R{\&}R) languages. Runyankore and Rukiga are two under-resourced Bantu Languages spoken by about 6 million people indigenous to South- Western Uganda, East Africa. We used Grammatical Framework (GF), a multilingual grammar formalism and a special- purpose functional programming language to formalise the descriptive grammar of these languages. To the best of our knowledge, these computational resource grammars are the first attempt to the creation of language resources for R{\&}R. In Future Work, we plan to use these grammars to bootstrap the generation of other linguistic resources such as multilingual corpora that make use of data-driven approaches to natural language processing feasible. In the meantime, they can be used to build Computer-Assisted Language Learning (CALL) applications for these languages among others.
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian
Authors: Seyed Arad Ashrafi Asli, Behnam Sabeti, Zahra Majdabadi, Preni Golazizian, reza fahmi, Omid Momenzadeh
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Active Learning, Persian Sentiment Analysis, Sentiment Analysis, Transfer Learning

Deep learning models are the current State-of-the-art methodologies towards many real-world problems. However, they need a substantial amount of labeled data to be trained appropriately. Acquiring labeled data can be challenging in some particular domains or less-resourced languages. There are some practical solutions regarding these issues, such as Active Learning and Transfer Learning. Active learning{'}s idea is simple: let the model choose the samples for annotation instead of labeling the whole dataset. This method leads to a more efficient annotation process. Active Learning models can achieve the baseline performance (the accuracy of the model trained on the whole dataset), with a considerably lower amount of labeled data. Several active learning approaches are tested in this work, and their compatibility with Persian is examined using a brand-new sentiment analysis dataset that is also introduced in this work. MirasOpinion, which to our knowledge is the largest Persian sentiment analysis dataset, is crawled from a Persian e-commerce website and annotated using a crowd-sourcing policy. LDA sampling, which is an efficient Active Learning strategy using Topic Modeling, is proposed in this research. Active Learning Strategies have shown promising results in the Persian language, and LDA sampling showed a competitive performance compared to other approaches.
Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions
Authors: Erich Round, Mark Ellison, Jayden Macklin-Cordes, Sacha Beniamine
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive

Linguists seek insight from all human languages, however accessing information from most of the full store of extant global linguistic descriptions is not easy. One of the most common kinds of information that linguists have documented is vernacular sentences, as recorded in descriptive grammars. Typically these sentences are formatted as interlinear glossed text (IGT). Most descriptive grammars, however, exist only as hardcopy or scanned pdf documents. Consequently, parsing IGTs in scanned grammars is a priority, in order to significantly increase the volume of documented linguistic information that is readily accessible. Here we demonstrate fundamental viability for a technology that can assist in making a large number of linguistic data sources machine readable: the automated identification and parsing of interlinear glossed text from scanned page images. For example, we attain high median precision and recall ({\textgreater}0.95) in the identification of examples sentences in IGT format. Our results will be of interest to those who are keen to see more of the existing documentation of human language, especially for less-resourced and endangered languages, become more readily accessible.
The Johns Hopkins University Bible Corpus: 1600+ Tongues for Typological Exploration
Authors: Arya D. McCarthy, Rachel Wicks, Dylan Lewis, Aaron Mueller, Winston Wu, Oliver Adams, Garrett Nicolai, Matt Post, David Yarowsky
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

We present findings from the creation of a massively parallel corpus in over 1600 languages, the Johns Hopkins University Bible Corpus (JHUBC). The corpus consists of over 4000 unique translations of the Christian Bible and counting. Our data is derived from scraping several online resources and merging them with existing corpora, combining them under a common scheme that is verse-parallel across all translations. We detail our effort to scrape, clean, align, and utilize this ripe multilingual dataset. The corpus captures the great typological variety of the world{'}s languages. We catalog this by showing highly similar proportions of representation of Ethnologue{'}s typological features in our corpus. We also give an example application: projecting pronoun features like clusivity across alignments to richly annotate languages which do not mark the distinction.
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu
Authors: Alex Zahrer, er, Andrej Zgank, Barbara Schuppler
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, speech-recognition, Speech Recognition

Since at least half of the world{'}s 6000 plus languages will vanish during the 21st century, language documentation has become a rapidly growing field in linguistics. A fundamental challenge for language documentation is the {''}transcription bottleneck{''}. Speech technology may deliver the decisive breakthrough for overcoming the transcription bottleneck. This paper presents first experiments from the development of ASR4LD, a new automatic speech recognition (ASR) based tool for language documentation (LD). The experiments are based on recordings from an ongoing documentation project for the endangered Muyu language in New Guinea. We compare phoneme recognition experiments with American English, Austrian German and Slovenian as source language and Muyu as target language. The Slovenian acoustic models achieve the by far best performance (43.71{\%} PER) in comparison to 57.14{\%} PER with American English, and 89.49{\%} PER with Austrian German. Whereas part of the errors can be explained by phonetic variation, the recording mismatch poses a major problem. On the long term, ASR4LD will not only be an integral part of the ongoing documentation project of Muyu, but will be further developed in order to facilitate also the language documentation process of other language groups.
Towards Flexible Cross-Resource Exploitation of Heterogeneous Language Documentation Data
Authors: Daniel Jettka, Timm Lehmberg
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper reports on challenges and solution approaches in the development of methods for language resource overarching data analysis in the field of language documentation. It is based on the successful outcomes of the initial phase of an 18 year long-term project on lesser resourced and mostly endangered indigenous languages of the Northern Eurasian area, which included the finalization and publication of multiple language corpora and additional language resources. While aiming at comprehensive cross-resource data analysis, the project at the same time is confronted with a dynamic and complex resource landscape, especially resulting from a vast amount of multi-layered information stored in the form of analogue primary data in different widespread archives on the territory of the Russian Federation. The methods described aim at solving the tension between unification of data sets and vocabularies on the one hand and maximum openness for the integration of future resources and adaption of external information on the other hand.
CantoMap: a Hong Kong Cantonese MapTask Corpus
Authors: Gr{\'e}goire Winterstein, Carmen Tang, Regine Lai
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This work reports on the construction of a corpus of connected spoken Hong Kong Cantonese. The corpus aims at providing an additional resource for the study of modern (Hong Kong) Cantonese and also involves several controlled elicitation tasks which will serve different projects related to the phonology and semantics of Cantonese. The word-segmented corpus offers recordings, phonemic transcription, and Chinese characters transcription. The corpus contains a total of 768 minutes of recordings and transcripts of forty speakers. All the audio material has been aligned at utterance level with the transcriptions, using the ELAN transcription and annotation tool. The controlled elicitation task was based on the design of HCRC MapTask corpus (Anderson et al., 1991), in which participants had to communicate using solely verbal means as eye contact was restricted. In this paper, we outline the design of the maps and their landmarks and the basic segmentation principles of the data and various transcription conventions we adopted. We also compare the contents of Cantomap to those of comparable Cantonese corpora.
No Data to Crawl? Monolingual Corpus Creation from PDF Files of Truly low-Resource Languages in Peru
Authors: Gina Bustamante, Arturo Oncevay, Roberto Zariquiey
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modelling

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.
Creating a Parallel Icelandic Dependency Treebank from Raw Text to Universal Dependencies
Authors: Hildur J{\'o}nsd{\'o}ttir, Anton Karl Ingason
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Making the low-resource language, Icelandic, accessible and usable in Language Technology is a work in progress and is supported by the Icelandic government. Creating resources and suitable training data (e.g., a dependency treebank) is a fundamental part of that work. We describe work on a parallel Icelandic dependency treebank based on Universal Dependencies (UD). This is important because it is the first parallel treebank resource for the language and since several other languages already have a resource based on the same text. Two Icelandic treebanks based on phrase-structure grammar have been built and ongoing work aims to convert them to UD. Previously, limited work has been done on dependency grammar for Icelandic. The current project aims to ameliorate this situation by creating a small dependency treebank from scratch. Creating a treebank is a laborious task so the process was implemented in an accessible manner using freely available tools and resources. The parallel data in the UD project was chosen as a source because this would furthermore give us the first parallel treebank for Icelandic. The Icelandic parallel UD corpus will be published as part of UD version 2.6.
Building a Universal Dependencies Treebank for Occitan
Authors: Aleks Miletic, ra, Myriam Bras, Marianne Vergez-Couret, Louise Esher, Clamen{\c{c}}a Poujade, Jean Sibille
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: POS

This paper outlines the ongoing effort of creating the first treebank for Occitan, a low-ressourced regional language spoken mainly in the south of France. We briefly present the global context of the project and report on its current status. We adopt the Universal Dependencies framework for this project. Our methodology is based on two main principles. Firstly, in order to guarantee the annotation quality, we use the agile annotation approach. Secondly, we rely on pre-processing using existing tools (taggers and parsers) to facilitate the work of human annotators, mainly through a delexicalized cross-lingual parsing approach. We present the results available at this point (annotation guidelines and a sub-corpus annotated with PoS tags and lemmas) and give the timeline for the rest of the work.
Building the Old Javanese Wordnet
Authors: David Moeljadi, Zakariya Pamuji Aminullah
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper discusses the construction and the ongoing development of the Old Javanese Wordnet. The words were extracted from the digitized version of the Old Javanese{--}English Dictionary (Zoetmulder, 1982). The wordnet is built using the {`}expansion{'} approach (Vossen, 1998), leveraging on the Princeton Wordnet{'}s core synsets and semantic hierarchy, as well as scientific names. The main goal of our project was to produce a high quality, human-curated resource. As of December 2019, the Old Javanese Wordnet contains 2,054 concepts or synsets and 5,911 senses. It is released under a Creative Commons Attribution 4.0 International License (CC BY 4.0). We are still developing it and adding more synsets and senses. We believe that the lexical data made available by this wordnet will be useful for a variety of future uses such as the development of Modern Javanese Wordnet and many language processing tasks and linguistic research on Javanese.
CPLM, a Parallel Corpus for Mexican Languages: Development and Interface
Authors: Gerardo Sierra Mart{\'\i}nez, Cynthia Monta{\~n}o, Gemma Bel-Enguix, Diego C{\'o}rdova, Margarita Mota Montoya
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diversity

Mexico is a Spanish speaking country that has a great language diversity, with 68 linguistic groups and 364 varieties. As they face a lack of representation in education, government, public services and media, they present high levels of endangerment. Due to the lack of data available on social media and the internet, few technologies have been developed for these languages. To analyze different linguistic phenomena in the country, the Language Engineering Group developed the Corpus Paralelo de Lenguas Mexicanas (CPLM) [The Mexican Languages Parallel Corpus], a collaborative parallel corpus for the low-resourced languages of Mexico. The CPLM aligns Spanish with six indigenous languages: Maya, Ch{'}ol, Mazatec, Mixtec, Otomi, and Nahuatl. First, this paper describes the process of building the CPLM: text searching, digitalization and alignment process. Furthermore, we present some difficulties regarding dialectal and orthographic variations. Second, we present the interface and types of searching as well as the use of filters.