pwc-archive/papers-with-abstracts huggingface dataset:
ReadAlong Studio: Practical Zero-Shot Text-Speech Alignment for Indigenous Language Audiobooks
Authors: Patrick Littell, Eric Joanis, Aidan Pine, Marc Tessier, David Huggins Daines, Delasie Torkornoo
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

While the alignment of audio recordings and text (often termed “forced alignment”) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise “solvable” problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.
Corpus Creation for Sentiment Analysis in Code-Mixed Tulu Text
Authors: Asha Hegde, Mudoor Devadas Anusha, Sharal Coelho, Hosahalli Lakshmaiah Shashirekha, Bharathi Raja Chakravarthi
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Decision Making, Sentiment Analysis

Sentiment Analysis (SA) employing code-mixed data from social media helps in getting insights to the data and decision making for various applications. One such application is to analyze users’ emotions from comments of videos on YouTube. Social media comments do not adhere to the grammatical norms of any language and they often comprise a mix of languages and scripts. The lack of annotated code-mixed data for SA in a low-resource language like Tulu makes the SA a challenging task. To address the lack of annotated code-mixed Tulu data for SA, a gold standard trlingual code-mixed Tulu annotated corpus of 7,171 YouTube comments is created. Further, Machine Learning (ML) algorithms are employed as baseline models to evaluate the developed dataset and the performance of the ML algorithms are found to be encouraging.
Crowd-sourcing for Less-resourced Languages: Lingua Libre for Polish
Authors: Mathilde Hutin, Marc Allassonnière-Tang
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

Oral corpora for linguistic inquiry are frequently built based on the content of news, radio, and/or TV shows, sometimes also of laboratory recordings. Most of these existing corpora are restricted to languages with a large amount of data available. Furthermore, such corpora are not always accessible under a free open-access license. We propose a crowd-sourced alternative to this gap. Lingua Libre is the participatory linguistic media library hosted by Wikimedia France. It includes recordings from more than 140 languages. These recordings have been provided by more than 750 speakers worldwide, who voluntarily recorded word entries of their native language and made them available under a Creative Commons license. In the present study, we take Polish, a less-resourced language in terms of phonetic data, as an example, and compare our phonetic observations built on the data from Lingua Libre with the phonetic observations found by previous linguistic studies. We observe that the data from Lingua Libre partially matches the phonetic inventory of Polish as described in previous studies, but that the acoustic values are less precise, thus showing both the potential and the limitations of Lingua Libre to be used for phonetic research.
Tupían Language Ressources: Data, Tools, Analyses
Authors: Lorena Martín Rodríguez, Tatiana Merzhevich, Wellington Silva, Tiago Tresoldi, Carolina Aragon, Fabrício F. Gerardi
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

TuLaR (Tupian Language Resources) is a project for collecting, documenting, analyzing, and developing computational and pedagogical material for low-resource Brazilian indigenous languages. It provides valuable data for language research regarding typological, syntactic, morphological, and phonological aspects. Here we present TuLaR’s databases, with special consideration to TuDeT (Tupian Dependency Treebanks), an annotated corpus under development for nine languages of the Tupian family, built upon the Universal Dependencies framework. The annotation within such a framework serves a twofold goal: enriching the linguistic documentation of the Tupian languages due to the rapid and consistent annotation, and providing computational resources for those languages, thanks to the suitability of our framework for developing NLP tools. We likewise present a related lexical database, some tools developed by the project, and examine future goals for our initiative.
Quality versus Quantity: Building Catalan-English MT Resources
Authors: Ona de Gibert Bonet, Ksenia Kharitonova, Blanca Calvo Figueras, Jordi Armengol-Estapé, Maite Melero
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Cross-Lingual Transfer, Transfer Learning, Translation

In this work, we make the case of quality over quantity when training a MT system for a medium-to-low-resource language pair, namely Catalan-English. We compile our training corpus out of existing resources of varying quality and a new high-quality corpus. We also provide new evaluation translation datasets in three different domains. In the process of building Catalan-English parallel resources, we evaluate the impact of drastically filtering alignments in the resulting MT engines. Our results show that even when resources are limited, as in this case, it is worth filtering for quality. We further explore the cross-lingual transfer learning capabilities of the proposed model for parallel corpus filtering by applying it to other languages. All resources generated in this work are released under open license to encourage the development of language technology in Catalan.
A Sentiment Corpus for South African Under-Resourced Languages in a Multilingual Context
Authors: Ronny Mabokela, Tim Schlippe
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Sentiment Analysis

Multilingual sentiment analysis is a process of detecting and classifying sentiment based on textual information written in multiple languages. There has been tremendous research advancement on high-resourced languages such as English. However, progress on under-resourced languages remains underrepresented with limited opportunities for further development of natural language processing (NLP) technologies. Sentiment analysis (SA) for under-resourced language still is a skewed research area. Although, there are some considerable efforts in emerging African countries to develop such resources for under-resourced languages, languages such as indigenous South African languages still suffer from a lack of datasets. To the best of our knowledge, there is currently no dataset dedicated to SA research for South African languages in a multilingual context, i.e. comments are in different languages and may contain code-switching. In this paper, we present the first subset of the multilingual sentiment corpus SAfriSenti for the three most widely spoken languages in South Africa—English, Sepedi (i.e. Northern Sotho), and Setswana. This subset consists of over 40,000 annotated tweets in all the three languages including even 36.6% of code-switched texts. We present data collection, cleaning and annotation strategies that were followed to curate the dataset for these languages. Furthermore, we describe how we developed language-specific sentiment lexicons, morpheme-based sentiment taggers, conduct linguistic analyses and present possible solutions for the challenges of this sentiment dataset. We will release the dataset and sentiment lexicons to the research communities to advance the NLP research of under-resourced languages.
CUNI Submission to MT4All Shared Task
Authors: Ivana Kvapilíková, Ondrej Bojar
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Denoising, Machine Translation, Translation, Unsupervised Machine Translation

This paper describes our submission to the MT4All Shared Task in unsupervised machine translation from English to Ukrainian, Kazakh and Georgian in the legal domain. In addition to the standard pipeline for unsupervised training (pretraining followed by denoising and back-translation), we used supervised training on a pseudo-parallel corpus retrieved from the provided mono-lingual corpora. Our system scored significantly higher than the baseline hybrid unsupervised MT system.
Resource: Indicators on the Presence of Languages in Internet
Authors: Daniel Pimienta
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

Reliable and maintained indicators of the space of languages on the Internet are required to support appropriate public policies and well-informed linguistic studies. Current sources are scarce and often strongly biased. The model to produce indicators on the presence of languages in the Internet, launched by the Observatory in 2017, has reached a sensible level of maturity and its data products are shared in CC-BY-SA 4.0 license. It reaches now 329 languages (L1 speakers > one million) and all the biases associated with the model have been controlled to an acceptable threshold, giving trust to the data, within an estimated confidence interval of +-20%. Some of the indicators (mainly the percentage of L1+L2 speakers connected to the Internet per language and derivates) rely on Ethnologue Global Dataset #24 for demo-linguistic data and ITU, completed by World Bank, for the percentage of persons connected to the Internet by country. The rest of indicators relies on the previous sources plus a large combination of hundreds of different sources for data related to Web contents per language. This research poster focuses the description of the new linguistic resources created. Methodological considerations are only exposed briefly and will be developed in another paper.
Language Technologies for Low Resource Languages: Sociolinguistic and Multilingual Insights
Authors: A. Seza Doğruöz, Sunayana Sitaram
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

There is a growing interest in building language technologies (LTs) for low resource languages (LRLs). However, there are flaws in the planning, data collection and development phases mostly due to the assumption that LRLs are similar to High Resource Languages (HRLs) but only smaller in size. In our paper, we first provide examples of failed LTs for LRLs and provide the reasons for these failures. Second, we discuss the problematic issues with the data for LRLs. Finally, we provide recommendations for building better LTs for LRLs through insights from sociolinguistics and multilingualism. Our goal is not to solve all problems around LTs for LRLs but to raise awareness about the existing issues, provide recommendations toward possible solutions and encourage collaboration across academic disciplines for developing LTs that actually serve the needs and preferences of the LRL communities.
Sentiment Analysis for Hausa: Classifying Students’ Comments
Authors: Ochilbek Rakhmanov, Tim Schlippe
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Sentiment Analysis

We describe our work on sentiment analysis for Hausa, where we investigated monolingual and cross-lingual approaches to classify student comments in course evaluations. Furthermore, we propose a novel stemming algorithm to improve accuracy. For studies in this area, we collected a corpus of more than 40,000 comments—the Hausa-English Sentiment Analysis Corpus For Educational Environments (HESAC). Our results demonstrate that the monolingual approaches for Hausa sentiment analysis slightly outperform the cross-lingual systems. Using our stemming algorithm in the pre-processing even improved the best model resulting in 97.4% accuracy on HESAC.
Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification
Authors: Utsav Maskey, Manish Bhatta, Shiva Bhatt, Sanket Dhungel, Bal Krishna Bal
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Language Modeling, Language Modelling, text-classification, Text Classification

Language model pre-training has significantly impacted NLP and resulted in performance gains on many NLP-related tasks, but comparative study of different approaches on many low-resource languages seems to be missing. This paper attempts to investigate appropriate methods for pretraining a Transformer-based model for the Nepali language. We focus on the language-specific aspects that need to be considered for modeling. Although some language models have been trained for Nepali, the study is far from sufficient. We train three distinct Transformer-based masked language models for Nepali text sequences: distilbert-base (Sanh et al., 2019) for its efficiency and minuteness, deberta-base (P. He et al., 2020) for its capability of modeling the dependency of nearby token pairs and XLM-ROBERTa (Conneau et al., 2020) for its capabilities to handle multilingual downstream tasks. We evaluate and compare these models with other Transformer-based models on a downstream classification task with an aim to suggest an effective strategy for training low-resource language models and their fine-tuning.
CoSwID, a Code Switching Identification Method Suitable for Under-Resourced Languages
Authors: Laurent Kevers
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Language Identification

We propose a method for identifying monolingual textual segments in multilingual documents. It requires only a minimal number of linguistic resources – word lists and monolingual corpora – and can therefore be adapted to many under-resourced languages. Taking these languages into account when processing multilingual documents in NLP tools is important as it can contribute to the creation of essential textual resources. This language identification task – code switching detection being its most complex form – can also provide added value to various existing data or tools. Our research demonstrates that a language identification module performing well on short texts can be used to efficiently analyse a document through a sliding window. The results obtained for code switching identification – between 87.29% and 97.97% accuracy – are state-of-the-art, which is confirmed by the benchmarks performed on the few available systems that have been used on our test data.
A Neural Network Approach to Create Minangkabau-Indonesia Bilingual Dictionary
Authors: Kartika Resiandi, Yohei Murakami, Arbi Haza Nasution
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Decoder

Indonesia has many varieties of ethnic languages, and most come from the same language family, namely Austronesian languages. Coming from that same language family, the words in Indonesian ethnic languages are very similar. However, there is research stating that Indonesian ethnic languages are endangered. Thus, to prevent that, we proposed to create a bilingual dictionary between ethnic languages using a neural network approach to extract transformation rules using character level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder functions read the input sequence, character by character, generate context, then extract a summary of the input. The decoder will produce an output sequence where every character in each time-step and the next character that comes out are affected by the previous character. The current case for experiment translation focuses on Minangkabau and Indonesian languages with 13761-word pairs. For evaluating the model’s performance, 5-Fold Cross-Validation is used.
Machine Translation from Standard German to Alemannic Dialects
Authors: Louisa Lambrecht, Felix Schneider, Alexander Waibel
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Machine Translation, Translation

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect.
Question Answering Classification for Amharic Social Media Community Based Questions
Authors: Tadesse Destaw, Seid Muhie Yimam, Abinew Ayele, Chris Biemann
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: 8k, Question Answering, Transliteration

In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like politics, economics, health, education, and so on. Since the questions are posed in a mixed-code, we apply different strategies to pre-process the dataset. Questions are posted in Amharic, English, or Amharic but in a Latin script. As part of the pre-processing tools, we build a Latin to Ethiopic Script transliteration tool. We collect 8k Amharic and 24K transliterated questions and develop deep learning-based questions answering classifiers that attain as high as an F-score of 57.29 in 20 different question classes or categories. The datasets and pre-processing scripts are open-sourced to facilitate further research on the Amharic community-based question answering.
Automatic Detection of Morphological Processes in the Yorùbá Language
Authors: Tunde Adegbola
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6

Automatic morphology induction is important for computational processing of natural language. In resource-scarce languages in particular, it offers the possibility of supplementing data-driven strategies of Natural Language Processing with morphological rules that may cater for out-of-vocabulary words. Unfortunately, popular approaches to unsupervised morphology induction do not work for some of the most productive morphological processes of the Yorùbá language. To the best of our knowledge, the automatic induction of such morphological processes as full and partial reduplication, infixation, interfixation, compounding and other morphological processes, particularly those based on the affixation of stem-derived morphemes have not been adequately addressed in the literature. This study proposes a method for the automatic detection of stem-derived morphemes in Yorùbá. Words in a Yorùbá lexicon of 14,670 word-tokens were clustered around “word-labels”. A word-label is a textual proxy of the patterns imposed on words by the morphological processes through which they were formed. Results confirm a conjectured significant difference between the predicted and observed probabilities of word-labels motivated by stem-derived morphemes. This difference was used as basis for automatic identification of words formed by the affixation of stem-derived morphemes. Keywords: Unsupervised Morphology Induction, Recurrent Partials, Recurrent Patterns, Stem-derived Morphemes, Word-labels.
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey
Authors: Diego Bear, Paul Cook
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Language Modelling, Morphological Analysis

Finite-state approaches to morphological analysis have been shown to improve the performance of natural language processing systems for polysynthetic languages, in-which words are generally composed of many morphemes, for tasks such as language modelling (Schwartz et al., 2020). However, finite-state morphological analyzers are expensive to construct and require expert knowledge of a language’s structure. Currently, there is no broad-coverage finite-state model of morphology for Wolastoqey, also known as Passamaquoddy-Maliseet, an endangered low-resource Algonquian language. As this is the case, in this paper, we investigate using two unsupervised models, MorphAGram and Morfessor, to obtain morphological segmentations for Wolastoqey. We train MorphAGram and Morfessor models on a small corpus of Wolastoqey words and evaluate using two an notated datasets. Our results indicate that MorphAGram outperforms Morfessor for morphological segmentation of Wolastoqey.
Baseline English and Maltese-English Classification Models for Subjectivity Detection, Sentiment Analysis, Emotion Analysis, Sarcasm Detection, and Irony Detection
Authors: Keith Cortis, Brian Davis
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Classification, Emotion Recognition, regression, Sarcasm Detection, Sentiment Analysis

This paper presents baseline classification models for subjectivity detection, sentiment analysis, emotion analysis, sarcasm detection, and irony detection. All models are trained on user-generated content gathered from newswires and social networking services, in three different languages: English —a high-resourced language, Maltese —a low-resourced language, and Maltese-English —a code-switched language. Traditional supervised algorithms namely, Support Vector Machines, Naïve Bayes, Logistic Regression, Decision Trees, and Random Forest, are used to build a baseline for each classification task, namely subjectivity, sentiment polarity, emotion, sarcasm, and irony. Baseline models are established at a monolingual (English) level and at a code-switched level (Maltese-English). Results obtained from all the classification models are presented.
Building Open-source Speech Technology for Low-resource Minority Languages with SáMi as an Example – Tools, Methods and Experiments
Authors: Katri Hiovain-Asikainen, Sjur Moshagen
Date: 2022-06-01 00:00:00
Proceeding: SIGUL (LREC) 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech

This paper presents a work-in-progress report of an open-source speech technology project for indigenous Sami languages. A less detailed description of this work has been presented in a more general paper about the whole GiellaLT language infrastructure, submitted to the LREC 2022 main conference. At this stage, we have designed and collected a text corpus specifically for developing speech technology applications, namely Text-to-speech (TTS) and Automatic speech recognition (ASR) for the Lule and North Sami languages. We have also piloted and experimented with different speech synthesis technologies using a miniature speech corpus as well as developed tools for effective processing of large spoken corpora. Additionally, we discuss effective and mindful use of the speech corpus and also possibilities to use found/archive materials for training an ASR model for these languages.