pwc-archive/papers-with-abstracts huggingface dataset:
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut
Authors: Vishwa Gupta, Gilles Boulianne
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This contribution presents our efforts to develop the automatic speech recognition (ASR) systems for three low resource languages: Kurmanji Kurdish, Cree and Inuktut. As a first step, we generate multilingual models from acoustic training data from 12 different languages in the hybrid DNN/HMM framework. We explore different strategies for combining the phones from different languages: either keep the phone labels separate for each language or merge the common phones. For Kurmanji Kurdish and Inuktut, keeping the phones separate gives much lower word error rate (WER), while merging phones gives lower WER for Cree. These WER are lower than training the acoustic models separately for each language. We also compare two different DNN architectures: factored time delay neural network (TDNN-F), and bidirectional long short-term memory (BLSTM) acoustic models. The TDNN-F acoustic models give significantly lower WER for Kurmanji Kurdish and Cree, while BLSTM acoustic models give significantly lower WER for Inuktut. We also show that for each language, training multilingual acoustic models by one more epoch with acoustic data from that language reduces the WER significantly. We also added 512-dimensional embedding features from cross-lingual pre-trained wav2vec2.0 XLSR-53 models, but they lead to only a small reduction in WER.
Extensions to Brahmic script processing within the Nisaba library: new scripts, languages and utilities
Authors: Alexander Gutkin, Cibu Johny, Raiomond Doctor, Lawrence Wolf-Sonkin, Brian Roark
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Transliteration

The Brahmic family of scripts is used to record some of the most spoken languages in the world and is arguably the most diverse family of writing systems. In this work, we present several substantial extensions to Brahmic script functionality within the open-source Nisaba library of finite-state script normalization and processing utilities (Johny et al., 2021). First, we extend coverage from the original ten scripts to an additional ten scripts of South Asia and beyond, including some used to record endangered languages such as Dogri. Second, we augment the language layer so that scripts used by multiple languages in distinct ways can be processed correctly for more languages, such as the Bengali script when used for the low-resource language Santali. We document key changes to the finite-state engine required to support these new languages and scripts. Finally, we add new script processing utilities, including lightweight script-level reading normalization that (unlike existing visual normalization) does not preserve visual invariance, and a fixed-input transliteration mechanism specifically tailored to Brahmic text entry with ASCII characters.
A Survey of Machine Translation Tasks on Nigerian Languages
Authors: Ebelechukwu Nwafor, Anietie Andy
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Machine Translation, Survey, Translation

Machine translation is an active area of research that has received a significant amount of attention over the past decade. With the advent of deep learning models, the translation of several languages has been performed with high accuracy and precision. In spite of the development in machine translation techniques, there is very limited work focused on translating low-resource African languages, particularly Nigerian languages. Nigeria is one of the most populous countries in Africa with diverse language and ethnic groups. In this paper, we survey the current state of the art of machine translation research on Nigerian languages with a major emphasis on neural machine translation techniques. We outline the limitations of research in machine translation on Nigerian languages and propose future directions in increasing research and participation.
Survey on Thai NLP Language Resources and Tools
Authors: Ratchakrit Arreerard, Stephen Mander, Scott Piao
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Survey

Over the past decades, Natural Language Processing (NLP) research has been expanding to cover more languages. Recently particularly, NLP community has paid increasing attention to under-resourced languages. However, there are still many languages for which NLP research is limited in terms of both language resources and software tools. Thai language is one of the under-resourced languages in the NLP domain, although it is spoken by nearly 70 million people globally. In this paper, we report on our survey on the past development of Thai NLP research to help understand its current state and future research directions. Our survey shows that, although Thai NLP community has achieved a significant achievement over the past three decades, particularly on NLP upstream tasks such as tokenisation, research on downstream tasks such as syntactic parsing and semantic analysis is still limited. But we foresee that Thai NLP research will advance rapidly as richer Thai language resources and more robust NLP techniques become available.
The Maaloula Aramaic Speech Corpus (MASC): From Printed Material to a Lemmatized and Time-Aligned Corpus
Authors: Ghattas Eid, Esther Seyffarth, Ingo Plag
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: LEMMA, Sentence

This paper presents the first electronic speech corpus of Maaloula Aramaic, an endangered Western Neo-Aramaic variety spoken in Syria. This 64,845-word corpus is available in four formats: (1) transcriptions, (2) lemmatized transcriptions, (3) audio files and time-aligned phonetic transcriptions, and (4) an SQLite database. The transcription files are a digitized and corrected version of authentic transcriptions of tape-recorded narratives coming from a fieldwork trip conducted in the 1980s and published in the early 1990s (Arnold, 1991a, 1991b). They contain no annotation, except for some informative tagging (e.g. to mark loanwords and misspoken words). In the lemmatized version of the files, each word form is followed by its lemma in angled brackets. The time-aligned TextGrid annotations consist of four tiers: the sentence level (Tier 1), the word level (Tiers 2 and 3), and the segment level (Tier 4). These TextGrid files are downloadable together with their audio files (for the original source of the audio data see Arnold, 2003). The SQLite database enables users to access the data on the level of tokens, types, lemmas, sentences, narratives, or speakers. The corpus is now available to the scientific community at https://doi.org/10.5281/zenodo.6496714.
VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering
Authors: Khang Le, Hien Nguyen, Tung Le Thanh, Minh Nguyen
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Multi-hop Question Answering, Question Answering, Sentence

Vietnamese is the native language of over 98 million people in the world. However, existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. We introduce VIMQA, a new Vietnamese dataset with over 10,000 Wikipedia-based multi-hop question-answer pairs. The dataset is human-generated and has four main features: (1) The questions require advanced reasoning over multiple paragraphs. (2) Sentence-level supporting facts are provided, enabling the QA model to reason and explain the answer. (3) The dataset offers various types of reasoning to test the model’s ability to reason and extract relevant proof. (4) The dataset is in Vietnamese, a low-resource language. We also conduct experiments on our dataset using state-of-the-art Multilingual single-hop and multi-hop QA methods. The results suggest that our dataset is challenging for existing methods, and there is room for improvement in Vietnamese QA systems. In addition, we propose a general process for data creation and publish a framework for creating multilingual multi-hop QA datasets. The dataset and framework are publicly available to encourage further research in Vietnamese QA systems.
A Mapudüngun FST Morphological Analyser and its Web Interface
Authors: Andrés Chandía
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

This paper describes the development and evaluation of a FST-based analyser-generator for Mapudüngun language, which is publicly available through a web interface. As far as we know, it is the first system of this kind for Mapudüngun. Following the Mapuche grammar by Smeets, we have developed a machine including the morphological and phonological aspects of Mapudüngun. Through this computational approach we have produced a finite state morphological analyser-generator capable of classifying and appropriately tagging all the components (roots and suffixes) interacting in a Mapuche word-form. A double evaluation has been carried out showing a good level of reliability. In order to face the lack of standardization of the language, additional components (an enhanced analyser, a spelling unifier and a root guesser) have been integrated in the tool. The generated corpora, the lexicons and the FST grammars are available for further development and comparison results.
Thirumurai: A Large Dataset of Tamil Shaivite Poems and Classification of Tamil Pann
Authors: Shankar Mahadevan, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Prabakaran Chandran, Ruba Priyadharshini, Sangeetha S, Bharathi Raja Chakravarthi
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Thirumurai, also known as Panniru Thirumurai, is a collection of Tamil Shaivite poems dating back to the Hindu revival period between the 6th and the 10th century. These poems are par excellence, in both literary and musical terms. They have been composed based on the ancient, now non-existent Tamil Pann system and can be set to music. We present a large dataset containing all the Thirumurai poems and also attempt to classify the Pann and author of each poem using transformer based architectures. Our work is the first of its kind in dealing with ancient Tamil text datasets, which are severely under-resourced. We explore several Deep Learning-based techniques for solving this challenge effectively and provide essential insights into the problem and how to address it.
Generating Monolingual Dataset for Low Resource Language Bodo from old books using Google Keep
Authors: Sanjib Narzary, Maharaj Brahma, Mwnthai Narzary, Gwmsrang Muchahary, Pranav Kumar Singh, Apurbalal Senapati, Sukumar Nandi, Bidisha Som
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Optical Character Recognition (OCR)

Bodo is a scheduled Indian language spoken largely by the Bodo community of Assam and other northeastern Indian states. Due to a lack of resources, it is difficult for young languages to communicate more effectively with the rest of the world. This leads to a lack of research in low-resource languages. The creation of a dataset is a tedious and costly process, particularly for languages with no participatory research. This is more visible for languages that are young and have recently adopted standard writing scripts. In this paper, we present a methodology using Google Keep for OCR to generate a monolingual Bodo corpus from different books. In this work, a Bodo text corpus of 192,327 tokens and 32,268 unique tokens is generated using free, accessible, and daily-usable applications. Moreover, some essential characteristics of the Bodo language are discussed that are neglected by Natural Language Progressing (NLP) researchers.
AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition
Authors: Dhrubajyoti Pathak, Sukumar Nandi, Priyankoo Sarmah
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, XLM-R

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.
GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages
Authors: Fitsum Gaim, Wonsuk Yang, Jong C. Park
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Identification, Machine Translation

Language identification is one of the fundamental tasks in natural language processing that is a prerequisite to data processing and numerous applications. Low-resourced languages with similar typologies are generally confused with each other in real-world applications such as machine translation, affecting the user’s experience. In this work, we present a language identification dataset for five typologically and phylogenetically related low-resourced East African languages that use the Ge’ez script as a writing system; namely Amharic, Blin, Ge’ez, Tigre, and Tigrinya. The dataset is built automatically from selected data sources, but we also performed a manual evaluation to assess its quality. Our approach to constructing the dataset is cost-effective and applicable to other low-resource languages. We integrated the dataset into an existing language-identification tool and also fine-tuned several Transformer based language models, achieving very strong results in all cases. While the task of language identification is easy for the informed person, such datasets can make a difference in real-world deployments and also serve as part of a benchmark for language understanding in the target languages. The data and models are made available at https://github.com/fgaim/geezswitch.
Handwritten Paleographic Greek Text Recognition: A Century-Based Approach
Authors: Paraskevi Platanou, John Pavlopoulos, Georgios Papaioannou
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Handwritten Text Recognition, HTR

Today classicists are provided with a great number of digital tools which, in turn, offer possibilities for further study and new research goals. In this paper we explore the idea that old Greek handwriting can be machine-readable and consequently, researchers can study the target material fast and efficiently. Previous studies have shown that Handwritten Text Recognition (HTR) models are capable of attaining high accuracy rates. However, achieving high accuracy HTR results for Greek manuscripts is still considered to be a major challenge. The overall aim of this paper is to assess HTR for old Greek manuscripts. To address this statement, we study and use digitized images of the Oxford University Bodleian Library Greek manuscripts. By manually transcribing 77 images, we created and present here a new dataset for Handwritten Paleographic Greek Text Recognition. The dataset instances were organized by establishing as a leading factor the century to which the manuscript and hence the image belongs. Experimenting then with an HTR model we show that the error rate depends on the century of the image.
Quality Control for Crowdsourced Bilingual Dictionary in Low-Resource Languages
Authors: Hiroki Chida, Yohei Murakami, Mondheera Pituxcoosuvarn
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

In conventional bilingual dictionary creation by using crowdsourcing, the main method is to ask multiple workers to translate the same words or sentences and take a majority vote. However, when this method is applied to the creation of bilingual dictionaries for low-resource languages with few speakers, many low-quality workers are expected to participate in the majority voting, which makes it difficult to maintain the quality of the evaluation by the majority voting. Therefore, we apply an effective aggregation method using a hyper question, which is a set of single questions, for quality control. Furthermore, to select high-quality workers, we design a task-allocation method based on the reliability of workers which is evaluated by their work results.
An Inflectional Database for Gitksan
Authors: Bruce Oliver, Clarissa Forbes, Changbing Yang, Farhan Samir, Edith Coates, Garrett Nicolai, Miikka Silfverberg
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Data Augmentation, Hallucination, Translation

This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.
PyCantonese: Cantonese Linguistics and NLP in Python
Authors: Jackson Lee, Litong Chen, Charles Lam, Chaak Ming Lau, Tsz-Him Tsui
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Part-Of-Speech Tagging

This paper introduces PyCantonese, an open-source Python library for Cantonese linguistics and natural language processing. After the library design, implementation, corpus data format, and key datasets included are introduced, the paper provides an overview of the currently implemented functionality: stop words, handling Jyutping romanization, word segmentation, part-of-speech tagging, and parsing Cantonese text.
Afaan Oromo Hate Speech Detection and Classification on Social Media
Authors: Teshome Mulugeta Ababu, Michael Melese Woldeyohannis
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Classification, Hate Speech Detection

Hate and offensive speech on social media is targeted to attack an individual or group of community based on protected characteristics such as gender, ethnicity, and religion. Hate and offensive speech on social media is a global problem that suffers the community especially, for an under-resourced language like Afaan Oromo language. One of the most widely spoken Cushitic language families is Afaan Oromo. Our objective is to develop and test a model used to detect and classify Afaan Oromo hate speech on social media. We developed numerous models that were used to detect and classify Afaan Oromo hate speech on social media by using different machine learning algorithms (classical, ensemble, and deep learning) with the combination of different feature extraction techniques such as BOW, TF-IDF, word2vec, and Keras Embedding layers. To perform the task, we required Afaan Oromo datasets, but the datasets were unavailable. By concentrating on four thematic areas of hate speech, such as gender, religion, race, and offensive speech, we were able to collect a total of 12,812 posts and comments from Facebook. BiLSTM with pre-trained word2vec feature extraction is an outperformed algorithm that achieves better accuracy of 0.84 and 0.88 for eight classes and two classes, respectively.
Cross-lingual Linking of Automatically Constructed Frames and FrameNet
Authors: Ryohei Sasano
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Cross-Lingual Word Embeddings, Word Embeddings

A semantic frame is a conceptual structure describing an event, relation, or object along with its participants. Several semantic frame resources have been manually elaborated, and there has been much interest in the possibility of applying semantic frames designed for a particular language to other languages, which has led to the development of cross-lingual frame knowledge. However, manually developing such cross-lingual lexical resources is labor-intensive. To support the development of such resources, this paper presents an attempt at automatic cross-lingual linking of automatically constructed frames and manually crafted frames. Specifically, we link automatically constructed example-based Japanese frames to English FrameNet by using cross-lingual word embeddings and a two-stage model that first extracts candidate FrameNet frames for each Japanese frame by taking only the frame-evoking words into account, then finds the best alignment of frames by also taking frame elements into account. Experiments using frame-annotated sentences in Japanese FrameNet indicate that our approach will facilitate the manual development of cross-lingual frame resources.
Aligning the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs
Authors: Ana-Maria Barbu, Verginica Barbu Mititelu, Cătălin Mititelu
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

We present here the efforts of aligning two language resources for Romanian: the Romanian Reference Treebank and the Valence Lexicon of Romanian Verbs: for each occurrence of those verbs in the treebank that were included as entries in the lexicon, a set of valence frames is automatically assigned, then manually validated by two linguists and, when necessary, corrected. Validating a valence frame also means semantically disambiguating the verb in the respective context. The validation is done by two linguists, on complementary datasets. However, a subset of verbs were validated by both annotators and Cohen’s κ is 0.87 for this subset. The alignment we have made also serves as a method of enhancing the quality of the two resources, as in the process we identify morpho-syntactic annotation mistakes, incomplete valence frames or missing ones. Information from each resource complements the information from the other, thus their value increases. The treebank and the lexicon are freely available, while the links discovered between them are also made available on GitHub.
PortiLexicon-UD: a Portuguese Lexical Resource according to Universal Dependencies Model
Authors: Lucelene Lopes, Magali Duran, Paulo Fernandes, Thiago Pardo
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: LEMMA, POS, TAG

This paper presents PortiLexicon-UD, a large and freely available lexicon for Portuguese delivering morphosyntactic information according to the Universal Dependencies model. This lexical resource includes part of speech tags, lemmas, and morphological information for words, with 1,221,218 entries (considering word duplication due to different combination of PoS tag, lemma, and morphological features). We report the lexicon creation process, its computational data structure, and its evaluation over an annotated corpus, showing that it has a high language coverage and good quality data.