Croatia

EACL 2023

Deep learning and low-resource languages: How much data is enough? A case study of three linguistically distinct South African languages

under-resourced languages

deep learning

workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Orthographic standardization is a milestone in a language's documentation and the development of its resources. However, texts written in former orthographies remain relevant to the language's history and development and therefore must be converted to the standardized orthography. Ensuring a language has access to the orthographically standardized version of all of its recorded texts is important in the development of resources as it provides additional textual resources for training, supports contribution of authors using former writing systems, and provides information about the development of the language. This paper evaluates the performance of natural language processing methods, specifically Finite State Transducers and Long Short-term Memory networks, for the orthographical conversion of Bàsàá texts from the Protestant missionary orthography to the now-standard AGLC orthography, with the conclusion that LSTMs are somewhat more effective in the absence of explicit lexical information.

Comparing methods of orthographic conversion for Bàsàá, a language of Cameroon

With the prominence of large pretrained language models, low-resource languages are rarely modelled monolingually and become victims of the “curse of multilinguality” in massively multilingual models. Recently, AfriBERTa showed that training transformer models from scratch on 1GB of data from many unrelated African languages outperforms massively multilingual models on downstream NLP tasks. Here we extend this direction, focusing on the use of related languages. We propose that training on smaller amounts of data but from related languages could match the performance of models trained on large, unrelated data. We test our hypothesis on the Niger-Congo family and its Bantu and Volta-Niger sub-families, pretraining models with data solely from Niger-Congo languages and finetuning on 4 downstream tasks: NER, part-of-speech tagging, sentiment analysis and text classification. We find that models trained on genetically related languages achieve equal performance on downstream tasks in low-resource languages despite using less training data. We recommend selecting training data based on language-relatedness when pretraining language models for low-resource languages.

Mini But Mighty: Efficient Multilingual Pretraining with Linguistically-Informed Data Selection

This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This repository can be updated periodically with contributions from other researchers. Our objective is to identify research gaps and disseminate the information to NLP researchers interested in Ethiopian languages and encourage future research in this domain.

Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities

This article describes the development of a list of frequently used words in written Sesotho. The list has been created with the aim of incorporating it into frequency-based text readability metrics. The list was derived using a corpus-based approach. By leveraging three existing Sesotho corpora, frequency lists could be derived, which were subsequently merged and qualitatively analysed and fine-tuned by an experienced speaker of Sesotho. The main challenges in compiling the list included reconciling the spelling variations, the treatment of abbreviations, and the presence of unexpected words in the preliminary lists. The final list comprises 3037 entries and is made publicly available to the research community.

A Corpus-Based List of Frequently Used Words in Sesotho

The aim of this article is to underscore the link between diacritic orthography and the sounds implicit in these orthographies that are applied in writing the Igala Language corpus. 30 vowels were identified plus 8 diphthongs. Examples in the form of sentences and the translation of these were provided. The article combines up to seven diacritic forms in order to better tackle the oft encountered problem of pronouncing words in texts written in foreign language by non-native speakers and learners with supporting indicators provided to guide end users on how to pronounce the words using the diacritic forms and vocal representation of these forms that are herein provided in the double slash oral transcription of the words. In the absence of a phonological symbol for representing the extra-low tones in the oral parenthesis, the reverse sans-serif capital L (˩)was used in lieu of the colon before the tone symbol (i.e.; /:a/) earlier imagined by this author while the low tones were recognized as short vowels, both orthographically and phonologically. The colon (:) and (::) were used to express the mid and the high pitches in a more limited extent in this very work.

Vowels and the Igala Language Resources

The goal of this paper is to learn more about how idiomatic information is structurally encoded in embeddings, using a structural probing method. We repurpose an existing English verbal multi-word expression (MWE) dataset to suit the probing framework and perform a comparative probing study of static (GloVe) and contextual (BERT) embeddings. Our experiments indicate that both encode some idiomatic information to varying degrees, but yield conflicting evidence as to whether idiomaticity is encoded in the vector norm, leaving this an open question. We also identify some limitations of the used dataset and highlight important directions for future work in improving its suitability for a probing analysis.

Idioms, Probing and Dangerous Things: Towards Structural Probing for Idiomaticity in Vector Space

An open question in language comprehension studies is whether non-compositional multiword expressions like idioms and compositional-but-frequent word sequences are processed differently. Are the latter constructed online, or are instead directly retrieved from the lexicon, with a degree of entrenchment depending on their frequency?

In this paper, we address this question with two different methodologies. First, we set up a self-paced reading experiment comparing human reading times for idioms and both high-frequency and low-frequency compositional word sequences. Then, we ran the same experiment using the Surprisal metrics computed with Neural Language Models (NLMs).

Our results provide evidence that idiomatic and high-frequency compositional expressions are processed similarly by both humans and NLMs.
Additional experiments were run to test the possible factors that could affect the NLMs' performance.

Are Frequent Phrases Directly Retrieved like Idioms? An Investigation with Self-Paced Reading and Language Models

Multiword expression (MWE) identification has been the focus of numerous research papers, especially in the context of the DiMSUM and PARSEME Shared Tasks (STs). This survey analyses 40 MWE identification papers with experiments on data from these STs. We look at corpus selection, pre- and post-processing, MWE encoding, evaluation metrics, statistical significance, and error analyses. We find that these aspects are usually considered minor and/or omitted in the literature. However, they may considerably impact the results and the conclusions drawn from them. Therefore, we advocate for more systematic descriptions of experimental conditions to reduce the risk of misleading conclusions drawn from poorly designed experimental setup.


A Survey of MWE Identification Experiments: The Devil is in the Details

We present version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. This fourth release (v.1.3) is the first one to cover the union of all the languages included in the previous three releases. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are (re-)split observing the PARSEME v.1.2 standard, which puts an impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.

PARSEME corpus release 1.3

The identification of Verbal Multiword Expressions (VMWEs) presents a greater challenge compared to non-verbal MWEs due to their higher surface variability. VMWEs are linguistic units that exhibit varying levels of semantic opaqueness and pose difficulties for computational models in terms of both their identification and the degree of compositionality. In this study, a new approach to predicting the compositional nature of VMWEs in Persian is presented. The method begins with an automatic identification of VMWEs in Persian sentences, which is approached as a sequence labeling problem for recognizing the components of VMWEs. The method then creates word embeddings that better capture the semantic properties of VMWEs and uses them to determine the degree of compositionality through multiple criteria. The study compares two neural architectures for identification, BiLSTM and ParsBERT, and shows that a fine-tuned BERT model surpasses the BiLSTM model in evaluation metrics with an F1 score of 89%. Next, a word2vec embedding model is trained to capture the semantics of identified VMWEs and is used to estimate their compositionality, resulting in an accuracy of 70.9% as demonstrated by experiments on a collected dataset of expert-annotated compositional and non-compositional VMWEs.

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Deep learning and low-resource languages: How much data is enough? A case study of three linguistically distinct South African languages

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Comparing methods of orthographic conversion for Bàsàá, a language of Cameroon

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