Croatia

EACL 2023

Investigating Sentiment-Bearing Words- and Emoji-based Distant Supervision Approaches for Sentiment Analysis

emojis

sentiment lexicon

sentiment analysis

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.

Dependency annotation can be a laborious process for under-resourced languages. However, in some cases, other resources are available. 
We investigate whether we can leverage such resources in the case of Swahili: We use the annotations of the Helsinki Corpus of Swahili for creating a Universal Dependencies treebank for Swahili. The Helsinki Corpus of Swahili provides word-level annotations for part of speech tags, morphological features, and functional syntactic tags. We train neural taggers for these types of annotations, then use those models to annotate our target corpus, the Swahili portion of the Global Voices Corpus. Based on the word-level annotations, we then manually create constraint grammar rules to annotate the target corpus for Universal Dependencies. We describe the process, discuss the annotation decisions we had to make, and we evaluate the approach.

Towards a Swahili Universal Dependency Treebank: Leveraging the Annotations of the Helsinki Corpus of Swahili

The purpose of this article is to demonstrate that the recently developed automated rule-based syllabification system for Sesotho can be used broadly across the officially recognised South African Sotho-Tswana language group encompassing Sepedi, Sesotho and Setswana. We evaluate the automatic syllabification system on 400 words comprising 100 most frequently used words and 100 least-used words in Sepedi and Setswana as provided in the Autshumato corpus publicly available online. It is found that the Sesotho rule-based syllabification system can be used to correctly identify vowel-only syllables, consonant-vowel syllables and consonant-only syllables in Sepedi and Setswana. Among other findings, it has been demonstrated that words with diacritics as in the case of Sepedi are correctly broken down into syllables. We make two main recommendations. First, the rules for syllabification should be updated so that Sepedi diacritics are accommodated. Second, the syllabification system should be updated so that it reflects the broader Sotho-Tswana language group instead of being limited to Sesotho. Further research is needed to ascertain whether the complex consonant behaves similarly in all three officially recognised Sotho-Tswana languages and evaluate the need for a specific rule for the nasal consonant. 


Evaluating the Sesotho rule-based syllabification system on Sepedi and Setswana words

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

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.

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Investigating Sentiment-Bearing Words- and Emoji-based Distant Supervision Approaches for Sentiment Analysis

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Towards a Swahili Universal Dependency Treebank: Leveraging the Annotations of the Helsinki Corpus of Swahili

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