ymoslem/acl-abstracts huggingface dataset:
Quartet@LT-EDI 2024: Support Vector Machine Based Approach For Homophobia/Transphobia Detection In Social Media Comments (2024)

Homophobia and transphobia are terms which are used to describe the fear or hatred towards people who are attracted to the same sex or people whose psychological gender differs from his biological sex. People use social media to exert this behaviour. The increased amount of abusive content negatively affects people in a lot of ways. It makes the environment toxic and unpleasant to LGBTQ+ people. The paper talks about the classification model for classifying the contents into 3 categories which are homophobic, transphobic and nonhomophobic/ transphobic. We used many traditional models like Support Vector Machine, Random Classifier, Logistic Regression and KNearest Neighbour to achieve this. The macro average F1 scores for Malayalam, Telugu, English, Marathi, Kannada, Tamil, Gujarati, Hindi are 0.88, 0.94, 0.96, 0.78, 0.93, 0.77, 0.94, 0.47 and the rank for these languages are 5, 6, 9, 6, 8, 6, 6, 4.
SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste And Migration Hate Speech Detection (2024)

This paper presents our submission for the shared task on Caste and Migration Hate Speech Detection: LT-EDI@EACL 20241 . This text classification task aims to foster the creation of models capable of identifying hate speech related to caste and migration. The dataset comprises social media comments, and the goal is to categorize them into negative and positive sentiments. Our approach explores back-translation for data augmentation to address sparse datasets in low-resource Dravidian languages. While Part-of-Speech (POS) tagging is valuable in natural language processing, our work highlights its ineffectiveness in Dravidian languages, with model performance drastically reducing from 0.73 to 0.67 on application. In analyzing boosting and ensemble methods, the voting classifier with traditional models outperforms others and the boosting techniques, underscoring the efficacy of simper models on low-resource data despite augmentation.
CUET\_NLP\_Manning@LT-EDI 2024: Transformer-based Approach on Caste and Migration Hate Speech Detection (2024)

The widespread use of online communication has caused a significant increase in the spread of hate speech on social media. However, there are also hate crimes based on caste and migration status. Despite several nations efforts to bring equality among their citizens, numerous crimes occur just based on caste. Migration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resourced language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and migration status on social media. To achieve this, this work investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. Experimental results revealed the highest macro f1-score of 0.80 using the M-BERT model, which enabled us to rank 3rd on the shared task.
DRAVIDIAN LANGUAGE@ LT-EDI 2024:Pretrained Transformer based Automatic Speech Recognition system for Elderly People (2024)

In this paper, the main goal of the study is to create an automatic speech recognition (ASR) system that is tailored to the Tamil language. The dataset that was employed includes audio recordings that were obtained from vulnerable populations in the Tamil region, such as elderly men and women and transgender individuals. The pre-trained model Rajaram1996/wav2vec2- large-xlsr-53-tamil is used in the engineering of the ASR system. This existing model is finetuned using a variety of datasets that include typical Tamil voices. The system is then tested with a specific test dataset, and the transcriptions that are produced are sent in for assessment. The Word Error Rate is used to evaluate the system`s performance. Our system has a WER of 37.733.
Transformers@LT-EDI-EACL2024: Caste and Migration Hate Speech Detection in Tamil Using Ensembling on Transformers (2024)

In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team {\textquotedblleft}Transformers{\textquotedblright} submission to the Caste and Migration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste and Migration Hate Speech in Tamil shared task.
Algorithm Alliance@LT-EDI-2024: Caste and Migration Hate Speech Detection (2024)

Caste and Migration speech refers to the use of language that distinguishes the offense, violence, and distress on their social, caste, and migration status. Here, caste hate speech targets the imbalance of an individual`s social status and focuses mainly on the degradation of their caste group. While the migration hate speech imposes the differences in nationality, culture, and individual status. These speeches are meant to affront the social status of these people. To detect this hate in the speech, our task on Caste and Migration Hate Speech Detection has been created which classifies human speech into genuine or stimulate categories. For this task, we used multiple classification models such as the train test split model to split the dataset into train and test data, Logistic regression, Support Vector Machine, MLP (multi-layer Perceptron) classifier, Random Forest classifier, KNN classifier, and Decision tree classification. Among these models, The SVM gave the highest macro average F1 score of 0.77 and the average accuracy for these models is around 0.75.
MEnTr@LT-EDI-2024: Multilingual Ensemble of Transformer Models for Homophobia/Transphobia Detection (2024)

Detection of Homophobia and Transphobia in social media comments serves as an important step in the overall development of Equality, Diversity and Inclusion (EDI). In this research, we describe the system we formulated while participating in the shared task of Homophobia/ Transphobia detection as a part of the Fourth Workshop On Language Technology For Equality, Diversity, Inclusion (LT-EDI- 2024) at EACL 2024. We used an ensemble of three state-of-the-art multilingual transformer models, namely Multilingual BERT (mBERT), Multilingual Representations for Indic Languages (MuRIL) and XLM-RoBERTa to detect the presence of Homophobia or Transphobia in YouTube comments. The task comprised of datasets in ten languages - Hindi, English, Telugu, Tamil, Malayalam, Kannada, Gujarati, Marathi, Spanish and Tulu. Our system achieved rank 1 for the Spanish and Tulu tasks, 2 for Telugu, 3 for Marathi and Gujarati, 4 for Tamil, 5 for Hindi and Kannada, 6 for English and 8 for Malayalam. These results speak for the efficacy of our ensemble model as well as the data augmentation strategy we adopted for the detection of anti-LGBT+ language in social media data.
CUET\_DUO@StressIdent\_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT (2024)

The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, {\textquotedblleft}Stress Identification in Dravidian Languages,{\textquotedblright} specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of \textbf{0.71} and \textbf{0.72}, respectively, and securing the $15^{th}$ position in Tamil code-mixed language and the $9^{th}$ position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu.
dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments (2024)

Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.
KEC\_AI\_MIRACLE\_MAKERS@LT-EDI-2024: Stress Identification in Dravidian Languages using Machine Learning Techniques (2024)

Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.
MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification (2024)

Misogynistic memes are a category of memes which contain disrespectful language targeting women on social media platforms. Hence, detecting such memes is necessary in order to maintain a healthy social media environment. To address the challenges of detecting misogynistic memes, {\textquotedblleft}Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes: LT-EDI@EACL 2024{\textquotedblright} shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024, invites researchers to develop models to detect misogynistic memes in Tamil and Malayalam. The shared task has two subtasks, and in this paper, we - team MUCS, describe the learning models submitted to Task 1 - Identification of Misogynistic Memes in Tamil and Malayalam. As memes represent multi-modal data of image and text, three models: i) Bidirectional Encoder Representations from Transformers (BERT)+Residual Network (ResNet)-50, ii) Multilingual Representations for Indian Languages (MuRIL)+ResNet-50, and iii) multilingual BERT (mBERT)+ResNet50, are proposed based on joint representation of text and image, for detecting misogynistic memes in Tamil and Malayalam. Among the proposed models, mBERT+ResNet-50 and MuRIL+ ResNet-50 models obtained macro F1 scores of 0.73 and 0.87 for Tamil and Malayalam datasets respectively securing 1st rank for both the datasets in the shared task.
MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification (2024)

Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to {\textquotedblleft}Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024{\textquotedblright} shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo{\_}Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo{\_}TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo{\_}probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo{\_}FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo{\_}Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo{\_}TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo{\_}probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo{\_}FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.
ASR TAMIL SSN@ LT-EDI-2024: Automatic Speech Recognition system for Elderly People (2024)

The results of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil (LT-EDI-2024) are discussed in this paper. The goal is to create an automated system for Tamil voice recognition. The older population that speaks Tamil is the source of the dataset used in this task. The proposed ASR system is designed with pre-trained model akashsivanandan/wav2vec2-large-xls-r300m-tamil-colab-final. The Tamil common speech dataset is utilized to fine-tune the pretrained model that powers our system. The suggested system receives the test data that was released from the task; transcriptions are then created for the test samples and delivered to the task. Word Error Rate (WER) is the evaluation statistic used to assess the provided result based on the task. Our Proposed system attained a WER of 29.297{\%}.
Goidelex: A Lexical Resource for Old Irish (2024)

We introduce Goidelex, a new lexical database resource for Old Irish. Goidelex is an openly accessible relational database in CSV format, linked by formal relationships. The launch version documents 695 headwords with extensive linguistic annotations, including orthographic forms using a normalised orthography, automatically generated phonemic transcriptions, and information about morphosyntactic features, such as gender, inflectional class, etc. Metadata in JSON format, following the Frictionless standard, provides detailed descriptions of the tables and dataset. The database is designed to be fully compatible with the Paralex and CLDF standards and is interoperable with existing lexical resources for Old Irish such as CorPH and eDIL. It is suited to both qualitative and quantitative investigation into Old Irish morphology and lexicon, as well as to comparative research. This paper outlines the creation process, rationale, and resulting structure of the database.
Developing a Part-of-speech Tagger for Diplomatically Edited Old Irish Text (2024)

POS-tagging is typically considered a fundamental text preprocessing task, with a variety of downstream NLP tasks and techniques being dependent on the availability of POS-tagged corpora. As such, POS-taggers are important precursors to further NLP tasks, and their accuracy can impact the potential accuracy of these dependent tasks. While a variety of POS-tagging methods have been developed which work well with modern languages, historical languages present orthographic and editorial challenges which require special attention. The effectiveness of POS-taggers developed for modern languages is reduced when applied to Old Irish, with its comparatively complex orthography and morphology. This paper examines some of the obstacles to POS-tagging Old Irish text, and shows that inconsistencies between extant annotated corpora reduce the quantity of data available for use in training POS-taggers. The development of a multi-layer neural network model for POS-tagging Old Irish text is described, and an experiment is detailed which demonstrates that this model outperforms a variety of off-the-shelf POS-taggers. Moreover, this model sets a new benchmark for POS-tagging diplomatically edited Old Irish text.
From YCOE to UD: Rule-based Root Identification in Old English (2024)

In this paper we apply a set of rules to identify the root of a dependency tree, following the Universal Dependencies formalism and starting from the constituency annotation of the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE). This rule-based root-identification task represents the first step towards a rule-based automatic conversion of this valuable resource into the UD format. After presenting Old English and the annotated resources available for this language, we describe the different rules we applied and then we discuss the results and the errors.
Too Young to NER: Improving Entity Recognition on Dutch Historical Documents (2024)

Named entity recognition (NER) on historical texts is beneficial for the field of digital humanities, as it allows to easily search for the names of people, places and other entities in digitised archives. While the task of historical NER in different languages has been gaining popularity in recent years, Dutch historical NER remains an underexplored topic. Using a recently released historical dataset from the Dutch Language Institute, we train three BERT-based models and analyse the errors to identify main challenges. All three models outperform a contemporary multilingual baseline by a large margin on historical test data.
Towards Named-Entity and Coreference Annotation of the Hebrew Bible (2024)

Named-entity annotation refers to the process of specifying what real-world (or, at least, external-to-the-text) entities various names and descriptions within a text refer to. Coreference annotation, meanwhile, specifies what context-dependent words or phrases, such as pronouns refer to. This paper describes an ongoing project to apply both of these to the Hebrew Bible, so far covering most of the book of Genesis, fully marking every person, place, object, and point in time which occurs in the text. The annotation process and possible future uses for the data are covered, along with the challenges involved in applying existing annotation guidelines to the Hebrew text.
LiMe: A Latin Corpus of Late Medieval Criminal Sentences (2024)

The Latin language has received attention from the computational linguistics research community, which has built, over the years, several valuable resources, ranging from detailed annotated corpora to sophisticated tools for linguistic analysis. With the recent advent of large language models, researchers have also started developing models capable of generating vector representations of Latin texts. The performances of such models remain behind the ones for modern languages, given the disparity in available data. In this paper, we present the LiMe dataset, a corpus of 325 documents extracted from a series of medieval manuscripts called Libri sententiarum potestatis Mediolani, and thoroughly annotated by experts, in order to be employed for masked language model, as well as supervised natural language processing tasks.
The Rise and Fall of Dependency Parsing in Dante Alighieri`s Divine Comedy (2024)

In this paper, we conduct parsing experiments on Dante Alighieri`s Divine Comedy, an Old Italian poem composed between 1306-1321 and organized into three Cantiche {---}Inferno, Purgatorio, and Paradiso. We perform parsing on subsets of the poem using both a Modern Italian training set and sections of the Divine Comedy itself to evaluate under which scenarios parsers achieve higher scores. We find that employing in-domain training data supports better results, leading to an increase of approximately +17{\%} in Unlabeled Attachment Score (UAS) and +25-30{\%} in Labeled Attachment Score (LAS). Subsequently, we provide brief commentary on the differences in scores achieved among subsections of Cantiche, and we conduct experimental parsing on a text from the same period and style as the Divine Comedy.
Unsupervised Authorship Attribution for Medieval Latin Using Transformer-Based Embeddings (2024)

We explore the potential of employing transformer-based embeddings in an unsupervised authorship attribution task for medieval Latin. The development of Large Language Models (LLMs) and recent advances in transfer learning alleviate many of the traditional issues associated with authorship attribution in lower-resourced (ancient) languages. Despite this, these methods remain heavily understudied within this domain. Concretely, we generate strong contextual embeddings using a variety of mono -and multilingual transformer models and use these as input for two unsupervised clustering methods: a standard agglomerative clustering algorithm and a self-organizing map. We show that these transformer-based embeddings can be used to generate high-quality and interpretable clusterings, resulting in an attractive alternative to the traditional feature-based methods.
\textquotedblleftTo Have the \textquoteleftMillion' Readers Yet\textquotedblright: Building a Digitally Enhanced Edition of the Bilingual Irish-English Newspaper an Gaodhal (1881-1898) (2024)

This paper introduces the {\textquoteleft}An Gaodhal' project, which aims to serve the historically under-resourced and endangered language of Irish (known as Gaeilge) by providing new digital tools and resources. The initial goal of the project was the extraction of full text of {\textquoteleft}An Gaodhal', a monthly bilingual Irish-English newspaper produced from 1881 to 1898, to the highest possible degree of accuracy via Optical Character Recognition (OCR), with a view to making its printed content searchable. The methodology applied toward achieving this goal yielded additional digital outputs including: 1. a new OCR model for the Irish language as printed in Cl{\'o} Gaelach type; 2. a new OCR model for bilingual Irish-English content printed in Cl{\'o} Gaelach and Roman types respectively; 3. a BART-based OCR post-correction model for historical bilingual Irish-English data; 4. a historical Irish training set for Named Entity Recognition (NER). All but the first of these four additional outputs appear to be the first of their kind. Each of the project outputs, including the full-text OCR outputs in ALTO XML format, is set for public release to enable open-access research. The paper also identifies the challenges historical Irish data poses to Natural Language Processing (NLP) in general and OCR in particular, and reports on project results and outputs to date. Finally, it contextualises the project within the wider field of NLP and considers its potential impact on under-resourced languages worldwide.