ymoslem/acl-abstracts huggingface dataset:
FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification (2022)

Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, mild, moderate, and even severe depression can be treated. This is why it is vital to detect signs of depression in time. One possibility for this is the use of text classification models on social media posts. Transformers have achieved state-of-the-art performance on a variety of similar text classification tasks. One drawback, however, is that when the dataset is imbalanced, the performance of these models may be negatively affected. Because of this, in this paper, we examine the effect of balancing a depression detection dataset using data augmentation. In particular, we use abstractive summarization techniques for data augmentation. We examine the effect of this method on the LT-EDI-ACL2022 task. Our results show that when increasing the multiplicity of the minority classes to the right degree, this data augmentation method can in fact improve classification scores on the task.
NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM (2022)

Analysing the contents of social media platforms such as YouTube, Facebook and Twitter gained interest due to the vast number of users. One of the important tasks is homophobia/transphobia detection. This paper illustrates the system submitted by our team for the homophobia/transphobia detection in social media comments shared task. A machine learning-based model has been designed and various classification algorithms have been implemented for automatic detection of homophobia in YouTube comments. TF/IDF has been used with a range of bigram model for vectorization of comments. Support Vector Machines has been used to develop the proposed model and our submission reported 0.91, 0.92, 0.88 weighted f1-score for English, Tamil and Tamil-English datasets respectively.
giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI (2022)

This paper describes team giniUs' submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English. We have secured a rank of 4 for the English task. We have open-sourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.
SSN\_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text (2022)

DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely {\textquotedblleft}not depressed{\textquotedblright},{\textquotedblleft}moderately depressed{\textquotedblright}, and {\textquotedblleft}severely de-pressed{\textquotedblright}. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN{\_}MLRG1 achieves 58.5{\%}accuracy on the DepSign-LT-EDI@ACL-2022test set.
DepressionOne@LT-EDI-ACL2022: Using Machine Learning with SMOTE and Random UnderSampling to Detect Signs of Depression on Social Media Text. (2022)

Depression is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act. Detecting depression is essential as it must be treated early to avoid painful consequences. Nowadays, people are broadcasting how they feel via posts and comments. Using social media, we can extract many comments related to depression and use NLP techniques to train and detect depression. This work presents the submission of the DepressionOne team at LT-EDI-2022 for the shared task, detecting signs of depression from social media text. The depression data is small and unbalanced. Thus, we have used oversampling and undersampling methods such as SMOTE and RandomUnderSampler to represent the data. Later, we used machine learning methods to train and detect the signs of depression.
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide (2022)

The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task $i$ and applied it to the dataset for Task $j$ to iteratively integrate new silver data for Task $i$. Our official submissions to the shared task obtained a macro-averaged F$_1$ score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach (2022)

Social media has seen enormous growth in its users recently and knowingly or unknowingly the behavior of a person will be reflected in the comments she/he posts on social media. Users having the sign of depression may post negative or disturbing content seeking the attention of other users. Hence, social media data can be analysed to check whether the users' have the sign of depression and help them to get through the situation if required. However, as analyzing the increasing amount of social media data manually in laborious and error-prone, automated tools have to be developed for the same. To address the issue of detecting the sign of depression content on social media, in this paper, we - team MUCS, describe an Ensemble of Machine Learning (ML) models and a Transfer Learning (TL) model submitted to {\textquotedblleft}Detecting Signs of Depression from Social Media Text-LT-EDI@ACL 2022{\textquotedblright} (DepSign-LT-EDI@ACL-2022) shared task at Association for Computational Linguistics (ACL) 2022. Both frequency and text based features are used to train an Ensemble model and Bidirectional Encoder Representations from Transformers (BERT) fine-tuned with raw text is used to train the TL model. Among the two models, the TL model performed better with a macro averaged F-score of 0.479 and placed 18th rank in the shared task. The code to reproduce the proposed models is available in github page1.
SSNCSE\_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models (2022)

Automatic speech recognition is a tool used to transform human speech into a written form. It is used in a variety of avenues, such as in voice commands, customer, service and more. It has emerged as an essential tool in the digitisation of daily life. It has been known to be of vital importance in making the lives of elderly and disabled people much easier. In this paper we describe an automatic speech recognition model, determined by using three pre-trained models, fine-tuned from the Facebook XLSR Wav2Vec2 model, which was trained using the Common Voice Dataset. The best model for speech recognition in Tamil is determined by finding the word error rate of the data. This work explains the submission made by SSNCSE{\_}NLP in the shared task organized by LT-EDI at ACL 2022. A word error rate of 39.4512 is achieved.
IDIAP\_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism (2022)

With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake. This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized. Hence, it is essential to have a system that automatically classifies the hatred content, especially on social media, to take it down. This paper presents a simple modular pipeline classifier with BERT embeddings and attention mechanism to classify hope speech content in the Hope Speech Detection shared task for Equality, Diversity, and Inclusion-ACL 2022. Our system submission ranks fourth with an F1-score of 0.84. We release our code-base here \url{https://github.com/Deepanshu-beep/hope-speech-attention} .
SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts (2022)

Depression is one of the most common mentalissues faced by people. Detecting signs ofdepression early on can help in the treatmentand prevention of extreme outcomes like suicide. Since the advent of the internet, peoplehave felt more comfortable discussing topicslike depression online due to the anonymityit provides. This shared task has used datascraped from various social media sites andaims to develop models that detect signs andthe severity of depression effectively. In thispaper, we employ transfer learning by applyingenhanced BERT model trained for Wikipediadataset to the social media text and performtext classification. The model gives a F1-scoreof 63.8{\%} which was reasonably better than theother competing models.
Findings of the Shared Task on Detecting Signs of Depression from Social Media (2022)

Social media is considered as a platform whereusers express themselves. The rise of social me-dia as one of humanity`s most important publiccommunication platforms presents a potentialprospect for early identification and manage-ment of mental illness. Depression is one suchillness that can lead to a variety of emotionaland physical problems. It is necessary to mea-sure the level of depression from the socialmedia text to treat them and to avoid the nega-tive consequences. Detecting levels of depres-sion is a challenging task since it involves themindset of the people which can change period-ically. The aim of the DepSign-LT-EDI@ACL-2022 shared task is to classify the social me-dia text into three levels of depression namely{\textquotedblleft}Not Depressed{\textquotedblright}, {\textquotedblleft}Moderately Depressed{\textquotedblright}, and{\textquotedblleft}Severely Depressed{\textquotedblright}. This overview presentsa description on the task, the data set, method-ologies used and an analysis on the results ofthe submissions. The models that were submit-ted as a part of the shared task had used a va-riety of technologies from traditional machinelearning algorithms to deep learning models. It could be observed from the result that thetransformer based models have outperformedthe other models. Among the 31 teams whohad submitted their results for the shared task,the best macro F1-score of 0.583 was obtainedusing transformer based model.
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil (2022)

This paper illustrates the overview of the sharedtask on automatic speech recognition in the Tamillanguage. In the shared task, spontaneousTamil speech data gathered from elderly andtransgender people was given for recognitionand evaluation. These utterances were collected from people when they communicatedin the public locations such as hospitals, markets, vegetable shop, etc. The speech corpusincludes utterances of male, female, and transgender and was split into training and testingdata. The given task was evaluated using WER(Word Error Rate). The participants used thetransformer-based model for automatic speechrecognition. Different results using differentpre-trained transformer models are discussedin this overview paper.
DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method (2022)

Depression is linked to the development of dementia. Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61{\%} and an Macro F1 score of 0.54.
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion (2022)

Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope significantly impacts the well-being of people, as suggested by health professionals. It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways. In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score ($2^{st}$ rank). For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score ($1^{st}$, $1^{st}$,$3^{rd}$ rank) respectively. For Kannada, we use Multilingual BERT with 0.32 F1 score($5^{th}$)position. We release our code-base here: \url{https://github.com/Muskaan-Singh/Hate-Speech-detection.git}.
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments (2022)

The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion. In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework. Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with ($1^{st}$, $1^{st}$,$8^{th}$) ranks respectively. We release our codebase here: \url{https://github.com/Muskaan-Singh/Homophobia-and-Transphobia-ACL-Submission.git}.
IDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text (2022)

Depression is a common illness involving sadness and lack of interest in all day-to-day activities. It is important to detect depression at an early stage as it is treated at an early stage to avoid consequences. In this paper, we present our system submission of ARGUABLY for DepSign-LT-EDI@ACL-2022. We aim to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The proposed system is an ensembled voting model with fine-tuned BERT, RoBERTa, and XLNet. Given social media postings in English, the submitted system classify the signs of depression into three labels, namely {\textquotedblleft}not depressed,{\textquotedblright} {\textquotedblleft}moderately depressed,{\textquotedblright} and {\textquotedblleft}severely depressed.{\textquotedblright} Our best model is ranked $3^{rd}$ position with 0.54{\%} accuracy . We make our codebase accessible here.
Overview of The Shared Task on Homophobia and Transphobia Detection in Social Media Comments (2022)

Homophobia and Transphobia Detection is the task of identifying homophobia, transphobia, and non-anti-LGBT+ content from the given corpus. Homophobia and transphobia are both toxic languages directed at LGBTQ+ individuals that are described as hate speech. This paper summarizes our findings on the {\textquotedblleft}Homophobia and Transphobia Detection in social media comments{\textquotedblright} shared task held at LT-EDI 2022 - ACL 2022 1. This shared taskfocused on three sub-tasks for Tamil, English, and Tamil-English (code-mixed) languages. It received 10 systems for Tamil, 13 systems for English, and 11 systems for Tamil-English. The best systems for Tamil, English, and Tamil-English scored 0.570, 0.870, and 0.610, respectively, on average macro F1-score.
Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion (2022)

Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection is focussed on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022) organised as a part of ACL 2022. The participants were provided with annotated training {\&} development datasets and unlabelled test datasets in all the five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes. The performances of the systems submitted by the participants were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available
Identifying Cleartext in Historical Ciphers (2022)

In historical encrypted sources we can find encrypted text sequences, also called ciphertext, as well as non-encrypted cleartexts written in a known language. While most of the cryptanalysis focuses on the decryption of ciphertext, cleartext is often overlooked although it can give us important clues about the historical interpretation and contextualisation of the manuscript. In this paper, we investigate to what extent we can automatically distinguish cleartext from ciphertext in historical ciphers and to what extent we are able to identify its language. The problem is challenging as cleartext sequences in ciphers are often short, up to a few words, in different languages due to historical code-switching. To identify the sequences and the language(s), we chose a rule-based approach and run 7 different models using historical language models on various ciphertexts.
Detecting Diachronic Syntactic Developments in Presence of Bias Terms (2022)

Corpus-based studies of diachronic syntactic changes are typically guided by the results of previous qualitative research. When such results are missing or, as is the case for Vedic Sanskrit, are restricted to small parts of a transmitted corpus, an exploratory framework that detects such changes in a data-driven fashion can substantially support the research process. In this paper, we introduce a customized version of the infinite relational model that groups syntactic constituents based on their structural similarities and their diachronic distributions. We propose a simple way to control for register and intellectual affiliation, and discuss our findings for four syntactic structures in Vedic texts.
Accurate Dependency Parsing and Tagging of Latin (2022)

Having access to high-quality grammatical annotations is important for downstream tasks in NLP as well as for corpus-based research. In this paper, we describe experiments with the Latin BERT word embeddings that were recently be made available by Bamman and Burns (2020). We show that these embeddings produce competitive results in the low-level task morpho-syntactic tagging. In addition, we describe a graph-based dependency parser that is trained with these embeddings and that clearly outperforms various baselines.
Annotating \textquotedblleftAbsolute\textquotedblright Preverbs in the Homeric and Vedic Treebanks (2022)

Indo-European preverbs are uninflected morphemes attaching to verbs and modifying their meaning. In Early Vedic and Homeric Greek, these morphemes held ambiguous morphosyntactic status raising issues for syntactic annotation. This paper focuses on the annotation of preverbs in so-called {\textquotedblleft}absolute{\textquotedblright} position in two Universal Dependencies treebanks. This issue is related to the broader topic of how to annotate ellipsis in Universal Dependencies. After discussing some of the current annotations, we propose a new scheme that better accounts for the variety of absolute constructions.
CHJ-WLSP: Annotation of \textquoteleftWord List by Semantic Principles' Labels for the Corpus of Historical Japanese (2022)

This article presents a word-sense annotation for the Corpus of Historical Japanese: a mashed-up Japanese lexicon based on the {\textquoteleft}Word List by Semantic Principles' (WLSP). The WLSP is a large-scale Japanese thesaurus that includes 98,241 entries with syntactic and hierarchical semantic categories. The historical WLSP is also compiled for the words in ancient Japanese. We utilized a morpheme-word sense alignment table to extract all possible word sense candidates for each word appearing in the target corpus. Then, we manually disambiguated the word senses for 647,751 words in the texts from the 10th century to 1910.
The IKUVINA Treebank (2022)

In this paper, we introduce the first dependency treebank for the Umbrian language (an extinct Indo-European language from the Italic family, once spoken in modern day Italy). We present the source of the corpus : a set of seven bronze tablets describing religious ceremonies, written using two different scripts, unearthed in Umbria in the XVth century. The corpus itself has already been studied extensively by specialists of old Italic and classical Indo-European languages. So we discuss a number of challenges that we encountered as we annotated the corpus following Universal Dependencies' guidelines from existing textual analyses.
Machine Translation of 16Th Century Letters from Latin to German (2022)

This paper outlines our work in collecting training data for and developing a Latin{--}German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin{--}German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.
A Treebank-based Approach to the Supprema Constructio in Dante`s Latin Works (2022)

This paper aims to apply a corpus-driven approach to Dante Alighieri`s Latin works using UDante, a treebank based on Dante Search and part of the Universal Dependencies project. We present a method based on the notion of barycentre applied to a dependency tree as a way to calculate the {\textquotedblleft}syntactic balance{\textquotedblright} of a sentence. Its application to Dante`s Latin works shows its potential in analysing the style of an author, and contributes to the interpretation of the supprema constructio mentioned in DVE II vi 7 as a well balanced syntactic pattern modeled on Latin literary writing.