Ireland

An Automatic Speech Recognition System is developed for addressing the Tamil conversational speech data of the elderly people and transgender. The speech corpus used in this system is collected from the people who adhere their communication in Tamil at some primary places like bank, hospital, vegetable markets. Our ASR system is designed with pre-trained model which is used to recognize the speech data. WER(Word Error Rate) calculation is used to analyse the performance of the ASR system. This evaluation could help to make a comparison of utterances between the elderly people and others. Similarly, the comparison between the transgender and other people is also done. Our proposed ASR system achieves the word error rate as 39.65

ACL 2022

Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil

tamil speech corpus

word error rate

pre-trained model

transformer model

automatic speech recognition

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no, moderate, or severe depression. We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks. To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output. In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training. Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.

Detecting signs of Depression from Social Media：Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster

Hope is an inherent part of human life and essential for improving the quality of life. Hope increases happiness and reduces stress and feelings of helplessness. Hope speech is the desired outcome for better and can be studied using text from various online sources where people express their desires and outcomes. In this paper, we address a deep-learning approach with a combination of linguistic and psycho-linguistic features for hope-speech detection. We report our best results submitted to LT-EDI-2022 which ranked 2nd and 3rd in English and Spanish respectively.

Are transformers the only hope? Hope speech detection for Spanish and English comments

The DepSign-LT-EDI-ACL2022 shared task focuses on early prediction of severity of depression over social media posts. The BioNLP group at Department of Data Science and Engineering in Indian Institute of Science Education and Research Bhopal (IISERB) has participated in this challenge and submitted three runs based on three different text mining models. The severity of depression were categorized into three classes, viz., no depression, moderate, and severe and the data to build models were released as part of this shared task. The objective of this work is to identify relevant features from the given social media texts for effective text classification. As part of our investigation, we explored features derived from text data using document embeddings technique and simple bag of words model following different weighting schemes. Subsequently, adaptive boosting, logistic regression, random forest and support vector machine (SVM) classifiers were used to identify the scale of depression from the given texts. The experimental analysis on the given validation data show that the SVM classifier using the bag of words model following term frequency and inverse document frequency weighting scheme outperforms the other models for identifying depression. However, this framework could not achieve a place among the top ten runs of the shared task. Here we will describe the potential of the proposed framework as well as the possible reasons behind mediocre performance on the given data.

A Bag of Words and Document Embeddings Based Framework to Identify Severity of Depression Over Social Media

Over the years, there has been a slow but steady change in the attitude of society towards different kinds of sexuality. However, on social media platforms, where people have the license to be anonymous, toxic comments targeted at homosexuals, transgenders and the LGBTQ+ community are not uncommon. Detection of homophobic comments on social media can be useful in making the internet a safer place for everyone. For this task, we used a combination of word embeddings and SVM Classifiers as well as some BERT-based transformers. We achieved a weighted F1-score of 0.93 on the English dataset, 0.75 on the Tamil dataset and 0.87 on the Tamil-English Code-Mixed dataset.

Homophobia/Transphobia Detection in Multiple Languages using SVM Classifiers and BERT-based Transformers

In this presentation I present our model for the shared task.

Detecting Signs of Depression from Social Media Text

Depression is a mental illness that negatively affects a person's well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it is important to recognize the signs of depression early. In the last decade, social media has become one of the most common places to express one's feelings. Hence, there is a possibility of text processing and applying machine learning techniques to detect possible signs of depression. In this paper, we present our approaches to solving the shared task titled Detecting Signs of Depression from Social Media Text. We explore three different approaches to solve the challenge: fine-tuning BERT model, leveraging AutoML for the construction of features and classifier selection and finally, we explore latent spaces derived from the combination of textual and knowledge-based representations. We ranked 9th out of 31 teams in the competition. Our best solution, based on knowledge graph and textual representations, was 4.9% behind the best model in terms of Macro F1, and only 1.9% behind in terms of Recall.

BERT, AutoML and Knowledge-graph backed Detection of Depression

In this paper, we describe our approach for the task of homophobia and transphobia detection in English social media comments. The dataset consists of YouTube comments, and it has been released for the shared task on Homophobia/Transphobia Detection in social media comments. Given the high class imbalance, we propose a solution based on data augmentation and ensemble modeling. We fine-tuned different large language models (BERT, RoBERTa, and HateBERT) and used the weighted majority vote on their predictions.
Our proposed model obtained 0.48 and 0.94 for macro and weighted F1-score, respectively, ranking at the third position.

Ensemble Modeling for Homophobia and Transphobia Detection

Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion. In this paper, we describe our approach to classifying homophobia and transphobia in social media comments. We used an
ensemble of transformer-based models to build our classifier. Our model ranked 2nd for English, 8th for Tamil and 10th for Tamil-English.


Ensembled Transformers Against Homophobia and Transphobia

This paper presents our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022. The task was to create a system that, given social media posts in English, should detect the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. We based our solution on transformer-based language models. We fine-tuned selected models: BERT, RoBERTa, XLNet, of which the best results were obtained for RoBERTa. Then, using the prepared corpus, we trained our own language model called DepRoBERTa (RoBERTa for Depression Detection). Fine-tuning of this model improved the results. The third solution was to use the ensemble averaging, which turned out to be the best solution. It achieved a macro-averaged F1-score of 0.583. The source code of prepared solution is available at https://github.com/rafalposwiata/depression-detection-lt-edi-2022.

Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models

Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, however, 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-ACL2022task. 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.

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