Search Results for author: Bharathi Raja Chakravarthi

Found 62 papers, 18 papers with code

Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation

no code implementations BioNLP (ACL) 2022 Usman Naseem, Ajay Bandi, Shaina Raza, Junaid Rashid, Bharathi Raja Chakravarthi

In this study, we propose a new method that addresses the challenges of medical dialogue generation by incorporating medical knowledge into transformer-based language models.

Dialogue Generation Medical Diagnosis

Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages

no code implementations VarDial (COLING) 2020 Bharathi Raja Chakravarthi, Navaneethan Rajasekaran, Mihael Arcan, Kevin McGuinness, Noel E. O’Connor, John P. McCrae

Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi-supervised approaches.

Bilingual Lexicon Induction Word Embeddings

IIITT at CASE 2021 Task 1: Leveraging Pretrained Language Models for Multilingual Protest Detection

1 code implementation ACL (CASE) 2021 Pawan Kalyan, Duddukunta Reddy, Adeep Hande, Ruba Priyadharshini, Ratnasingam Sakuntharaj, Bharathi Raja Chakravarthi

In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media.

Pretrained Language Models Sentence Classification

Improving Wordnets for Under-Resourced Languages Using Machine Translation

no code implementations GWC 2018 Bharathi Raja Chakravarthi, Mihael Arcan, John P. McCrae

In addition to that, we carried out a manual evaluation of the translations for the Tamil language, where we demonstrate that our approach can aid in improving wordnet resources for under-resourced Dravidian languages.

Machine Translation Natural Language Processing +1

ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021

no code implementations NAACL (SMM4H) 2021 Atul Kr. Ojha, Priya Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. McCrae

Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information.

named-entity-recognition Named Entity Recognition

Findings of the Shared Task on Troll Meme Classification in Tamil

no code implementations EACL (DravidianLangTech) 2021 Shardul Suryawanshi, Bharathi Raja Chakravarthi

On the other hand, this freedom of expression or free speech can be abused by its user or a troll to demean an individual or a group.

Meme Classification

An Overview of Fairness in Data – Illuminating the Bias in Data Pipeline

no code implementations EACL (LTEDI) 2021 Senthil Kumar B, Aravindan Chandrabose, Bharathi Raja Chakravarthi

Data in general encodes human biases by default; being aware of this is a good start, and the research around how to handle it is ongoing.

Fairness

Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction

1 code implementation DravidianLangTech (ACL) 2022 Manikandan Ravikiran, Bharathi Raja Chakravarthi

This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset.

Data Augmentation

Multimodal Hate Speech Detection from Bengali Memes and Texts

no code implementations19 Apr 2022 Md. Rezaul Karim, Sumon Kanti Dey, Tanhim Islam, Bharathi Raja Chakravarthi

Like English, Bengali social media content also includes images along with texts (e. g., multimodal contents are posted by embedding short texts into images on Facebook), only the textual data is not enough to judge them (e. g., to determine they are hate speech).

Hate Speech Detection Natural Language Processing +1

Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification

1 code implementation31 Dec 2021 Sean Benhur, Roshan Nayak, Kanchana Sivanraju, Adeep Hande, Subalalitha Chinnaudayar Navaneethakrishnan, Ruba Priyadharshini, Bharathi Raja Chakravarthi

Due to the exponentially increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence.

TrollsWithOpinion: A Dataset for Predicting Domain-specific Opinion Manipulation in Troll Memes

no code implementations8 Sep 2021 Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, Suzanne Little, Paul Buitelaar

To enable this analysis, we enhanced an existing dataset by annotating the data with our defined classes, resulting in a dataset of 8, 881 IWT or multimodal memes in the English language (TrollsWithOpinion dataset).

Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments

no code implementations1 Sep 2021 Bharathi Raja Chakravarthi, Ruba Priyadharshini, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kayalvizhi Sampath, Durairaj Thenmozhi, Sathiyaraj Thangasamy, Rajendran Nallathambi, John Phillip McCrae

We provide a new hierarchical taxonomy for online homophobia and transphobia, as well as an expert-labelled dataset that will allow homophobic/transphobic content to be automatically identified.

Hope Speech detection in under-resourced Kannada language

2 code implementations10 Aug 2021 Adeep Hande, Ruba Priyadharshini, Anbukkarasi Sampath, Kingston Pal Thamburaj, Prabakaran Chandran, Bharathi Raja Chakravarthi

Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms.

Hope Speech Detection Text Classification +1

Do Images really do the Talking? Analysing the significance of Images in Tamil Troll meme classification

1 code implementation9 Aug 2021 Siddhanth U Hegde, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, Ratnasingam Sakuntharaj, Sathiyaraj Thangasamy, B Bharathi, Bharathi Raja Chakravarthi

Our work illustrates different textual analysis methods and contrasting multimodal methods ranging from simple merging to cross attention to utilising both worlds' - best visual and textual features.

Language Modelling Meme Classification

IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil , Malayalam and English

1 code implementation19 Apr 2021 Nikhil Ghanghor, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi

This paper describes the IIITK’s team submissions to the hope speech detection for equality, diversity and inclusion in Dravidian languages shared task organized by LT-EDI 2021 workshop@EACL 2021.

Hope Speech Detection

IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers

1 code implementation19 Apr 2021 Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Sajeetha Thavareesan, Bharathi Raja Chakravarthi

In a world filled with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for.

Hope Speech Detection

IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada

1 code implementation17 Apr 2021 Nikhil Ghanghor, Parameswari Krishnamurthy, Sajeetha Thavareesan, Ruba Priyadharshini, Bharathi Raja Chakravarthi

This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021.

Classification Language Identification +1

DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language

1 code implementation28 Dec 2020 Md. Rezaul Karim, Sumon Kanti Dey, Tanhim Islam, Sagor Sarker, Mehadi Hasan Menon, Kabir Hossain, Bharathi Raja Chakravarthi, Md. Azam Hossain, Stefan Decker

The exponential growths of social media and micro-blogging sites not only provide platforms for empowering freedom of expressions and individual voices, but also enables people to express anti-social behaviour like online harassment, cyberbullying, and hate speech.

Hate Speech Detection Natural Language Processing +1

Unsupervised Deep Language and Dialect Identification for Short Texts

no code implementations COLING 2020 Koustava Goswami, Rajdeep Sarkar, Bharathi Raja Chakravarthi, Theodorus Fransen, John P. McCrae

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines.

Dialect Identification Natural Language Processing +1

A Survey of Orthographic Information in Machine Translation

no code implementations4 Aug 2020 Bharathi Raja Chakravarthi, Priya Rani, Mihael Arcan, John P. McCrae

It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation.

Bilingual Lexicon Induction Natural Language Processing +1

Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

1 code implementation LREC 2020 Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, Paul Buitelaar

Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U. S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset.

Abuse Detection Meme Classification

Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

1 code implementation11 Apr 2020 Md. Rezaul Karim, Bharathi Raja Chakravarthi, John P. McCrae, Michael Cochez

Evaluations against several baseline embedding models, e. g., Word2Vec and GloVe yield up to 92. 30%, 82. 25%, and 90. 45% F1-scores in case of document classification, sentiment analysis, and hate speech detection, respectively during 5-fold cross-validation tests.

Classification Document Classification +4

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