Vijayasaradhi Indurthi


2023

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Francis Wilde at SemEval-2023 Task 5: Clickbait Spoiler Type Identification with Transformers
Vijayasaradhi Indurthi | Vasudeva Varma
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Clickbait is the text or a thumbnail image that entices the user to click the accompanying link. Clickbaits employ strategies while deliberately hiding the critical elements of the article and revealing partial information in the title, which arouses sufficient curiosity and motivates the user to click the link. In this work, we identify the kind of spoiler given a clickbait title. We formulate this as a text classification problem. We finetune pretrained transformer models on the title of the post and build models for theclickbait-spoiler classification. We achieve a balanced accuracy of 0.70 which is close to the baseline.

2020

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Predicting Clickbait Strength in Online Social Media
Vijayasaradhi Indurthi | Bakhtiyar Syed | Manish Gupta | Vasudeva Varma
Proceedings of the 28th International Conference on Computational Linguistics

Hoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to click on them and satisfy the curiosity gap in their mind. Low quality material pointed to by clickbaits leads to time wastage and annoyance for users. Even for enterprises publishing clickbaits, it hurts more than it helps as it erodes user trust, attracts wrong visitors, and produces negative signals for ranking algorithms. Hence, identifying and flagging clickbait titles is very essential. Previous work on clickbaits has majorly focused on binary classification of clickbait titles. However not all clickbaits are equally clickbaity. It is not only essential to identify a click-bait, but also to identify the intensity of the clickbait based on the strength of the clickbait. In this work, we model clickbait strength prediction as a regression problem. While previous methods have relied on traditional machine learning or vanilla recurrent neural networks, we rigorously investigate the use of transformers for clickbait strength prediction. On a benchmark dataset with ∼39K posts, our methods outperform all the existing methods in the Clickbait Challenge.

2019

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FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter
Vijayasaradhi Indurthi | Bakhtiyar Syed | Manish Shrivastava | Nikhil Chakravartula | Manish Gupta | Vasudeva Varma
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team - Fermi’s model achieved an accuracy of 65.00% for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language. In this paper we provide a detailed description of the approach, as well as the results obtained in the task.

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EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text
Nikhil Chakravartula | Vijayasaradhi Indurthi
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our participation in the SemEval 2019 Task 3 - Contextual Emotion Detection in Text. This task aims to identify emotions, viz. happiness, anger, sadness in the context of a text conversation. Our system is a stacked Bidirectional LSTM, equipped with attention on top of word embeddings pre-trained on a large collection of Twitter data. In this paper, apart from describing our official submission, we elucidate how different deep learning models respond to this task.

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Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings
Vijayasaradhi Indurthi | Bakhtiyar Syed | Manish Shrivastava | Manish Gupta | Vasudeva Varma
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system (Fermi) for Task 6: OffensEval: Identifying and Categorizing Offensive Language in Social Media of SemEval-2019. We participated in all the three sub-tasks within Task 6. We evaluate multiple sentence embeddings in conjunction with various supervised machine learning algorithms and evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team Fermi’s model achieved an F1-score of 64.40%, 62.00% and 62.60% for sub-task A, B and C respectively on the official leaderboard. Our model for sub-task C which uses pre-trained ELMo embeddings for transforming the input and uses SVM (RBF kernel) for training, scored third position on the official leaderboard. Through the paper we provide a detailed description of the approach, as well as the results obtained for the task.

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Fermi at SemEval-2019 Task 4: The sarah-jane-smith Hyperpartisan News Detector
Nikhil Chakravartula | Vijayasaradhi Indurthi | Bakhtiyar Syed
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate the performance of these text classification algorithms. Our team - Fermi’s model achieved an accuracy of 59.10% and an F1 score of 69.5% on the official test data set. In this paper, we provide a detailed description of the approach as well as the results obtained in the task.

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Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA Forums
Bakhtiyar Syed | Vijayasaradhi Indurthi | Manish Shrivastava | Manish Gupta | Vasudeva Varma
Proceedings of the 13th International Workshop on Semantic Evaluation

Online Community Question Answering Forums (cQA) have gained massive popularity within recent years. The rise in users for such forums have led to the increase in the need for automated evaluation for question comprehension and fact evaluation of the answers provided by various participants in the forum. Our team, Fermi, participated in sub-task A of Task 8 at SemEval 2019 - which tackles the first problem in the pipeline of factual evaluation in cQA forums, i.e., deciding whether a posed question asks for a factual information, an opinion/advice or is just socializing. This information is highly useful in segregating factual questions from non-factual ones which highly helps in organizing the questions into useful categories and trims down the problem space for the next task in the pipeline for fact evaluation among the available answers. Our system uses the embeddings obtained from Universal Sentence Encoder combined with XGBoost for the classification sub-task A. We also evaluate other combinations of embeddings and off-the-shelf machine learning algorithms to demonstrate the efficacy of the various representations and their combinations. Our results across the evaluation test set gave an accuracy of 84% and received the first position in the final standings judged by the organizers.

2017

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Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language
Vijayasaradhi Indurthi | Subba Reddy Oota
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper we provide detailed description of the approach, as well as the results obtained in the task. Our models achieved a F1-score of 77.65% for Subtask 1 and 52.15% for Subtask 2.