Search Results for author: Debanjan Mahata

Found 28 papers, 4 papers with code

A Preliminary Exploration of GANs for Keyphrase Generation

no code implementations EMNLP 2020 Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent

We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques.

GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations

no code implementations17 Apr 2021 Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle Lee, Anish Acharya, Rajiv Ratn Shah

Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6, 831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English.

Abstractive Text Summarization

Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers

no code implementations21 Dec 2020 Yaman Kumar, Swati Aggarwal, Debanjan Mahata, Rajiv Ratn Shah, Ponnurangam Kumaraguru, Roger Zimmermann

In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS).

An Annotated Dataset of Discourse Modes in Hindi Stories

no code implementations LREC 2020 Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Am Stent, a

In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative.

#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement

no code implementations14 Dec 2019 Akash Gautam, Puneet Mathur, Rakesh Gosangi, Debanjan Mahata, Ramit Sawhney, Rajiv Ratn Shah

In this paper, we present a dataset containing 9, 973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts.

Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings

no code implementations19 Oct 2019 Dhruva Sahrawat, Debanjan Mahata, Mayank Kulkarni, Haimin Zhang, Rakesh Gosangi, Amanda Stent, Agniv Sharma, Yaman Kumar, Rajiv Ratn Shah, Roger Zimmermann

In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings.

Keyphrase Extraction Word Embeddings

BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories

no code implementations9 Oct 2019 Yaman Kumar, Debanjan Mahata, Sagar Aggarwal, Anmol Chugh, Rajat Maheshwari, Rajiv Ratn Shah

In this paper, we introduce the first and largest Hindi text corpus, named BHAAV, which means emotions in Hindi, for analyzing emotions that a writer expresses through his characters in a story, as perceived by a narrator/reader.

Emotion Recognition

Keyphrase Generation for Scientific Articles using GANs

1 code implementation24 Sep 2019 Avinash Swaminathan, Raj Kuwar Gupta, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah

In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN).

\#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media

1 code implementation ACL 2019 Arijit Ghosh Chowdhury, Ramit Sawhney, Rajiv Ratn Shah, Debanjan Mahata

The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat- ing to our social lives, particularly our health and well-being.

Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

no code implementations NAACL 2019 Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah

The {\#}MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment.

Classification General Classification +2

MobiVSR: A Visual Speech Recognition Solution for Mobile Devices

no code implementations10 May 2019 Nilay Shrivastava, Astitwa Saxena, Yaman Kumar, Rajiv Ratn Shah, Debanjan Mahata, Amanda Stent

Visual speech recognition (VSR) is the task of recognizing spoken language from video input only, without any audio.

Lip Reading Quantization +1

Identifying Offensive Posts and Targeted Offense from Twitter

no code implementations19 Apr 2019 Haimin Zhang, Debanjan Mahata, Simra Shahid, Laiba Mehnaz, Sarthak Anand, Yaman Singla, Rajiv Ratn Shah, Karan Uppal

In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media.

Suggestion Mining from Online Reviews using ULMFiT

1 code implementation19 Apr 2019 Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Ratn Shah, Karan Uppal

In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.

Classification General Classification +2

Harnessing GANs for Zero-shot Learning of New Classes in Visual Speech Recognition

1 code implementation29 Jan 2019 Yaman Kumar, Dhruva Sahrawat, Shubham Maheshwari, Debanjan Mahata, Amanda Stent, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann

To solve this problem, we present a novel approach to zero-shot learning by generating new classes using Generative Adversarial Networks (GANs), and show how the addition of unseen class samples increases the accuracy of a VSR system by a significant margin of 27% and allows it to handle speaker-independent out-of-vocabulary phrases.

Visual Speech Recognition Zero-Shot Learning

Kiki Kills: Identifying Dangerous Challenge Videos from Social Media

no code implementations2 Dec 2018 Nupur Baghel, Yaman Kumar, Paavini Nanda, Rajiv Ratn Shah, Debanjan Mahata, Roger Zimmermann

There has been upsurge in the number of people participating in challenges made popular through social media channels.

Did you take the pill? - Detecting Personal Intake of Medicine from Twitter

no code implementations3 Aug 2018 Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang

We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance and affective computing for tracking moods, emotions and sentiments of patients expressing intake of medicine in social media.

Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings

no code implementations16 Jul 2018 Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann, John R. Talburt

Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases.

Keyword Extraction

A Multimodal Approach to Predict Social Media Popularity

no code implementations16 Jul 2018 Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann

In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction.

Detecting Offensive Tweets in Hindi-English Code-Switched Language

no code implementations WS 2018 Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata

The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script.

Classification General Classification +2

#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter

no code implementations16 May 2018 Debanjan Mahata, Jasper Friedrichs, Hitkul, Rajiv Ratn Shah

Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research.

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