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.
no code implementations • EACL (AdaptNLP) 2021 • Abhinav Ramesh Kashyap, Laiba Mehnaz, Bhavitvya Malik, Abdul Waheed, Devamanyu Hazarika, Min-Yen Kan, Rajiv Ratn Shah
The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains.
1 code implementation • EMNLP 2020 • Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, Rajiv Ratn Shah
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.
Ranked #1 on Stock Market Prediction on stocknet (using extra training data)
no code implementations • EMNLP 2020 • Ramit Sawhney, Harshit Joshi, Saumya Gandhi, Rajiv Ratn Shah
Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention.
1 code implementation • EMNLP 2020 • Ramit Sawhney, Piyush Khanna, Arshiya Aggarwal, Taru Jain, Puneet Mathur, Rajiv Ratn Shah
Natural language processing has recently made stock movement forecasting and volatility forecasting advances, leading to improved financial forecasting.
1 code implementation • EMNLP 2021 • Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah
Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation.
no code implementations • 2 May 2024 • Somesh Singh, Harini S I, Yaman K Singla, Veeky Baths, Rajiv Ratn Shah, Changyou Chen, Balaji Krishnamurthy
Specifically, we show that training LLMs to predict the receiver behavior of likes and comments improves the LLM's performance on a wide variety of downstream content understanding tasks.
no code implementations • 22 Apr 2024 • Avinash Anand, Kritarth Prasad, Ujjwal Goel, Mohit Gupta, Naman Lal, Astha Verma, Rajiv Ratn Shah
This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents.
1 code implementation • 19 Apr 2024 • Avinash Anand, Mohit Gupta, Kritarth Prasad, Navya Singla, Sanjana Sanjeev, Jatin Kumar, Adarsh Raj Shivam, Rajiv Ratn Shah
Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems.
no code implementations • 19 Apr 2024 • Avinash Anand, Janak Kapuriya, Chhavi Kirtani, Apoorv Singh, Jay Saraf, Naman Lal, Jatin Kumar, Adarsh Raj Shivam, Astha Verma, Rajiv Ratn Shah, Roger Zimmermann
We employ the LLaVA open-source model to answer multimodal physics MCQs and compare the performance with and without using RLHF.
no code implementations • 16 Apr 2024 • Avinash Anand, Raj Jaiswal, Pijush Bhuyan, Mohit Gupta, Siddhesh Bangar, Md. Modassir Imam, Rajiv Ratn Shah, Shin'ichi Satoh
Our proposed approach achieves an IOU of 0. 96 and an OCR Accuracy of 78%, showcasing a remarkable improvement of approximately 25% in the OCR Accuracy compared to the previous Table Transformer approach.
no code implementations • 15 Apr 2024 • Avinash Anand, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma, Rajiv Ratn Shah
Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document.
1 code implementation • 15 Apr 2024 • Avinash Anand, Raj Jaiswal, Mohit Gupta, Siddhesh S Bangar, Pijush Bhuyan, Naman Lal, Rajeev Singh, Ritika Jha, Rajiv Ratn Shah, Shin'ichi Satoh
To solve this problem, domain adaptation approaches have been developed that use a small quantity of labeled data to adjust the model to the target domain.
no code implementations • 20 Mar 2024 • Shivam Ratnakant Mhaskar, Nirmesh J. Shah, Mohammadi Zaki, Ashishkumar P. Gudmalwar, Pankaj Wasnik, Rajiv Ratn Shah
In this paper, we present the development of an isometric NMT system using Reinforcement Learning (RL), with a focus on optimizing the alignment of phoneme counts in the source and target language sentence pairs.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
1 code implementation • 21 Feb 2024 • Ritwik Mishra, Pooja Desur, Rajiv Ratn Shah, Ponnurangam Kumaraguru
We introduce a Translated dataset for Multilingual Coreference Resolution (TransMuCoRes) in 31 South Asian languages using off-the-shelf tools for translation and word-alignment.
no code implementations • 18 Nov 2023 • Varun Khurana, Yaman K Singla, Jayakumar Subramanian, Rajiv Ratn Shah, Changyou Chen, Zhiqiang Xu, Balaji Krishnamurthy
We show that BoigLLM outperforms 13x larger models such as GPT-3. 5 and GPT-4 in this task, demonstrating that while these state-of-the-art models can understand images, they lack information on how these images perform in the real world.
no code implementations • 19 Oct 2023 • Mann Khatri, Mirza Yusuf, Yaman Kumar, Rajiv Ratn Shah, Ponnurangam Kumaraguru
We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance.
1 code implementation • 8 Oct 2023 • Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram, Rajiv Ratn Shah
That is the partial rank similarity is measured (PRS) rather than the individual MOS values as with the L1 loss.
no code implementations • 1 Sep 2023 • Harini S I, Somesh Singh, Yaman K Singla, Aanisha Bhattacharyya, Veeky Baths, Changyou Chen, Rajiv Ratn Shah, Balaji Krishnamurthy
Finally, with the intent of memorable ad generation, we present a scalable method to build a high-quality memorable ad generation model by leveraging automatically annotated data.
no code implementations • 1 Sep 2023 • Ashmit Khandelwal, Aditya Agrawal, Aanisha Bhattacharyya, Yaman K Singla, Somesh Singh, Uttaran Bhattacharya, Ishita Dasgupta, Stefano Petrangeli, Rajiv Ratn Shah, Changyou Chen, Balaji Krishnamurthy
We call these models Large Content and Behavior Models (LCBMs).
1 code implementation • 16 May 2023 • Aanisha Bhattacharya, Yaman K Singla, Balaji Krishnamurthy, Rajiv Ratn Shah, Changyou Chen
Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities.
no code implementations • 3 May 2023 • Mann Khatri, Pritish Wadhwa, Gitansh Satija, Reshma Sheik, Yaman Kumar, Rajiv Ratn Shah, Ponnurangam Kumaraguru
In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments.
no code implementations • 13 Apr 2023 • Astha Verma, Siddhesh Bangar, A V Subramanyam, Naman Lal, Rajiv Ratn Shah, Shin'ichi Satoh
However, these methods suffer from high model variance with low performance on high-dimensional datasets due to the ineffective design of the denoiser and are limited in their utilization of ZO techniques.
1 code implementation • 21 Mar 2023 • Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Yi Yu, Yifang Yin, Rajiv Ratn Shah
To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing.
Ranked #1 on Talking Face Generation on CREMA-D
1 code implementation • 28 Feb 2023 • Shubhankar Singh, Anirudh Pupneja, Shivaansh Mital, Cheril Shah, Manish Bawkar, Lakshman Prasad Gupta, Ajit Kumar, Yaman Kumar, Rushali Gupta, Rajiv Ratn Shah
In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain.
1 code implementation • 12 Feb 2023 • Mohit Sharma, Amit Deshpande, Rajiv Ratn Shah
In this paper, we consider a theoretical model for injecting data bias, namely, under-representation and label bias (Blum & Stangl, 2019).
no code implementations • ACM Multimedia Asia 2022 • Sahil Goyal, Shagun Uppal, Sarthak Bhagat, Dhroov Goel, Sakshat Mali, Yi Yu, Yifang Yin, Rajiv Ratn Shah
Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields.
no code implementations • 27 Nov 2022 • Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Manan Suri, Rajiv Ratn Shah
Based on early-fusion and self-attention-based multimodal interaction between text and acoustic modalities, in this paper, we propose a novel multimodal architecture for disfluency detection from individual utterances.
1 code implementation • 2 Oct 2022 • Mohit Agrawal, Pragyan Mehrotra, Rajesh Kumar, Rajiv Ratn Shah
Zero-effort, Population, and Random-vector.
1 code implementation • 20 Aug 2022 • Yaman Kumar Singla, Rajat Jha, Arunim Gupta, Milan Aggarwal, Aditya Garg, Tushar Malyan, Ayush Bhardwaj, Rajiv Ratn Shah, Balaji Krishnamurthy, Changyou Chen
Motivated by persuasion literature in social psychology and marketing, we introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies.
no code implementations • 28 May 2022 • Devansh Gupta, Aditya Saini, Drishti Bhasin, Sarthak Bhagat, Shagun Uppal, Rishi Raj Jain, Ponnurangam Kumaraguru, Rajiv Ratn Shah
Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification.
1 code implementation • 30 Mar 2022 • Sreyan Ghosh, Sonal Kumar, Yaman Kumar Singla, Rajiv Ratn Shah, S. Umesh
Existing approaches in disfluency detection focus on solving a token-level classification task for identifying and removing disfluencies in text.
no code implementations • 29 Mar 2022 • Debanjan Mahata, Navneet Agarwal, Dibya Gautam, Amardeep Kumar, Swapnil Parekh, Yaman Kumar Singla, Anish Acharya, Rajiv Ratn Shah
Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval.
no code implementations • 18 Dec 2021 • Zaki Mustafa Farooqi, Sreyan Ghosh, Rajiv Ratn Shah
In the current era of the internet, where social media platforms are easily accessible for everyone, people often have to deal with threats, identity attacks, hate, and bullying due to their association with a cast, creed, gender, religion, or even acceptance or rejection of a notion.
no code implementations • 30 Nov 2021 • Pakhi Bamdev, Manraj Singh Grover, Yaman Kumar Singla, Payman Vafaee, Mika Hama, Rajiv Ratn Shah
English proficiency assessments have become a necessary metric for filtering and selecting prospective candidates for both academia and industry.
1 code implementation • 17 Nov 2021 • Yaman Kumar Singla, Sriram Krishna, Rajiv Ratn Shah, Changyou Chen
Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services.
no code implementations • 18 Oct 2021 • Hemant Yadav, Akshat Gupta, Sai Krishna Rallabandi, Alan W Black, Rajiv Ratn Shah
We perform experiments across three different languages: English, Sinhala, and Tamil each with different data sizes to simulate high, medium, and low resource scenarios.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
1 code implementation • 14 Oct 2021 • Sreyan Ghosh, Samden Lepcha, S Sakshi, Rajiv Ratn Shah, S. Umesh
We believe that our dataset would act as a benchmark for the relatively new and un-explored Spoken Language Processing task of detecting toxicity from spoken utterances and boost further research in this space.
no code implementations • 13 Oct 2021 • Anuj Saraswat, Mehar Bhatia, Yaman Kumar Singla, Changyou Chen, Rajiv Ratn Shah
Recent studies in speech perception have been closely linked to fields of cognitive psychology, phonology, and phonetics in linguistics.
1 code implementation • 13 Oct 2021 • Mohit Sharma, Raj Patra, Harshal Desai, Shruti Vyas, Yogesh Rawat, Rajiv Ratn Shah
We present this as a benchmark dataset in noisy learning for video understanding.
1 code implementation • 25 Sep 2021 • Swapnil Parekh, Yaman Singla Kumar, Somesh Singh, Changyou Chen, Balaji Krishnamurthy, Rajiv Ratn Shah
It is well known that natural language models are vulnerable to adversarial attacks, which are mostly input-specific in nature.
no code implementations • 24 Sep 2021 • Yaman Kumar Singla, Swapnil Parekh, Somesh Singh, Junyi Jessy Li, Rajiv Ratn Shah, Changyou Chen
This is in stark contrast to recent probing studies on pre-trained representation learning models, which show that rich linguistic features such as parts-of-speech and morphology are encoded by them.
no code implementations • 30 Aug 2021 • Yaman Kumar Singla, Avykat Gupta, Shaurya Bagga, Changyou Chen, Balaji Krishnamurthy, Rajiv Ratn Shah
In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate.
no code implementations • ACL 2021 • Ramit Sawhney, Mihir Goyal, Prakhar Goel, Puneet Mathur, Rajiv Ratn Shah
We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multi-speaker input to forecast the financial risk associated with the M{\&}A calls.
1 code implementation • 15 Jun 2021 • Mohit Agrawal, Pragyan Mehrotra, Rajesh Kumar, Rajiv Ratn Shah
Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack.
no code implementations • NAACL 2021 • Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, Lucie Flek
Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners.
no code implementations • NAACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.
1 code implementation • NAACL 2021 • Ramit Sawhney, Arshiya Aggarwal, Rajiv Ratn Shah
In this work, we present the first study to discover the gender bias in multimodal volatility prediction due to gender-sensitive audio features and fewer female executives in earnings calls of one of the world{'}s biggest stock indexes, the S{\&}P 500 index.
1 code implementation • NAACL 2021 • Ramit Sawhney, Puneet Mathur, Taru Jain, Akash Kumar Gautam, Rajiv Ratn Shah
We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.
no code implementations • 17 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.
no code implementations • EACL 2021 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.
1 code implementation • EACL 2021 • Ramit Sawhney, Harshit Joshi, Lucie Flek, Rajiv Ratn Shah
Building on clinical studies, PHASE learns phase-like progressions in users{'} historical Plutchik-wheel-based emotions to contextualize suicidal intent.
no code implementations • 23 Feb 2021 • Shivangi Singhal, Rajiv Ratn Shah, Ponnurangam Kumaraguru
The majority of studies on automatic fact-checking and fake news detection is restricted to English only.
1 code implementation • 10 Jan 2021 • Sreyan Ghosh, Sonal Kumar, Harsh Jalan, Hemant Yadav, Rajiv Ratn Shah
This paper describes our proposed system for the AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides.
no code implementations • 6 Jan 2021 • Vidit Jain, Maitree Leekha, Rajiv Ratn Shah, Jainendra Shukla
To analyze our identification module's feasibility, we compared the backchannel prediction models trained on (a) manually-annotated and (b) semi-supervised labels.
no code implementations • 2 Jan 2021 • Jui Shah, Yaman Kumar Singla, Changyou Chen, Rajiv Ratn Shah
In recent times, BERT based transformer models have become an inseparable part of the 'tech stack' of text processing models.
no code implementations • 31 Dec 2020 • Rajaswa Patil, Yaman Kumar Singla, Rajiv Ratn Shah, Mika Hama, Roger Zimmermann
While there has been significant progress towards modelling coherence in written discourse, the work in modelling spoken discourse coherence has been quite limited.
1 code implementation • 27 Dec 2020 • Swapnil Parekh, Yaman Kumar Singla, Changyou Chen, Junyi Jessy Li, Rajiv Ratn Shah
However, little research has been put to understand and interpret the black-box nature of these deep-learning based scoring models.
no code implementations • 21 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).
2 code implementations • Asian Chapter of the Association for Computational Linguistics 2020 • Shagun Uppal, Vivek Gupta, Avinash Swaminathan, Haimin Zhang, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah, Amanda Stent
We further improve the performance by using a joint-objective for classification and textual entailment.
no code implementations • COLING 2020 • Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.
no code implementations • COLING 2020 • Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah
Models with a large number of parameters are prone to over-fitting and often fail to capture the underlying input distribution.
1 code implementation • 25 Nov 2020 • Hemant Yadav, Atul Anshuman Singh, Rachit Mittal, Sunayana Sitaram, Yi Yu, Rajiv Ratn Shah
Training a robust system, e. g., Speech to Text (STT), requires large datasets.
1 code implementation • 30 Oct 2020 • Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah, Roger Zimmermann
We also release several datasets to test computer vision video generation models of their speech understanding.
no code implementations • 18 Sep 2020 • Yi Yu, Abhishek Srivastava, Rajiv Ratn Shah
Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (a form of unsupervised learning with supervision information from data itself).
no code implementations • SEMEVAL 2020 • Sarthak Anand, Pradyumna Gupta, Hemant Yadav, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah
This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text.
no code implementations • 31 Aug 2020 • Shahid Nawaz Khan, Maitree Leekha, Jainendra Shukla, Rajiv Ratn Shah
Automatically detecting personality traits can aid several applications, such as mental health recognition and human resource management.
1 code implementation • 4 Aug 2020 • Dhruv Verma, Kshitij Gulati, Rajiv Ratn Shah
Despite various cutting-edge solutions proposed in the past for personalising fashion recommendation, the technology is still limited by its poor performance on new entities, i. e. the cold-start problem.
1 code implementation • 4 Aug 2020 • Dikshant Sagar, Jatin Garg, Prarthana Kansal, Sejal Bhalla, Rajiv Ratn Shah, Yi Yu
The rise in the fashion industry and its effect on social influencing have made outfit compatibility a need.
Ranked #1 on Preference Mapping on IQOON3000
1 code implementation • 14 Jul 2020 • Anubha Kabra, Mehar Bhatia, Yaman Kumar, Junyi Jessy Li, Rajiv Ratn Shah
This number is increasing further due to COVID-19 and the associated automation of education and testing.
no code implementations • ACL 2020 • Sharan Pai, Nikhil Sachdeva, Prince Sachdeva, Rajiv Ratn Shah
Aphasia is a speech and language disorder which results from brain damage, often characterized by word retrieval deficit (anomia) resulting in naming errors (paraphasia).
no code implementations • WS 2020 • Akash Gautam, Debanjan Mahata, Rakesh Gosangi, Rajiv Ratn Shah
These indicators are then used to expand the dataset.
no code implementations • 16 Jun 2020 • Vishaal Udandarao, Mohit Agrawal, Rajesh Kumar, Rajiv Ratn Shah
On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors.
no code implementations • 12 Jun 2020 • Dhruva Sahrawat, Yaman Kumar, Shashwat Aggarwal, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann
To close the gap between speech understanding and multimedia video applications, in this paper, we show the initial experiments by modelling the perception on visual speech and showing its use case on video compression.
1 code implementation • 9 Jun 2020 • Manraj Singh Grover, Pakhi Bamdev, Ratin Kumar Brala, Yaman Kumar, Mika Hama, Rajiv Ratn Shah
The tool allows audio data and their corresponding annotations to be uploaded and assigned to a user through a key-based API.
1 code implementation • 22 May 2020 • Hemant Yadav, Sreyan Ghosh, Yi Yu, Rajiv Ratn Shah
Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • 17 May 2020 • Manraj Singh Grover, Yaman Kumar, Sumit Sarin, Payman Vafaee, Mika Hama, Rajiv Ratn Shah
In this study, we propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech using attention fusion.
1 code implementation • 15 May 2020 • Shagun Uppal, Anish Madan, Sarthak Bhagat, Yi Yu, Rajiv Ratn Shah
In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.
1 code implementation • 7 May 2020 • Vishaal Udandarao, Abhishek Maiti, Deepak Srivatsav, Suryatej Reddy Vyalla, Yifang Yin, Rajiv Ratn Shah
In this paper, we present a novel framework COBRA that aims to train two modalities (image and text) in a joint fashion inspired by the Contrastive Predictive Coding (CPC) and Noise Contrastive Estimation (NCE) paradigms which preserve both inter and intra-class relationships.
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.
no code implementations • 24 Jan 2020 • Gyanesh Anand, Akash Gautam, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah, Ramit Sawhney
Twitter is a social media platform where users express opinions over a variety of issues.
1 code implementation • 24 Jan 2020 • Shivam Rustagi, Aakash Garg, Pranay Raj Anand, Rajesh Kumar, Yaman Kumar, Rajiv Ratn Shah
The modified GRU-based model outperforms the standard CNN-RNN and Conv3D models for three of the four scenarios.
Human-Computer Interaction I.2.7
no code implementations • 14 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.
no code implementations • 26 Nov 2019 • Baani Leen Kaur Jolly, Palash Aggrawal, Surabhi S. Nath, Viresh Gupta, Manraj Singh Grover, Rajiv Ratn Shah
Brain Computer Interfaces (BCI) have become very popular with Electroencephalography (EEG) being one of the most commonly used signal acquisition techniques.
1 code implementation • 26 Nov 2019 • Osaid Rehman Nasir, Shailesh Kumar Jha, Manraj Singh Grover, Yi Yu, Ajit Kumar, Rajiv Ratn Shah
We then model the highly multi-modal problem of text to face generation as learning the conditional distribution of faces (conditioned on text) in same latent space.
no code implementations • 4 Nov 2019 • Jagriti Sikka, Kushal Satya, Yaman Kumar, Shagun Uppal, Rajiv Ratn Shah, Roger Zimmermann
Predicting the runtime complexity of a programming code is an arduous task.
no code implementations • 19 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.
1 code implementation • 9 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.
no code implementations • 30 Sep 2019 • Vedant Bhatia, Prateek Rawat, Ajit Kumar, Rajiv Ratn Shah
We present an end-to-end solution for ranking candidates based on their suitability to a job description.
1 code implementation • 24 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).
no code implementations • ACL 2019 • Arijit Ghosh Chowdhury, Aniket Didolkar, Ramit Sawhney, Rajiv Ratn Shah
The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language.
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.
no code implementations • ACL 2019 • Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, Rajiv Ratn Shah
Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today.
no code implementations • 28 Jun 2019 • Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, Roger Zimmermann
The model takes silent videos as input and produces speech as the output.
no code implementations • NAACL 2019 • Rohan Mishra, Pradyumn Prakhar Sinha, Ramit Sawhney, Debanjan Mahata, Puneet Mathur, Rajiv Ratn Shah
Suicide is a leading cause of death among youth and the use of social media to detect suicidal ideation is an active line of research.
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.
no code implementations • 10 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.
1 code implementation • 19 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.
no code implementations • 19 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.
1 code implementation • 29 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.
no code implementations • 2 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.
no code implementations • 23 Sep 2018 • Raghav Kapoor, Yaman Kumar, Kshitij Rajput, Rajiv Ratn Shah, Ponnurangam Kumaraguru, Roger Zimmermann
In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users.
no code implementations • 3 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.
no code implementations • 16 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.
no code implementations • 16 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.
no code implementations • 2 Jul 2018 • Yaman Kumar, Mayank Aggarwal, Pratham Nawal, Shin'ichi Satoh, Rajiv Ratn Shah, Roger Zimmerman
Recently, research has started venturing into generating (audio) speech from silent video sequences but there have been no developments thus far in dealing with divergent views and poses of a speaker.
Sound Audio and Speech Processing
no code implementations • NAACL 2018 • Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann
Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases.
no code implementations • 16 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.