Search Results for author: Abhinav Joshi

Found 7 papers, 5 papers with code

ISLTranslate: Dataset for Translating Indian Sign Language

1 code implementation11 Jul 2023 Abhinav Joshi, Susmit Agrawal, Ashutosh Modi

To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language.

Sentence Sign Language Translation +1

U-CREAT: Unsupervised Case Retrieval using Events extrAcTion

1 code implementation11 Jul 2023 Abhinav Joshi, Akshat Sharma, Sai Kiran Tanikella, Ashutosh Modi

To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus.

Retrieval

ScriptWorld: Text Based Environment For Learning Procedural Knowledge

1 code implementation8 Jul 2023 Abhinav Joshi, Areeb Ahmad, Umang Pandey, Ashutosh Modi

Text-based games provide a framework for developing natural language understanding and commonsense knowledge about the world in reinforcement learning based agents.

Language Modelling Natural Language Understanding +1

SemEval 2023 Task 6: LegalEval - Understanding Legal Texts

no code implementations19 Apr 2023 Ashutosh Modi, Prathamesh Kalamkar, Saurabh Karn, Aman Tiwari, Abhinav Joshi, Sai Kiran Tanikella, Shouvik Kumar Guha, Sachin Malhan, Vivek Raghavan

LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction.

named-entity-recognition Named Entity Recognition

Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments

no code implementations7 Nov 2022 Abhinav Joshi, Naman Gupta, Jinang Shah, Binod Bhattarai, Ashutosh Modi, Danail Stoyanov

In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times.

3D Hand Pose Estimation Representation Learning +2

COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

2 code implementations NAACL 2022 Abhinav Joshi, Ashwani Bhat, Ayush Jain, Atin Vikram Singh, Ashutosh Modi

Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions.

Multimodal Emotion Recognition

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