Search Results for author: Ishan Jindal

Found 17 papers, 5 papers with code

Label Definitions Improve Semantic Role Labeling

1 code implementation NAACL 2022 Li Zhang, Ishan Jindal, Yunyao Li

Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.

Semantic Role Labeling Sentence

Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

1 code implementation22 May 2023 Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang

Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations.

Active Learning Decision Making +2

PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation

1 code implementation12 Oct 2022 Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li

In this paper, we address key practical issues with existing evaluation scripts and propose a more strict SRL evaluation metric PriMeSRL.

Semantic Role Labeling Sentence

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis

no code implementations1 Oct 2021 Sumit Shekhar, Bhanu Prakash Reddy Guda, Ashutosh Chaubey, Ishan Jindal, Avneet Jain

We propose novel rewards to account for class imbalance and user feedback in the annotation interface, to improve the active learning method.

Active Learning document understanding +5

CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling

no code implementations Findings of the Association for Computational Linguistics 2020 Ishan Jindal, Yunyao Li, Siddhartha Brahma, Huaiyu Zhu

Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages.

Semantic Role Labeling Sentence

Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

no code implementations11 Nov 2018 Ishan Jindal, Zhiwei Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand.

reinforcement-learning Reinforcement Learning (RL) +1

Tensor Matched Kronecker-Structured Subspace Detection for Missing Information

no code implementations25 Oct 2018 Ishan Jindal, Matthew Nokleby

We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace.

A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip

no code implementations12 Oct 2017 Ishan Jindal, Tony, Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye

In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management.

Feature Engineering Management +1

Learning Deep Networks from Noisy Labels with Dropout Regularization

no code implementations9 May 2017 Ishan Jindal, Matthew Nokleby, Xue-wen Chen

Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance.

Classification and Representation via Separable Subspaces: Performance Limits and Algorithms

no code implementations7 May 2017 Ishan Jindal, Matthew Nokleby

We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning for better classification and representation of multidimensional signals by exploiting the structure in the signal.

Classification Dictionary Learning +1

Effective Object Tracking in Unstructured Crowd Scenes

no code implementations2 Oct 2015 Ishan Jindal, Shanmuganathan Raman

The analysis shows the advantages and limitations of the proposed approach for tracking an object in unstructured crowd scenes.

Object Object Tracking

SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks

no code implementations17 Feb 2015 Aalok Gangopadhyay, Shivam Mani Tripathi, Ishan Jindal, Shanmuganathan Raman

The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years.

Classification General Classification +1

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