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20172021
most citedHarnessing the Vulnerability of Latent Layers in Adversarially Trained Models

23 citations · 69 across the 28 of their papers we have counts for

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cs.CL2021

COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews

Shruti Singh, Mayank Singh, Pawan Goyal

Comparing research papers is a conventional method to demonstrate progress in experimental research. We present COMPARE, a taxonomy and a dataset of comparison discussions in peer…

cs.CL2021

Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text

Vivek Srivastava, Mayank Singh

In this shared task, we seek the participating teams to investigate the factors influencing the quality of the code-mixed text generation systems. We synthetically generate code-mi…

cs.CL2021

MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation

Ayush Garg, Sammed S Kagi, Vivek Srivastava +1

Code-mixing is a phenomenon of mixing words and phrases from two or more languages in a single utterance of speech and text. Due to the high linguistic diversity, code-mixing prese…

cs.CL2021

HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text

Vivek Srivastava, Mayank Singh

Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and…

cs.CL2021

TweeNLP: A Twitter Exploration Portal for Natural Language Processing

Viraj Shah, Shruti Singh, Mayank Singh

We present TweeNLP, a one-stop portal that organizes Twitter's natural language processing (NLP) data and builds a visualization and exploration platform. It curates 19,395 tweets…

cs.CL2021

Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text

Vivek Srivastava, Mayank Singh

Code-mixing is a frequent communication style among multilingual speakers where they mix words and phrases from two different languages in the same utterance of text or speech. Ide…