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  • šŸ‘‹ Hi, Iā€™m Ashutosh Kumar (@ashutoshml).
  • šŸ‘€ Iā€™m interested in NLP (primarily: Natural Language Generation), and Machine learning
  • šŸŒ± Iā€™m currently learning about applications of Paraphrasing models, and shortcomings of current NLG models.
  • šŸ’žļø Iā€™m looking to collaborate on data-augmentation strategies, NLG, NLG evaluation, NLP model reliability. If you have some interesting problems in these areas, and believe I would be able to contribute in solving the problem, do reach out to me.
  • šŸ“« How to reach me: [email protected]

Ashutosh Kumar's Projects

alleviating-inconsistency icon alleviating-inconsistency

ACL 2022 (Findings): Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks

convlab-2 icon convlab-2

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

datasets icon datasets

šŸ¤— The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

lightning-tutorials icon lightning-tutorials

Collection of Pytorch lightning tutorial form as rich scripts automatically transformed to ipython notebooks.

metrics icon metrics

Machine learning metrics for distributed, scalable PyTorch applications.

multiwoz icon multiwoz

Source code for end-to-end dialogue model from the MultiWOZ paper (Budzianowski et al. 2018, EMNLP)

nl-augmenter icon nl-augmenter

NL-Augmenter šŸ¦Ž ā†’ šŸ A Collaborative Repository of Natural Language Transformations

pydpp icon pydpp

A python package for sampling from determinantal point processes

pytorch-lightning icon pytorch-lightning

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

sentence-vae icon sentence-vae

PyTorch Implementation of "Generating Sentences from a Continuous Space" by Bowman et al 2015 https://arxiv.org/abs/1511.06349

ss_on_decoding_steps. icon ss_on_decoding_steps.

codes for "Scheduled Sampling Based on Decoding Steps for Neural Machine Translation" (long paper of EMNLP-2022)

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