Akhilesh Sudhakar


2019

pdf bib
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer
Akhilesh Sudhakar | Bhargav Upadhyay | Arjun Maheswaran
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Text style transfer is the task of transferring the style of text having certain stylistic attributes, while preserving non-stylistic or content information. In this work we introduce the Generative Style Transformer (GST) - a new approach to rewriting sentences to a target style in the absence of parallel style corpora. GST leverages the power of both, large unsupervised pre-trained language models as well as the Transformer. GST is a part of a larger ‘Delete Retrieve Generate’ framework, in which we also propose a novel method of deleting style attributes from the source sentence by exploiting the inner workings of the Transformer. Our models outperform state-of-art systems across 5 datasets on sentiment, gender and political slant transfer. We also propose the use of the GLEU metric as an automatic metric of evaluation of style transfer, which we found to compare better with human ratings than the predominantly used BLEU score.

2017

pdf bib
Experiments on Morphological Reinflection: CoNLL-2017 Shared Task
Akhilesh Sudhakar | Anil Kumar Singh
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection

pdf bib
Reference Scope Identification for Citances Using Convolutional Neural Networks
Saurav Jha | Aanchal Chaurasia | Akhilesh Sudhakar | Anil Kumar Singh
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

pdf bib
Neural Morphological Disambiguation Using Surface and Contextual Morphological Awareness
Akhilesh Sudhakar | Anil Kumar Singh
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)