Vijil Chenthamarakshan


Learning Implicit Text Generation via Feature Matching
Inkit Padhi | Pierre Dognin | Ke Bai | Cícero Nogueira dos Santos | Vijil Chenthamarakshan | Youssef Mroueh | Payel Das
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Generative feature matching network (GFMN) is an approach for training state-of-the-art implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.


Syntax Based Reordering with Automatically Derived Rules for Improved Statistical Machine Translation
Karthik Visweswariah | Jiri Navratil | Jeffrey Sorensen | Vijil Chenthamarakshan | Nandakishore Kambhatla
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

Urdu and Hindi: Translation and sharing of linguistic resources
Karthik Visweswariah | Vijil Chenthamarakshan | Nandakishore Kambhatla
Coling 2010: Posters