Ashok Popat

Also published as: Ashok C. Popat


ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Chen-Yu Lee | Chun-Liang Li | Chu Wang | Renshen Wang | Yasuhisa Fujii | Siyang Qin | Ashok Popat | Tomas Pfister
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score.


Language-independent compound splitting with morphological operations
Klaus Macherey | Andrew Dai | David Talbot | Ashok Popat | Franz Och
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


Large Scale Parallel Document Mining for Machine Translation
Jakob Uszkoreit | Jay Ponte | Ashok Popat | Moshe Dubiner
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)


Large Language Models in Machine Translation
Thorsten Brants | Ashok C. Popat | Peng Xu | Franz J. Och | Jeffrey Dean
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)