<filename>files/bibs/tallip-2018.bib<gh_stars>1-10
@article{10.1145/3359988,
author = {<NAME> <NAME>},
title = {A Deep Neural Network Framework for English Hindi Question Answering},
year = {2019},
issue_date = {March 2020},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {19},
number = {2},
issn = {2375-4699},
url = {https://doi.org/10.1145/3359988},
doi = {10.1145/3359988},
abstract = {In this article, we propose a unified deep neural network framework for multilingual question answering (QA). The proposed network deals with the multilingual questions and answers snippets. The input to the network is a pair of factoid question and snippet in the multilingual environment (English and Hindi), and output is the relevant answer from the snippet. We begin by generating the snippet using a graph-based language-independent algorithm, which exploits the lexico-semantic similarity between the sentences. The soft alignment of the question words from the English and Hindi languages has been used to learn the shared representation of the question. The learned shared representation of question and attention-based snippet representation are passed as an input to the answer extraction layer of the network, which extracts the answer span from the snippet. Evaluation on a standard multilingual QA dataset shows the state-of-the-art performance with 39.44 Exact Match (EM) and 44.97 F1 values. Similarly, we achieve the performance of 50.11 Exact Match (EM) and 53.77 F1 values on Translated SQuAD dataset.},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = nov,
articleno = {25},
numpages = {22},
keywords = {low-resourced languages, gated recurrent units, Question answering, snippet generation, neural networks, character embedding, attention mechanism}
}
