@InProceedings{10.1007/978-3-030-63830-6_55,
author="Saikh, Tanik
and De, Arkadipta
and Bandyopadhyay, Dibyanayan
and Gain, Baban
and Ekbal, Asif",
editor="Yang, Haiqin
and Pasupa, Kitsuchart
and Leung, Andrew Chi-Sing
and Kwok, James T.
and Chan, Jonathan H.
and King, Irwin",
title="A Neural Framework for English-Hindi Cross-Lingual Natural Language Inference",
booktitle="Neural Information Processing",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="655--667",
abstract="Recognizing Textual Entailment (RTE) between two pieces of texts is a very crucial problem in Natural Language Processing (NLP), and it adds further challenges when involving two different languages, i.e. in cross-lingual scenario. The paucity of a large volume of datasets for this problem has become the key bottleneck of nourishing research in this line. In this paper, we provide a deep neural framework for cross-lingual textual entailment involving English and Hindi. As there are no large dataset available for this task, we first create this by translating the premises and hypotheses pairs of Stanford Natural Language Inference (SNLI) (https://nlp.stanford.edu/projects/snli/) dataset into Hindi. We develop a Bidirectional Encoder Representations for Transformers (BERT) based baseline on this newly created dataset. We perform experiments in both mono-lingual and cross-lingual settings. For the mono-lingual setting, we obtain the accuracy scores of 83{\%} and 72{\%} for English and Hindi languages, respectively. In the cross-lingual setting, we obtain the accuracy scores of 69{\%} and 72{\%} for English-Hindi and Hindi-English language pairs, respectively. We hope this dataset can serve as valuable resource for research and evaluation of Cross Lingual Textual Entailment (CLTE) models.",
isbn="978-3-030-63830-6"
}