TITLE: XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU) 

ABSTRACT: Natural Language Processing systems are heavily dependent on the availability
of annotated data to train practical models. Primarily, models are trained on
English datasets. In recent times, significant advances have been made in
multilingual understanding due to the steeply increasing necessity of working
in different languages. One of the points that stands out is that since there
are now so many pre-trained multilingual models, we can utilize them for
cross-lingual understanding tasks. Using cross-lingual understanding and
Natural Language Inference, it is possible to train models whose applications
extend beyond the training language. We can leverage the power of machine
translation to skip the tiresome part of translating datasets from one language
to another. In this work, we focus on improving the original XNLI dataset by
re-translating the MNLI dataset in all of the 14 different languages present in
XNLI, including the test and dev sets of XNLI using Google Translate. We also
perform experiments by training models in all 15 languages and analyzing their
performance on the task of natural language inference. We then expand our
boundary to investigate if we could improve performance in low-resource
languages such as Swahili and Urdu by training models in languages other than
English.
Natural Language Processing systems are heavily dependent on the availability
of annotated data to train practical models. Primarily, models are trained on
English datasets. In recent times, significant advances have been made in
multilingual understanding due to the steeply increasing necessity of working
in different languages. One of the points that stands out is that since there
are now so many pre-trained multilingual models, we can utilize them for
cross-lingual understanding tasks. Using cross-lingual understanding and
Natural Language Inference, it is possible to train models whose applications
extend beyond the training language. We can leverage the power of machine
translation to skip the tiresome part of translating datasets from one language
to another. In this work, we focus on improving the original XNLI dataset by
re-translating the MNLI dataset in all of the 14 different languages present in
XNLI, including the test and dev sets of XNLI using Google Translate. We also
perform experiments by training models in all 15 languages and analyzing their
performance on the task of natural language inference. We then expand our
boundary to investigate if we could improve performance in low-resource
languages such as Swahili and Urdu by training models in languages other than
English.
XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)
http://arxiv.org/abs/2301.06527v1