Papers by Ishan Tarunesh

2 papers
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)

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Challenge: Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages.
Approach: They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model .
Outcome: The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset.
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text (2021.acl-long)

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Challenge: a computational model for code-switching text is lacking in the corpus of real text.
Approach: They propose a neural machine translation model to generate Hindi-English code-switched sentences using monolingual Hindi sentences.
Outcome: The proposed model reduces perplexity on a language modeling task and improves on linguistic inference tasks.

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