Search Results for author: Krithika Ramesh

Found 7 papers, 2 papers with code

Revisiting Queer Minorities in Lexicons

1 code implementation NAACL (WOAH) 2022 Krithika Ramesh, Sumeet Kumar, Ashiqur KhudaBukhsh

Lexicons play an important role in content moderation often being the first line of defense.

MEGA: Multilingual Evaluation of Generative AI

1 code implementation22 Mar 2023 Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Maxamed Axmed, Kalika Bali, Sunayana Sitaram

Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages.

Benchmarking

Fairness in Language Models Beyond English: Gaps and Challenges

no code implementations24 Feb 2023 Krithika Ramesh, Sunayana Sitaram, Monojit Choudhury

With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors.

Fairness

'Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube

no code implementations17 Feb 2022 Krithika Ramesh, Ashiqur R. KhudaBukhsh, Sumeet Kumar

Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Curb Your Carbon Emissions: Benchmarking Carbon Emissions in Machine Translation

no code implementations26 Sep 2021 Mirza Yusuf, Praatibh Surana, Gauri Gupta, Krithika Ramesh

In recent times, there has been definitive progress in the field of NLP, with its applications growing as the utility of our language models increases with advances in their performance.

Benchmarking Machine Translation +1

Evaluating Gender Bias in Hindi-English Machine Translation

no code implementations ACL (GeBNLP) 2021 Gauri Gupta, Krithika Ramesh, Sanjay Singh

The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject.

Fairness Machine Translation +2

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