In recent years, scientists have employed artificial intelligence to enhance translation across programming languages and automatically fix problems. Machines can now construct increasingly sophisticated word representations thanks to advances in natural language processing (NLP).
NLP models can recognize the human voice and written text, interpret it in a machine-readable format, and communicate in human language rather than code. Every year new iterations of existing NLP models are introduced that can perform tasks like writing emails, articles, sentiment analysis, text extraction, etc., with better accuracy. Despite these advancements, a lack of diversity in artificial intelligence can result in additional systemic issues. For instance, NLP research primarily concentrates on spoken languages, ignoring the more than 200 signed languages in the world and the nearly 70 million people who use them to communicate.
Although signed languages make up a large portion of the world’s languages, they fail to be included in the training dataset for NLP models. Hence there is a rising demand for technology that can handle signed languages, as well as their importance.
Kayo Yin, a master’s student at the Language Technologies Institute, recently co-authored an article advocating for the inclusion of signed languages in NLP research. The paper titled, “Including Signed Languages in Natural Language Processing,” won the Best Theme Paper Award at this month’s 59th Annual Meeting of the Association for Computational Linguistics.
However, bringing this change won’t be easy as sign languages are not universal. They vary from country to country and even in different regions of a large country. For instance, the thumbs-up gesture is considered as approval in India and the USA. However, giving somebody a thumbs-up in Greece, Iran, Russia, Sardinia and parts of West Africa could get you in trouble! Similarly, while the V gesture means victory in the USA, in ASL, it stands for number 2, and in China and Thailand, it is used during posing for photos. But the same sign created considerable controversy for George W. Bush when he flashed it to an Australian audience with the palm pointing inside, which is considered a huge insult.
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While humans can completely comprehend the nuances of a language, computers may not be adept in the same. For example, it may find it challenging to process abstract input like a sarcastic comment or learn that books are the plural form of the word book, yet the plural of deer is deer. So now, when it comes to signed languages, the NLP models must be carefully trained on the basis of the target audience and then slowly be upgraded to function in diverse situations.
Yin says researchers need to work hand in hand while developing the NLP models using signed language datasets. “We can’t fully understand signed language if we only look at the visuals,” she adds. Meanwhile, Yin is happy that her paper was received well by natural language processing researchers and people studying and using signed languages. Yin hopes that the paper motivates people to make a significant change in the community.
