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Short abstract:

This paper explores Queer representation in Chinese-English machine translation, employing a focused case study on the DeepL. we seek to unravel the complexities surrounding the queerness of NLP, questioning the underlying biases and assumptions embedded in machine translation algorithms.

Long abstract:

This paper explores the complex intersection of Queer and Natural Language Processing (NLP) within the realm of Chinese-English machine translation, employing a focused case study on the popular DeepL translation tool. Our investigation reveals significant differences in Queer identity representations, especially when translating Chinese terms related to homosexuality and Queer.

The key to our analysis is the apparent mistranslation of DeepL, which interprets the Chinese word " homosexual " as " homophobia ". Furthermore, the tool consistently performs poorly in accurately rendering the word "Queer" across various contextual dimensions. This paper critically examines the implications of such misrepresentations and their potential impact on communication, identity expression and social understanding.

Through an exploration of the DeepL case study, we seek to unravel the complexities surrounding the queerness of NLP, questioning the underlying biases and assumptions embedded in machine translation algorithms. In dissecting this case through the lens of socio-technical imaginaries, we delve into the embedded cultural and societal assumptions that shape AI algorithms. By scrutinizing the inadequacies of current AI models in handling diverse and evolving expressions of sexuality and identity, this paper contributes to the broader discourse on inclusivity, representation, and ethics within the field of Science and Technology Studies (STS).