MuST-SHE: a multilingual benchmark allowing for a fine-grained analysis of gender bias in Machine Translation and Speech Translation.

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MuST-Speakers is a resource designed to i) foster research around gender bias in speech translation (ST) and machine translation (MT), and ii) facilitate the development of gender-enhanced translation models.

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‼️ *Paper, code, models, and outputs out* for one of our last papers "AlignAtt" about Simultaneous Speech Translation recently published at #Interspeech2023!

📍Official Paper:
📍Repo (code, etc.):

#NLProc #NLP #speech #translation

Our pick of the week by @apierg: "Prompt-Driven Neural Machine Translation" by Li et al., Findings ACL 2022.

#machine #translation #MT #NLP #NLProc #ACL #computational #linguistics #prompt

The call for diversity and inclusion (D&I) subsidies for #EMNLP2023 is online!
EMNLP 2023 is providing D&I funds for registration, caregiving, bandwidth, travel and VPN subsidies.

Deadline: October 20, 2023 11:59pm (Anywhere on Earth)


One of the very first multilingual and multimodal model to obtain performance competitive with dedicated models, maybe the first of many? Anyway, a very interesting read:

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