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Yingshan Susan Wang · Linlu Qiu · Zhaofeng Wu · Roger P. Levy · Yoon Kim
Grammaticality and likelihood are distinct notions in human language. Pretrained language models (LMs), which are probabilistic models of language fitted to maximize corpus likelihood, generate grammatically well-formed text and discriminate well between grammatical and ungrammatical sentences in...
Danlu Chen · Ka Sing He · Jiahe Tian · Chenghao Xiao · Zhaofeng Wu · Taylor Berg-Kirkpatrick · Freda Shi
The landscape of extremely low-resource machine translation (MT) is characterized by perplexing variability in reported performance, often making results across different language pairs difficult to contextualize. For researchers focused on specific language groups—such as ancient languages—it is...
Zhaofeng Wu · Shiqi Wang · Boya Peng · Anuj Kumar Goyal · Melanie Kambadur · Sebastian Ruder · Yoon Kim · Chloe Bi
Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance in lower-resource PLs is often limited by training data availability. In principle, however, most programming skills are universal across PLs, so...