9 citations · 10 across the 10 of their papers we have counts for
14 papers · 1 filter
In-Context Learning in Speech Language Models: Analyzing the Role of Acoustic Features, Linguistic Structure, and Induction Heads
Charlotte Pouw, Hosein Mohebbi, Afra Alishahi +1
In-Context Learning (ICL) has been extensively studied in text-only Language Models, but remains largely unexplored in the speech domain. Here, we investigate how linguistic and ac…
Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures
Chiara Manna, Hosein Mohebbi, Afra Alishahi +2
While Large Language Models achieve state-of-the-art results across a wide range of NLP tasks, they remain prone to systematic biases. Among these, gender bias is particularly sali…
On the reliability of feature attribution methods for speech classification
Gaofei Shen, Hosein Mohebbi, Arianna Bisazza +2
As the capabilities of large-scale pre-trained models evolve, understanding the determinants of their outputs becomes more important. Feature attribution aims to reveal which parts…
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation
Chiara Manna, Afra Alishahi, Frédéric Blain +1
While gender bias in modern Neural Machine Translation (NMT) systems has received much attention, traditional evaluation metrics do not to fully capture the extent to which these s…
A Linguistically Motivated Analysis of Intonational Phrasing in Text-to-Speech Systems: Revealing Gaps in Syntactic Sensitivity
Charlotte Pouw, Afra Alishahi, Willem Zuidema
We analyze the syntactic sensitivity of Text-to-Speech (TTS) systems using methods inspired by psycholinguistic research. Specifically, we focus on the generation of intonational p…
How Language Models Prioritize Contextual Grammatical Cues?
Hamidreza Amirzadeh, Afra Alishahi, Hosein Mohebbi
Transformer-based language models have shown an excellent ability to effectively capture and utilize contextual information. Although various analysis techniques have been used to…