6 papers
Cross-Modal Robustness Transfer (CMRT): Training Robust Speech Translation Models Using Adversarial Text
Abderrahmane Issam, Yusuf Can Semerci, Jan Scholtes +1
End-to-End Speech Translation (E2E-ST) has seen significant advancements, yet current models are primarily benchmarked on curated, "clean" datasets. This overlooks critical real-wo…
A Study of Crosslinguistic Influence in Language Models
Abderrahmane Issam, Yusuf Can Semerci, Jan Scholtes +1
The sequential acquisition of languages inevitably leads to Crosslinguistic Influence (CLI), where the syntactic properties of a first language (L1) impact the processing of a seco…
DTW-Align: Bridging the Modality Gap in End-to-End Speech Translation with Dynamic Time Warping Alignment
Abderrahmane Issam, Yusuf Can Semerci, Jan Scholtes +1
End-to-End Speech Translation (E2E-ST) is the task of translating source speech directly into target text bypassing the intermediate transcription step. The representation discrepa…
You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
PaweÅ MÄ ka, Yusuf Can Semerci, Jan Scholtes +1
Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples…
A Representation Level Analysis of NMT Model Robustness to Grammatical Errors
Abderrahmane Issam, Yusuf Can Semerci, Jan Scholtes +1
Understanding robustness is essential for building reliable NLP systems. Unfortunately, in the context of machine translation, previous work mainly focused on documenting robustnes…
Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models
PaweÅ MÄ ka, Yusuf Can Semerci, Jan Scholtes +1
In this paper, we investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English-to-French lang…