Papers by Yusuf Can Semerci
A Representation Level Analysis of NMT Model Robustness to Grammatical Errors (2025.findings-acl)
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| Challenge: | Existing work on robustness failures or improving robustness has focused on documenting failures . however, there has been limited analysis of model representations in response to noise. |
| Approach: | They perform Grammatical Error Detection probing and representational similarity analysis to examine model representations of ungrammatical inputs and how they evolve through model layers. |
| Outcome: | The proposed model detects and corrects the grammatical error by moving its representation toward the correct form. |
You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models (2025.emnlp-main)
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| Challenge: | Using sparse contextually rich examples, we demonstrate a strong association between training data sparsity and model performance. |
| Approach: | They propose two training strategies to leverage contextually rich examples in training data . they demonstrate strong association between sparsity and model performance . |
| Outcome: | The proposed training strategies improve translation accuracy by 6 and 8 percentage points on the ctxPro evaluation. |
Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models (2025.coling-main)
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| Challenge: | In Context-aware Machine Translation, the context sentences are available to the system and can be used to maintain coherence of translation and resolve ambiguities. |
| Approach: | They investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English- to-French directions. |
| Outcome: | The attention heads influence the models' ability to disambiguate pronouns in the English-to-German and English- to-French directions. |