Papers by Sinan Mutlu

2 papers
Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER (2025.emnlp-industry)

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Challenge: nave multitask pre-finetuning introduces conflicting optimization signals that degrade overall performance.
Approach: They propose a framework that enables a single shared encoder backbone with modular adapters.
Outcome: The proposed framework achieves comparable performance to individual pre-finetuning while meeting practical deployment constraint.
Decoding Text Spans for Efficient and Accurate Named-Entity Recognition (2026.acl-industry)

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Challenge: Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughput constraints in addition to strong accuracy.
Approach: They propose a span-based NER framework that can be used to compute span representations at the final transformer stage, avoiding redundant computation in earlier layers.
Outcome: The proposed framework matches competitive baselines while improving throughput and reducing computational cost.

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