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<a href="https://huggingface.co/spaces/TurCOMET/paper"><img src="https://huggingface.co/spaces/TurCOMET/README/resolve/main/banner.png" alt="TurCOMET: a morphologically-aware evaluation system for document-level Turkish-English machine translation. EMNLP 2026."></a>
# TurCOMET: A Morphologically-Aware Evaluation System for Document-Level Turkish-English Machine Translation
**Atahan Uz · Hüseyin Emir Akdağ · Susan Üsküdarlı**<br>
Boğaziçi University · *EMNLP 2026, Budapest, Hungary*
[📄 **Paper (PDF)**](https://huggingface.co/spaces/TurCOMET/paper/resolve/main/TurCOMET_EMNLP2026_paper.pdf) ·
[🖼️ **Poster (PDF)**](https://huggingface.co/spaces/TurCOMET/paper/resolve/main/TurCOMET_EMNLP2026_poster.pdf) ·
[🌐 **Project page**](https://huggingface.co/spaces/TurCOMET/paper) ·
[🤖 **Model**](https://huggingface.co/TurCOMET/XCOMET-XL-TR) ·
[📚 **TurkDoc-MT**](https://huggingface.co/datasets/TurCOMET/TurkDoc-MT)
TurCOMET is the first Turkish-specific augmentation of COMET. It adds a 330K-parameter bottleneck to a frozen xCOMET-XL that fuses five Turkish-aware lexical features (chrF++, lemma-TER via Zemberek, LaBSE cosine, length-z and a direction flag) into the pooled representation. This counters the sub-word over-segmentation that makes neural metrics miss errors in agglutinative Turkish. On **TurkDoc-MT**, our new 1,322-document, 7-domain EN↔TR benchmark, an expert MQM audit of 11 LLMs shows that TurCOMET agrees with professional translators better than both BLEU and xCOMET-XL.
## 📖 Read the paper & poster
| Full paper · 21 pages | EMNLP 2026 poster · A0 |
|:---:|:---:|
| <a href="https://huggingface.co/spaces/TurCOMET/paper#paper"><img src="https://huggingface.co/spaces/TurCOMET/README/resolve/main/paper.webp" width="300" alt="First page of the TurCOMET paper"></a> | <a href="https://huggingface.co/spaces/TurCOMET/paper#poster"><img src="https://huggingface.co/spaces/TurCOMET/README/resolve/main/poster.webp" width="300" alt="TurCOMET EMNLP 2026 poster"></a> |
| [Read online](https://huggingface.co/spaces/TurCOMET/paper#paper) · [PDF](https://huggingface.co/spaces/TurCOMET/paper/resolve/main/TurCOMET_EMNLP2026_paper.pdf) | [View online](https://huggingface.co/spaces/TurCOMET/paper#poster) · [PDF](https://huggingface.co/spaces/TurCOMET/paper/resolve/main/TurCOMET_EMNLP2026_poster.pdf) |
## 📊 Key results
Agreement with expert MQM judgments from 4 professional translators (550 rated translations, 22 model × direction systems):
| Metric | Segment Kendall τ | System Kendall τ | System Pearson r |
|:---|---:|---:|---:|
| BLEU | 0.256 | 0.628 | 0.828 |
| xCOMET-XL | 0.267 | 0.636 | 0.950 |
| **TurCOMET (ours)** | **0.291** | **0.749** | **0.976** |
The segment-level gain is significant overall (Δτ = +0.0247, p = 0.022) and comes almost entirely from TR→EN (Δτ = +0.049, p < 0.001). In EN→TR, TurCOMET and xCOMET-XL are statistically tied.
## 🤗 Released resources
| | Repository | Contents |
|:---|:---|:---|
| 🤖 Model | [`TurCOMET/XCOMET-XL-TR`](https://huggingface.co/TurCOMET/XCOMET-XL-TR) | TurCOMET checkpoint (bf16, ~7 GB) for scoring EN↔TR translations |
| 📚 Benchmark | [`TurCOMET/TurkDoc-MT`](https://huggingface.co/datasets/TurCOMET/TurkDoc-MT) | 1,322 document-level EN↔TR pairs across 7 domains |
| ✍️ Annotations | [`TurCOMET/TurkDoc-MT-MQM-Annotations`](https://huggingface.co/datasets/TurCOMET/TurkDoc-MT-MQM-Annotations) | 2,200 expert MQM annotations with error spans, categories and severities |
| 🔁 Outputs | [`TurCOMET/TurkDoc-MT_with_model-translations`](https://huggingface.co/datasets/TurCOMET/TurkDoc-MT_with_model-translations) | TurkDoc-MT translations from all 11 evaluated LLMs, both directions |
| 🏋️ Training data | [`TurCOMET/wmt-da-en-tr_tr-en`](https://huggingface.co/datasets/TurCOMET/wmt-da-en-tr_tr-en) | 35,357 WMT EN↔TR DA ratings with features and the exact 90/5/5 splits |
## 📝 Citation
```bibtex
@inproceedings{uz-etal-2026-turcomet,
title = {{TurCOMET}: A Morphologically-Aware Evaluation System for
Document-Level {T}urkish-{E}nglish Machine Translation},
author = {Uz, Atahan and Akda{\u{g}}, H{\"u}seyin Emir and
{\"U}sk{\"u}darl{\i}, Susan},
booktitle = {Proceedings of the 2026 Conference on Empirical Methods
in Natural Language Processing},
year = {2026},
address = {Budapest, Hungary},
publisher = {Association for Computational Linguistics}
}
```
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