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<!doctype html>
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<title>TurCOMET · EMNLP 2026</title>
<meta name="description" content="TurCOMET: A Morphologically-Aware Evaluation System for Document-Level Turkish-English Machine Translation. Full paper, poster, results and resources (EMNLP 2026).">
<meta property="og:title" content="TurCOMET · EMNLP 2026">
<meta property="og:description" content="A morphologically-aware evaluation system for document-level Turkish↔English MT. Full paper, poster and resources.">
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<header class="hero">
  <div class="wrap">
    <span class="venue"><i></i>EMNLP 2026 · Budapest, Hungary</span>
    <h1>TurCOMET</h1>
    <p class="subtitle">A Morphologically-Aware Evaluation System for Document-Level Turkish-English Machine Translation</p>
    <div class="authors"><span>Atahan Uz</span><span>Hüseyin Emir Akdağ</span><span>Susan Üsküdarlı</span></div>
    <div class="affil">Boğaziçi University · <code>{atahan.uz, huseyin.akdag}@std.bogazici.edu.tr · suzan.uskudarli@bogazici.edu.tr</code></div>
    <div class="btns">
      <a class="btn primary" href="TurCOMET_EMNLP2026_paper.pdf" target="_blank" rel="noopener"><svg><use href="#i-doc"/></svg>Paper (PDF)</a>
      <a class="btn" href="TurCOMET_EMNLP2026_poster.pdf" target="_blank" rel="noopener"><svg><use href="#i-img"/></svg>Poster (PDF)</a>
      <a class="btn" href="https://huggingface.co/TurCOMET/XCOMET-XL-TR" target="_blank" rel="noopener"><svg><use href="#i-box"/></svg>Model</a>
      <a class="btn" href="https://huggingface.co/datasets/TurCOMET/TurkDoc-MT" target="_blank" rel="noopener"><svg><use href="#i-db"/></svg>TurkDoc-MT</a>
      <a class="btn" href="https://huggingface.co/datasets/TurCOMET/TurkDoc-MT-MQM-Annotations" target="_blank" rel="noopener"><svg><use href="#i-db"/></svg>MQM annotations</a>
      <a class="btn" href="#cite"><svg><use href="#i-quote"/></svg>BibTeX</a>
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<main class="wrap">

  <div class="stats" role="list">
    <div class="stat" role="listitem"><b class="hl">+0.113</b><span>system-level Kendall τ</span><small>vs. xCOMET-XL: 0.636 → 0.749</small></div>
    <div class="stat" role="listitem"><b class="hl">+0.024</b><span>segment-level Kendall τ</span><small>0.267 → 0.291, p = 0.022</small></div>
    <div class="stat" role="listitem"><b>330K</b><span>trainable parameters</span><small>&lt;0.01% of frozen xCOMET-XL</small></div>
    <div class="stat" role="listitem"><b>1,322</b><span>documents, 7 domains</span><small>TurkDoc-MT benchmark, EN↔TR</small></div>
    <div class="stat" role="listitem"><b>2,200</b><span>expert MQM annotations</span><small>by 4 professional translators</small></div>
  </div>

  <section id="abstract">
    <h2>Abstract</h2>
    <div class="card abstract">
      <p>We introduce TurCOMET, the first Turkish-specific augmentation of COMET for evaluating translations between Turkish and English. By extending xCOMET-XL with Turkish morphological features and training it on additional Turkish-English data via an additive bottleneck layer, TurCOMET resolves neural evaluation failures caused by sub-word over-segmentation. To validate our metric, we introduce TurkDoc-MT, a meticulously curated Turkish-English document-level aligned dataset of 1,322 instances spanning seven domains. Meta-evaluation demonstrates that TurCOMET significantly outperforms the off-the-shelf baseline, yielding absolute Kendall’s τ correlation gains of +0.113 (system-level) and +0.024 (segment-level), with the largest gains in the TR→EN direction. All models, datasets, code, and annotations are publicly released for the benefit of Turkish natural language processing and benchmarking any translation system.</p>
    </div>
  </section>

  <section id="read">
    <h2>Paper &amp; poster</h2>
    <p class="lede">Read the full camera-ready paper or the EMNLP 2026 poster right here. Click any page to open the PDF at that page.</p>
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        <div class="title">EMNLP 2026 poster<span>A0 portrait</span></div>
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        <a href="TurCOMET_EMNLP2026_poster.pdf" target="_blank" rel="noopener"><img src="img/poster.webp" alt="TurCOMET EMNLP 2026 poster" loading="lazy" width="2600" height="3680"></a>
        <p class="hint">Click the poster to open the vector PDF and zoom in on any detail.</p>
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  </section>

  <section id="method">
    <h2>How TurCOMET works</h2>
    <p class="lede">Turkish is agglutinative: a single root can carry a long chain of derivational and inflectional suffixes, which the XLM-R sub-word tokenizer fragments aggressively. This over-segmentation depresses surface overlap and dilutes the pooled sentence representation, so TurCOMET feeds the metric explicit Turkish-aware signals.</p>
    <figure class="card figure" style="margin:0">
      <div class="img"><img src="img/architecture.webp" alt="TurCOMET architecture: frozen xCOMET-XL backbone with a trainable bottleneck that fuses five Turkish lexical features into the pooled representation" loading="lazy" width="1939" height="1053"></div>
      <figcaption><b>Figure 1.</b> The frozen xCOMET-XL backbone (3.5B parameters) is kept intact. The only addition is a violet bottleneck that fuses a five-dimensional Turkish feature vector into the pooled embedding: <i>h′ = h + W₂ tanh(W₁[h; f] + b₁) + b₂</i>, width 64.</figcaption>
    </figure>
    <div class="grid3">
      <div class="card point">
        <h3>Five Turkish-aware features</h3>
        <p>Character overlap that survives suffix chains, lemma-level edit rate, a reference-free semantic anchor, an omission detector and a direction flag.</p>
        <div class="chips"><span class="chip">chrF++</span><span class="chip">lemma-TER</span><span class="chip">LaBSE cos</span><span class="chip">length-z</span><span class="chip">direction</span></div>
      </div>
      <div class="card point">
        <h3>Never worse at step zero</h3>
        <p>W₂ and b₂ are zero-initialized, so training starts from scores identical to xCOMET-XL. Only ~330K parameters (&lt;0.01% of the network) are trained.</p>
      </div>
      <div class="card point">
        <h3>One model, both directions</h3>
        <p>Fine-tuned on 35,357 WMT EN↔TR direct-assessment ratings (2017–18) with AdamW and MSE loss on a single A100. Held-out segment τ: 0.368 → 0.394.</p>
      </div>
    </div>
  </section>

  <section id="results">
    <h2>Results</h2>
    <p class="lede">An expert MQM audit of 550 translations (4 professional translators, 11 LLMs × 2 directions) shows that TurCOMET agrees best with human judgment.</p>
    <div class="twocol">
      <div class="card tcard">
        <h3>Correlation with expert MQM judgments</h3>
        <p class="cap">Segment level n = 550, system level n = 22. Higher is better.</p>
        <div class="corr-wrap">
        <div class="scroll">
          <table class="corr">
            <thead><tr><th></th><th style="text-align:left">Metric</th><th>Pearson</th><th>Spearman</th><th>Kendall</th></tr></thead>
            <tbody>
              <tr><td class="lvl">Segment</td><td>BLEU</td><td>0.385</td><td>0.335</td><td>0.256</td></tr>
              <tr><td class="lvl"></td><td>xCOMET-XL</td><td>0.419</td><td>0.350</td><td>0.267</td></tr>
              <tr class="ours"><td class="lvl"></td><td>TurCOMET</td><td>0.436</td><td>0.383</td><td>0.291</td></tr>
              <tr class="sep"><td class="lvl">System</td><td>BLEU</td><td>0.828</td><td>0.852</td><td>0.628</td></tr>
              <tr><td class="lvl"></td><td>xCOMET-XL</td><td>0.950</td><td>0.802</td><td>0.636</td></tr>
              <tr class="ours"><td class="lvl"></td><td>TurCOMET</td><td>0.976</td><td>0.913</td><td>0.749</td></tr>
            </tbody>
          </table>
        </div>
        <p class="note"><b>Where the gain comes from.</b> Segment-level Δτ = +0.0247 overall (95% CI [+0.004, +0.049], p = 0.022), carried almost entirely by TR→EN (Δτ = +0.049, p &lt; 0.001). In EN→TR the two neural metrics are statistically indistinguishable.</p>
        </div>
      </div>
      <div class="card tcard">
        <h3>LLM leaderboard on TurkDoc-MT</h3>
        <p class="cap">Averaged over both directions, sorted by the human MQM judges (negated z-score, higher = better).</p>
        <div class="scroll">
          <table>
            <thead><tr><th>Model</th><th>Humans</th><th>BLEU</th><th>xCOMET-XL</th><th>TurCOMET</th></tr></thead>
            <tbody>
              <tr><td><b>gemma-4-31B-it</b></td><td><b>+0.32</b></td><td><b>31.3</b></td><td><b>0.814</b></td><td><b>0.714</b></td></tr>
              <tr><td>translategemma-27b-it</td><td>+0.26</td><td>21.4</td><td>0.805</td><td>0.709</td></tr>
              <tr><td>translategemma-12b-it</td><td>+0.22</td><td>19.0</td><td>0.785</td><td>0.695</td></tr>
              <tr><td>gpt-oss-120b</td><td>+0.21</td><td>22.5</td><td>0.782</td><td>0.688</td></tr>
              <tr><td>gemma-3-12b-it</td><td>+0.21</td><td>25.5</td><td>0.766</td><td>0.675</td></tr>
              <tr><td>gemma-3-27b-it</td><td>+0.17</td><td>28.7</td><td>0.782</td><td>0.686</td></tr>
              <tr><td>gpt-oss-20b</td><td class="neg">−0.19</td><td>22.4</td><td>0.749</td><td>0.665</td></tr>
              <tr><td>Qwen3.5-9B</td><td class="neg">−0.20</td><td>22.6</td><td>0.734</td><td>0.657</td></tr>
              <tr><td>translategemma-4b-it</td><td class="neg">−0.30</td><td>10.1</td><td>0.732</td><td>0.651</td></tr>
              <tr><td>Llama-3.1-8B-Instruct</td><td class="neg">−0.42</td><td>16.2</td><td>0.668</td><td>0.607</td></tr>
              <tr><td>gemma-3-4b-it</td><td class="neg">−0.43</td><td>14.6</td><td>0.665</td><td>0.606</td></tr>
            </tbody>
          </table>
        </div>
      </div>
    </div>
  </section>

  <section id="benchmark">
    <h2>TurkDoc-MT benchmark</h2>
    <p class="lede">1,322 English–Turkish document pairs kept at their native document boundaries, preserving discourse-level context, across seven domains from curated Wikipedia prose to multi-thousand-word TED talks.</p>
    <div class="card tcard">
      <div class="scroll">
        <table>
          <thead><tr><th>Domain</th><th style="text-align:left">Source</th><th>Docs</th><th>Mean words</th><th>90th pct.</th><th>99th pct.</th></tr></thead>
          <tbody>
            <tr><td>Wikipedia articles</td><td class="src">FLORES-200</td><td>200</td><td>72.8</td><td>106.1</td><td>124.2</td></tr>
            <tr><td>Technical &amp; scientific</td><td class="src">TWiST</td><td>200</td><td>119.9</td><td>205.4</td><td>363.3</td></tr>
            <tr><td>Educational dialogue</td><td class="src">XQuAD</td><td>200</td><td>134.1</td><td>190.4</td><td>330.5</td></tr>
            <tr><td>Journalistic prose</td><td class="src">NTREX-128</td><td>122</td><td>342.9</td><td>543.6</td><td>689.1</td></tr>
            <tr><td>Legal rulings</td><td class="src">AYM</td><td>200</td><td>107.2</td><td>193.1</td><td>426.3</td></tr>
            <tr><td>Web crawl</td><td class="src">DocHPLT</td><td>200</td><td>225.1</td><td>348.1</td><td>1,260.0</td></tr>
            <tr><td>Spoken language</td><td class="src">TED2020</td><td>200</td><td>2,646.3</td><td>3,285.4</td><td>4,495.6</td></tr>
            <tr class="sep"><td><b>Total</b></td><td></td><td><b>1,322</b></td><td></td><td></td><td></td></tr>
          </tbody>
        </table>
      </div>
    </div>
  </section>

  <section id="resources">
    <h2>Released resources</h2>
    <p class="lede">Everything needed to use TurCOMET, reproduce the training, and re-run the meta-evaluation.</p>
    <div class="res">
      <a class="card r" href="https://huggingface.co/TurCOMET/XCOMET-XL-TR" target="_blank" rel="noopener">
        <span class="kind"><i style="background:#F0374B"></i>Model</span>
        <span class="name">TurCOMET/XCOMET-XL-TR</span>
        <p>The TurCOMET checkpoint (bf16, ~7 GB). Scores (source, translation, reference) triplets in both directions.</p>
      </a>
      <a class="card r" href="https://huggingface.co/datasets/TurCOMET/TurkDoc-MT" target="_blank" rel="noopener">
        <span class="kind"><i style="background:#38BDF8"></i>Dataset</span>
        <span class="name">TurCOMET/TurkDoc-MT</span>
        <p>The benchmark: 1,322 document-level EN↔TR pairs across seven domains.</p>
      </a>
      <a class="card r" href="https://huggingface.co/datasets/TurCOMET/TurkDoc-MT-MQM-Annotations" target="_blank" rel="noopener">
        <span class="kind"><i style="background:#38BDF8"></i>Dataset</span>
        <span class="name">TurCOMET/TurkDoc-MT-MQM-Annotations</span>
        <p>2,200 expert MQM annotations (error spans, categories, severities, z-scores) from four professional translators.</p>
      </a>
      <a class="card r" href="https://huggingface.co/datasets/TurCOMET/TurkDoc-MT_with_model-translations" target="_blank" rel="noopener">
        <span class="kind"><i style="background:#38BDF8"></i>Dataset</span>
        <span class="name">TurCOMET/TurkDoc-MT_with_model-translations</span>
        <p>TurkDoc-MT with the EN→TR and TR→EN translations produced by every evaluated LLM.</p>
      </a>
      <a class="card r" href="https://huggingface.co/datasets/TurCOMET/wmt-da-en-tr_tr-en" target="_blank" rel="noopener">
        <span class="kind"><i style="background:#38BDF8"></i>Dataset</span>
        <span class="name">TurCOMET/wmt-da-en-tr_tr-en</span>
        <p>Training data: 35,357 WMT EN↔TR DA ratings with precomputed features and the exact 90/5/5 splits.</p>
      </a>
    </div>
  </section>

  <section id="cite">
    <h2>Citation</h2>
    <p class="lede">If you use TurCOMET or TurkDoc-MT, please cite:</p>
    <div class="card cite">
      <button class="copy" type="button" id="copy">Copy</button>
<pre id="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}
}</pre>
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