File size: 34,165 Bytes
b094444 f34d883 7103ac1 f34d883 7103ac1 f34d883 279ced1 f34d883 47127f5 f34d883 b094444 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 | <!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<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.">
<meta property="og:image" content="https://huggingface.co/spaces/TurCOMET/paper/resolve/main/img/banner.png">
<meta name="twitter:card" content="summary_large_image">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
<script>
// Hugging Face passes the viewer's theme to Space iframes as ?__theme=light|dark
(function () {
try {
var t = new URLSearchParams(location.search).get('__theme');
if (t === 'light' || t === 'dark') document.documentElement.setAttribute('data-theme', t);
} catch (e) {}
})();
</script>
<style>
:root {
--bg: #F6F7F9;
--surface: #FFFFFF;
--surface-2: #F1F4F8;
--text: #0F172A;
--muted: #475569;
--faint: #64748B;
--border: #E2E8F0;
--accent: #D21F3C;
--accent-fill: #F0374B;
--accent-soft: rgba(240, 55, 75, .09);
--link: #1D4ED8;
--viewer-bg: #E5E9F0;
--shadow: 0 1px 2px rgba(15, 23, 42, .06), 0 8px 24px rgba(15, 23, 42, .06);
--hero: #0B1220;
--mono: "JetBrains Mono", ui-monospace, Menlo, Consolas, monospace;
--sans: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
}
@media (prefers-color-scheme: dark) {
:root:not([data-theme="light"]) {
--bg: #0B1220; --surface: #111A2E; --surface-2: #16213A; --text: #E2E8F0; --muted: #A3B1C6;
--faint: #7C8BA1; --border: #22304A; --accent: #FB7185; --accent-fill: #F0374B;
--accent-soft: rgba(240, 55, 75, .14); --link: #7DD3FC; --viewer-bg: #0A101C;
--shadow: 0 1px 2px rgba(0, 0, 0, .3), 0 8px 24px rgba(0, 0, 0, .25);
}
}
:root[data-theme="dark"] {
--bg: #0B1220; --surface: #111A2E; --surface-2: #16213A; --text: #E2E8F0; --muted: #A3B1C6;
--faint: #7C8BA1; --border: #22304A; --accent: #FB7185; --accent-fill: #F0374B;
--accent-soft: rgba(240, 55, 75, .14); --link: #7DD3FC; --viewer-bg: #0A101C;
--shadow: 0 1px 2px rgba(0, 0, 0, .3), 0 8px 24px rgba(0, 0, 0, .25);
}
* { box-sizing: border-box; }
html { scroll-behavior: smooth; }
body { margin: 0; background: var(--bg); color: var(--text); font-family: var(--sans); font-size: 16px; line-height: 1.6; -webkit-font-smoothing: antialiased; }
a { color: var(--link); text-decoration: none; }
a:hover { text-decoration: underline; }
img { max-width: 100%; height: auto; display: block; }
.wrap { max-width: 1080px; margin: 0 auto; padding: 0 24px; }
@media (max-width: 640px) { .wrap { padding: 0 16px; } }
/* ---------- hero ---------- */
.hero { background: radial-gradient(ellipse 70% 90% at 85% 10%, #1B3352 0%, rgba(11,18,32,0) 70%), var(--hero); color: #F8FAFC; position: relative; }
.hero::before { content: ""; position: absolute; inset: 0 0 auto 0; height: 4px; background: linear-gradient(90deg, #F0374B 0%, #B4467F 55%, #38BDF8 100%); }
.hero .wrap { padding-top: 56px; padding-bottom: 48px; }
.venue { display: inline-flex; align-items: center; gap: 10px; padding: 6px 14px 6px 12px; border-radius: 999px; border: 1px solid rgba(240,55,75,.6); background: rgba(240,55,75,.12); color: #FDA4AF; font-weight: 600; font-size: 14px; letter-spacing: .02em; }
.venue i { width: 8px; height: 8px; border-radius: 50%; background: #F0374B; }
.hero h1 { margin: 22px 0 6px; font-size: clamp(44px, 8vw, 76px); line-height: 1; font-weight: 800; letter-spacing: -0.03em; }
.hero .subtitle { margin: 14px 0 0; font-size: clamp(18px, 2.4vw, 24px); line-height: 1.35; color: #CBD5E1; font-weight: 500; max-width: 820px; }
.authors { margin: 28px 0 4px; font-size: 18px; font-weight: 600; display: flex; flex-wrap: wrap; gap: 4px 22px; }
.affil { color: #94A3B8; font-size: 15px; overflow-wrap: anywhere; }
.affil code { font-family: var(--mono); font-size: 13px; color: #7C8BA1; }
.btns { display: flex; flex-wrap: wrap; gap: 10px; margin-top: 30px; }
.btn { display: inline-flex; align-items: center; gap: 8px; padding: 10px 16px; border-radius: 10px; font-weight: 600; font-size: 15px; color: #E2E8F0; background: rgba(255,255,255,.06); border: 1px solid rgba(255,255,255,.14); transition: background .15s, border-color .15s; }
.btn:hover { text-decoration: none; background: rgba(255,255,255,.12); border-color: rgba(255,255,255,.28); }
.btn.primary { background: #F0374B; border-color: #F0374B; color: #fff; }
.btn.primary:hover { background: #E11D48; border-color: #E11D48; }
.btn svg { width: 17px; height: 17px; flex: none; }
/* ---------- stats ---------- */
.stats { display: grid; grid-template-columns: repeat(5, 1fr); gap: 12px; margin-top: -28px; position: relative; }
.stat { background: var(--surface); border: 1px solid var(--border); border-radius: 14px; padding: 18px 16px 16px; box-shadow: var(--shadow); }
.stat b { display: block; font-size: 30px; line-height: 1.1; font-weight: 800; letter-spacing: -0.02em; font-variant-numeric: tabular-nums; }
.stat b.hl { color: var(--accent); }
.stat span { display: block; margin-top: 6px; font-weight: 600; font-size: 14px; line-height: 1.3; }
.stat small { display: block; margin-top: 4px; color: var(--faint); font-size: 12.5px; line-height: 1.35; }
@media (max-width: 920px) { .stats { grid-template-columns: repeat(3, 1fr); } }
@media (max-width: 560px) { .stats { grid-template-columns: repeat(2, 1fr); } .stat b { font-size: 26px; } .stat:last-child { grid-column: span 2; } }
/* ---------- sections ---------- */
section { padding: 56px 0 8px; }
h2 { font-size: 26px; line-height: 1.2; letter-spacing: -0.015em; margin: 0 0 6px; font-weight: 700; }
h2 .num { color: var(--accent); margin-right: 10px; font-variant-numeric: tabular-nums; }
.lede { color: var(--muted); margin: 0 0 22px; max-width: 760px; }
.card { background: var(--surface); border: 1px solid var(--border); border-radius: 14px; box-shadow: var(--shadow); }
.abstract { padding: 26px 28px; font-size: 16.5px; line-height: 1.7; }
.abstract p { margin: 0; }
@media (max-width: 640px) { .abstract { padding: 20px; } }
/* ---------- viewer ---------- */
.tabs { display: flex; gap: 6px; padding: 6px; background: var(--surface-2); border: 1px solid var(--border); border-radius: 12px; width: max-content; max-width: 100%; }
.tab { appearance: none; border: 0; background: transparent; color: var(--muted); font: 600 15px var(--sans); padding: 9px 18px; border-radius: 8px; cursor: pointer; display: inline-flex; gap: 8px; align-items: center; }
.tab[aria-selected="true"] { background: var(--surface); color: var(--text); box-shadow: 0 1px 3px rgba(15,23,42,.12); }
.tab .count { font-weight: 500; color: var(--faint); font-size: 13px; }
.viewer { margin-top: 14px; overflow: hidden; }
.toolbar { display: flex; align-items: center; justify-content: space-between; gap: 12px; flex-wrap: wrap; padding: 12px 16px; border-bottom: 1px solid var(--border); }
.toolbar .title { font-weight: 600; font-size: 15px; }
.toolbar .title span { color: var(--faint); font-weight: 500; margin-left: 8px; font-variant-numeric: tabular-nums; }
.toolbar .actions { display: flex; gap: 8px; flex-wrap: wrap; }
.tbtn { display: inline-flex; align-items: center; gap: 6px; font-size: 14px; font-weight: 600; padding: 7px 12px; border-radius: 8px; border: 1px solid var(--border); color: var(--text); background: var(--surface); }
.tbtn:hover { text-decoration: none; background: var(--surface-2); }
.tbtn svg { width: 15px; height: 15px; }
.pages { background: var(--viewer-bg); height: min(88vh, 1180px); overflow-y: auto; padding: 24px 16px; overscroll-behavior: contain; }
.page { max-width: 860px; margin: 0 auto 18px; background: #fff; box-shadow: 0 2px 10px rgba(0,0,0,.18); position: relative; }
.page img { width: 100%; height: auto; aspect-ratio: 1654 / 2339; }
.page a { display: block; }
.page .pn { position: absolute; right: 10px; bottom: 8px; font: 500 12px var(--mono); color: #64748B; background: rgba(255,255,255,.85); padding: 2px 6px; border-radius: 4px; }
.poster-pane { background: var(--viewer-bg); padding: 24px 16px; }
.poster-pane a { display: block; max-width: 860px; margin: 0 auto; box-shadow: 0 2px 10px rgba(0,0,0,.18); }
.poster-pane img { width: 100%; height: auto; aspect-ratio: 2600 / 3680; background: #fff; }
.hint { text-align: center; color: var(--faint); font-size: 13px; margin: 14px 0 0; }
[hidden] { display: none !important; }
/* ---------- method ---------- */
.figure { padding: 20px; }
.figure .img { background: #fff; border-radius: 10px; padding: 8px; }
.figure figcaption { color: var(--muted); font-size: 14px; margin-top: 14px; line-height: 1.55; }
.grid3 { display: grid; grid-template-columns: repeat(3, 1fr); gap: 14px; margin-top: 14px; }
.point { padding: 18px 18px 16px; }
.point h3 { margin: 0 0 6px; font-size: 16px; }
.point p { margin: 0; color: var(--muted); font-size: 14.5px; line-height: 1.55; }
.chips { display: flex; flex-wrap: wrap; gap: 6px; margin-top: 10px; }
.chip { font: 500 12.5px var(--mono); padding: 3px 8px; border-radius: 6px; background: var(--surface-2); border: 1px solid var(--border); color: var(--text); }
@media (max-width: 820px) { .grid3 { grid-template-columns: 1fr; } }
/* ---------- tables ---------- */
.twocol { display: grid; grid-template-columns: 1fr; gap: 14px; }
.twocol > *, .grid3 > *, .res > *, .stats > * { min-width: 0; }
.tcard { padding: 18px 18px 14px; }
.tcard h3 { margin: 0 0 2px; font-size: 16px; }
.tcard .cap { color: var(--faint); font-size: 13px; margin: 0 0 12px; line-height: 1.45; }
.corr-wrap { display: grid; grid-template-columns: minmax(0, 1.4fr) minmax(0, 1fr); gap: 24px; align-items: center; }
.corr-wrap .note { margin: 0; }
@media (max-width: 820px) { .corr-wrap { grid-template-columns: 1fr; gap: 14px; } }
.scroll { overflow-x: auto; -webkit-overflow-scrolling: touch; }
table { border-collapse: collapse; width: 100%; font-size: 14px; font-variant-numeric: tabular-nums; }
th, td { padding: 7px 10px; text-align: right; white-space: nowrap; }
th:first-child, td:first-child, table.corr td:nth-child(2) { text-align: left; }
td.src { text-align: left; color: var(--muted); }
thead th { font-size: 12px; text-transform: uppercase; letter-spacing: .05em; color: var(--faint); font-weight: 600; border-bottom: 1px solid var(--border); }
tbody td { border-bottom: 1px solid var(--border); }
tbody tr:last-child td { border-bottom: 0; }
td.lvl { color: var(--faint); font-size: 12.5px; text-transform: uppercase; letter-spacing: .05em; }
tr.ours td { font-weight: 700; }
tr.ours td:not(.lvl) { color: var(--accent); }
tr.sep td { border-top: 2px solid var(--border); }
td.neg { color: var(--faint); }
.note { margin: 14px 0 0; padding: 12px 14px; border-left: 3px solid var(--accent-fill); background: var(--accent-soft); border-radius: 0 8px 8px 0; color: var(--muted); font-size: 14px; }
.note b { color: var(--text); }
/* ---------- resources ---------- */
.res { display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 14px; }
.r { padding: 18px; display: flex; flex-direction: column; gap: 6px; color: var(--text); transition: border-color .15s, transform .15s; }
.r:hover { text-decoration: none; border-color: var(--accent-fill); transform: translateY(-1px); }
.r .kind { font-size: 12px; font-weight: 600; text-transform: uppercase; letter-spacing: .06em; color: var(--faint); display: flex; align-items: center; gap: 8px; }
.r .kind i { width: 8px; height: 8px; border-radius: 2px; }
.r .name { font: 500 14.5px var(--mono); word-break: break-word; }
.r p { margin: 0; color: var(--muted); font-size: 14px; line-height: 1.5; }
/* ---------- cite ---------- */
.cite { position: relative; }
.cite pre { margin: 0; padding: 20px 22px; overflow-x: auto; font: 13.5px/1.6 var(--mono); color: var(--text); }
.copy { position: absolute; top: 12px; right: 12px; font: 600 13px var(--sans); padding: 6px 12px; border-radius: 8px; border: 1px solid var(--border); background: var(--surface-2); color: var(--text); cursor: pointer; }
.copy:hover { border-color: var(--faint); }
footer { margin-top: 64px; border-top: 1px solid var(--border); padding: 28px 0 40px; }
footer .wrap { display: flex; align-items: center; justify-content: space-between; gap: 20px; flex-wrap: wrap; }
.logos { display: flex; align-items: center; gap: 14px; background: #fff; padding: 8px 14px; border-radius: 12px; border: 1px solid var(--border); }
.logos img { height: 44px; width: auto; }
footer p { margin: 0; color: var(--faint); font-size: 14px; }
</style>
</head>
<body>
<svg width="0" height="0" style="position:absolute" aria-hidden="true">
<symbol id="i-doc" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M14 2H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h12a2 2 0 0 0 2-2V8z"/><path d="M14 2v6h6M8 13h8M8 17h5"/></symbol>
<symbol id="i-img" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><rect x="3" y="3" width="18" height="18" rx="2"/><circle cx="8.5" cy="8.5" r="1.5"/><path d="m21 15-5-5L5 21"/></symbol>
<symbol id="i-box" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M21 16V8a2 2 0 0 0-1-1.73l-7-4a2 2 0 0 0-2 0l-7 4A2 2 0 0 0 3 8v8a2 2 0 0 0 1 1.73l7 4a2 2 0 0 0 2 0l7-4A2 2 0 0 0 21 16z"/><path d="M3.27 6.96 12 12.01l8.73-5.05M12 22.08V12"/></symbol>
<symbol id="i-db" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><ellipse cx="12" cy="5" rx="9" ry="3"/><path d="M21 12c0 1.66-4 3-9 3s-9-1.34-9-3"/><path d="M3 5v14c0 1.66 4 3 9 3s9-1.34 9-3V5"/></symbol>
<symbol id="i-quote" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M3 21c3 0 7-1 7-8V5c0-1.25-.76-2-2-2H4c-1.25 0-2 .75-2 2v6c0 1.25.75 2 2 2h3c0 4-2 6-4 6M15 21c3 0 7-1 7-8V5c0-1.25-.76-2-2-2h-4c-1.25 0-2 .75-2 2v6c0 1.25.75 2 2 2h3c0 4-2 6-4 6"/></symbol>
<symbol id="i-ext" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M18 13v6a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h6M15 3h6v6M10 14 21 3"/></symbol>
<symbol id="i-dl" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M21 15v4a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2v-4M7 10l5 5 5-5M12 15V3"/></symbol>
</svg>
<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>
</div>
</div>
</header>
<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><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 & 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>
<div class="tabs" role="tablist" aria-label="Document">
<button class="tab" role="tab" id="tab-paper" aria-controls="pane-paper" aria-selected="true">Paper <span class="count">21 pages</span></button>
<button class="tab" role="tab" id="tab-poster" aria-controls="pane-poster" aria-selected="false">Poster <span class="count">A0</span></button>
</div>
<div class="card viewer" id="pane-paper" role="tabpanel" aria-labelledby="tab-paper">
<div class="toolbar">
<div class="title">TurCOMET — full paper<span id="pageind">Page 1 / 21</span></div>
<div class="actions">
<a class="tbtn" href="TurCOMET_EMNLP2026_paper.pdf" target="_blank" rel="noopener"><svg><use href="#i-ext"/></svg>Open PDF</a>
<a class="tbtn" href="TurCOMET_EMNLP2026_paper.pdf" download target="_blank" rel="noopener"><svg><use href="#i-dl"/></svg>Download</a>
</div>
</div>
<div class="pages" id="pages" tabindex="0" aria-label="Paper pages"></div>
<noscript><p class="hint"><a href="TurCOMET_EMNLP2026_paper.pdf">Open the paper (PDF)</a></p></noscript>
</div>
<div class="card viewer" id="pane-poster" role="tabpanel" aria-labelledby="tab-poster" hidden>
<div class="toolbar">
<div class="title">EMNLP 2026 poster<span>A0 portrait</span></div>
<div class="actions">
<a class="tbtn" href="TurCOMET_EMNLP2026_poster.pdf" target="_blank" rel="noopener"><svg><use href="#i-ext"/></svg>Open PDF</a>
<a class="tbtn" href="TurCOMET_EMNLP2026_poster.pdf" download target="_blank" rel="noopener"><svg><use href="#i-dl"/></svg>Download</a>
</div>
</div>
<div class="poster-pane">
<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>
</div>
</div>
</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 (<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 < 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 & 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>
</div>
</section>
</main>
<footer>
<div class="wrap">
<div class="logos"><img src="img/bogazici.png" alt="Boğaziçi University" width="246" height="240"><img src="img/tabilab.png" alt="TABILAB" width="151" height="240"></div>
<p>Boğaziçi University · EMNLP 2026, October 24–29, Budapest, Hungary</p>
</div>
</footer>
<script>
(function () {
var N = 21, pdf = 'TurCOMET_EMNLP2026_paper.pdf';
var box = document.getElementById('pages'), ind = document.getElementById('pageind');
for (var i = 1; i <= N; i++) {
var n = (i < 10 ? '0' : '') + i;
var d = document.createElement('div');
d.className = 'page'; d.dataset.page = i;
d.innerHTML = '<a href="' + pdf + '#page=' + i + '" target="_blank" rel="noopener" aria-label="Open page ' + i + ' in the PDF">' +
'<img src="pages/page-' + n + '.webp" alt="Paper page ' + i + '" width="1654" height="2339"' + (i > 2 ? ' loading="lazy"' : '') + '></a>' +
'<span class="pn">' + i + ' / ' + N + '</span>';
box.appendChild(d);
}
if ('IntersectionObserver' in window) {
var io = new IntersectionObserver(function (es) {
es.forEach(function (e) { if (e.isIntersecting) ind.textContent = 'Page ' + e.target.dataset.page + ' / ' + N; });
}, { root: box, rootMargin: '-45% 0px -45% 0px' });
box.querySelectorAll('.page').forEach(function (p) { io.observe(p); });
}
var tabs = { paper: document.getElementById('tab-paper'), poster: document.getElementById('tab-poster') };
var panes = { paper: document.getElementById('pane-paper'), poster: document.getElementById('pane-poster') };
function show(k) {
Object.keys(tabs).forEach(function (t) {
var on = t === k;
tabs[t].setAttribute('aria-selected', on ? 'true' : 'false');
tabs[t].tabIndex = on ? 0 : -1;
panes[t].hidden = !on;
});
}
tabs.paper.addEventListener('click', function () { show('paper'); });
tabs.poster.addEventListener('click', function () { show('poster'); });
document.querySelector('.tabs').addEventListener('keydown', function (e) {
if (e.key === 'ArrowRight' || e.key === 'ArrowLeft') {
var k = tabs.paper.getAttribute('aria-selected') === 'true' ? 'poster' : 'paper';
show(k); tabs[k].focus();
}
});
if (/^#(poster|paper)$/.test(location.hash)) {
if (location.hash === '#poster') show('poster');
setTimeout(function () { document.getElementById('read').scrollIntoView(); }, 0);
}
document.getElementById('copy').addEventListener('click', function () {
var b = this, txt = document.getElementById('bibtex').textContent;
function done(ok) { b.textContent = ok ? 'Copied' : 'Select & copy'; setTimeout(function () { b.textContent = 'Copy'; }, 1600); }
if (navigator.clipboard && navigator.clipboard.writeText) {
navigator.clipboard.writeText(txt).then(function () { done(true); }, function () { done(fallback(txt)); });
} else { done(fallback(txt)); }
function fallback(t) {
try { var ta = document.createElement('textarea'); ta.value = t; document.body.appendChild(ta); ta.select(); var ok = document.execCommand('copy'); ta.remove(); return ok; } catch (e) { return false; }
}
});
})();
</script>
</body>
</html>
|