File size: 126,714 Bytes
6fa4bc9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 | {
"paper_id": "P12-1002",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:29:01.834142Z"
},
"title": "Joint Feature Selection in Distributed Stochastic Learning for Large-Scale Discriminative Training in SMT",
"authors": [
{
"first": "Patrick",
"middle": [],
"last": "Simianer",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Heidelberg University",
"location": {
"postCode": "69120",
"settlement": "Heidelberg",
"country": "Germany"
}
},
"email": "simianer@cl.uni-heidelberg.de"
},
{
"first": "Stefan",
"middle": [],
"last": "Riezler",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Heidelberg University",
"location": {
"postCode": "69120",
"settlement": "Heidelberg",
"country": "Germany"
}
},
"email": "riezler@cl.uni-heidelberg.de"
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Carnegie Mellon University Pittsburgh",
"location": {
"postCode": "15213",
"region": "PA",
"country": "USA"
}
},
"email": "cdyer@cs.cmu.edu"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "With a few exceptions, discriminative training in statistical machine translation (SMT) has been content with tuning weights for large feature sets on small development data. Evidence from machine learning indicates that increasing the training sample size results in better prediction. The goal of this paper is to show that this common wisdom can also be brought to bear upon SMT. We deploy local features for SCFG-based SMT that can be read off from rules at runtime, and present a learning algorithm that applies 1 / 2 regularization for joint feature selection over distributed stochastic learning processes. We present experiments on learning on 1.5 million training sentences, and show significant improvements over tuning discriminative models on small development sets.",
"pdf_parse": {
"paper_id": "P12-1002",
"_pdf_hash": "",
"abstract": [
{
"text": "With a few exceptions, discriminative training in statistical machine translation (SMT) has been content with tuning weights for large feature sets on small development data. Evidence from machine learning indicates that increasing the training sample size results in better prediction. The goal of this paper is to show that this common wisdom can also be brought to bear upon SMT. We deploy local features for SCFG-based SMT that can be read off from rules at runtime, and present a learning algorithm that applies 1 / 2 regularization for joint feature selection over distributed stochastic learning processes. We present experiments on learning on 1.5 million training sentences, and show significant improvements over tuning discriminative models on small development sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The standard SMT training pipeline combines scores from large count-based translation models and language models with a few other features and tunes these using the well-understood line-search technique for error minimization of Och (2003) . If only a handful of dense features need to be tuned, minimum error rate training can be done on small tuning sets and is hard to beat in terms of accuracy and efficiency. In contrast, the promise of largescale discriminative training for SMT is to scale to arbitrary types and numbers of features and to provide sufficient statistical support by parameter estimation on large sample sizes. Features may be lexicalized and sparse, non-local and overlapping, or be designed to generalize beyond surface statistics by incorporating part-of-speech or syntactic labels. The modeler's goals might be to identify complex properties of translations, or to counter errors of pretrained translation models and language models by explicitly down-weighting translations that exhibit certain undesired properties. Various approaches to feature engineering for discriminative models have been presented (see Section 2), however, with a few exceptions, discriminative learning in SMT has been confined to training on small tuning sets of a few thousand examples. This contradicts theoretical and practical evidence from machine learning that suggests that larger training samples should be beneficial to improve prediction also in SMT. Why is this?",
"cite_spans": [
{
"start": 229,
"end": 239,
"text": "Och (2003)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "One possible reason why discriminative SMT has mostly been content with small tuning sets lies in the particular design of the features themselves. For example, the features introduced by Chiang et al. (2008) and Chiang et al. (2009) for an SCFG model for Chinese/English translation are of two types: The first type explicitly counters overestimates of rule counts, or rules with bad overlap points, bad rewrites, or with undesired insertions of target-side terminals. These features are specified in handcrafted lists based on a thorough analysis of a tuning set. Such finely hand-crafted features will find sufficient statistical support on a few thousand examples and thus do not benefit from larger training sets. The second type of features deploys external information such as syntactic parses or word alignments to penalize bad reorderings or undesired translations of phrases that cross syntactic constraints. At large scale, extraction of such features quickly becomes infeasible because of costly generation and storage of linguistic annotations. Another possible reason why large training data did not yet show the expected improvements in discriminative SMT is a special overfitting problem of current popular online learning techniques. This is due to stochastic learning on a per-example basis where a weight update on a misclassified example may apply only to a small fraction of data that have been seen before. Thus many features will not generalize well beyond the training examples on which they were introduced.",
"cite_spans": [
{
"start": 188,
"end": 208,
"text": "Chiang et al. (2008)",
"ref_id": "BIBREF3"
},
{
"start": 213,
"end": 233,
"text": "Chiang et al. (2009)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(1) X \u2192 X 1 hat X 2 versprochen, X 1 promised X 2 (2) X \u2192 X 1 hat mir X 2 versprochen, X 1 promised me X 2 (3) X \u2192 X 1 versprach X 2 , X 1 promised X 2",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The goal of this paper is to investigate if and how it is possible to benefit from scaling discriminative training for SMT to large training sets. We deploy generic features for SCFG-based SMT that can efficiently be read off from rules at runtime. Such features include rule ids, rule-local n-grams, or types of rule shapes. Another crucial ingredient of our approach is a combination of parallelized stochastic learning with feature selection inspired by multi-task learning. The simple but effective idea is to randomly divide training data into evenly sized shards, use stochastic learning on each shard in parallel, while performing 1 / 2 regularization for joint feature selection on the shards after each epoch, before starting a new epoch with a reduced feature vector averaged across shards. Iterative feature selection procedure is the key to both efficiency and improved prediction: Without interleaving parallelized stochastic learning with feature selection our largest experiments would not be feasible. Selecting features jointly across shards and averaging does counter the overfitting effect that is inherent to stochastic updating. Our resulting models are learned on large data sets, but they are small and outperform models that tune feature sets of various sizes on small development sets. Our software is freely available as a part of the cdec 1 framework.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1 https://github.com/redpony/cdec",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The great promise of discriminative training for SMT is the possibility to design arbitrarily expressive, complex, or overlapping features in great numbers. The focus of many approaches thus has been on feature engineering and on adaptations of machine learning algorithms to the special case of SMT (where gold standard rankings have to be created automatically). Examples for adapted algorithms include Maximum-Entropy Models (Och and Ney, 2002; Blunsom et al., 2008) , Pairwise Ranking Perceptrons (Shen et al., 2004; Watanabe et al., 2006; Hopkins and May, 2011) , Structured Perceptrons (Liang et al., 2006a) , Boosting (Duh and Kirchhoff, 2008; Wellington et al., 2009) , Structured SVMs (Tillmann and Zhang, 2006; Hayashi et al., 2009) , MIRA (Watanabe et al., 2007; Chiang et al., 2008; Chiang et al., 2009) , and others. Adaptations of the loss functions underlying such algorithms to SMT have recently been described as particular forms of ramp loss optimization (McAllester and Keshet, 2011; Gimpel and Smith, 2012) .",
"cite_spans": [
{
"start": 428,
"end": 447,
"text": "(Och and Ney, 2002;",
"ref_id": "BIBREF33"
},
{
"start": 448,
"end": 469,
"text": "Blunsom et al., 2008)",
"ref_id": "BIBREF0"
},
{
"start": 501,
"end": 520,
"text": "(Shen et al., 2004;",
"ref_id": "BIBREF41"
},
{
"start": 521,
"end": 543,
"text": "Watanabe et al., 2006;",
"ref_id": "BIBREF45"
},
{
"start": 544,
"end": 566,
"text": "Hopkins and May, 2011)",
"ref_id": "BIBREF19"
},
{
"start": 592,
"end": 613,
"text": "(Liang et al., 2006a)",
"ref_id": "BIBREF24"
},
{
"start": 625,
"end": 650,
"text": "(Duh and Kirchhoff, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 651,
"end": 675,
"text": "Wellington et al., 2009)",
"ref_id": "BIBREF48"
},
{
"start": 694,
"end": 720,
"text": "(Tillmann and Zhang, 2006;",
"ref_id": "BIBREF43"
},
{
"start": 721,
"end": 742,
"text": "Hayashi et al., 2009)",
"ref_id": "BIBREF17"
},
{
"start": 750,
"end": 773,
"text": "(Watanabe et al., 2007;",
"ref_id": "BIBREF46"
},
{
"start": 774,
"end": 794,
"text": "Chiang et al., 2008;",
"ref_id": "BIBREF3"
},
{
"start": 795,
"end": 815,
"text": "Chiang et al., 2009)",
"ref_id": "BIBREF4"
},
{
"start": 973,
"end": 1002,
"text": "(McAllester and Keshet, 2011;",
"ref_id": "BIBREF27"
},
{
"start": 1003,
"end": 1026,
"text": "Gimpel and Smith, 2012)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "All approaches have been shown to scale to large feature sets and all include some kind of regularization method. However, most approaches have been confined to training on small tuning sets. Exceptions where discriminative SMT has been used on large training data are Liang et al. (2006a) who trained 1.5 million features on 67,000 sentences, Blunsom et al. (2008) who trained 7.8 million rules on 100,000 sentences, or Tillmann and Zhang (2006) who used 230,000 sentences for training.",
"cite_spans": [
{
"start": 269,
"end": 289,
"text": "Liang et al. (2006a)",
"ref_id": "BIBREF24"
},
{
"start": 344,
"end": 365,
"text": "Blunsom et al. (2008)",
"ref_id": "BIBREF0"
},
{
"start": 421,
"end": 446,
"text": "Tillmann and Zhang (2006)",
"ref_id": "BIBREF43"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our approach is inspired by Duh et al. (2010) who applied multi-task learning for improved generalization in n-best reranking. In contrast to our work, Duh et al. (2010) did not incorporate multitask learning into distributed learning, but defined tasks as n-best lists, nor did they develop new algorithms, but used off-the-shelf multi-task tools.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The work described in this paper is based on the SMT framework of hierarchical phrase-based translation (Chiang, 2005; Chiang, 2007) . Translation rules are extracted from word-aligned parallel sentences and can be seen as productions of a synchronous CFG. Examples are rules like (1)- 3shown in Figure 1 . Local features are designed to be readable directly off the rule at decoding time. We use three rule templates in our work:",
"cite_spans": [
{
"start": 104,
"end": 118,
"text": "(Chiang, 2005;",
"ref_id": "BIBREF5"
},
{
"start": 119,
"end": 132,
"text": "Chiang, 2007)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 296,
"end": 304,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Local Features for Synchronous CFGs",
"sec_num": "3"
},
{
"text": "Rule identifiers: These features identify each rule by a unique identifier. Such features correspond to the relative frequencies of rewrites rules used in standard models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local Features for Synchronous CFGs",
"sec_num": "3"
},
{
"text": "Rule n-grams: These features identify n-grams of consecutive items in a rule. We use bigrams on source-sides of rules. Such features identify possible source side phrases and thus can give preference to rules including them. 2",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local Features for Synchronous CFGs",
"sec_num": "3"
},
{
"text": "Rule shape: These features are indicators that abstract away from lexical items to templates that identify the location of sequences of terminal symbols in relation to non-terminal symbols, on both the source-and target-sides of each rule used. For example, both rules (1) and 2map to the same indicator, namely that a rule is being used that consists of a (NT, term*, NT, term*) pattern on its source side, and an (NT, term*, NT) pattern on its target side. Rule 3maps to a different template, that of (NT, term*, NT) on source and target sides.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local Features for Synchronous CFGs",
"sec_num": "3"
},
{
"text": "The following discussion of learning methods is based on pairwise ranking in a Stochastic Gradient Descent (SGD) framework. The resulting algorithms can be seen as variants of the perceptron algorithm. Let each translation candidate be represented by a feature vector x \u2208 IR D where preference pairs for training are prepared by sorting translations according to smoothed sentence-wise BLEU score (Liang et al., 2006a) against the reference. For a preference pair",
"cite_spans": [
{
"start": 397,
"end": 418,
"text": "(Liang et al., 2006a)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "x j = (x (1) j , x (2) j ) where x (1) j is pre- ferred over x (2) j , andx j = x (1) j \u2212 x (2)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "j , we consider the following hinge loss-type objective function:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "l j (w) = (\u2212 w,x j ) +",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "where (a) + = max(0, a) , w \u2208 IR D is a weight vector, and \u2022, \u2022 denotes the standard vector dot product. Instantiating SGD to the following stochastic subgradient leads to the perceptron algorithm for pairwise ranking 3 (Shen and Joshi, 2005) :",
"cite_spans": [
{
"start": 220,
"end": 242,
"text": "(Shen and Joshi, 2005)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "\u2207l j (w) = \u2212x j if w,x j \u2264 0, 0 else.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "Our baseline algorithm 1 (SDG) scales pairwise ranking to large scale scenarios. The algorithm takes an average over the final weight updates of each epoch instead of keeping a record of all weight updates for final averaging (Collins, 2002) or for voting (Freund and Schapire, 1999) .",
"cite_spans": [
{
"start": 226,
"end": 241,
"text": "(Collins, 2002)",
"ref_id": "BIBREF7"
},
{
"start": 256,
"end": 283,
"text": "(Freund and Schapire, 1999)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "Algorithm 1 SGD: int I, T , float \u03b7 Initialize w0,0,0 \u2190 0. for epochs t \u2190 0 . . . T \u2212 1: do for all i \u2208 {0 . . . I \u2212 1}: do Decode i th input with wt,i,0. for all pairs xj, j \u2208 {0 . . . P \u2212 1}: do wt,i,j+1 \u2190 wt,i,j \u2212 \u03b7\u2207lj(wt,i,j) end for wt,i+1,0 \u2190 wt,i,P end for wt+1,0,0 \u2190 wt,I,0 end for return 1 T T t=1 wt,0,0",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "While stochastic learning exhibits a runtime behavior that is linear in sample size (Bottou, 2004) , very large datasets can make sequential processing infeasible. Algorithm 2 (MixSGD) addresses this problem by parallelization in the framework of MapReduce (Dean and Ghemawat, 2004) .",
"cite_spans": [
{
"start": 84,
"end": 98,
"text": "(Bottou, 2004)",
"ref_id": "BIBREF1"
},
{
"start": 257,
"end": 282,
"text": "(Dean and Ghemawat, 2004)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "Algorithm 2 MixSGD: int I, T, Z, float \u03b7 Partition data into Z shards, each of size S \u2190 I/Z; distribute to machines. for all shards z \u2208 {1 . . . Z}: parallel do Initialize wz,0,0,0 \u2190 0. for epochs t \u2190 0 . . . T \u2212 1: do for all i \u2208 {0 . . . S \u2212 1}: do Decode i th input with wz,t,i,0. for all pairs xj, j \u2208 {0 . . . P \u2212 1}: do wz,t,i,j+1 \u2190 wz,t,i,j \u2212 \u03b7\u2207lj(wz,t,i,j) end for wz,t,i+1,0 \u2190 wz,t,i,P end for wz,t+1,0,0 \u2190 wz,t,S,0 end for end for Collect final weights from each machine,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "return 1 Z Z z=1 1 T T t=1 wz,t,0,0 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "Algorithm 2 is a variant of the SimuParallelSGD algorithm of Zinkevich et al. (2010) or equivalently of the parameter mixing algorithm of McDonald et al. (2010) . The key idea of algorithm 2 is to partition the data into disjoint shards, then train SGD on each shard in parallel, and after training mix the final parameters from each shard by averaging. The algorithm requires no communication between machines until the end.",
"cite_spans": [
{
"start": 61,
"end": 84,
"text": "Zinkevich et al. (2010)",
"ref_id": "BIBREF49"
},
{
"start": 138,
"end": 160,
"text": "McDonald et al. (2010)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "McDonald et al. 2010also present an iterative mixing algorithm where weights are mixed from each shard after training a single epoch of the perceptron in parallel on each shard. The mixed weight vector is re-sent to each shard to start another epoch of training in parallel on each shard. This algorithm corresponds to our algorithm 3 (IterMixSGD).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Feature Selection in Distributed Stochastic Learning",
"sec_num": "4"
},
{
"text": "Partition data into Z shards, each of size S \u2190 I/Z; distribute to machines.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Initialize v \u2190 0. for epochs t \u2190 0 . . . T \u2212 1: do for all shards z \u2208 {1 . . . Z}: parallel do wz,t,0,0 \u2190 v for all i \u2208 {0 . . . S \u2212 1}: do Decode i th input with wz,t,i,0. for all pairs xj, j \u2208 {0 . . . P \u2212 1}: do wz,t,i,j+1 \u2190 wz,t,i,j \u2212 \u03b7\u2207lj(wz,t,i,j) end for wz,t,i+1,0 \u2190 wz,t,i,P end for end for Collect weights v \u2190 1 Z Z z=1 wz,t,S,0. end for return v",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Parameter mixing by averaging will help to ease the feature sparsity problem, however, keeping feature vectors on the scale of several million features in memory can be prohibitive. If network latency is a bottleneck, the increased amount of information sent across the network after each epoch may be a further problem.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Our algorithm 4 (IterSelSGD) introduces feature selection into distributed learning for increased efficiency and as a more radical measure against overfitting. The key idea is to view shards as tasks, and to apply methods for joint feature selection from multi-task learning to achieve small sets of features that are useful across all tasks or shards. Our algorithm represents weights in a Z-by-",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "D matrix W = [w z 1 | . . . |w z Z ] T of stacked D-dimensional weight",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "vectors across Z shards. We compute the 2 norm of the weights in each feature column, sort features by this value, and keep K features in the model. This feature selection procedure is done after each epoch. Reduced weight vectors are mixed and the result is re-sent to each shard to start another epoch of parallel training on each shard.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Algorithm 4 IterSelSGD: int I, T, Z, K, float \u03b7 Partition data into Z shards, each of size S = I/Z; distribute to machines.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Initialize v \u2190 0. for epochs t \u2190 0 . . . T \u2212 1: do for all shards z \u2208 {1 . . . Z}: parallel do wz,t,0,0 \u2190 v for all i \u2208 {0 . . . S \u2212 1}: do Decode i th input with wz,t,i,0. for all pairs xj, j \u2208 {0 . . . P \u2212 1}: do wz,t,i,j+1 \u2190 wz,t,i,j \u2212 \u03b7\u2207lj(wz,t,i,j) end for wz,t,i+1,0 \u2190 wz,t,i,P end for end for Collect/stack weights W \u2190 [w1,t,S,0| . . . |wZ,t,S,0] T Select top K feature columns of W by 2 norm and for k \u2190 1 . . . K do v[k] = 1 Z Z z=1 W[z][k]. end for end for return v",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "This algorithm can be seen as an instance of 1 / 2 regularization as follows: Let w d be the dth column vector of W, representing the weights for the dth feature across tasks/shards. 1 / 2 regularization penalizes weights W by the weighted 1 / 2 norm",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "\u03bb||W|| 1,2 = \u03bb D d=1 ||w d || 2 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Each 2 norm of a weight column represents the relevance of the corresponding feature across tasks/shards. The 1 sum of the 2 norms enforces a selection among features based on these norms. Consider for example the two 5-feature, 3task weight matrices in Figure 2 . Assuming the same loss for both matrices, the right-hand side matrix is preferred because of a smaller 1 / 2 norm (12 instead of 18). This matrix shares features across tasks which leads to larger 2 norms for some columns (here ||w 1 || 2 and ||w 2 || 2 ) and forces other columns to zero. This results in shrinking the matrix to those features that are useful across all tasks.",
"cite_spans": [],
"ref_spans": [
{
"start": 254,
"end": 262,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "w 1 w 2 w 3 w 4 w 5 w 1 w 2 w 3 w 4 w 5 w z1 [ 6 4 0 0 0 ] [ 6 4 0 0 0 ] w z2 [ 0 0 3 0 0 ] [ 3 0 0 0 0 ] w z3 [ 0 0 0 2 3 ] [ 2 3 0 0 0 ] column 2 norm: 6 4 3 2 3 7 5 0 0 0 1 sum: \u21d2 18 \u21d2 12",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Figure 2: 1 / 2 regularization enforcing feature selection.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "Our algorithm is related to Obozinski et al. (2010)'s approach to 1 / 2 regularization where feature columns are incrementally selected based on the 2 norms of the gradient vectors corresponding to feature columns. Their algorithm is itself an extension of gradient-based feature selection based on the 1 norm, e.g., Perkins et al. (2003) . 4 In contrast to these approaches we approximate the gradient by using the weights given by the ranking algorithm itself. This relates our work to weight-based recursive feature elimination (RFE) (Lal et al., 2006) . Furthermore, algorithm 4 performs feature selection based on a choice of meta-parameter of K features instead of by thresholding a regularization meta-parameter \u03bb, however, these techniques are equivalent and can be transformed into each other.",
"cite_spans": [
{
"start": 317,
"end": 338,
"text": "Perkins et al. (2003)",
"ref_id": "BIBREF37"
},
{
"start": 341,
"end": 342,
"text": "4",
"ref_id": null
},
{
"start": 537,
"end": 555,
"text": "(Lal et al., 2006)",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 3 IterMixSGD: int I, T, Z, float \u03b7",
"sec_num": null
},
{
"text": "The datasets used in our experiments are versions of the News Commentary (nc), News Crawl (crawl) and Europarl (ep) corpora described in Table 1 . The translation direction is German-to-English.",
"cite_spans": [],
"ref_spans": [
{
"start": 137,
"end": 144,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Data, Systems, Experiment Settings",
"sec_num": "5.1"
},
{
"text": "The SMT framework used in our experiments is hierarchical phrase-based translation (Chiang, 2007) . We use the cdec decoder 5 (Dyer et al., 2010) and induce SCFG grammars from two sets of symmetrized alignments using the method described by Chiang (2007) . All data was tokenized and lowercased; German compounds were split (Dyer, 2009) . For word alignment of the news-commentary data, we used GIZA++ (Och and Ney, 2000) ; for aligning the Europarl data, we used the Berkeley aligner (Liang et al., 2006b) . Before training, we collect all the grammar rules necessary to translate each individual sentence into separate files (so-called per-sentence grammars) (Lopez, 2007) . When decoding, cdec loads the appropriate file immediately prior to translation of the sentence. The computational overhead is minimal compared to the expense of decoding. Also, deploying disk space instead of memory fits perfectly into the MapReduce framework we are working in. Furthermore, the extraction of grammars for training is done in a leave-one-out fashion (Zollmann and Sima'an, 2005) where rules are extracted for a parallel sentence pair only if the same rules are found in other sentences of the corpus as well.",
"cite_spans": [
{
"start": 83,
"end": 97,
"text": "(Chiang, 2007)",
"ref_id": "BIBREF6"
},
{
"start": 126,
"end": 145,
"text": "(Dyer et al., 2010)",
"ref_id": "BIBREF12"
},
{
"start": 241,
"end": 254,
"text": "Chiang (2007)",
"ref_id": "BIBREF6"
},
{
"start": 324,
"end": 336,
"text": "(Dyer, 2009)",
"ref_id": "BIBREF14"
},
{
"start": 402,
"end": 421,
"text": "(Och and Ney, 2000)",
"ref_id": "BIBREF31"
},
{
"start": 485,
"end": 506,
"text": "(Liang et al., 2006b)",
"ref_id": "BIBREF25"
},
{
"start": 661,
"end": 674,
"text": "(Lopez, 2007)",
"ref_id": "BIBREF26"
},
{
"start": 1045,
"end": 1073,
"text": "(Zollmann and Sima'an, 2005)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data, Systems, Experiment Settings",
"sec_num": "5.1"
},
{
"text": "3-gram (news-commentary) and 5-gram (Europarl) language models are trained on the data described in Table 1 , using the SRILM toolkit (Stolcke, 2002) and binarized for efficient querying using kenlm (Heafield, 2011) . For the 5-gram language models, we replaced every word in the lm training data with <unk> that did not appear in the English part of the parallel training data to build an open vocabulary language model. Training data for discriminative learning are prepared by comparing a 100-best list of translations against a single reference using smoothed persentence BLEU (Liang et al., 2006a) . From the BLEU-reordered n-best list, translations were put into sets for the top 10% level (HI), the middle 80% level (MID), and the bottom 10% level (LOW). These level sets are used for multipartite ranking (ep) training data and also News Crawl (crawl) dev/test data were taken from the WMT11 translation task (http://statmt.org/ wmt11/translation-task.html). The dev/test data of nc are the sets provided with the WMT07 shared task (http://statmt.org/wmt07/shared-task.html). Ep dev/test data is from WMT08 shared task (http://statmt.org/wmt08/shared-task.html). The numbers in brackets for the rule counts of ep/nc training data are total counts of rules in the per-sentence grammars.",
"cite_spans": [
{
"start": 134,
"end": 149,
"text": "(Stolcke, 2002)",
"ref_id": "BIBREF42"
},
{
"start": 199,
"end": 215,
"text": "(Heafield, 2011)",
"ref_id": "BIBREF18"
},
{
"start": 581,
"end": 602,
"text": "(Liang et al., 2006a)",
"ref_id": "BIBREF24"
},
{
"start": 813,
"end": 817,
"text": "(ep)",
"ref_id": null
}
],
"ref_spans": [
{
"start": 100,
"end": 107,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Data, Systems, Experiment Settings",
"sec_num": "5.1"
},
{
"text": "where translation pairs are built between the elements in HI-MID, HI-LOW, and MID-LOW, but not between translations inside sets on the same level. This idea is depicted graphically in Figure 3 . The intuition is to ensure that good translations are preferred over bad translations without teasing apart small differences. For evaluation, we used the mteval-v11b.pl script to compute lowercased BLEU-4 scores (Papineni et al., 2001) . Statistical significance was measured using an Approximate Randomization test (Noreen, 1989; Riezler and Maxwell, 2005) .",
"cite_spans": [
{
"start": 408,
"end": 431,
"text": "(Papineni et al., 2001)",
"ref_id": "BIBREF36"
},
{
"start": 512,
"end": 526,
"text": "(Noreen, 1989;",
"ref_id": "BIBREF29"
},
{
"start": 527,
"end": 553,
"text": "Riezler and Maxwell, 2005)",
"ref_id": "BIBREF38"
}
],
"ref_spans": [
{
"start": 184,
"end": 192,
"text": "Figure 3",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Data, Systems, Experiment Settings",
"sec_num": "5.1"
},
{
"text": "All experiments for training on dev sets were carried out on a single computer. For grammar extraction and training of the full data set we used a 30 node hadoop Map/Reduce cluster that can handle 300 jobs at once. We split the data into 2290 shards for the ep runs and 141 shards for the nc runs, each shard holding about 1,000 sentences, which corresponds to the dev set size of the nc data set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data, Systems, Experiment Settings",
"sec_num": "5.1"
},
{
"text": "The baseline learner in our experiments is a pairwise ranking perceptron that is used on various features and training data and plugged into various meta- algorithms for distributed processing. The perceptron algorithm itself compares favorably to related learning techniques such as the MIRA adaptation of Chiang et al. (2008) . Figure 4 gives a boxplot depicting BLEU-4 results for 100 runs of the MIRA implementation of the cdec package, tuned on dev-nc, and evaluated on the respective test set test-nc. 6 We see a high variance (whiskers denote standard deviations) around a median of 27.2 BLEU and a mean of 27.1 BLEU. The fluctuation of results is due to sampling training examples from the translation hy- Table 2 : BLEU-4 results for algorithms 1 (SGD), 2 (MixSGD), 3 (IterMixSDG), and 4 (IterSelSGD) on newscommentary (nc) data. Feature groups are 12 dense features (default), rule identifiers (id), rule n-gram (ng), and rule shape (shape). Statistical significance at p-level < 0.05 of a result difference on the test set to a different algorithm applied to the same feature group is indicated by raised algorithm number. \u2020 indicates statistically significant differences to best result across features groups for same algorithm, indicated in bold face. @ indicates the optimal number of epochs chosen on the devtest set.",
"cite_spans": [
{
"start": 307,
"end": 327,
"text": "Chiang et al. (2008)",
"ref_id": "BIBREF3"
},
{
"start": 508,
"end": 509,
"text": "6",
"ref_id": null
}
],
"ref_spans": [
{
"start": 330,
"end": 338,
"text": "Figure 4",
"ref_id": "FIGREF2"
},
{
"start": 714,
"end": 721,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "5.2"
},
{
"text": "pergraph as is done in the cdec implementation of MIRA. We found similar fluctuations for the cdec implementations of PRO (Hopkins and May, 2011) or hypergraph-MERT (Kumar et al., 2009) both of which depend on hypergraph sampling. In contrast, the perceptron is deterministic when started from a zero-vector of weights and achieves favorable 28.0 BLEU on the news-commentary test set. Since we are interested in relative improvements over a stable baseline, we restrict our attention in all following experiments to the perceptron. 7 Table 2 shows the results of the experimental comparison of the 4 algorithms of Section 4. The 7 Absolute improvements would be possible, e.g., by using larger language models or by adding news data to the ep training set when evaluating on crawl test sets (see, e.g., Dyer et al. (2011) ), however, this is not the focus of this paper. default features include 12 dense models defined on SCFG rules; 8 The sparse features are the 3 templates described in Section 3. All feature weights were tuned together using algorithms 1-4. If not indicated otherwise, the perceptron was run for 10 epochs with learning rate \u03b7 = 0.0001, started at zero weight vector, using deduplicated 100-best lists.",
"cite_spans": [
{
"start": 122,
"end": 145,
"text": "(Hopkins and May, 2011)",
"ref_id": "BIBREF19"
},
{
"start": 165,
"end": 185,
"text": "(Kumar et al., 2009)",
"ref_id": "BIBREF21"
},
{
"start": 803,
"end": 821,
"text": "Dyer et al. (2011)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [
{
"start": 534,
"end": 541,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "5.2"
},
{
"text": "The results on the news-commentary (nc) data show that training on the development set does not benefit from adding large feature sets -BLEU result differences between tuning 12 default features Table 3 : BLEU-4 results for algorithms 1 (SGD) and 4 (IterSelSGD) on Europarl (ep) and news crawl (crawl) test data. Feature groups are 12 dense features (default), rule identifiers (id), rule n-gram (ng), and rule shape (shape). Statistical significance at p-level < 0.05 of a result difference on the test set to a different algorithm applied to the same feature group is indicated by raised algorithm number. \u2020 indicates statistically significant differences to best result across features groups for same algorithm, indicated in bold face. @ indicates the optimal number of epochs chosen on the devtest set.",
"cite_spans": [],
"ref_spans": [
{
"start": 195,
"end": 202,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "5.2"
},
{
"text": "and tuning the full set of 180,000 features are not significant. However, scaling all features to the full training set shows significant improvements for algorithm 3, and especially for algorithm 4, which gains 0.8 BLEU points over tuning 12 features on the development set. The number of features rises to 4.7 million without feature selection, which iteratively selects 100,000 features with best 2 norm values across shards. Feature templates such as rule n-grams and rule shapes only work if iterative mixing (algorithm 3) or feature selection (algorithm 4) are used. Adding rule id features works in combination with other sparse features. Table 3 shows results for algorithms 1 and 4 on the Europarl data (ep) for different devtest and test sets. Europarl data were used in all runs for training and for setting the meta-parameter of number of epochs. Testing was done on the Europarl test set and news crawl test data from the years 2010 and 2011. Here tuning large feature sets on the respective dev sets yields significant improvements of around 2 BLEU points over tuning the 12 default features on the dev sets. Another 0.5 BLEU points (test-crawl11) or even 1.3 BLEU points (test-crawl10) are gained when scaling to the full training set using iterative features selection. Result differences on the Europarl test set were not significant for moving from dev to full train set. Algorithms 2 and 3 were infeasible to run on Europarl data beyond one epoch because features vectors grew too large to be kept in memory.",
"cite_spans": [],
"ref_spans": [
{
"start": 646,
"end": 653,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "5.2"
},
{
"text": "We presented an approach to scaling discriminative learning for SMT not only to large feature sets but also to large sets of parallel training data. Since inference for SMT (unlike many other learning problems) is very expensive, especially on large training sets, good parallelization is key. Our approach is made feasible and effective by applying joint feature selection across distributed stochastic learning processes. Furthermore, our local features are efficiently computable at runtime. Our algorithms and features are generic and can easily be reimplemented and make our results relevant across datasets and language pairs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6"
},
{
"text": "In future work, we would like to investigate more sophisticated features, better learners, and in general improve the components of our system that have been neglected in the current investigation of relative improvements by scaling the size of data and feature sets. Ultimately, since our algorithms are inspired by multi-task learning, we would like to apply them to scenarios where a natural definition of tasks is given. For example, patent data can be characterized along the dimensions of patent classes and patent text fields (W\u00e4schle and Riezler, 2012) and thus are well suited for multi-task translation.",
"cite_spans": [
{
"start": 533,
"end": 560,
"text": "(W\u00e4schle and Riezler, 2012)",
"ref_id": "BIBREF44"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6"
},
{
"text": "Similar \"monolingual parse features\" have been used inDyer et al. (2011).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Other loss functions lead to stochastic versions of SVMs(Collobert and Bengio, 2004;Shalev-Shwartz et al., 2007;Chapelle and Keerthi, 2010).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Note that by definition of ||W||1,2, standard 1 regularization is a special case of 1/ 2 regularization for a single task.5 cdec metaparameters were set to a non-terminal span limit of 15 and standard cube pruning with a pop limit of 200.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "MIRA was used with default meta parameters: 250 hypothesis list to search for oracles, regularization strength C = 0.01 and using 15 passes over the input. It optimized IBM BLEU-4. The initial weight vector was 0.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "negative log relative frequency p(e|f ); log count(f ); log count(e, f ); lexical translation probability p(f |e) and p(e|f )(Koehn et al., 2003); indicator variable on singleton phrase e; indicator variable on singleton phrase pair f, e; word penalty; language model weight; OOV count of language model; number of untranslated words; Hiero glue rules(Chiang, 2007).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "Stefan Riezler and Patrick Simianer were supported in part by DFG grant \"Cross-language Learning-to-Rank for Patent Retrieval\". Chris Dyer was supported in part by a MURI grant \"The linguisticcore approach to structured translation and analysis of low-resource languages\" from the US Army Research Office and a grant \"Unsupervised Induction of Multi-Nonterminal Grammars for SMT\" from Google, Inc.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "A discriminative latent variable models for statistical machine translation",
"authors": [
{
"first": "Phil",
"middle": [],
"last": "Blunsom",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
},
{
"first": "Miles",
"middle": [],
"last": "Osborne",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT'08)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Phil Blunsom, Trevor Cohn, and Miles Osborne. 2008. A discriminative latent variable models for statistical machine translation. In Proceedings of the 46th An- nual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL- HLT'08), Columbus, OH.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Stochastic learning",
"authors": [
{
"first": "L\u00e9on",
"middle": [],
"last": "Bottou",
"suffix": ""
}
],
"year": 2004,
"venue": "Advanced Lectures on Machine Learning",
"volume": "",
"issue": "",
"pages": "146--168",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L\u00e9on Bottou. 2004. Stochastic learning. In Olivier Bousquet, Ulrike von Luxburg, and Gunnar R\u00e4tsch, editors, Advanced Lectures on Machine Learning, pages 146-168. Springer, Berlin.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Efficient algorithms for ranking with SVMs",
"authors": [
{
"first": "Olivier",
"middle": [],
"last": "Chapelle",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Sathiya Keerthi",
"suffix": ""
}
],
"year": 2010,
"venue": "Information Retrieval Journal",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Olivier Chapelle and S. Sathiya Keerthi. 2010. Efficient algorithms for ranking with SVMs. Information Re- trieval Journal.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Online large-margin training of syntactic and structural translation features",
"authors": [
{
"first": "David",
"middle": [],
"last": "Chiang",
"suffix": ""
},
{
"first": "Yuval",
"middle": [],
"last": "Marton",
"suffix": ""
},
{
"first": "Philip",
"middle": [],
"last": "Resnik",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP'08)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Chiang, Yuval Marton, and Philip Resnik. 2008. Online large-margin training of syntactic and struc- tural translation features. In Proceedings of the 2008 Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP'08), Waikiki, Honolulu, Hawaii.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "001 new features for statistical machine translation",
"authors": [
{
"first": "David",
"middle": [],
"last": "Chiang",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Knight",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT'09)",
"volume": "11",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Chiang, Kevin Knight, and Wei Wang. 2009. 11,001 new features for statistical machine transla- tion. In Proceedings of the 2009 Annual Confer- ence of the North American Chapter of the Associa- tion for Computational Linguistics (NAACL-HLT'09), Boulder, CO.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A hierarchical phrase-based model for statistical machine translation",
"authors": [
{
"first": "David",
"middle": [],
"last": "Chiang",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Chiang. 2005. A hierarchical phrase-based model for statistical machine translation. In Proceedings of the 43rd Annual Meeting of the Association for Com- putational Linguistics (ACL'05), Ann Arbor, MI.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Hierarchical phrase-based translation",
"authors": [
{
"first": "David",
"middle": [],
"last": "Chiang",
"suffix": ""
}
],
"year": 2007,
"venue": "Computational Linguistics",
"volume": "",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Chiang. 2007. Hierarchical phrase-based transla- tion. Computational Linguistics, 33(2).",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP'02)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Collins. 2002. Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms. In Proceedings of the con- ference on Empirical Methods in Natural Language Processing (EMNLP'02), Philadelphia, PA.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Links between perceptrons, MLPs, and SVMs",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Samy",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 21st International Conference on Machine Learning (ICML'04)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert and Samy Bengio. 2004. Links be- tween perceptrons, MLPs, and SVMs. In Proceed- ings of the 21st International Conference on Machine Learning (ICML'04), Banff, Canada.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Mapreduce: Simplified data processing on large clusters",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Dean",
"suffix": ""
},
{
"first": "Sanjay",
"middle": [],
"last": "Ghemawat",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI'04)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Dean and Sanjay Ghemawat. 2004. Mapre- duce: Simplified data processing on large clusters. In Proceedings of the 6th Symposium on Operating Sys- tem Design and Implementation (OSDI'04), San Fran- cisco, CA.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Beyond loglinear models: Boosted minimum error rate training for n-best ranking",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Duh",
"suffix": ""
},
{
"first": "Katrin",
"middle": [],
"last": "Kirchhoff",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL'08)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kevin Duh and Katrin Kirchhoff. 2008. Beyond log- linear models: Boosted minimum error rate training for n-best ranking. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguis- tics (ACL'08), Short Paper Track, Columbus, OH.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "N-best reranking by multitask learning",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Duh",
"suffix": ""
},
{
"first": "Katsuhito",
"middle": [],
"last": "Sudoh",
"suffix": ""
},
{
"first": "Hajime",
"middle": [],
"last": "Tsukada",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
},
{
"first": "Masaaki",
"middle": [],
"last": "Nagata",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 5th Joint Workshop on Statistical Machine Translation and Met-ricsMATR",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kevin Duh, Katsuhito Sudoh, Hajime Tsukada, Hideki Isozaki, and Masaaki Nagata. 2010. N-best reranking by multitask learning. In Proceedings of the 5th Joint Workshop on Statistical Machine Translation and Met- ricsMATR, Uppsala, Sweden.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "cdec: A decoder, alignment, and learning framework for finitestate and context-free translation models",
"authors": [
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Lopez",
"suffix": ""
},
{
"first": "Juri",
"middle": [],
"last": "Ganitkevitch",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Weese",
"suffix": ""
},
{
"first": "Ferhan",
"middle": [],
"last": "Ture",
"suffix": ""
},
{
"first": "Phil",
"middle": [],
"last": "Blunsom",
"suffix": ""
},
{
"first": "Hendra",
"middle": [],
"last": "Setiawan",
"suffix": ""
},
{
"first": "Vladimir",
"middle": [],
"last": "Eidelman",
"suffix": ""
},
{
"first": "Philip",
"middle": [],
"last": "Resnik",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the ACL 2010 System Demonstrations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chris Dyer, Adam Lopez, Juri Ganitkevitch, Jonathan Weese, Ferhan Ture, Phil Blunsom, Hendra Setiawan, Vladimir Eidelman, and Philip Resnik. 2010. cdec: A decoder, alignment, and learning framework for finite- state and context-free translation models. In Proceed- ings of the ACL 2010 System Demonstrations, Upp- sala, Sweden.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "The CMU-ARK germanenglish translation system",
"authors": [
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Gimpel",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [
"H"
],
"last": "Clark",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 6th Workshop on Machine Translation (WMT11)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chris Dyer, Kevin Gimpel, Jonathan H. Clark, and Noah A. Smith. 2011. The CMU-ARK german- english translation system. In Proceedings of the 6th Workshop on Machine Translation (WMT11), Edin- burgh, UK.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Using a maximum entropy model to build segmentation lattices for MT",
"authors": [
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics -Human Language Technologies (NAACL-HLT'09)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chris Dyer. 2009. Using a maximum entropy model to build segmentation lattices for MT. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics -Hu- man Language Technologies (NAACL-HLT'09), Boul- der, CO.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Large margin classification using the perceptron algorithm",
"authors": [
{
"first": "Yoav",
"middle": [],
"last": "Freund",
"suffix": ""
},
{
"first": "Robert",
"middle": [
"E"
],
"last": "Schapire",
"suffix": ""
}
],
"year": 1999,
"venue": "Journal of Machine Learning Research",
"volume": "37",
"issue": "",
"pages": "277--296",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoav Freund and Robert E. Schapire. 1999. Large margin classification using the perceptron algorithm. Journal of Machine Learning Research, 37:277-296.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Structured ramp loss minimization for machine translation",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Gimpel",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kevin Gimpel and Noah A. Smith. 2012. Structured ramp loss minimization for machine translation. In Proceedings of 2012 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL- HLT 2012), Montreal, Canada.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Structural support vector machines for log-linear approach in statistical machine translation",
"authors": [
{
"first": "Katsuhiko",
"middle": [],
"last": "Hayashi",
"suffix": ""
},
{
"first": "Taro",
"middle": [],
"last": "Watanabe",
"suffix": ""
},
{
"first": "Hajime",
"middle": [],
"last": "Tsukada",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of IWSLT",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Katsuhiko Hayashi, Taro Watanabe, Hajime Tsukada, and Hideki Isozaki. 2009. Structural support vector machines for log-linear approach in statistical machine translation. In Proceedings of IWSLT, Tokyo, Japan.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "KenLM: faster and smaller language model queries",
"authors": [
{
"first": "Kenneth",
"middle": [],
"last": "Heafield",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Translation (WMT'11)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenneth Heafield. 2011. KenLM: faster and smaller lan- guage model queries. In Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Transla- tion (WMT'11), Edinburgh, UK.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Tuning as ranking",
"authors": [
{
"first": "Mark",
"middle": [],
"last": "Hopkins",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP'11)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mark Hopkins and Jonathan May. 2011. Tuning as ranking. In Proceedings of 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP'11), Edinburgh, Scotland.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Statistical phrase-based translation",
"authors": [
{
"first": "Philipp",
"middle": [],
"last": "Koehn",
"suffix": ""
},
{
"first": "Franz",
"middle": [
"Josef"
],
"last": "Och",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Marcu",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the Human Language Technology Conference and the 3rd Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL'03)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceed- ings of the Human Language Technology Conference and the 3rd Meeting of the North American Chapter of the Association for Computational Linguistics (HLT- NAACL'03), Edmonton, Cananda.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Efficient minimum error rate training and minimum Bayes-risk decoding for translation hypergraphs and lattices",
"authors": [
{
"first": "Shankar",
"middle": [],
"last": "Kumar",
"suffix": ""
},
{
"first": "Wolfgang",
"middle": [],
"last": "Macherey",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Franz",
"middle": [],
"last": "Och",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 47th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shankar Kumar, Wolfgang Macherey, Chris Dyer, and Franz Och. 2009. Efficient minimum error rate train- ing and minimum Bayes-risk decoding for translation hypergraphs and lattices. In Proceedings of the 47th",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Annual Meeting of the Association for Computational Linguistics and the 4th IJCNLP of the AFNLP (ACL-IJCNLP'09",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computational Linguistics and the 4th IJCNLP of the AFNLP (ACL- IJCNLP'09, Suntec, Singapore.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Embedded methods",
"authors": [
{
"first": "Olivier",
"middle": [],
"last": "Thomas Navin Lal",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Chapelle",
"suffix": ""
},
{
"first": "Andr\u00e9",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Elisseeff",
"suffix": ""
}
],
"year": 2006,
"venue": "Feature Extraction: Foundations and Applications",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thomas Navin Lal, Olivier Chapelle, Jason Weston, and Andr\u00e9 Elisseeff. 2006. Embedded methods. In I.M. Guyon, S.R. Gunn, M. Nikravesh, and L. Zadeh, ed- itors, Feature Extraction: Foundations and Applica- tions. Springer.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "An end-to-end discriminative approach to machine translation",
"authors": [
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
},
{
"first": "Alexandre",
"middle": [],
"last": "Bouchard-C\u00f4t\u00e9",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "Ben",
"middle": [],
"last": "Taskar",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the joint conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL'06)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Percy Liang, Alexandre Bouchard-C\u00f4t\u00e9, Dan Klein, and Ben Taskar. 2006a. An end-to-end discriminative approach to machine translation. In Proceedings of the joint conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL'06), Syd- ney, Australia.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Alignment by agreement",
"authors": [
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
},
{
"first": "Ben",
"middle": [],
"last": "Taskar",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Human Language Technology Conference -North American Chapter of the Association for Computational Linguistics annual meeting (HLT-NAACL'06)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Percy Liang, Ben Taskar, and Dan Klein. 2006b. Align- ment by agreement. In Proceedings of the Human Language Technology Conference -North American Chapter of the Association for Computational Linguis- tics annual meeting (HLT-NAACL'06), New York, NY.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Hierarchical phrase-based translation with suffix arrays",
"authors": [
{
"first": "Adam",
"middle": [],
"last": "Lopez",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of EMNLP-CoNLL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Adam Lopez. 2007. Hierarchical phrase-based transla- tion with suffix arrays. In Proceedings of EMNLP- CoNLL, Prague, Czech Republic.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Generalization bounds and consistency for latent structural probit and ramp loss",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcallester",
"suffix": ""
},
{
"first": "Joseph",
"middle": [],
"last": "Keshet",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 25th Annual Conference on Neural Information Processing Sytems (NIPS 2011)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David McAllester and Joseph Keshet. 2011. Generaliza- tion bounds and consistency for latent structural pro- bit and ramp loss. In Proceedings of the 25th Annual Conference on Neural Information Processing Sytems (NIPS 2011), Granada, Spain.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Distributed training strategies for the structured perceptron",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Keith",
"middle": [],
"last": "Hall",
"suffix": ""
},
{
"first": "Gideon",
"middle": [],
"last": "Mann",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of Human Language Technologies: The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT'10)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ryan McDonald, Keith Hall, and Gideon Mann. 2010. Distributed training strategies for the structured per- ceptron. In Proceedings of Human Language Tech- nologies: The 11th Annual Conference of the North American Chapter of the Association for Compu- tational Linguistics (NAACL-HLT'10), Los Angeles, CA.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Computer Intensive Methods for Testing Hypotheses. An Introduction",
"authors": [
{
"first": "Eric",
"middle": [
"W"
],
"last": "Noreen",
"suffix": ""
}
],
"year": 1989,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eric W. Noreen. 1989. Computer Intensive Methods for Testing Hypotheses. An Introduction. Wiley, New York.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Joint covariate selection and joint subspace selection for multiple classification problems",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Obozinski",
"suffix": ""
},
{
"first": "Ben",
"middle": [],
"last": "Taskar",
"suffix": ""
},
{
"first": "Michael",
"middle": [
"I"
],
"last": "Jordan",
"suffix": ""
}
],
"year": 2010,
"venue": "Statistics and Computing",
"volume": "20",
"issue": "",
"pages": "231--252",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guillaume Obozinski, Ben Taskar, and Michael I. Jordan. 2010. Joint covariate selection and joint subspace se- lection for multiple classification problems. Statistics and Computing, 20:231-252.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Improved statistical alignment models",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Franz",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Och",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ney",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of the 38th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franz Josef Och and Hermann Ney. 2000. Improved sta- tistical alignment models. In Proceedings of the 38th",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Annual Meeting of the Association for Computational Linguistics (ACL'00)",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computational Linguistics (ACL'00), Hongkong, China.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Discriminative training and maximum entropy models for statistical machine translation",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Franz",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Och",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ney",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the 40th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franz Josef Och and Hermann Ney. 2002. Discrimina- tive training and maximum entropy models for statis- tical machine translation. In Proceedings of the 40th",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Annual Meeting of the Association for Computational Linguistics (ACL'02)",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computational Linguistics (ACL'02), Philadelphia, PA.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Minimum error rate training in statistical machine translation",
"authors": [
{
"first": "Franz Josef",
"middle": [],
"last": "Och",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the Human Language Technology Conference and the 3rd Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL'03)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franz Josef Och. 2003. Minimum error rate train- ing in statistical machine translation. In Proceedings of the Human Language Technology Conference and the 3rd Meeting of the North American Chapter of the Association for Computational Linguistics (HLT- NAACL'03), Edmonton, Cananda.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Bleu: a method for automatic evaluation of machine translation",
"authors": [
{
"first": "Kishore",
"middle": [],
"last": "Papineni",
"suffix": ""
},
{
"first": "Salim",
"middle": [],
"last": "Roukos",
"suffix": ""
},
{
"first": "Todd",
"middle": [],
"last": "Ward",
"suffix": ""
},
{
"first": "Wei-Jing",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2001. Bleu: a method for automatic evaluation of machine translation. Technical Report IBM Research Division Technical Report, RC22176 (W0190-022), Yorktown Heights, N.Y.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Grafting: Fast, incremental feature selection by gradient descent in function space",
"authors": [
{
"first": "Simon",
"middle": [],
"last": "Perkins",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Lacker",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Theiler",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1333--1356",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Simon Perkins, Kevin Lacker, and James Theiler. 2003. Grafting: Fast, incremental feature selection by gra- dient descent in function space. Journal of Machine Learning Research, 3:1333-1356.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "On some pitfalls in automatic evaluation and significance testing for MT",
"authors": [
{
"first": "Stefan",
"middle": [],
"last": "Riezler",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Maxwell",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the ACL-05 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stefan Riezler and John Maxwell. 2005. On some pit- falls in automatic evaluation and significance testing for MT. In Proceedings of the ACL-05 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization, Ann Arbor, MI.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Pegasos: Primal Estimated sub-GrAdient SOlver for SVM",
"authors": [
{
"first": "Shai",
"middle": [],
"last": "Shalev-Shwartz",
"suffix": ""
},
{
"first": "Yoram",
"middle": [],
"last": "Singer",
"suffix": ""
},
{
"first": "Nathan",
"middle": [],
"last": "Srebro",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 24th International Conference on Machine Learning (ICML'07)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shai Shalev-Shwartz, Yoram Singer, and Nathan Sre- bro. 2007. Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In Proceedings of the 24th Inter- national Conference on Machine Learning (ICML'07), Corvallis, OR.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Ranking and reranking with perceptron",
"authors": [
{
"first": "Libin",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Aravind",
"middle": [
"K"
],
"last": "Joshi",
"suffix": ""
}
],
"year": 2005,
"venue": "Journal of Machine Learning Research",
"volume": "60",
"issue": "1-3",
"pages": "73--96",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Libin Shen and Aravind K. Joshi. 2005. Ranking and reranking with perceptron. Journal of Machine Learn- ing Research, 60(1-3):73-96.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Discriminative reranking for machine translation",
"authors": [
{
"first": "Libin",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Anoop",
"middle": [],
"last": "Sarkar",
"suffix": ""
},
{
"first": "Franz Josef",
"middle": [],
"last": "Och",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the Human Language Technology conference / North American chapter of the Association for Computational Linguistics annual meeting (HLT/NAACL'04)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Libin Shen, Anoop Sarkar, and Franz Josef Och. 2004. Discriminative reranking for machine translation. In Proceedings of the Human Language Technology con- ference / North American chapter of the Associa- tion for Computational Linguistics annual meeting (HLT/NAACL'04), Boston, MA.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "SRILM -an extensible language modeling toolkit",
"authors": [
{
"first": "Andreas",
"middle": [],
"last": "Stolcke",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the International Conference on Spoken Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andreas Stolcke. 2002. SRILM -an extensible language modeling toolkit. In Proceedings of the International Conference on Spoken Language Processing, Denver, CO.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "A discriminatie global training algorithm for statistical MT",
"authors": [
{
"first": "Christoph",
"middle": [],
"last": "Tillmann",
"suffix": ""
},
{
"first": "Tong",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the joint conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL'06)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Christoph Tillmann and Tong Zhang. 2006. A dis- criminatie global training algorithm for statistical MT. In Proceedings of the joint conference of the In- ternational Committee on Computational Linguistics and the Association for Computational Linguistics (COLING-ACL'06), Sydney, Australia.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "Structural and topical dimensions in multi-task patent translation",
"authors": [
{
"first": "Katharina",
"middle": [],
"last": "W\u00e4schle",
"suffix": ""
},
{
"first": "Stefan",
"middle": [],
"last": "Riezler",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Katharina W\u00e4schle and Stefan Riezler. 2012. Structural and topical dimensions in multi-task patent translation. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguis- tics, Avignon, France.",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "NTT statistical machine translation for IWSLT",
"authors": [
{
"first": "Taro",
"middle": [],
"last": "Watanabe",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Suzuki",
"suffix": ""
},
{
"first": "Hajime",
"middle": [],
"last": "Tsukada",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the International Workshop on Spoken Language Translation (IWSLT)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Taro Watanabe, Jun Suzuki, Hajime Tsukada, and Hideki Isozaki. 2006. NTT statistical machine translation for IWSLT 2006. In Proceedings of the International Workshop on Spoken Language Translation (IWSLT), Kyoto, Japan.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "Online large-margin training for statistical machine translation",
"authors": [
{
"first": "Taro",
"middle": [],
"last": "Watanabe",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Suzuki",
"suffix": ""
},
{
"first": "Hajime",
"middle": [],
"last": "Tsukada",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Taro Watanabe, Jun Suzuki, Hajime Tsukada, and Hideki Isozaki. 2007. Online large-margin training for statis- tical machine translation. In Proceedings of the 2007",
"links": null
},
"BIBREF47": {
"ref_id": "b47",
"title": "Natural Language Processing and Computational Language Learning (EMNLP'07)",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joint Conference on Empirical Mehtods in Natural Language Processing and Computational Language Learning (EMNLP'07), Prague, Czech Republic.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "Toward purely discriminative training for treestructured translation models",
"authors": [
{
"first": "Benjamin",
"middle": [],
"last": "Wellington",
"suffix": ""
},
{
"first": "Joseph",
"middle": [],
"last": "Turian",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Melamed",
"suffix": ""
}
],
"year": 2009,
"venue": "Learning Machine Translation",
"volume": "",
"issue": "",
"pages": "132--149",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Benjamin Wellington, Joseph Turian, and Dan Melamed. 2009. Toward purely discriminative training for tree- structured translation models. In Cyril Goutte, Nicola Cancedda, and Marc Dymetman, editors, Learning Machine Translation, pages 132-149, Cambridge, MA. The MIT Press.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Parallelized stochastic gradient descent",
"authors": [
{
"first": "Martin",
"middle": [
"A"
],
"last": "Zinkevich",
"suffix": ""
},
{
"first": "Markus",
"middle": [],
"last": "Weimer",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Smola",
"suffix": ""
},
{
"first": "Lihong",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 24th Annual Conference on Neural Information Processing Sytems (NIPS'10)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Martin A. Zinkevich, Markus Weimer, Alex Smola, and Lihong Li. 2010. Parallelized stochastic gradient de- scent. In Proceedings of the 24th Annual Conference on Neural Information Processing Sytems (NIPS'10), Vancouver, Canada.",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "A consistent and efficient estimator for data-oriented parsing",
"authors": [
{
"first": "Andreas",
"middle": [],
"last": "Zollmann",
"suffix": ""
},
{
"first": "Khalil",
"middle": [],
"last": "Sima",
"suffix": ""
}
],
"year": 2005,
"venue": "Journal of Automata, Languages and Combinatorics",
"volume": "10",
"issue": "2/3",
"pages": "367--388",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andreas Zollmann and Khalil Sima'an. 2005. A consis- tent and efficient estimator for data-oriented parsing. Journal of Automata, Languages and Combinatorics, 10(2/3):367-388.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "SCFG rules for translation.",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF1": {
"text": "Multipartite pairwise ranking.",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF2": {
"text": "Boxplot of BLEU-4 results for 100 runs of MIRA on news commentary data, depicting median (M), mean (x), interquartile range (box), standard deviation (whiskers), outliers (end points).",
"uris": null,
"type_str": "figure",
"num": null
},
"TABREF1": {
"type_str": "table",
"html": null,
"num": null,
"text": "",
"content": "<table/>"
}
}
}
} |