File size: 165,556 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 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 | {
"paper_id": "P19-1015",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:27:49.595737Z"
},
"title": "Massively Multilingual Transfer for NER",
"authors": [
{
"first": "Afshin",
"middle": [],
"last": "Rahimi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The University of Melbourne",
"location": {}
},
"email": "rahimia@unimelb.edu.au"
},
{
"first": "Yuan",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The University of Melbourne",
"location": {}
},
"email": "yuanl4@student.unimelb.edu.au"
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The University of Melbourne",
"location": {}
},
"email": "t.cohn@unimelb.edu.au"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In cross-lingual transfer, NLP models over one or more source languages are applied to a lowresource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a \"massive\" setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model. 1",
"pdf_parse": {
"paper_id": "P19-1015",
"_pdf_hash": "",
"abstract": [
{
"text": "In cross-lingual transfer, NLP models over one or more source languages are applied to a lowresource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a \"massive\" setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model. 1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Supervised learning remains king in natural language processing, with most tasks requiring large quantities of annotated corpora. The majority of the world's 6,000+ languages however have limited or no annotated text, and therefore much of the progress in NLP has yet to be realised widely. Cross-lingual transfer learning is a technique which can compensate for the dearth of data, by transferring knowledge from high-to lowresource languages, which has typically taken the form of annotation projection over parallel corpora or other multilingual resources (Yarowsky et al., 2001; Hwa et al., 2005) , or making use of transferable representations, such as phonetic transcriptions (Bharadwaj et al., 2016) , closely related languages (Cotterell and Duh, 2017) or bilingual dictionaries (Mayhew et al., 2017; Xie et al., 2018) .",
"cite_spans": [
{
"start": 559,
"end": 582,
"text": "(Yarowsky et al., 2001;",
"ref_id": "BIBREF49"
},
{
"start": 583,
"end": 600,
"text": "Hwa et al., 2005)",
"ref_id": "BIBREF18"
},
{
"start": 682,
"end": 706,
"text": "(Bharadwaj et al., 2016)",
"ref_id": "BIBREF3"
},
{
"start": 735,
"end": 760,
"text": "(Cotterell and Duh, 2017)",
"ref_id": "BIBREF5"
},
{
"start": 787,
"end": 808,
"text": "(Mayhew et al., 2017;",
"ref_id": "BIBREF28"
},
{
"start": 809,
"end": 826,
"text": "Xie et al., 2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Most methods proposed for cross-lingual transfer rely on a single source language, which limits the transferable knowledge to only one source.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The target language might be similar to many source languages, on the grounds of the script, word order, loan words etc, and transfer would benefit from these diverse sources of information. There are a few exceptions, which use transfer from several languages, ranging from multitask learning (Duong et al., 2015; Ammar et al., 2016; , and annotation projection from several languages (T\u00e4ckstr\u00f6m, 2012; Fang and Cohn, 2016; Plank and Agi\u0107, 2018) . However, to the best of our knowledge, none of these approaches adequately account for the quality of transfer, but rather \"weight\" the contribution of each language uniformly.",
"cite_spans": [
{
"start": 294,
"end": 314,
"text": "(Duong et al., 2015;",
"ref_id": "BIBREF8"
},
{
"start": 315,
"end": 334,
"text": "Ammar et al., 2016;",
"ref_id": "BIBREF1"
},
{
"start": 386,
"end": 403,
"text": "(T\u00e4ckstr\u00f6m, 2012;",
"ref_id": "BIBREF41"
},
{
"start": 404,
"end": 424,
"text": "Fang and Cohn, 2016;",
"ref_id": "BIBREF13"
},
{
"start": 425,
"end": 446,
"text": "Plank and Agi\u0107, 2018)",
"ref_id": "BIBREF34"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we propose a novel method for zero-shot multilingual transfer, inspired by research in truth inference in crowd-sourcing, a related problem, in which the 'ground truth' must be inferred from the outputs of several unreliable annotators (Dawid and Skene, 1979) . In this problem, the best approaches estimate each model's reliability, and their patterns of mistakes (Kim and Ghahramani, 2012 ). Our proposed model adapts these ideas to a multilingual transfer setting, whereby we learn the quality of transfer, and language-specific transfer errors, in order to infer the best labelling in the target language, as part of a Bayesian graphical model. The key insight is that while the majority of poor models make lots of mistakes, these mistakes are diverse, while the few good models consistently provide reliable input. This allows the model to infer which are the reliable models in an unsupervised manner, i.e., without explicit supervision in the target language, and thereby make accurate inferences despite the substantial noise.",
"cite_spans": [
{
"start": 251,
"end": 274,
"text": "(Dawid and Skene, 1979)",
"ref_id": "BIBREF7"
},
{
"start": 380,
"end": 405,
"text": "(Kim and Ghahramani, 2012",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In the paper, we also consider a supervised setting, where a tiny annotated corpus is available in the target language. We present two methods to use this data: 1) estimate reliability parameters of the Bayesian model, and 2) explicit model selection and fine-tuning of a low-resource supervised model, thus allowing for more accurate modelling of language specific parameters, such as character embeddings, shown to be important in previous work (Xie et al., 2018) .",
"cite_spans": [
{
"start": 447,
"end": 465,
"text": "(Xie et al., 2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Experimenting on two NER corpora, one with as many as 41 languages, we show that single model transfer has highly variable performance, and uniform ensembling often substantially underperforms the single best model. In contrast, our zero-shot approach does much better, exceeding the performance of the single best model, and our few-shot supervised models result in further gains.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We frame the problem of multilingual transfer as follows. We assume a collection of H models, all trained in a high resource setting, denoted",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach",
"sec_num": "2"
},
{
"text": "M h = {M h i , i \u2208 (1, H)}.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach",
"sec_num": "2"
},
{
"text": "Each of these models are not well matched to our target data setting, for instance these may be trained on data from different domains, or on different languages, as we evaluate in our experiments, where we use crosslingual embeddings for model transfer. This is a problem of transfer learning, namely, how best we can use the H models for best results in the target language. 2 Simple approaches in this setting include a) choosing a single model M \u2208 M h , on the grounds of practicality, or the similarity between the model's native data condition and the target, and this model is used to label the target data; or b) allowing all models to 'vote' in an classifier ensemble, such that the most frequent outcome is selected as the ensemble output. Unfortunately neither of these approaches are very accurate in a cross-lingual transfer setting, as we show in \u00a74, where we show a fixed source language model (en) dramatically underperforms compared to oracle selection of source language, and the same is true for uniform voting.",
"cite_spans": [
{
"start": 377,
"end": 378,
"text": "2",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach",
"sec_num": "2"
},
{
"text": "Motivated by these findings, we propose novel methods for learning. For the \"zero-shot\" setting where no labelled data is available in the target, we propose the BEA uns method inspired by work 2 We limit our attention to transfer in a 'black-box' setting, that is, given predictive models, but not assuming access to their data, nor their implementation. This is the most flexible scenario, as it allows for application to settings with closed APIs, and private datasets. It does, however, preclude multitask learning, as the source models are assumed to be static. in truth inference from crowd-sourced datasets or diverse classifiers ( \u00a72.1). To handle the \"few-shot\" case \u00a72.2 presents a rival supervised technique, RaRe, based on using very limited annotations in the target language for model selection and classifier fine-tuning.",
"cite_spans": [
{
"start": 194,
"end": 195,
"text": "2",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach",
"sec_num": "2"
},
{
"text": "V (j) \u03c0 z i y ij \u03b2 \u03b1 i = 1 . . . N j = 1 . . . H",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach",
"sec_num": "2"
},
{
"text": "One way to improve the performance of the ensemble system is to select a subset of component models carefully, or more generally, learn a non-uniform weighting function. Some models do much better than others, on their own, so it stands to reason that identifying these handful of models will give rise to better ensemble performance. How might we proceed to learn the relative quality of models in the setting where no annotations are available in the target language? This is a classic unsupervised inference problem, for which we propose a probabilistic graphical model, inspired by Kim and Ghahramani (2012) .",
"cite_spans": [
{
"start": 586,
"end": 611,
"text": "Kim and Ghahramani (2012)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "We develop a generative model, illustrated in Figure 1 , of the transfer models' predictions, y ij , where i \u2208 [1, N ] is an instance (a token or an entity span), and j \u2208 [1, H] indexes a transfer model. The generative process assumes a 'true' label, z i \u2208 [1, K], which is corrupted by each transfer model, in producing the prediction, y ij . The corruption process is described by",
"cite_spans": [],
"ref_spans": [
{
"start": 46,
"end": 54,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "P (y ij = l|z i = k, V (j) ) = V (j)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "kl , where V (j) \u2208 R K\u00d7K is the confusion matrix specific to a transfer model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "To complete the story, the confusion matrices are drawn from vague row-wise independent Dirichlet priors, with a parameter \u03b1 = 1, and the true labels are governed by a Dirichlet prior, \u03c0, which is drawn from an uninformative Dirichlet distribution with a parameter \u03b2 = 1. This generative model is referred to as BEA.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "Inference under the BEA model involves ex-plaining the observed predictions Y in the most efficient way. Where several transfer models have identical predictions, k, on an instance, this can be explained by letting z i = k, 3 and the confusion matrices of those transfer models assigning high probability to",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "V (j)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "kk . Other, less reliable, transfer models will have divergent predictions, which are less likely to be in agreement, or else are heavily biased towards a particular class. Accordingly, the BEA model can better explain these predictions through label confusion, using the off-diagonal elements of the confusion matrix. Aggregated over a corpus of instances, the BEA model can learn to differentiate between those reliable transfer models, with high V kl , l = k. This procedure applies perlabel, and thus the 'reliability' of a transfer model is with respect to a specific label, and may differ between classes. This helps in the NER setting where many poor transfer models have excellent accuracy for the outside label, but considerably worse performance for entity labels.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "For inference, we use mean-field variational Bayes (Jordan, 1998), which learns a variational distribution, q(Z, V, \u03c0) to optimise the evidence lower bound (ELBO),",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "log P (Y |\u03b1, \u03b2) \u2265 E q(Z,V,\u03c0) log P (Y, Z, V, \u03c0|\u03b1, \u03b2) q(Z, V, \u03c0)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "assuming a fully factorised variational distribution, q(Z, V, \u03c0) = q(Z)q(V )q(\u03c0). This gives rise to an iterative learning algorithm with update rules: where \u03c8 is the digamma function, defined as the logarithmic derivative of the gamma function. The sets of rules (1) and (2) are applied alternately, to update the values of",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "E q log \u03c0 k (1a) =\u03c8 \u03b2 + i q(z i = k) \u2212 \u03c8 (K\u03b2 + N ) E q log V (j) kl (1b) =\u03c8 \u03b1 + i q(z i = k)1[y ij = l] \u2212 \u03c8 K\u03b1 + i q(z i = k) q(z i = k) \u221d exp \uf8f1 \uf8f2 \uf8f3 E q log \u03c0 k + j E q log V (j) ky ij \uf8fc \uf8fd \uf8fe (2) w1 w2 w3 w4 [1, 4] [2, 4] [3, 4] M h 1 B-ORG I-ORG I-ORG I-ORG ORG O O M h 2 O B-ORG I-ORG I-ORG O ORG O M h 3 O O B-ORG I-ORG O O ORG M h 4 O B-PER I-PER I-PER O PER O M h 5 O B-PER I-PER I-PER O PER O Agg. O B-PER I-ORG I-ORG O PER O",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "E q log \u03c0 k , E q log V (j)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "kl , and q(z ij = k) respectively. This repeats until convergence, when the difference in the ELBO between two iterations is smaller than a threshold.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "The final prediction of the model is based on q(Z), using the maximum a posteriori label z i = arg max z q(z i = z). This method is referred to as BEA uns . In our NER transfer task, classifiers are diverse in their F1 scores ranging from almost 0 to around 80, motivating spammer removal (Raykar and Yu, 2012) to filter out the worst of the transfer models. We adopt a simple strategy that first estimates the confusion matrices for all transfer models on all labels, then ranks them based on their mean recall on different entity categories (elements on the diagonals of their confusion matrices), and then runs the BEA model again using only labels from the top k transfer models only. We call this method BEA uns\u00d72 and its results are reported in \u00a74.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zero-Shot Transfer",
"sec_num": "2.1"
},
{
"text": "Our proposed aggregation method in \u00a72.1 is based on an assumption that the true annotations are independent from each other, which simplifies the model but may generate undesired results. That is, entities predicted by different transfer models could be mixed, resulting in labels inconsistent with the BIO scheme. Table 1 shows an example, where a sentence with 4 words is annotated by 5 transfer models with 4 different predictions, among which at most one is correct as they overlap. However, the aggregated result in the token view is a mixture of two predictions, which is supported by no transfer models.",
"cite_spans": [],
"ref_spans": [
{
"start": 315,
"end": 322,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Token versus Entity Granularity",
"sec_num": "2.1.1"
},
{
"text": "To deal with this problem, we consider aggre-gating the predictions in the entity view. As shown in Table 1 , we convert the predictions for tokens to predictions for ranges, aggregate labels for every range, and then resolve remaining conflicts. A prediction is ignored if it conflicts with another one with higher probability. By using this greedy strategy, we can solve the conflicts raised in entitylevel aggregation. We use superscripts tok and ent to denote token-level and entity-level aggregations, i.e. BEA tok uns and BEA ent uns .",
"cite_spans": [],
"ref_spans": [
{
"start": 100,
"end": 107,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Token versus Entity Granularity",
"sec_num": "2.1.1"
},
{
"text": "Until now, we have assumed no access to annotations in the target language. However, when some labelled text is available, how might this best be used? In our experimental setting, we assume a modest set of 100 labelled sentences, in keeping with a low-resource setting (Garrette and Baldridge, 2013) . 4 We propose two models BEA sup and RaRe in this setting.",
"cite_spans": [
{
"start": 270,
"end": 300,
"text": "(Garrette and Baldridge, 2013)",
"ref_id": "BIBREF16"
},
{
"start": 303,
"end": 304,
"text": "4",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "Supervising BEA (BEA sup ) One possibility is to use the labelled data to find the posterior for the parameters V (j) and \u03c0 of the Bayesian model described in \u00a72.1. Let n k be the number of instances in the labelled data whose true label is k, and n jkl the number of instances whose true label is k and classifier j labels them as l. Then the quantities in Equation (1) can be calculated as",
"cite_spans": [
{
"start": 114,
"end": 117,
"text": "(j)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "E log \u03c0 k =\u03c8(n k ) \u2212 \u03c8(N ) E log v jkl =\u03c8(n jkl ) \u2212 \u03c8 l n jkl .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "These are used in Equation 2for inference on the test set. We refer to this setting as BEA sup .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "Ranking and Retraining (RaRe) We also propose an alternative way of exploiting the limited annotations, RaRe, which first ranks the systems, and then uses the top ranked models' outputs alongside the gold data to retrain a model on the target language. The motivation is that the above technique is agnostic to the input text, and therefore is unable to exploit situations where regularities occur, such as common words or character patterns that are indicative of specific class labels, including names, titles, etc. These signals are unlikely to be consistently captured by crosslingual transfer. Training a model on the target language with a character encoder component, can distil the signal that are captured by the transfer models, while relating this towards generalisable lexical and structural evidence in the target language. This on its own will not be enough, as many tokens will be consistently misclassified by most or all of the transfer models, and for this reason we also perform model fine-tuning using the supervised data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "The ranking step in RaRe proceeds by evaluating each of the H transfer models on the target gold set, to produce scores s h (using the F 1 score). The scores are then truncated to the top k \u2264 H values, such that s h = 0 for those systems h not ranked in the top k, and normalised",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "\u03c9 h = s h k j=1 s j .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "The range of scores are quite wide, covering 0.00 \u2212 0.81 (see Figure 2) , and accordingly this simple normalisation conveys a strong bias towards the top scoring transfer systems.",
"cite_spans": [],
"ref_spans": [
{
"start": 62,
"end": 71,
"text": "Figure 2)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "The next step is a distillation step, where a model is trained on a large unannotated dataset in the target language, such that the model predictions match those of a weighted mixture of transfer models, using \u03c9 = (\u03c9 1 , . . . , \u03c9 H ) as the mixing weights. This process is implemented as minibatch scheduling, where the labels for each minibatch are randomly sampled from transfer model h with probability \u03c9 h . 5 This is repeated over the course of several epochs of training. Finally, the model is fine-tuned using the small supervised dataset, in order to correct for phenomena that are not captured from model transfer, particularly character level information which is not likely to transfer well for all but the most closely related languages. Fine-tuning proceeds for a fixed number of epochs on the supervised dataset, to limit overtraining of richly parameterise models on a tiny dataset. Note that in all stages, the same supervised dataset is used, both in ranking and fine-tuning, and moreover, we do not use a development set. This is not ideal, and generalisation performance would likely improve were we to use additional annotated data, however our meagre use of data is designed for a low resource setting where labelled data is at a premium.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Few-Shot Transfer",
"sec_num": "2.2"
},
{
"text": "Our primarily evaluation is over a subset of the Wikiann NER corpus (Pan et al., 2017) , using 41 out of 282 languages, where the langauges were chosen based on their overlap with multilingual word embedding resources from Lample et al. (2018) . 6 The NER taggs are in IOB2 format comprising of LOC, PER, and ORG. The distribution of labels is highly skewed, so we created balanced datasets, and partitioned into training, development, and test sets, details of which are in the Appendix. For comparison with prior work, we also evaluate on the CoNLL 2002 and 2003 datasets (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003 ), which we discuss further in \u00a74.",
"cite_spans": [
{
"start": 68,
"end": 86,
"text": "(Pan et al., 2017)",
"ref_id": "BIBREF32"
},
{
"start": 223,
"end": 243,
"text": "Lample et al. (2018)",
"ref_id": "BIBREF25"
},
{
"start": 246,
"end": 247,
"text": "6",
"ref_id": null
},
{
"start": 581,
"end": 596,
"text": "Kim Sang, 2002;",
"ref_id": "BIBREF43"
},
{
"start": 597,
"end": 632,
"text": "Tjong Kim Sang and De Meulder, 2003",
"ref_id": "BIBREF44"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "3.1"
},
{
"text": "For language-independent word embedding features we use fastText 300 dimensional Wikipedia embeddings (Bojanowski et al., 2017) , and map them to the English embedding space using character-identical words as the seed for the Procrustes rotation method for learning bingual embedding spaces from MUSE (Lample et al., 2018) . 7 Similar to Xie et al. 2018we don't rely on a bilingual dictionary, so the method can be easily applied to other languages.",
"cite_spans": [
{
"start": 102,
"end": 127,
"text": "(Bojanowski et al., 2017)",
"ref_id": "BIBREF4"
},
{
"start": 301,
"end": 322,
"text": "(Lample et al., 2018)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "3.1"
},
{
"text": "As the sequential tagger, we use a BiLSTM-CRF (Lample et al., 2016) , which has been shown to result in state-of-the-art results in high resource settings (Ma and Hovy, 2016; Lample et al., 2016) . This model includes both word embeddings (for which we used fixed cross-lingual embeddings) and character embeddings, to form a parameterised potential function in a linear chain conditional random field. With the exception of batch size and learning rate which were tuned (details in Appendix), we kept the architecture and the hyperparameters the same as the published code. 8",
"cite_spans": [
{
"start": 46,
"end": 67,
"text": "(Lample et al., 2016)",
"ref_id": "BIBREF24"
},
{
"start": 155,
"end": 174,
"text": "(Ma and Hovy, 2016;",
"ref_id": "BIBREF26"
},
{
"start": 175,
"end": 195,
"text": "Lample et al., 2016)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "6 With ISO 639-1 codes: af, ar, bg, bn, bs, ca, cs, da, de, el, en, es, et, fa, fi, fr, he, hi, hr, hu, id, it, lt, lv, mk, ms, nl, no, pl, pt, ro, ru, sk, sl, sq, sv, ta, tl, tr, uk and vi. 7 We also experimented with other bilingual embedding methods, including: supervised learning over bilingual dictionaries, which barely affected system performance; and pure-unsupervised methods (Lample et al., 2018; Artetxe et al., 2018) , which performed substantially worse. For this reason we use identical word type seeding, which is preferred as it imposes no additional supervision requirement. 8 https://github.com/guillaumegenthial/ sequence_tagging",
"cite_spans": [
{
"start": 56,
"end": 192,
"text": "de, el, en, es, et, fa, fi, fr, he, hi, hr, hu, id, it, lt, lv, mk, ms, nl, no, pl, pt, ro, ru, sk, sl, sq, sv, ta, tl, tr, uk and vi. 7",
"ref_id": null
},
{
"start": 386,
"end": 407,
"text": "(Lample et al., 2018;",
"ref_id": "BIBREF25"
},
{
"start": 408,
"end": 429,
"text": "Artetxe et al., 2018)",
"ref_id": "BIBREF2"
},
{
"start": 593,
"end": 594,
"text": "8",
"ref_id": "BIBREF50"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "We trained models on all 41 languages in both high-resource (HSup) and naive supervised lowresource (LSup) settings, where HSup pre-trained models were used for transfer in a leave-one-out setting, i.e., taking the predictions of 40 models into a single target language. The same BiLSTM-CRF is also used for RaRe.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "To avoid overfitting, we use early stopping based on a validation set for the HSup, and LSup baselines. For RaRe, given that the model is already trained on noisy data, we stop fine-tuning after only 5 iterations, chosen based on the performance for the first four languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "We compare the supervised HSup and LSup monolingual baselines with our proposed transfer models:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "MV uniform ensemble, a.k.a.\"majority vote\"; BEA uns\u00d72 , BEA uns unsupervised aggregation models, applied to entities or tokens (see \u00a72.1); BEA sup supervised estimation of BEA prior ( \u00a72.2); RaRe, RaRe uns supervised ranking and retraining model ( \u00a72.2), and uniform ranking without fine-tuning, respectively; and Oracle selecting the best performing single transfer model, based on test performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "We also compare with BWET (Xie et al., 2018) as state-of-the-art in unsupervised NER transfer. BWET transfers the source English training and development data to the target language using bilingual dictionary induction (Lample et al., 2018) , and then uses a transformer architecture to compensate for missing sequential information. We used BWET in both CoNLL, and Wikiann datasets by transferring from their corresponding source English data to the target language. 9",
"cite_spans": [
{
"start": 26,
"end": 44,
"text": "(Xie et al., 2018)",
"ref_id": "BIBREF48"
},
{
"start": 219,
"end": 240,
"text": "(Lample et al., 2018)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model Variations",
"sec_num": "3.2"
},
{
"text": "We report the results for single source direct transfer, and then show that our proposed multilingual methods outperform majority voting. Then we analyse the choice of source languages, and how it affects transfer. Figure 3 : The mean and standard deviation for the F 1 score of the proposed unsupervised models (BEA tok uns and BEA ent uns ), supervised models (RaRe and BEA ent sup t10) compared with state-of-the-art unsupervised model BWET (Xie et al., 2018) , high-and lowresource supervised models HSup and LSup, and majority voting (MV tok ) in terms of entity level F 1 over the 41 languages (40 for BWET) summarised from Table 4. The x axis shows the annotation requirement of each model in the target language where \"200\" means 100 sentences each for training and development, and \"5K+\" means using all the available annotation for training and development sets. Points with the same colour/shape have equal data requirement.",
"cite_spans": [
{
"start": 444,
"end": 462,
"text": "(Xie et al., 2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [
{
"start": 215,
"end": 223,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "4"
},
{
"text": "The first research question we consider is the utility of direct transfer, and the simple majority vote ensembling method. As shown in Figure 2 , using a single model for direct transfer (English: en) is often a terrible choice. The oracle choice of source language model does much better, however it is not always a closely related language (e.g., Italian: it does best for Indonesian: id, despite the target being closer to Malay: ms). Note the collection of Cyrillic languages (bg, mk, uk) where the oracle is substantially better than the majority vote, which is likely due to script differences. The role of script appears to be more important than language family, as seen for Slavic languages where direct transfer works well between between pairs languages using the same alphabet (Cyrillic versus Latin), but much more poorly when there is an alphabet mismatch. 11 The transfer relationship is not symmetric e.g., Persian: fa does best for Arabic: ar, but German: de does best for Persian. Figure 2 also shows that ensemble voting is well below the oracle best language, which is likely to be a result of overall high error rates coupled with error correlation between models, and little can be gained from ensembling.",
"cite_spans": [
{
"start": 871,
"end": 873,
"text": "11",
"ref_id": null
}
],
"ref_spans": [
{
"start": 135,
"end": 143,
"text": "Figure 2",
"ref_id": null
},
{
"start": 999,
"end": 1007,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Direct Transfer",
"sec_num": null
},
{
"text": "We report the results for the proposed low-resource supervised models (RaRe and BEA sup ), and unsupervised models (BEA uns and BEA uns\u00d72 ), summarised as an average over the 41 languages in Figure 3 (see Appendix A for the full table of results). The figure compares against high-and low-resource supervised baselines (HSup and LSup, respectively), and BWET. The best performance is achieved with a high supervision (HSup, F 1 = 89.2), while very limited supervision (LSup) results in a considerably lower F 1 of 62.1. The results for MV tok show that uniform ensembling of multiple source models is even worse, by about 5 points.",
"cite_spans": [],
"ref_spans": [
{
"start": 191,
"end": 199,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Multilingual Transfer",
"sec_num": null
},
{
"text": "Unsupervised zero-shot learning dramatically improves upon MV tok , and BEA ent uns outperforms BEA tok uns , showing the effectiveness of inference Further analysis show that majority voting works reasonably well for Romance and Germanic languages, which are well represented in the dataset, but fails miserably compared to single best for Slavic languages (e.g. ru, uk, bg) where there are only a few related languages. For most of the isolated languages (ar, fa, he, vi, ta), explicitly training a model in RaRe outperforms BEA ent sup , showing that relying only on aggregation of annotated data has limitations, in that it cannot exploit character and structural features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Multilingual Transfer",
"sec_num": null
},
{
"text": "Choice of Source Languages An important question is how the other models, particularly the unsupervised variants, are affected by the number and choice of sources languages. Figure 4 charts the performance of MV, BEA, and RaRe against the number of source models, comparing the use of ideal or realistic selection methods to attempt to find the best source models. MV ent , BEA ent sup , and RaRe use a small labeled dataset to rank the source models. BEA ent uns, oracle has the access to the perfect ranking of source models based on their real F 1 on the test set. BEA uns\u00d72 is completely unsupervised in that it uses its own estimates to rank all source models.",
"cite_spans": [],
"ref_spans": [
{
"start": 174,
"end": 182,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Multilingual Transfer",
"sec_num": null
},
{
"text": "MV doesn't show any benefit with more than 3 source models. 12 In contrast, BEA and RaRe con-tinue to improve with up to 10 languages. We show that BEA in two realistic scenarios (unsupervised: BEA ent uns\u00d72 , and supervised: BEA ent sup ) is highly effective at discriminating between good and bad source models, and thus filtering out the bad models gives the best results. The BEA ent uns\u00d72 curve shows the effect of filtering using purely unsupervised signal, which has a positive, albeit mild effect on performance. In BEA ent uns, oracle although the source model ranking is perfect, it narrowly outperforms BEA. Note also that neither of the BEA curves show evidence of the sawtooth pattern, i.e., they largely benefit from more inputs, irrespective of their parity. Finally, adding supervision in the target language in RaRe further improves upon the unsupervised models.",
"cite_spans": [
{
"start": 60,
"end": 62,
"text": "12",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Multilingual Transfer",
"sec_num": null
},
{
"text": "CoNLL Dataset Finally, we apply our model to the CoNLL-02/03 datasets, to benchmark our technique against related work. This corpus is much less rich than Wikiann used above, as it includes only four languages (en, de, nl, es), and furthermore, the languages are closely related and share the same script. Results in Table 2 show that our methods are competitive with benchmark methods, and, moreover, the use of 100 annotated sentences in the target language (RaRe l ) gives good improvements over unsupervised models. 13 Results also show that MV does very well, especially MV ent , and its performance is comparable to BEA's. Note that there are only 3 source models and none of them is clearly bad, so BEA estimates that they are similarly reliable which results in little difference in terms of performance between BEA and MV.",
"cite_spans": [
{
"start": 520,
"end": 522,
"text": "13",
"ref_id": null
}
],
"ref_spans": [
{
"start": 317,
"end": 324,
"text": "Table 2",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Multilingual Transfer",
"sec_num": null
},
{
"text": "Two main approaches for cross-lingual transfer are representation and annotation projection. Representation projection learns a model in a highresource source language using representations that are cross-linguistically transferable, and then directly applies the model to data in the target language. This can include the use of crosslingual word clusters and word embeddings (Ammar et al., 2016; Ni et al., 2017) , multitask learning with a closely related high-resource language (e.g. Spanish for Galician) (Cotterell and Duh, 2017) the source and target languages through phonemic transcription (Bharadwaj et al., 2016) or Wikification (Tsai et al., 2016) . In annotation projection, the annotations of tokens in a source sentence are projected to their aligned tokens in the target language through a parallel corpus. Annotation projection has been applied to POS tagging (Yarowsky et al., 2001; Das and Petrov, 2011; Duong et al., 2014; Fang and Cohn, 2016) , NER (Zitouni and Florian, 2008; Ehrmann et al., 2011; Agerri et al., 2018) , and parsing (Hwa et al., 2005; Ma and Xia, 2014; Rasooli and Collins, 2015a,b) . The Bible, Europarl, and recently the Watchtower has been used as parallel corpora, which are limited in genre, size, and language coverage, motivating the use of Wikipedia to create weak annotation for multilingual tasks such as NER (Nothman et al., 2013) . Recent advances in (un)supervised bilingual dictionary induction (Gouws and S\u00f8gaard, 2015; Duong et al., 2016; Lample et al., 2018; Artetxe et al., 2018; Schuster et al., 2019) have enabled cross-lingual alignment with bilingual dictionaries (Mayhew et al., 2017; Xie et al., 2018) . Most annotation projection methods with few exceptions (T\u00e4ckstr\u00f6m, 2012; Plank and Agi\u0107, 2018) use only one language (often English) as the source language. In multi-source language setting, majority voting is often used to aggregate noisy annotations (e.g. Plank and Agi\u0107 (2018) ). Fang and Cohn (2016) show the importance of modelling the annotation biases that the source language(s) might project to the target language.",
"cite_spans": [
{
"start": 377,
"end": 397,
"text": "(Ammar et al., 2016;",
"ref_id": "BIBREF1"
},
{
"start": 398,
"end": 414,
"text": "Ni et al., 2017)",
"ref_id": "BIBREF30"
},
{
"start": 510,
"end": 535,
"text": "(Cotterell and Duh, 2017)",
"ref_id": "BIBREF5"
},
{
"start": 599,
"end": 623,
"text": "(Bharadwaj et al., 2016)",
"ref_id": "BIBREF3"
},
{
"start": 640,
"end": 659,
"text": "(Tsai et al., 2016)",
"ref_id": "BIBREF45"
},
{
"start": 877,
"end": 900,
"text": "(Yarowsky et al., 2001;",
"ref_id": "BIBREF49"
},
{
"start": 901,
"end": 922,
"text": "Das and Petrov, 2011;",
"ref_id": "BIBREF6"
},
{
"start": 923,
"end": 942,
"text": "Duong et al., 2014;",
"ref_id": "BIBREF9"
},
{
"start": 943,
"end": 963,
"text": "Fang and Cohn, 2016)",
"ref_id": "BIBREF13"
},
{
"start": 970,
"end": 997,
"text": "(Zitouni and Florian, 2008;",
"ref_id": "BIBREF50"
},
{
"start": 998,
"end": 1019,
"text": "Ehrmann et al., 2011;",
"ref_id": null
},
{
"start": 1020,
"end": 1040,
"text": "Agerri et al., 2018)",
"ref_id": "BIBREF0"
},
{
"start": 1055,
"end": 1073,
"text": "(Hwa et al., 2005;",
"ref_id": "BIBREF18"
},
{
"start": 1074,
"end": 1091,
"text": "Ma and Xia, 2014;",
"ref_id": "BIBREF27"
},
{
"start": 1092,
"end": 1121,
"text": "Rasooli and Collins, 2015a,b)",
"ref_id": null
},
{
"start": 1358,
"end": 1380,
"text": "(Nothman et al., 2013)",
"ref_id": "BIBREF31"
},
{
"start": 1448,
"end": 1473,
"text": "(Gouws and S\u00f8gaard, 2015;",
"ref_id": "BIBREF17"
},
{
"start": 1474,
"end": 1493,
"text": "Duong et al., 2016;",
"ref_id": "BIBREF10"
},
{
"start": 1494,
"end": 1514,
"text": "Lample et al., 2018;",
"ref_id": "BIBREF25"
},
{
"start": 1515,
"end": 1536,
"text": "Artetxe et al., 2018;",
"ref_id": "BIBREF2"
},
{
"start": 1537,
"end": 1559,
"text": "Schuster et al., 2019)",
"ref_id": "BIBREF39"
},
{
"start": 1625,
"end": 1646,
"text": "(Mayhew et al., 2017;",
"ref_id": "BIBREF28"
},
{
"start": 1647,
"end": 1664,
"text": "Xie et al., 2018)",
"ref_id": "BIBREF48"
},
{
"start": 1722,
"end": 1739,
"text": "(T\u00e4ckstr\u00f6m, 2012;",
"ref_id": "BIBREF41"
},
{
"start": 1740,
"end": 1761,
"text": "Plank and Agi\u0107, 2018)",
"ref_id": "BIBREF34"
},
{
"start": 1925,
"end": 1946,
"text": "Plank and Agi\u0107 (2018)",
"ref_id": "BIBREF34"
},
{
"start": 1950,
"end": 1970,
"text": "Fang and Cohn (2016)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Transfer from multiple source languages: Previous work has shown the improvements of multi-source transfer in NER (T\u00e4ckstr\u00f6m, 2012; Enghoff et al., 2018) , POS tagging (Snyder et al., 2009; Plank and Agi\u0107, 2018) , and parsing (Ammar et al., 2016) compared to single source transfer, however, multi-source transfer might be noisy as a result of divergence in script, phonology, morphology, syntax, and semantics between the source languages, and the target language. To capture such differences, various methods have been proposed: latent variable models (Snyder et al., 2009) , majority voting (Plank and Agi\u0107, 2018) , utilising typological features (Ammar et al., 2016) , or explicitly learning annotation bias . Our work is also related to knowledge distillation from multiple source models applied in parsing (Kuncoro et al., 2016) and machine translation (Kim and Rush, 2016; Johnson et al., 2017) . In this work, we use truth inference to model the transfer annotation bias from diverse source models. Finally, our work is related to truth inference from crowd-sourced annotations (Whitehill et al., 2009; Welinder et al., 2010) , and most importantly from diverse classifiers (Kim and Ghahramani, 2012; Ratner et al., 2017) . Nguyen et al. (2017) propose a hidden Markov model for aggregating crowdsourced sequence labels, but only learn per-class accuracies for workers instead of full confusion matrices in order to address the data sparsity problem in crowdsourcing.",
"cite_spans": [
{
"start": 114,
"end": 131,
"text": "(T\u00e4ckstr\u00f6m, 2012;",
"ref_id": "BIBREF41"
},
{
"start": 132,
"end": 153,
"text": "Enghoff et al., 2018)",
"ref_id": "BIBREF12"
},
{
"start": 168,
"end": 189,
"text": "(Snyder et al., 2009;",
"ref_id": "BIBREF40"
},
{
"start": 190,
"end": 211,
"text": "Plank and Agi\u0107, 2018)",
"ref_id": "BIBREF34"
},
{
"start": 226,
"end": 246,
"text": "(Ammar et al., 2016)",
"ref_id": "BIBREF1"
},
{
"start": 554,
"end": 575,
"text": "(Snyder et al., 2009)",
"ref_id": "BIBREF40"
},
{
"start": 594,
"end": 616,
"text": "(Plank and Agi\u0107, 2018)",
"ref_id": "BIBREF34"
},
{
"start": 650,
"end": 670,
"text": "(Ammar et al., 2016)",
"ref_id": "BIBREF1"
},
{
"start": 812,
"end": 834,
"text": "(Kuncoro et al., 2016)",
"ref_id": "BIBREF23"
},
{
"start": 859,
"end": 879,
"text": "(Kim and Rush, 2016;",
"ref_id": "BIBREF22"
},
{
"start": 880,
"end": 901,
"text": "Johnson et al., 2017)",
"ref_id": "BIBREF19"
},
{
"start": 1086,
"end": 1110,
"text": "(Whitehill et al., 2009;",
"ref_id": "BIBREF47"
},
{
"start": 1111,
"end": 1133,
"text": "Welinder et al., 2010)",
"ref_id": "BIBREF46"
},
{
"start": 1182,
"end": 1208,
"text": "(Kim and Ghahramani, 2012;",
"ref_id": null
},
{
"start": 1209,
"end": 1229,
"text": "Ratner et al., 2017)",
"ref_id": "BIBREF37"
},
{
"start": 1232,
"end": 1252,
"text": "Nguyen et al. (2017)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Cross-lingual transfer does not work out of the box, especially when using large numbers of source languages, and distantly related target languages. In an NER setting using a collection of 41 languages, we showed that simple methods such as uniform ensembling do not work well. We proposed two new multilingual transfer models (RaRe and BEA), based on unsupervised transfer, or a supervised transfer setting with a small 100 sentence labelled dataset in the target language. We also compare our results with BWET (Xie et al., 2018) , a state-of-the-art unsupervised single source (English) transfer model, and showed that multilingual transfer outperforms it, however, our work is orthogonal to their work in that if training data from multiple source models is created, RaRe and BEA can still combine them, and outperform majority voting. Our unsupervised method, BEA uns , provides a fast and simple way of annotating data in the target language, which is capable of reasoning under noisy annotations, and outperforms several competitive baselines, including the majority voting ensemble, a low-resource supervised baseline, and the oracle single best transfer model. We show that light supervision improves performance further, and that our second approach, RaRe, based on ranking transfer models and then retraining on the target language, results in further and more consistent performance improvements.",
"cite_spans": [
{
"start": 514,
"end": 532,
"text": "(Xie et al., 2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "We tuned the batch size and the learning rate using development sets in four languages, 14 and then fixed these hyperparameters for all other languages in each model. The batch size was 1 sentence in low-resource scenarios (in baseline LSup and fine-tuning of RaRe), and to 100 sentences, in high-resource settings (HSup and the pretraining phase of RaRe). The learning rate was set to 0.001 and 0.01 for the high-resource and low-resource baseline models, respectively, and to 0.005, 0.0005 for the pretraining and fine-tuning phase of RaRe based on development results for the four languages. For CoNLL datasets, we had to decrease the batch size of the pre-training phase from 100 to 20 (because of GPU memory issues).",
"cite_spans": [
{
"start": 88,
"end": 90,
"text": "14",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "A.1 Hyperparameters",
"sec_num": null
},
{
"text": "We experimented with Wiki and CommonCrawl monolingual embeddings from fastText (Bojanowski et al., 2017) . Each of the 41 languages is mapped to English embedding space using three methods from MUSE: 1) supervised with bilingual dictionaries; 2) seeding using identical character sequences; and 3) unsupervised training using adversarial learning (Lample et al., 2018) . The crosslingual mappings are evaluated by precision at k = 1. The resulting cross-lingual embeddings are then used in NER direct transfer in a leave-one-out setting for the 41 languages (41\u00d740 transfers), and we report the mean F 1 in Table 3 . CommonCrawl doesn't perform well in bilingual induction despite having larger text corpora, and underperforms in direct transfer NER. It is also evident that using identical character strings instead of a bilingual dictionary as the seed for learning a supervised bilingual mapping barely affects the performance. This finding also applies to few-shot learning over larger ensembles: running RaRe over 40 source languages achieves an average F 1 of 77.9 when using embeddings trained with a dictionary, versus 76.9 using string identity instead. For this reason we have used the string identity method in the paper (e.g., Table 4 ), providing greater portability to language pairs without a bilingual dictionary. Experiments with unsupervised mappings performed substantially worse than supervised methods, and so we didn't explore these further.",
"cite_spans": [
{
"start": 79,
"end": 104,
"text": "(Bojanowski et al., 2017)",
"ref_id": "BIBREF4"
},
{
"start": 347,
"end": 368,
"text": "(Lample et al., 2018)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [
{
"start": 607,
"end": 614,
"text": "Table 3",
"ref_id": "TABREF6"
},
{
"start": 1239,
"end": 1246,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.2 Cross-lingual Word Embeddings",
"sec_num": null
},
{
"text": "14 Afrikaans, Arabic, Bulgarian and Bengali. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A.2 Cross-lingual Word Embeddings",
"sec_num": null
},
{
"text": "In Figure 5 the performance of an NER model trained in a high-resource setting on a source language applied on the other 40 target languages (leave-one-out) is shown. An interesting finding is that symmetry does not always hold (e.g. id vs. ms or fa vs. ar).",
"cite_spans": [],
"ref_spans": [
{
"start": 3,
"end": 11,
"text": "Figure 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.3 Direct Transfer Results",
"sec_num": null
},
{
"text": "The result of applying baselines, proposed models and their variations, and unsupervised transfer model of Xie et al. (2018) are shown in Table 4 .",
"cite_spans": [
{
"start": 107,
"end": 124,
"text": "Xie et al. (2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [
{
"start": 138,
"end": 145,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.4 Detailed Low-resource Results",
"sec_num": null
},
{
"text": "ar he id ms tl vi af nl en de da no sv el tr fa bn hi ta ca es fr it pt ro bg mk ru uk bs cs hr pl sk sl sq lt lv et fi hu Figure 5: The direct transfer performance of a source NER model trained in a high-resource setting applied on the other 40 target languages, and evaluated in terms of phrase-level F 1 . The languages are roughly sorted by language family. Slavic languages in Cyrillic script are from bg to uk, and those in Latin script are from bs to sl. 48 89 61 74 79 78 80 76 49 80 79 77 73 \u00b5 ---89.2 62.1 74.3 77.4 76.9 74.8 60.2 50.5 72.8 69.7 64.5 56.7 71.6 \u03c3 ---2.8 5.2 7.3 6.4 6.4 9.6 24.1 14.7 11.5 12.6 13.7 25 11.5 Table 4 : The size of training and test sets (development set size equals test set size) in thousand sentences, and the precision at 1 for Bilingual dictionaries induced from mapping languages to the English embedding space (using identical characters) is shown (BiDic.P@1). F 1 scores on the test set, comparing baseline supervised models (HSup, LSup), multilingual transfer from top k source languages (RaRe, 5 runs, k = 1, 10, 40), an unsupervised RaRe with uniform expertise and no fine-tuning (RaRe uns ), and aggregation methods: majority voting (MV tok ), BEA tok uns and BEA ent uns (Bayesian aggregation in token-and entity-level), and the oracle single best annotation (Oracle). We also compare with BWET (Xie et al., 2018) , an unsupervised transfer model with stateof-the-art on CoNLL NER datasets. The mean and standard deviation over all 41 languages, \u00b5, \u03c3, are also reported.",
"cite_spans": [
{
"start": 1348,
"end": 1366,
"text": "(Xie et al., 2018)",
"ref_id": "BIBREF48"
}
],
"ref_spans": [
{
"start": 633,
"end": 640,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.4 Detailed Low-resource Results",
"sec_num": null
},
{
"text": "Although there is no explicit breaking of the symmetry of the model, we initialise inference using the majority vote, which results in a bias towards this solution.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Garrette and Baldridge (2013) showed that about 100 sentences can be annotated with POS tags in two hours by non-native annotators.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "We show that uniform sampling with few source languages achieves worse performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Because BWET uses identical characters for bilingual dictionary induction, we observed many English loan words in the target language mapped to the same word in the induced bilingual dictionaries. Filtering such dictionary items might improve BWET.10 For detailed results seeTable 4in the Appendix.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Detailed direct transfer results are shown inFigure 5in the Appendix.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "The sawtooth pattern arises from the increased numbers of ties (broken randomly) with even numbers of inputs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "For German because of its capitalisation pattern, we lowercase all the source and target data, and also remove German as a source model for other languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Building named entity recognition taggers via parallel corpora",
"authors": [
{
"first": "Rodrigo",
"middle": [],
"last": "Agerri",
"suffix": ""
},
{
"first": "Yiling",
"middle": [],
"last": "Chung",
"suffix": ""
},
{
"first": "Itziar",
"middle": [],
"last": "Aldabe",
"suffix": ""
},
{
"first": "Nora",
"middle": [],
"last": "Aranberri",
"suffix": ""
},
{
"first": "Gorka",
"middle": [],
"last": "Labaka",
"suffix": ""
},
{
"first": "German",
"middle": [],
"last": "Rigau",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). European Language Resource Association",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rodrigo Agerri, Yiling Chung, Itziar Aldabe, Nora Aranberri, Gorka Labaka, and German Rigau. 2018. Building named entity recognition taggers via paral- lel corpora. In Proceedings of the Eleventh Interna- tional Conference on Language Resources and Eval- uation (LREC-2018). European Language Resource Association.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Many languages, one parser",
"authors": [
{
"first": "Waleed",
"middle": [],
"last": "Ammar",
"suffix": ""
},
{
"first": "George",
"middle": [],
"last": "Mulcaire",
"suffix": ""
},
{
"first": "Miguel",
"middle": [],
"last": "Ballesteros",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Noah",
"middle": [],
"last": "Smith",
"suffix": ""
}
],
"year": 2016,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "4",
"issue": "",
"pages": "431--444",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Waleed Ammar, George Mulcaire, Miguel Ballesteros, Chris Dyer, and Noah Smith. 2016. Many lan- guages, one parser. Transactions of the Association for Computational Linguistics, 4:431-444.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings",
"authors": [
{
"first": "Mikel",
"middle": [],
"last": "Artetxe",
"suffix": ""
},
{
"first": "Gorka",
"middle": [],
"last": "Labaka",
"suffix": ""
},
{
"first": "Eneko",
"middle": [],
"last": "Agirre",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "789--798",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018. A robust self-learning method for fully unsuper- vised cross-lingual mappings of word embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 789-798, Melbourne, Aus- tralia.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Phonologically aware neural model for named entity recognition in low resource transfer settings",
"authors": [
{
"first": "Akash",
"middle": [],
"last": "Bharadwaj",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Mortensen",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Jaime",
"middle": [],
"last": "Carbonell",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1462--1472",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1153"
]
},
"num": null,
"urls": [],
"raw_text": "Akash Bharadwaj, David Mortensen, Chris Dyer, and Jaime Carbonell. 2016. Phonologically aware neu- ral model for named entity recognition in low re- source transfer settings. In Proceedings of the 2016 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1462-1472.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Enriching word vectors with subword information",
"authors": [
{
"first": "Piotr",
"middle": [],
"last": "Bojanowski",
"suffix": ""
},
{
"first": "Edouard",
"middle": [],
"last": "Grave",
"suffix": ""
},
{
"first": "Armand",
"middle": [],
"last": "Joulin",
"suffix": ""
},
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
}
],
"year": 2017,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "5",
"issue": "",
"pages": "135--146",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Associa- tion for Computational Linguistics, 5:135-146.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Lowresource named entity recognition with crosslingual, character-level neural conditional random fields",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Cotterell",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Duh",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the Eighth International Joint Conference on Natural Language Processing",
"volume": "2",
"issue": "",
"pages": "91--96",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ryan Cotterell and Kevin Duh. 2017. Low- resource named entity recognition with cross- lingual, character-level neural conditional random fields. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 91-96. Asian Fed- eration of Natural Language Processing.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Unsupervised part-of-speech tagging with bilingual graph-based projections",
"authors": [
{
"first": "Dipanjan",
"middle": [],
"last": "Das",
"suffix": ""
},
{
"first": "Slav",
"middle": [],
"last": "Petrov",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "600--609",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dipanjan Das and Slav Petrov. 2011. Unsupervised part-of-speech tagging with bilingual graph-based projections. In Proceedings of the 49th Annual Meeting of the Association for Computational Lin- guistics: Human Language Technologies, pages 600-609.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Maximum likelihood estimation of observer errorrates using the em algorithm",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Philip Dawid",
"suffix": ""
},
{
"first": "Allan M",
"middle": [],
"last": "Skene",
"suffix": ""
}
],
"year": 1979,
"venue": "Applied statistics",
"volume": "",
"issue": "",
"pages": "20--28",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexander Philip Dawid and Allan M Skene. 1979. Maximum likelihood estimation of observer error- rates using the em algorithm. Applied statistics, pages 20-28.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser",
"authors": [
{
"first": "Long",
"middle": [],
"last": "Duong",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bird",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Cook",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing",
"volume": "2",
"issue": "",
"pages": "845--850",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd Annual Meet- ing of the Association for Computational Linguistics and the 7th International Joint Conference on Natu- ral Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26- 31, 2015, Beijing, China, Volume 2: Short Papers, pages 845-850.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "What can we get from 1000 tokens? A case study of multilingual POS tagging for resource-poor languages",
"authors": [
{
"first": "Long",
"middle": [],
"last": "Duong",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
},
{
"first": "Karin",
"middle": [],
"last": "Verspoor",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bird",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Cook",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "886--897",
"other_ids": {
"DOI": [
"10.3115/v1/D14-1096"
]
},
"num": null,
"urls": [],
"raw_text": "Long Duong, Trevor Cohn, Karin Verspoor, Steven Bird, and Paul Cook. 2014. What can we get from 1000 tokens? A case study of multilingual POS tag- ging for resource-poor languages. In Proceedings of the 2014 Conference on Empirical Methods in Natu- ral Language Processing (EMNLP), pages 886-897.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Learning crosslingual word embeddings without bilingual corpora",
"authors": [
{
"first": "Long",
"middle": [],
"last": "Duong",
"suffix": ""
},
{
"first": "Hiroshi",
"middle": [],
"last": "Kanayama",
"suffix": ""
},
{
"first": "Tengfei",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bird",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1285--1295",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1136"
]
},
"num": null,
"urls": [],
"raw_text": "Long Duong, Hiroshi Kanayama, Tengfei Ma, Steven Bird, and Trevor Cohn. 2016. Learning crosslingual word embeddings without bilingual corpora. In Pro- ceedings of the 2016 Conference on Empirical Meth- ods in Natural Language Processing, pages 1285- 1295.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Low-resource named entity recognition via multi-source projection: Not quite there yet?",
"authors": [
{
"first": "Jan",
"middle": [],
"last": "Vium Enghoff",
"suffix": ""
},
{
"first": "S\u00f8ren",
"middle": [],
"last": "Harrison",
"suffix": ""
},
{
"first": "Agi\u0107",
"middle": [],
"last": "And\u017eeljko",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text",
"volume": "",
"issue": "",
"pages": "195--201",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jan Vium Enghoff, S\u00f8ren Harrison, and\u017deljko Agi\u0107. 2018. Low-resource named entity recognition via multi-source projection: Not quite there yet? In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 195-201.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Learning when to trust distant supervision: An application to lowresource POS tagging using cross-lingual projection",
"authors": [
{
"first": "Meng",
"middle": [],
"last": "Fang",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "178--186",
"other_ids": {
"DOI": [
"10.18653/v1/K16-1018"
]
},
"num": null,
"urls": [],
"raw_text": "Meng Fang and Trevor Cohn. 2016. Learning when to trust distant supervision: An application to low- resource POS tagging using cross-lingual projection. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pages 178-186.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Model transfer for tagging low-resource languages using a bilingual dictionary",
"authors": [
{
"first": "Meng",
"middle": [],
"last": "Fang",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "587--593",
"other_ids": {
"DOI": [
"10.18653/v1/P17-2093"
]
},
"num": null,
"urls": [],
"raw_text": "Meng Fang and Trevor Cohn. 2017. Model transfer for tagging low-resource languages using a bilingual dictionary. In Proceedings of the 55th Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 587-593.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Learning how to active learn: A deep reinforcement learning approach",
"authors": [
{
"first": "Meng",
"middle": [],
"last": "Fang",
"suffix": ""
},
{
"first": "Yuan",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Trevor",
"middle": [],
"last": "Cohn",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "595--605",
"other_ids": {
"DOI": [
"10.18653/v1/D17-1063"
]
},
"num": null,
"urls": [],
"raw_text": "Meng Fang, Yuan Li, and Trevor Cohn. 2017. Learning how to active learn: A deep reinforcement learning approach. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Process- ing, pages 595-605.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Learning a part-of-speech tagger from two hours of annotation",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Garrette",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Baldridge",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "138--147",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Garrette and Jason Baldridge. 2013. Learning a part-of-speech tagger from two hours of annotation. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 138-147.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Simple task-specific bilingual word embeddings",
"authors": [
{
"first": "Stephan",
"middle": [],
"last": "Gouws",
"suffix": ""
},
{
"first": "Anders",
"middle": [],
"last": "S\u00f8gaard",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "1386--1390",
"other_ids": {
"DOI": [
"10.3115/v1/N15-1157"
]
},
"num": null,
"urls": [],
"raw_text": "Stephan Gouws and Anders S\u00f8gaard. 2015. Simple task-specific bilingual word embeddings. In Pro- ceedings of the 2015 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1386-1390.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Bootstrapping parsers via syntactic projection across parallel texts",
"authors": [
{
"first": "Rebecca",
"middle": [],
"last": "Hwa",
"suffix": ""
},
{
"first": "Philip",
"middle": [],
"last": "Resnik",
"suffix": ""
},
{
"first": "Amy",
"middle": [],
"last": "Weinberg",
"suffix": ""
},
{
"first": "Clara",
"middle": [
"I"
],
"last": "Cabezas",
"suffix": ""
},
{
"first": "Okan",
"middle": [],
"last": "Kolak",
"suffix": ""
}
],
"year": 2005,
"venue": "Natural Language Engineering",
"volume": "11",
"issue": "3",
"pages": "311--325",
"other_ids": {
"DOI": [
"10.1017/S1351324905003840"
]
},
"num": null,
"urls": [],
"raw_text": "Rebecca Hwa, Philip Resnik, Amy Weinberg, Clara I. Cabezas, and Okan Kolak. 2005. Bootstrapping parsers via syntactic projection across parallel texts. Natural Language Engineering, 11(3):311-325.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Google's multilingual neural machine translation system: Enabling zero-shot translation",
"authors": [
{
"first": "Melvin",
"middle": [],
"last": "Johnson",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "Quoc",
"middle": [
"V"
],
"last": "Le",
"suffix": ""
},
{
"first": "Maxim",
"middle": [],
"last": "Krikun",
"suffix": ""
},
{
"first": "Yonghui",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Zhifeng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Nikhil",
"middle": [],
"last": "Thorat",
"suffix": ""
},
{
"first": "Fernanda",
"middle": [],
"last": "Vi\u00e9gas",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Wattenberg",
"suffix": ""
},
{
"first": "Greg",
"middle": [],
"last": "Corrado",
"suffix": ""
},
{
"first": "Macduff",
"middle": [],
"last": "Hughes",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2017,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "5",
"issue": "",
"pages": "339--351",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Vi\u00e9gas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017. Google's multilingual neural machine translation system: En- abling zero-shot translation. Transactions of the As- sociation for Computational Linguistics, 5:339-351.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Learning in graphical models",
"authors": [
{
"first": "Jordan",
"middle": [],
"last": "Michael Irwin",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "89",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Irwin Jordan. 1998. Learning in graphical models, volume 89. Springer Science & Business Media.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Sequencelevel knowledge distillation",
"authors": [
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Alexander",
"middle": [
"M"
],
"last": "Rush",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1317--1327",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1139"
]
},
"num": null,
"urls": [],
"raw_text": "Yoon Kim and Alexander M. Rush. 2016. Sequence- level knowledge distillation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1317-1327.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Distilling an ensemble of greedy dependency parsers into one mst parser",
"authors": [
{
"first": "Adhiguna",
"middle": [],
"last": "Kuncoro",
"suffix": ""
},
{
"first": "Miguel",
"middle": [],
"last": "Ballesteros",
"suffix": ""
},
{
"first": "Lingpeng",
"middle": [],
"last": "Kong",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1744--1753",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1180"
]
},
"num": null,
"urls": [],
"raw_text": "Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, and Noah A. Smith. 2016. Dis- tilling an ensemble of greedy dependency parsers into one mst parser. In Proceedings of the 2016 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1744-1753.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Neural architectures for named entity recognition",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Miguel",
"middle": [],
"last": "Ballesteros",
"suffix": ""
},
{
"first": "Sandeep",
"middle": [],
"last": "Subramanian",
"suffix": ""
},
{
"first": "Kazuya",
"middle": [],
"last": "Kawakami",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "260--270",
"other_ids": {
"DOI": [
"10.18653/v1/N16-1030"
]
},
"num": null,
"urls": [],
"raw_text": "Guillaume Lample, Miguel Ballesteros, Sandeep Sub- ramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 260-270.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Word translation without parallel data",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Alexis",
"middle": [],
"last": "Conneau",
"suffix": ""
},
{
"first": "Marc'aurelio",
"middle": [],
"last": "Ranzato",
"suffix": ""
},
{
"first": "Ludovic",
"middle": [],
"last": "Denoyer",
"suffix": ""
},
{
"first": "Herv\u00e9",
"middle": [],
"last": "J\u00e9gou",
"suffix": ""
}
],
"year": 2018,
"venue": "International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guillaume Lample, Alexis Conneau, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herv\u00e9 J\u00e9gou. 2018. Word translation without parallel data. In Interna- tional Conference on Learning Representations.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF",
"authors": [
{
"first": "Xuezhe",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Eduard",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "1064--1074",
"other_ids": {
"DOI": [
"10.18653/v1/P16-1101"
]
},
"num": null,
"urls": [],
"raw_text": "Xuezhe Ma and Eduard Hovy. 2016. End-to-end sequence labeling via bi-directional LSTM-CNNs- CRF. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), pages 1064-1074, Berlin, Germany. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Unsupervised dependency parsing with transferring distribution via parallel guidance and entropy regularization",
"authors": [
{
"first": "Xuezhe",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Fei",
"middle": [],
"last": "Xia",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1337--1348",
"other_ids": {
"DOI": [
"10.3115/v1/P14-1126"
]
},
"num": null,
"urls": [],
"raw_text": "Xuezhe Ma and Fei Xia. 2014. Unsupervised depen- dency parsing with transferring distribution via par- allel guidance and entropy regularization. In Pro- ceedings of the 52nd Annual Meeting of the Asso- ciation for Computational Linguistics (Volume 1: Long Papers), pages 1337-1348, Baltimore, Mary- land. Association for Computational Linguistics.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Cheap translation for cross-lingual named entity recognition",
"authors": [
{
"first": "Stephen",
"middle": [],
"last": "Mayhew",
"suffix": ""
},
{
"first": "Chen-Tse",
"middle": [],
"last": "Tsai",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2536--2545",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephen Mayhew, Chen-Tse Tsai, and Dan Roth. 2017. Cheap translation for cross-lingual named entity recognition. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Pro- cessing, pages 2536-2545.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Aggregating and predicting sequence labels from crowd annotations",
"authors": [
{
"first": "An",
"middle": [
"Thanh"
],
"last": "Nguyen",
"suffix": ""
},
{
"first": "Byron",
"middle": [],
"last": "Wallace",
"suffix": ""
},
{
"first": "Junyi",
"middle": [
"Jessy"
],
"last": "Li",
"suffix": ""
},
{
"first": "Ani",
"middle": [],
"last": "Nenkova",
"suffix": ""
},
{
"first": "Matthew",
"middle": [],
"last": "Lease",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "299--309",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "An Thanh Nguyen, Byron Wallace, Junyi Jessy Li, Ani Nenkova, and Matthew Lease. 2017. Aggregating and predicting sequence labels from crowd annota- tions. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol- ume 1: Long Papers), volume 1, pages 299-309.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Weakly supervised cross-lingual named entity recognition via effective annotation and representation projection",
"authors": [
{
"first": "Jian",
"middle": [],
"last": "Ni",
"suffix": ""
},
{
"first": "Georgiana",
"middle": [],
"last": "Dinu",
"suffix": ""
},
{
"first": "Radu",
"middle": [],
"last": "Florian",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1470--1480",
"other_ids": {
"DOI": [
"10.18653/v1/P17-1135"
]
},
"num": null,
"urls": [],
"raw_text": "Jian Ni, Georgiana Dinu, and Radu Florian. 2017. Weakly supervised cross-lingual named entity recognition via effective annotation and representa- tion projection. In Proceedings of the 55th Annual Meeting of the Association for Computational Lin- guistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pages 1470-1480.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Learning multilingual named entity recognition from wikipedia",
"authors": [
{
"first": "Joel",
"middle": [],
"last": "Nothman",
"suffix": ""
},
{
"first": "Nicky",
"middle": [],
"last": "Ringland",
"suffix": ""
},
{
"first": "Will",
"middle": [],
"last": "Radford",
"suffix": ""
},
{
"first": "Tara",
"middle": [],
"last": "Murphy",
"suffix": ""
},
{
"first": "James",
"middle": [
"R"
],
"last": "Curran",
"suffix": ""
}
],
"year": 2013,
"venue": "Artificial Intelligence",
"volume": "194",
"issue": "",
"pages": "151--175",
"other_ids": {
"DOI": [
"10.1016/j.artint.2012.03.006"
]
},
"num": null,
"urls": [],
"raw_text": "Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, and James R. Curran. 2013. Learning mul- tilingual named entity recognition from wikipedia. Artificial Intelligence, 194:151-175.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Crosslingual name tagging and linking for 282 languages",
"authors": [
{
"first": "Xiaoman",
"middle": [],
"last": "Pan",
"suffix": ""
},
{
"first": "Boliang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "May",
"suffix": ""
},
{
"first": "Joel",
"middle": [],
"last": "Nothman",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Knight",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017. Cross- lingual name tagging and linking for 282 languages.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "1",
"issue": "",
"pages": "1946--1958",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1946-1958.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Distant supervision from disparate sources for low-resource partof-speech tagging",
"authors": [
{
"first": "Barbara",
"middle": [],
"last": "Plank",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Agi\u0107",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "614--620",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Barbara Plank and\u017deljko Agi\u0107. 2018. Distant super- vision from disparate sources for low-resource part- of-speech tagging. In Proceedings of the 2018 Con- ference on Empirical Methods in Natural Language Processing, pages 614-620.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Density-driven cross-lingual transfer of dependency parsers",
"authors": [
{
"first": "Mohammad",
"middle": [],
"last": "Sadegh",
"suffix": ""
},
{
"first": "Rasooli",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "328--338",
"other_ids": {
"DOI": [
"10.18653/v1/D15-1039"
]
},
"num": null,
"urls": [],
"raw_text": "Mohammad Sadegh Rasooli and Michael Collins. 2015a. Density-driven cross-lingual transfer of de- pendency parsers. In Proceedings of the 2015 Con- ference on Empirical Methods in Natural Language Processing, pages 328-338.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Density-driven cross-lingual transfer of dependency parsers",
"authors": [
{
"first": "Mohammad",
"middle": [],
"last": "Sadegh",
"suffix": ""
},
{
"first": "Rasooli",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "328--338",
"other_ids": {
"DOI": [
"10.18653/v1/D15-1039"
]
},
"num": null,
"urls": [],
"raw_text": "Mohammad Sadegh Rasooli and Michael Collins. 2015b. Density-driven cross-lingual transfer of de- pendency parsers. In Proceedings of the 2015 Con- ference on Empirical Methods in Natural Language Processing, pages 328-338, Lisbon, Portugal. Asso- ciation for Computational Linguistics.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Snorkel: Rapid training data creation with weak supervision",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Ratner",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Stephen",
"suffix": ""
},
{
"first": "Henry",
"middle": [],
"last": "Bach",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Ehrenberg",
"suffix": ""
},
{
"first": "Sen",
"middle": [],
"last": "Fries",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "R\u00e9",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the VLDB Endowment",
"volume": "11",
"issue": "",
"pages": "269--282",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher R\u00e9. 2017. Snorkel: Rapid training data creation with weak su- pervision. Proceedings of the VLDB Endowment, 11(3):269-282.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Eliminating spammers and ranking annotators for crowdsourced labeling tasks",
"authors": [
{
"first": "C",
"middle": [],
"last": "Vikas",
"suffix": ""
},
{
"first": "Shipeng",
"middle": [],
"last": "Raykar",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2012,
"venue": "J. Mach. Learn. Res",
"volume": "13",
"issue": "",
"pages": "491--518",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vikas C. Raykar and Shipeng Yu. 2012. Eliminating spammers and ranking annotators for crowdsourced labeling tasks. J. Mach. Learn. Res., 13:491-518.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Cross-lingual alignment of contextual word embeddings",
"authors": [
{
"first": "Tal",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "Ori",
"middle": [],
"last": "Ram",
"suffix": ""
},
{
"first": "Regina",
"middle": [],
"last": "Barzilay",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Globerson",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tal Schuster, Ori Ram, Regina Barzilay, and Amir Globerson. 2019. Cross-lingual alignment of con- textual word embeddings, with applications to zero- shot dependency parsing.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Adding more languages improves unsupervised multilingual part-of-speech tagging: a bayesian non-parametric approach",
"authors": [
{
"first": "Benjamin",
"middle": [],
"last": "Snyder",
"suffix": ""
},
{
"first": "Tahira",
"middle": [],
"last": "Naseem",
"suffix": ""
},
{
"first": "Jacob",
"middle": [],
"last": "Eisenstein",
"suffix": ""
},
{
"first": "Regina",
"middle": [],
"last": "Barzilay",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "83--91",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Benjamin Snyder, Tahira Naseem, Jacob Eisenstein, and Regina Barzilay. 2009. Adding more languages improves unsupervised multilingual part-of-speech tagging: a bayesian non-parametric approach. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Lin- guistics, pages 83-91.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Nudging the envelope of direct transfer methods for multilingual named entity recognition",
"authors": [
{
"first": "Oscar",
"middle": [],
"last": "T\u00e4ckstr\u00f6m",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure",
"volume": "",
"issue": "",
"pages": "55--63",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oscar T\u00e4ckstr\u00f6m. 2012. Nudging the envelope of di- rect transfer methods for multilingual named entity recognition. In Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure, pages 55-63.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "Cross-lingual word clusters for direct transfer of linguistic structure",
"authors": [
{
"first": "Oscar",
"middle": [],
"last": "T\u00e4ckstr\u00f6m",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "477--487",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oscar T\u00e4ckstr\u00f6m, Ryan McDonald, and Jakob Uszko- reit. 2012. Cross-lingual word clusters for direct transfer of linguistic structure. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, pages 477-487.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "Introduction to the conll-2002 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "",
"suffix": ""
},
{
"first": "Tjong Kim",
"middle": [],
"last": "Sang",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the 6th Conference on Natural Language Learning",
"volume": "20",
"issue": "",
"pages": "1--4",
"other_ids": {
"DOI": [
"10.3115/1118853.1118877"
]
},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang. 2002. Introduction to the conll-2002 shared task: Language-independent named entity recognition. In Proceedings of the 6th Conference on Natural Language Learning -Volume 20, COLING-02, pages 1-4, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "Introduction to the conll-2003 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "Tjong",
"suffix": ""
},
{
"first": "Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Fien",
"middle": [],
"last": "De Meulder",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003",
"volume": "4",
"issue": "",
"pages": "142--147",
"other_ids": {
"DOI": [
"10.3115/1119176.1119195"
]
},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 -Volume 4, CONLL '03, pages 142-147, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "Cross-lingual named entity recognition via wikification",
"authors": [
{
"first": "Chen-Tse",
"middle": [],
"last": "Tsai",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Mayhew",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "219--228",
"other_ids": {
"DOI": [
"10.18653/v1/K16-1022"
]
},
"num": null,
"urls": [],
"raw_text": "Chen-Tse Tsai, Stephen Mayhew, and Dan Roth. 2016. Cross-lingual named entity recognition via wikifica- tion. In Proceedings of The 20th SIGNLL Confer- ence on Computational Natural Language Learning, pages 219-228.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "The multidimensional wisdom of crowds",
"authors": [
{
"first": "Peter",
"middle": [],
"last": "Welinder",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Branson",
"suffix": ""
},
{
"first": "Serge",
"middle": [
"J"
],
"last": "Belongie",
"suffix": ""
},
{
"first": "Pietro",
"middle": [],
"last": "Perona",
"suffix": ""
}
],
"year": 2010,
"venue": "Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "2424--2432",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter Welinder, Steve Branson, Serge J. Belongie, and Pietro Perona. 2010. The multidimensional wis- dom of crowds. In Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Pro- ceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada., pages 2424- 2432. Curran Associates, Inc.",
"links": null
},
"BIBREF47": {
"ref_id": "b47",
"title": "Whose vote should count more: Optimal integration of labels from labelers of unknown expertise",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Whitehill",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Ruvolo",
"suffix": ""
},
{
"first": "Tingfan",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Jacob",
"middle": [],
"last": "Bergsma",
"suffix": ""
},
{
"first": "Javier",
"middle": [
"R"
],
"last": "Movellan",
"suffix": ""
}
],
"year": 2009,
"venue": "Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "2035--2043",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jacob Whitehill, Paul Ruvolo, Tingfan Wu, Jacob Bergsma, and Javier R. Movellan. 2009. Whose vote should count more: Optimal integration of la- bels from labelers of unknown expertise. In Ad- vances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meet- ing held 7-10 December 2009, Vancouver, British Columbia, Canada., pages 2035-2043. Curran As- sociates, Inc.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "Neural crosslingual named entity recognition with minimal resources",
"authors": [
{
"first": "Jiateng",
"middle": [],
"last": "Xie",
"suffix": ""
},
{
"first": "Zhilin",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
},
{
"first": "Jaime",
"middle": [],
"last": "Carbonell",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "369--379",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, and Jaime Carbonell. 2018. Neural cross- lingual named entity recognition with minimal re- sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 369-379.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Inducing multilingual text analysis tools via robust projection across aligned corpora",
"authors": [
{
"first": "David",
"middle": [],
"last": "Yarowsky",
"suffix": ""
},
{
"first": "Grace",
"middle": [],
"last": "Ngai",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Wicentowski",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of the first international conference on Human language technology research",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Yarowsky, Grace Ngai, and Richard Wicen- towski. 2001. Inducing multilingual text analysis tools via robust projection across aligned corpora. In Proceedings of the first international conference on Human language technology research, pages 1-",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "Mention detection crossing the language barrier",
"authors": [
{
"first": "Imed",
"middle": [],
"last": "Zitouni",
"suffix": ""
},
{
"first": "Radu",
"middle": [],
"last": "Florian",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "600--609",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Imed Zitouni and Radu Florian. 2008. Mention detec- tion crossing the language barrier. In Proceedings of the 2008 Conference on Empirical Methods in Nat- ural Language Processing, pages 600-609.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"uris": null,
"num": null,
"text": "Plate diagram for the BEA model."
},
"TABREF0": {
"content": "<table><tr><td>: An example sentence with its aggregated la-</td></tr><tr><td>bels in both token view and entity view. Aggregation</td></tr><tr><td>in token view may generate results inconsistent with</td></tr><tr><td>the BIO scheme.</td></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": ""
},
"TABREF1": {
"content": "<table><tr><td>F 1</td><td>40 60 80</td><td>nl</td><td>fa</td><td>ru</td><td>ar</td><td colspan=\"3\">hr fr sk</td><td colspan=\"2\">nl</td><td>fr</td><td>fi</td><td>de</td><td colspan=\"2\">et it</td><td>es</td><td>pt</td><td colspan=\"4\">cs nl it cs</td><td>sk</td><td colspan=\"3\">bg id es</td><td>ar</td><td colspan=\"2\">en nl</td><td>ru ca</td></tr><tr><td/><td>20</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td>af</td><td>ar</td><td>bg</td><td>bn</td><td>bs</td><td>ca</td><td>cs</td><td colspan=\"2\">de</td><td>el</td><td>et</td><td>fa</td><td>fi</td><td>fr</td><td>he</td><td>hi</td><td>hr</td><td>hu</td><td>id</td><td>lt</td><td>lv</td><td>mk</td><td>ms</td><td>ro</td><td>ta</td><td>tl</td><td>tr</td><td>uk</td><td>vi</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"6\">Target Language</td><td/><td/><td colspan=\"6\">Top En MV</td></tr><tr><td colspan=\"4\">Figure 2: 40 0 100 200 5K+ Annotation Requirement (#sentences)</td><td/><td/><td>60</td><td/><td colspan=\"2\">LSup BWET</td><td colspan=\"6\">80 BEA tok RaRe t10 100 HSup BEA ent sup t10 MV ent t3 MV tok t3 BEA ent uns\u00d72 t10 BEA ent uns uns MV ent MV tok</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"5\">F1 over 41 langs.</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"7\">10 Finally we report results on</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"8\">CoNLL NER datasets.</td><td/><td/><td/><td/></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": "Best source language ( ) compared with en ( ), and majority voting ( ) over all source languages in terms of F 1 performance in direct transfer shown for a subset of the 41 target languages (x axis). Worst transfer score, not shown here, is about 0. See \u00a73 for details of models and datasets."
},
"TABREF2": {
"content": "<table><tr><td/><td/><td>BEA ent uns\u00d72</td><td>MV ent</td><td>BEA ent uns, oracle</td></tr><tr><td/><td>80</td><td>BEA ent sup</td><td>RaRe</td><td/></tr><tr><td/><td>75</td><td/><td/><td/></tr><tr><td>1</td><td/><td/><td/><td/></tr><tr><td>F</td><td>70</td><td/><td/><td/></tr><tr><td/><td>65</td><td/><td/><td/></tr><tr><td/><td>0</td><td>5</td><td>10</td><td>15</td><td>20</td></tr><tr><td/><td/><td colspan=\"3\">#source languages</td></tr><tr><td colspan=\"6\">Figure 4: The mean F 1 performance of MV ent ,</td></tr><tr><td colspan=\"3\">BEA ent sup , BEA ent uns\u00d72 ,</td><td/><td/></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": "BEA ent uns, oracle , and RaRe over the 41 languages by the number of source languages. over entities rather than tokens. It is clear that having access to limited annotation in the target language makes a substantial difference in BEA ent sup and RaRe with F 1 of 74.8 and 77.4, respectively."
},
"TABREF3": {
"content": "<table><tr><td>lang.</td><td>de es nl en</td></tr><tr><td>Tsai et al. (2016) w</td><td>48.1 60.6 61.6 -</td></tr><tr><td>Ni et al. (2017) w, p, d</td><td>58.5 65.1 65.4 -</td></tr><tr><td colspan=\"2\">Mayhew et al. (2017) w, d 59.1 66.0 66.5 -</td></tr><tr><td>Xie et al. (2018) 0</td><td>57.8 72.4 70.4 -</td></tr><tr><td>our work</td><td/></tr><tr><td>MV tok, 0</td><td>57.4 66.4 71.0 62.1</td></tr><tr><td>MV ent, 0</td><td>57.7 69.0 70.3 64.6</td></tr><tr><td>BEA tok, 0 uns</td><td>58.2 64.7 70.1 61.2</td></tr><tr><td>BEA ent, 0 uns</td><td>57.8 63.4 70.3 64.8</td></tr><tr><td>RaRe 0 uns</td><td>59.1 71.8 67.6 67.5</td></tr><tr><td>RaRe l</td><td>64.0 72.5 72.5 70.0</td></tr><tr><td>HSup</td><td>79.1 85.7 87.1 89.5</td></tr><tr><td/><td>, or bridging</td></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": "T\u00e4ckstr\u00f6m et al. (2012) p 40.4 59.3 58.4 -Nothman et al. (2013) w 55.8 61.0 64.0 61.3"
},
"TABREF4": {
"content": "<table><tr><td>of phrase-based F 1 on CoNLL NER datasets compared</td></tr><tr><td>with state-of-the-art benchmark methods. Resource re-</td></tr><tr><td>quirements are indicated with superscripts, p: parallel</td></tr><tr><td>corpus, w: Wikipedia, d: dictionary, l: 100 NER anno-</td></tr><tr><td>tation, 0: no extra resources.</td></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": "The performance of RaRe and BEA in terms"
},
"TABREF5": {
"content": "<table><tr><td>Unsup</td><td>crawl wiki</td><td>34 24</td><td>26 21</td></tr><tr><td>IdentChar</td><td>crawl wiki</td><td>43 53</td><td>37 44</td></tr><tr><td>Sup</td><td>crawl wiki</td><td>50 54</td><td>39 45</td></tr></table>",
"type_str": "table",
"html": null,
"num": null,
"text": "Transl. Acc. Dir.Transf. F 1"
},
"TABREF6": {
"content": "<table/>",
"type_str": "table",
"html": null,
"num": null,
"text": "The effect of the choice of monolingual word embeddings (Common Crawl and Wikipedia), and their cross-lingual mapping on NER direct transfer. Word translation accuracy, and direct transfer NER F 1 are averaged over 40 languages."
}
}
}
} |