File size: 152,085 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 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T01:13:09.114902Z"
},
"title": "Morphological Segmentation for Seneca",
"authors": [
{
"first": "Zoey",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Boston College",
"location": {}
},
"email": ""
},
{
"first": "Robbie",
"middle": [],
"last": "Jimerson",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Emily",
"middle": [],
"last": "Prud'hommeaux",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Boston College",
"location": {}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario. The labeled data in our experiments comes from two sources: one digitized from a publicly available grammar book and the other collected from informal sources. We treat these two sources as distinct domains and investigate different evaluation designs for model selection. The first design abides by standard practices and evaluates models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set. Across a series of monolingual and cross-linguistic training settings, our results demonstrate the utility of neural encoderdecoder architecture when coupled with multitask learning.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario. The labeled data in our experiments comes from two sources: one digitized from a publicly available grammar book and the other collected from informal sources. We treat these two sources as distinct domains and investigate different evaluation designs for model selection. The first design abides by standard practices and evaluates models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set. Across a series of monolingual and cross-linguistic training settings, our results demonstrate the utility of neural encoderdecoder architecture when coupled with multitask learning.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "A member of the Hodin\u00f6h\u0161\u00f6ni (Iroquoian) language family in North America, the Seneca language is spoken mainly in three reservations located in Western New York: Allegany, Cattaraugus and Tonawanda. Seneca is considered acutely endangered and is currently estimated to have fewer than 50 first-language speakers left, most of whom are elders. Motivated by the Seneca community's language reclamation and revitalization program, a few hundred children and adults are actively learning and speaking Seneca as a second language.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To further facilitate the documentation process of Seneca, recent years have witnessed the scholarly bridge between the language community and academic research, taking advantage of rapidly evolving technologies in natural language processing (NLP) (Neubig et al., 2020; Jimerson and Prud'hommeaux, 2018) . In particular, ongoing work has mainly been devoted to developing automatic speech recognition (ASR) systems for Seneca (Thai et al., 2020 (Thai et al., , 2019 . Their findings demonstrated that when combined with synthetic data augmentation and machine learning techniques, robust acoustic models could be built even with a very limited amount of recorded naturalistic speech. More importantly, the research output was incorporated into the Seneca people's documentation endeavors, illustrating the potential of collaborations between language communities and academic researchers.",
"cite_spans": [
{
"start": 249,
"end": 270,
"text": "(Neubig et al., 2020;",
"ref_id": null
},
{
"start": 271,
"end": 304,
"text": "Jimerson and Prud'hommeaux, 2018)",
"ref_id": "BIBREF32"
},
{
"start": 427,
"end": 445,
"text": "(Thai et al., 2020",
"ref_id": "BIBREF62"
},
{
"start": 446,
"end": 466,
"text": "(Thai et al., , 2019",
"ref_id": "BIBREF61"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The current study contributes to this line of research with the same ethical considerations (Meek, 2012) . Specifically, we focus on morphological segmentation for Seneca, an area that has not yet been investigated thus far. Given a Seneca word, the task of morphological segmentation is to decompose it into individual morphemes (e.g., hasgatgw\u00eb's \u2192 ha + sgatgw\u00eb' + s).",
"cite_spans": [
{
"start": 92,
"end": 104,
"text": "(Meek, 2012)",
"ref_id": "BIBREF44"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "With a series of in-domain, cross-domain and cross-linguistic experiments, the goal of our work is to build effective segmentation models that can support the community's ongoing language reclamation and revitalization efforts. Particularly for morphologically rich languages, it has been shown that morphological segmentation is a useful component in certain NLP tasks such as machine translation (Clifton and Sarkar, 2011) , dependency parsing (Seeker and \u00c7etinoglu, 2015) , keyword spotting (Narasimhan et al., 2014) , and automatic speech recognition (ASR) (Afify et al., 2006) . Given that Seneca is a highly polysynthetic language (see Section 2), good morphological segmentation models show promise for the development of other computational systems such as ASR, which would facilitate the documentation process of the language itself.",
"cite_spans": [
{
"start": 398,
"end": 424,
"text": "(Clifton and Sarkar, 2011)",
"ref_id": "BIBREF9"
},
{
"start": 446,
"end": 474,
"text": "(Seeker and \u00c7etinoglu, 2015)",
"ref_id": "BIBREF55"
},
{
"start": 494,
"end": 519,
"text": "(Narasimhan et al., 2014)",
"ref_id": "BIBREF46"
},
{
"start": 561,
"end": 581,
"text": "(Afify et al., 2006)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Another motivation for our experiments lies in the fact that previous research on morphological segmentation has mostly concentrated on Indo-European languages in high-resource settings (Goldsmith, 2001; Cotterell et al., 2016b) , sometimes relying on external large-scale corpora in order to derive morpheme or lexical frequency information (Cotterell et al., 2015; Ruokolainen et al., 2014; Lind\u00e9n et al., 2009) . By contrast, work on morphological segmentation of augmented low-resource settings or truly underresourced languages is lacking in general (Kann et al., 2016) . Hence demonstrations of what model architecture and training settings could be beneficial with data sets of very small size would be informative to other researchers whose work shares similar goals and ethical considerations as ours.",
"cite_spans": [
{
"start": 186,
"end": 203,
"text": "(Goldsmith, 2001;",
"ref_id": "BIBREF23"
},
{
"start": 204,
"end": 228,
"text": "Cotterell et al., 2016b)",
"ref_id": "BIBREF12"
},
{
"start": 342,
"end": 366,
"text": "(Cotterell et al., 2015;",
"ref_id": "BIBREF11"
},
{
"start": 367,
"end": 392,
"text": "Ruokolainen et al., 2014;",
"ref_id": "BIBREF54"
},
{
"start": 393,
"end": 413,
"text": "Lind\u00e9n et al., 2009)",
"ref_id": "BIBREF40"
},
{
"start": 555,
"end": 574,
"text": "(Kann et al., 2016)",
"ref_id": "BIBREF34"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Following recently advocated scientific practices (Bender and Friedman, 2018; Gebru et al., 2018) , we would like to first introduce the data of the indigenous languages to be explored.",
"cite_spans": [
{
"start": 50,
"end": 77,
"text": "(Bender and Friedman, 2018;",
"ref_id": "BIBREF7"
},
{
"start": 78,
"end": 97,
"text": "Gebru et al., 2018)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data Statements",
"sec_num": "2"
},
{
"text": "The protagonist in our experiments is Seneca, the data of which came from three sources: the book The Seneca Verb: Labeling the Ancient Voice by Bardeau 20071 , informal transcriptions provided by members from the community, and a recently digitized Bible translated into Seneca. The grammar book provides morphological segmentation for only verbs and the morpheme boundaries were based on rules defined by grammarians. By contrast, the informal sources contain labeled segmentation for a mix of verbs and nouns conducted by community speakers. The Bible offers only unlabeled data. One of the most distinct features of Seneca morphology is that it is highly polysynthetic. This means that a single word can consist of multiple morphemes and may contain more than one stem; and this single word is able to express the meaning of a whole phrase or even sentences at times (Aikhenvald et al., 2007; Greenberg, 1960) . As a demonstration, consider the following example (the indicated morphological characteristics here abide by the annotation standards of Sylak-Glassman (2016)). Breaking the Seneca word into individual morphemes, ye:n\u00f6 is the stem which has the verbal meaning of grab in present tense; the prefix ke denotes that ye:n\u00f6 is a transitive action, with I being the subject and her/them being the object; the single apostrophe ' at the end marks the 1 https://senecalanguage.com/ wp-content/uploads/Verb-Book-Vol.1.pdf stative state.",
"cite_spans": [
{
"start": 871,
"end": 896,
"text": "(Aikhenvald et al., 2007;",
"ref_id": "BIBREF1"
},
{
"start": 897,
"end": 913,
"text": "Greenberg, 1960)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data Statements",
"sec_num": "2"
},
{
"text": "(1) keyen\u00f6' ke I+her/them yen\u00f6 grab ' STAT I've grabbed her/them.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data Statements",
"sec_num": "2"
},
{
"text": "A large number of words in Seneca have agglutinative morphological features, meaning when multiple morphemes are combined during word formation, their original forms remain unchanged. Consider the example presented above again. When the prefix and the stem are combined into the word, neither of them goes through any phonological and orthographic changes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data Statements",
"sec_num": "2"
},
{
"text": "On the other hand, Seneca also has fusional properties; this means that during the formation of some words, the combining morphemes can undergo phonological (and orthographical) changes. As an illustration, consider the following word in Seneca. When combining the four morphemes together, the masculine singular subject hra, the verb stem k and the s that marks habitual state do not undergo any changes; whereas the initial i is replaced with \u00ed to make sure that the verbs or verb phrases have at least two syllables (Chafe, 2015). In addition to Seneca, we include four Mexican indigenous languages from the Yuto-Aztecan language family (Baker, 1997) for our crosslinguisitic training experiments: Mexicanero (888 words), Nahuatl (1,123 words), Wixarika (1,385 words), and Yorem Nokki (1,063 words). The data for these languages contains morphological segmentation that was initially digitized from the book collections of Archive of Indigenous Language (Mexicanero (Una, 2001 ), Nahuatl (de Su\u00e1rez, 1980 , Wixarika (G\u00f3mez and L\u00f3pez, 1999) , Yorem Nokki (Freeze, 1989)). The data collection was carried out by the authors of Kann et al. (2018) based on the descriptions in their work, and their preprocessed data sets are publicly available. The four Yuto-Aztecan languages are also polysynthetic.",
"cite_spans": [
{
"start": 969,
"end": 979,
"text": "(Una, 2001",
"ref_id": "BIBREF63"
},
{
"start": 980,
"end": 1007,
"text": "), Nahuatl (de Su\u00e1rez, 1980",
"ref_id": null
},
{
"start": 1019,
"end": 1042,
"text": "(G\u00f3mez and L\u00f3pez, 1999)",
"ref_id": "BIBREF25"
},
{
"start": 1128,
"end": 1146,
"text": "Kann et al. (2018)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data Statements",
"sec_num": "2"
},
{
"text": "The task of morphological segmentation has been cast in distinct ways in previous work. One line of research focuses on surface segmentation (Ruokolainen et al., 2016) , while the other attends to canonical segmentation (Cotterell et al., 2016b) . Both involve correctly decomposing a given word into distinct morphemes, which also typically includes words that stand alone as free morphemes. Nevertheless, the two tasks differ in one key aspect: whether the combination 2 of the segmented morpheme sequence stays true to the initial orthography of the word. For surface segmentation, the answer is yes (e.g., Indonnesian dihapus \u2192 di+hapus). On the other hand, canonical segmentation sometimes involves the addition and/or deletion of characters from the surface form of the initial word, in order to capture phonological or orthographic characteristics of the component morphemes when uncombined. For example, the word measurable in English would be segmented as measure + able, recovering the orthographic e that was lost during word formation. For surface segmentation, both supervised and unsupervised approaches have gained in popularity over the years. Within the realm of supervised methods, a large number of experiments have developed rule-based finite-state transducers (FST) (Kaplan and Kay, 1994) with weights usually determined by rich linguistic feature sets. The high functionality of hand-crafted FST for morphological analyses has been demonstrated for languages such as Persian (Amtrup, 2003 ), Finnish (Lind\u00e9n et al., 2009 , Semitic languages such as Tigrinya (Gasser, 2009) and Arabic (Beesley, 1996; Shaalan and Attia, 2012) , as well as various African languages (Gasser, 2011) . Other work has shifted to more data-driven machine learning techniques, including but not limited to memory-based learning (van den Bosch and Daelemans, 1999; Marsi et al., 2005) , conditional random field models (CRF) (Cotterell et al., 2015; Ruokolainen et al., 2013 Ruokolainen et al., , 2014 , and convolutional networks (Sorokin and Kravtsova, 2018; Sorokin, 2019) .",
"cite_spans": [
{
"start": 141,
"end": 167,
"text": "(Ruokolainen et al., 2016)",
"ref_id": "BIBREF52"
},
{
"start": 220,
"end": 245,
"text": "(Cotterell et al., 2016b)",
"ref_id": "BIBREF12"
},
{
"start": 1497,
"end": 1510,
"text": "(Amtrup, 2003",
"ref_id": "BIBREF2"
},
{
"start": 1511,
"end": 1542,
"text": "), Finnish (Lind\u00e9n et al., 2009",
"ref_id": null
},
{
"start": 1580,
"end": 1594,
"text": "(Gasser, 2009)",
"ref_id": "BIBREF20"
},
{
"start": 1606,
"end": 1621,
"text": "(Beesley, 1996;",
"ref_id": "BIBREF6"
},
{
"start": 1622,
"end": 1646,
"text": "Shaalan and Attia, 2012)",
"ref_id": "BIBREF56"
},
{
"start": 1686,
"end": 1700,
"text": "(Gasser, 2011)",
"ref_id": "BIBREF21"
},
{
"start": 1835,
"end": 1861,
"text": "Bosch and Daelemans, 1999;",
"ref_id": "BIBREF64"
},
{
"start": 1862,
"end": 1881,
"text": "Marsi et al., 2005)",
"ref_id": "BIBREF42"
},
{
"start": 1922,
"end": 1946,
"text": "(Cotterell et al., 2015;",
"ref_id": "BIBREF11"
},
{
"start": 1947,
"end": 1971,
"text": "Ruokolainen et al., 2013",
"ref_id": "BIBREF53"
},
{
"start": 1972,
"end": 1998,
"text": "Ruokolainen et al., , 2014",
"ref_id": "BIBREF54"
},
{
"start": 2028,
"end": 2057,
"text": "(Sorokin and Kravtsova, 2018;",
"ref_id": "BIBREF59"
},
{
"start": 2058,
"end": 2072,
"text": "Sorokin, 2019)",
"ref_id": "BIBREF57"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Unsupervised methods have perhaps enjoyed a longer history (Harris, 1955) , with earlier studies relying on information-theoretic measures as indexes of character-level predictability, which were then used to determine morpheme boundaries (Hafer and Weiss, 1974) . Later work such as Linguistica (Goldsmith, 2001) and Morfessor (Creutz and Lagus, 2002) applied the analyses of Minimum Description Length for morpheme induction (Rissanen, 1998; Poon et al., 2009) . developed Bayesian generative models that would also take into account the context of individual words, which were able to simulate the process of how children learn to segment words given child-directed speech.",
"cite_spans": [
{
"start": 59,
"end": 73,
"text": "(Harris, 1955)",
"ref_id": "BIBREF30"
},
{
"start": 239,
"end": 262,
"text": "(Hafer and Weiss, 1974)",
"ref_id": "BIBREF28"
},
{
"start": 296,
"end": 313,
"text": "(Goldsmith, 2001)",
"ref_id": "BIBREF23"
},
{
"start": 328,
"end": 352,
"text": "(Creutz and Lagus, 2002)",
"ref_id": "BIBREF13"
},
{
"start": 427,
"end": 443,
"text": "(Rissanen, 1998;",
"ref_id": "BIBREF50"
},
{
"start": 444,
"end": 462,
"text": "Poon et al., 2009)",
"ref_id": "BIBREF49"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "In contrast to surface segmentation, the problem of canonical segmentation has mainly been addressed with supervised methods. Cotterell et al. (2016b) extended a previous semi-CRF (Cotterell et al., 2015) for surface segmentation to jointly predict morpheme boundaries and orthographic changes, leading to improved results for German and Indonesian. With the same datasets, Kann et al. (2016) adopted character-based neural sequence models coupled with a neural reranker, presenting further improvement from Cotterell et al. (2016b) . There has, however, been some unsupervised induction of canonical segmentation (see Hammarstr\u00f6m and Borin (2011) for a thorough review). For instance, Dasgupta and Ng (2007) showed that certain spelling rules (e.g. insertion, deletion) derived heuristically from corpus frequency were able to handle orthographic changes during word formation. In comparison, Naradowsky and Goldwater (2009) provided a Bayesian model that formulate spelling rules probabilistically with character-level contextual information; the simultaneous learning process of both the rules and morpheme boundaries in turn boosted segmentation performance.",
"cite_spans": [
{
"start": 126,
"end": 150,
"text": "Cotterell et al. (2016b)",
"ref_id": "BIBREF12"
},
{
"start": 180,
"end": 204,
"text": "(Cotterell et al., 2015)",
"ref_id": "BIBREF11"
},
{
"start": 508,
"end": 532,
"text": "Cotterell et al. (2016b)",
"ref_id": "BIBREF12"
},
{
"start": 619,
"end": 647,
"text": "Hammarstr\u00f6m and Borin (2011)",
"ref_id": "BIBREF29"
},
{
"start": 686,
"end": 708,
"text": "Dasgupta and Ng (2007)",
"ref_id": "BIBREF14"
},
{
"start": 894,
"end": 925,
"text": "Naradowsky and Goldwater (2009)",
"ref_id": "BIBREF45"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "Although Seneca has fusional morphological features, meaning that certain morpheme boundaries within words are not necessarily clear-cut, the Seneca morphological data currently does not provide labeled canonical segmentation. We therefore focus on the task of surface segmentation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "3"
},
{
"text": "As mentioned in Section 2, the labeled words for Seneca came from both the verbal paradigm book by Bardeau (2007) and informal sources. We treated the two sources as separate domains and constructed a dataset for each. The number of morphemes per word on average in the grammar book is 3.87 (95% confidence intervals: (3.86, 3.88); see Section 4.4), which is slightly lower than that in the informal sources (4.12 (4.10, 4.13)). On the other hand, the number of unique morphemes is much higher in the data from the informal sources (N = 1,641) than that in the grammar book (N = 631). This difference in the amount of morphological variation between the two domains raises the expectation that with the same model architecture, morphological segmentation of the words from the informal sources is possibly more challenging.",
"cite_spans": [
{
"start": 99,
"end": 113,
"text": "Bardeau (2007)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data preprocessing",
"sec_num": "4.1"
},
{
"text": "For each data set, to construct the low-resource settings, we set the train/dev/test ratio to be 2:1:2, then randomly generated five splits for every dataset with this ratio (Gorman and Bedrick, 2019) . 3 We used the first random split of both domains for model evaluation as well as selection of training settings; the setting(s) eventually selected would then be applied to data from each of the five random splits to test the stability of the model performance.",
"cite_spans": [
{
"start": 174,
"end": 200,
"text": "(Gorman and Bedrick, 2019)",
"ref_id": "BIBREF26"
},
{
"start": 203,
"end": 204,
"text": "3",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data preprocessing",
"sec_num": "4.1"
},
{
"text": "We took advantage of the fact that the two data sets for Seneca came from different domains by investigating two experimental designs: evaluating with a development set versus evaluating with a development domain. The former carried out the standard practices. When building models for morphological segmentation of a particular domain, only the in-domain training set would be (part of) the training data for the models, along with possible addition of training data from the other domain or indigenous languages. The development set from the same domain would be used to evaluate models and the one(s) with the best performance would be selected (e.g. segmentation for the grammar book data using the development set of the grammar book for evaluation). However, realistically development sets are luxuries to critically endangered languages (Kann et al., 3 Data, code, and models are available at https:// github.com/zoeyliu18/Seneca. 2019). To help with the documentation of these languages more effectively, one would want to use as much training data as possible, ideally from the same domain or language. Yet acquiring more data for languages like Seneca, whether with or without manual annotations, faces extreme difficulty. It requires not only extensive time and financial resources, but also expertise from the very few native speakers left, most of whom are elders.",
"cite_spans": [
{
"start": 844,
"end": 857,
"text": "(Kann et al.,",
"ref_id": null
},
{
"start": 858,
"end": 859,
"text": "3",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation design",
"sec_num": "4.2"
},
{
"text": "To increase the utility of the already-limited data for Seneca, we experimented with a second design of using a development domain for model evaluation. That is, for morphological segmentation of a particular domain, the new in-domain training data would be the concatenation of the initial training set along with the development set from the same domain. This new combination would be (part of) the training data for the models. In this case the development set of the other domain would then be applied instead to evaluate model performance (e.g. segmentation for the grammar book using the development set of the informal sources for evaluation). Again, the model(s) with the best performance on the development domain would be selected.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation design",
"sec_num": "4.2"
},
{
"text": "Comparing the two designs, taking into account the different configurations of the training data, it is possible that evaluation with a development domain would lead to different model architectures/settings being selected. On the other hand, it is also possible that the same model architecture or setting would be favored regardless of the particular design. In addition, because using a development domain essentially means that there is more indomain training data, it remains to be seen whether this evaluation design would achieve better results when testing the stability of the model setting.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation design",
"sec_num": "4.2"
},
{
"text": "We experimented with three general settings: indomain training, cross-domain training, and crosslinguistic training. For all settings, we adopted character-based sequence-to-sequence (seq2seq) recurrent neural network (RNN) (Elman, 1990) trained with OpenNMT (Klein et al., 2017) . This model architecture has been previously demonstrated to perform well for polysynthetic indigenous languages (Kann et al., 2018) .",
"cite_spans": [
{
"start": 259,
"end": 279,
"text": "(Klein et al., 2017)",
"ref_id": "BIBREF37"
},
{
"start": 394,
"end": 413,
"text": "(Kann et al., 2018)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model training",
"sec_num": "4.3"
},
{
"text": "In cases where applicable, we also compared the performance of the neural seq2seq models to unsupervised Morfessor 4 (Creutz and Lagus, 2002) . In what follows, we describe the details of the seq2seq models in each training setting.",
"cite_spans": [
{
"start": 117,
"end": 141,
"text": "(Creutz and Lagus, 2002)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model training",
"sec_num": "4.3"
},
{
"text": "Naive baseline Our first baseline applied the default parameters in OpenNMT -an encoder-decoder long-short term memory model (LSTM) (Hochreiter and Schmidhuber, 1997) with the attention mechanism from Luong et al. (2015) . All embeddings have 500 dimensions. Both the encoder and the decoder contain two hidden layers with 500 hidden units in each layer. Training was performed with SGD (Robbins and Monro, 1951) and a batch size of 64. Abiding by our experimental designs, for all the baseline models, when evaluating with the development set, the in-domain training data came from just the training set. By contrast, when evaluating with the development domain, the in-domain training data was the concatenation of the training and the development sets.",
"cite_spans": [
{
"start": 132,
"end": 166,
"text": "(Hochreiter and Schmidhuber, 1997)",
"ref_id": "BIBREF31"
},
{
"start": 201,
"end": 220,
"text": "Luong et al. (2015)",
"ref_id": "BIBREF41"
},
{
"start": 387,
"end": 412,
"text": "(Robbins and Monro, 1951)",
"ref_id": "BIBREF51"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "Less naive baseline Going beyond the default settings in the first baseline, our second baseline experimented with different combinations of parameter settings and attention mechanisms (Bahdanau et al., 2015 ):",
"cite_spans": [
{
"start": 185,
"end": 207,
"text": "(Bahdanau et al., 2015",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "\u2022 RNN type: LSTM / GRU \u2022 embedding dimesions: {128, 300, 500}",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "\u2022 hidden layers: {1, 2}",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "\u2022 hidden units: {128, 300, 500}",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "\u2022 batch size: {16, 32, 64}",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "\u2022 optimizer: SGD / ADADELTA (Zeiler, 2012) These models were trained and evaluated in the same way as the first baseline. Based on results from either the development set or the development domain (after statistical tests; see Section 4.4), the model architecture that was selected was an attention-based encoder-decoder (Bahdanau et al., 2015) , where the encoder is composed of a bidirectional GRU while the decoder consists of a unidirectional GRU. Both the encoder and the decoder have two hidden layers with 100 hidden states in each layer. All embeddings have 300 dimensions. Training was performed with ADADELTA and a batch size of 16.",
"cite_spans": [
{
"start": 321,
"end": 344,
"text": "(Bahdanau et al., 2015)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "sor (Kohonen et al., 2010) was also explored; yet the performance was worse than the unsupervised method. Thus we eventually chose the unsupervised variant for systematic comparisons with the seq2seq models.",
"cite_spans": [
{
"start": 4,
"end": 26,
"text": "(Kohonen et al., 2010)",
"ref_id": "BIBREF38"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "In-domain training",
"sec_num": "4.3.1"
},
{
"text": "With the model architecture of our less naive baseline, we turned to our cross-domain training experiments using four different methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "Self-training The first method utilized selftraining (McClosky et al., 2008) and resorted to the unlabeled words from the Bible, which were first automatically segmented with the second baseline model from in-domain training. These words were then added to the in-domain training data given each of the two evaluation designs (Section 4.2).",
"cite_spans": [
{
"start": 53,
"end": 76,
"text": "(McClosky et al., 2008)",
"ref_id": "BIBREF43"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "Multi-task learning The second method applied multi-task learning (Kann et al., 2018) . In this case, in addition to the task of morphological segmentation, we added a new task where the training objective is to generate output that is identical to the input. In the seq2seq model, the decoder does not always generate every character in the input sequence, which prevents accurate morphological segmentation of the full word. Thus the ulterior goal of this additional task is simple yet important: helping the model learn to copy.",
"cite_spans": [
{
"start": 66,
"end": 85,
"text": "(Kann et al., 2018)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "In particular, words from the in-domain training data were used for the segmentation task, while words from the Bible were used for mapping input to output. Every word in the eventual training data was appended with a task-specific input symbol. For instance, let X represent the task of morphological segmentation, Y the task of mapping input to output, the goal of the model is to jointly perform the following :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "\u2022 \u00ebw\u00ebn\u00f6tg\u00ebh + X \u2192 \u00eb + w\u00ebn + \u00f6tg\u00ebh \u2022 oiwa' + Y \u2192 oiwa'",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "Transfer learning The third method adopts domain transfer learning. Consider morphological segmentation of the grammar book as an example. When using a development set, the in-domain training data, which includes only the training set of the grammar book, would be combined with all data from the informal sources. On the other hand, when using a development domain, the indomain training data, which includes the training and development sets of the grammar book, would be concatenated with just the training and test sets from the informal sources.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "Fine-tuning With the model trained from transfer learning, we fine-tuned it further with in-domain training data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "One point to note is when evaluating with a development domain, we expected that the model trained with domain transfer learning (with finetuning) would yield the best results. However, these results would not be directly informative about whether this setting is indeed better than the others, the latter of which only included in-domain training data. Hence for this particular evaluation design, while we still carried out the domain transfer experiments for consistency, we selected models only based on the other training settings.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-domain training",
"sec_num": "4.3.2"
},
{
"text": "In order to examine whether data from other polysynthetic languages would improve model performance, we carried out cross-linguistic training with three different settings: multi-task learning, transfer learning (Kann et al., 2018) , and finetuning. These settings are similar to those in crossdomain training, except that the data from the four Mexican languages was used as additional training data instead of the Bible or out-of-domain data.",
"cite_spans": [
{
"start": 212,
"end": 231,
"text": "(Kann et al., 2018)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Cross-linguistic training",
"sec_num": "4.3.3"
},
{
"text": "Three measures were computed as indexes of model performance (Cotterell et al., 2016a; van den Bosch and Daelemans, 1999) : full form accuracy, morpheme F1, and average Levenshtein distance (Levenshtein, 1966) . Significance testing of each metric was conducted with bootstrapping (Efron and Tibshirani, 1994) . As an illustration, take full form accuracy as an example. After applying a model to the development set (or domain) with a total of N words, we: (1) randomly selected N words from the development set with replacement; (2) calculated the full form accuracy of the selected sample; (3) repeated step (1) and (2) for 10,000 iterations, which yielded an empirical distribution of full form accuracy; (4) measured the mean and the 95% confidence interval (CI) of the empirical distribution.",
"cite_spans": [
{
"start": 61,
"end": 86,
"text": "(Cotterell et al., 2016a;",
"ref_id": "BIBREF10"
},
{
"start": 87,
"end": 121,
"text": "van den Bosch and Daelemans, 1999)",
"ref_id": "BIBREF64"
},
{
"start": 190,
"end": 209,
"text": "(Levenshtein, 1966)",
"ref_id": "BIBREF39"
},
{
"start": 281,
"end": 309,
"text": "(Efron and Tibshirani, 1994)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Metrics",
"sec_num": "4.4"
},
{
"text": "For evaluation, we considered a training setting to be better than another based on at least one of the three metrics calculated. As presented in Table 2 , when evaluating with the development set, it appears that for the grammar book, the simple less naive baseline with careful parameter tuning is able to yield excellent performance, while other more complicated training configurations such as including additional out-of-domain data do not lead to further improvement (no significant differences in the results). Therefore we chose the less naive baseline from in-domain training for the final testing given its simplicity and average score for each of the three metrics.",
"cite_spans": [],
"ref_spans": [
{
"start": 146,
"end": 153,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Evaluation with development set",
"sec_num": "5.1"
},
{
"text": "By contrast, with the same training settings, the models show weaker performance for informal sources. This corresponds to our initial expectation that due to the higher number of unique morphemes in informal sources, accurately labeling the boundaries of these morphemes would be comparatively more challenging. Similar to results for the grammar book, none of the other training configurations seems to significantly surpass the two baselines. With that being said, we selected the cross-linguistic training with multi-task learning for the final testing, again because it has the best average score for each of the three measures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation with development set",
"sec_num": "5.1"
},
{
"text": "On the other hand, when evaluating with the development domain, as shown in Table 3 , almost all other training configurations appear to be better than the two baselines, a pattern that holds for data from the grammar book as well as that from the informal sources. When compared to the two baselines, while the other settings do not show significant improvement in terms of accuracy or F1 score, the average Levenshtein distance is shorter when the models are trained with multi-task learning and/or additional cross-linguistic data. Given the results, for both the grammar book and the informal sources, we selected cross-domain multi-task learning as the setting for final model testing.",
"cite_spans": [],
"ref_spans": [
{
"start": 76,
"end": 83,
"text": "Table 3",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Evaluation with development domain",
"sec_num": "5.2"
},
{
"text": "Combining the results from Table 2 and Table 3 together, it appears that regardless of the particular evaluation design, in any of the settings where unsupervised Morfessor is applicable (Creutz and Lagus, 2002) , the neural encoder-decoder models consistently yielded significantly better performance in relation to all three measures. This observation also speaks to previous findings from Kann et al. (2018) , except that they adopted semi-supervised variants of Morfessor.",
"cite_spans": [
{
"start": 187,
"end": 211,
"text": "(Creutz and Lagus, 2002)",
"ref_id": "BIBREF13"
},
{
"start": 392,
"end": 410,
"text": "Kann et al. (2018)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [
{
"start": 27,
"end": 46,
"text": "Table 2 and Table 3",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Evaluation with development domain",
"sec_num": "5.2"
},
{
"text": "Comparing the segmentation results from the seq2seq models to those from Morfessor, overall there does not seem to be aspects where the latter systematically falls short, in the sense that the segmentation patterns by Morfessor are more or less \"all over the place\". One potential explanation lies in the fact that in both our data sets, the majority of the words have a frequency of one (95.28% for the grammar book; 95.57% for the informal sources). On the other hand, successful segmentation by unsupervised Morfessor relies heavily on the frequency of a given word and accordingly the number of overlapping or common morphemes shared by different words, whether the occurrence frequency information was computed from the training data or from additional unlabeled data. In addition to the complex morphological features of Seneca and the high frequency of unique morphemes in the two data sets used in our experiments, the Bible dataset, despite containing more unlabeled words, is still relatively small (N = 8,588), and thus is not especially useful for deriving frequency estimates.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation with development domain",
"sec_num": "5.2"
},
{
"text": "For both the grammar book and the informal sources, we tested the stability of the selected model settings across the five random splits (Section 4.1). With each random split, we trained a model following the selected setting for each of the evaluation designs; the model was then applied to the test set of the random split.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Testing",
"sec_num": "5.3"
},
{
"text": "Based on Figure 1 , within each evaluation design, the test performance of the model setting is stable across the random splits. Morphological segmentation of data from the grammar book was able to achieve consistently better results than that for the informal sources. Regardless of the data source, while there does not appear to be significant differences in model performance between the two evaluation designs, comparing to using a development set, evaluating with a development domain led to slight improvement of average scores for each of the three metrics.",
"cite_spans": [],
"ref_spans": [
{
"start": 9,
"end": 17,
"text": "Figure 1",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Testing",
"sec_num": "5.3"
},
{
"text": "We have investigated morphological segmentation for Seneca, an indigenous Native American language with highly complex morphological characteristics. In a series of in-domain, cross-domain, and cross-linguistic training settings, the results demonstrate that neural seq2seq models are quite effective at correctly labeling morpheme boundaries, at least at the surface level. With the two evaluation designs explored here, the model settings were able to achieve above 96% F1 score for data from the grammar book, and above 85% for the informal sources.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Many of the languages indigenous to North America are as endangered as Seneca and have available resources comparable in both size and scope to those used in the current work. Our thorough investigation of how to effectively integrate these limited and varied resources can potentially serve as a model for other community-driven collaborations to document endangered languages for future generations, and to produce materials suitable for language immersion and revitalization. For our future work, in addition to refining and improving our models, we also plan to explore the utility of morphological segmentation for improving language modeling in ASR. This would be able to support transcription of both archival recordings and new recordings captured by community members involved in language revitalization projects.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Here we used the term combination instead of concatenation, because surface segmentation is applicable to words with concatenative morphology as well as those with nonconcatenative morphology.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "In preliminary experiments, semi-supervised Morfes-",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We are grateful for the cooperation and support of the Seneca Nation of Indians. This material is based upon work supported by the National Science Foundation under Grant No. 1761562. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "On the use of morphological analysis for dialectal arabic speech recognition",
"authors": [
{
"first": "Mohamed",
"middle": [],
"last": "Afify",
"suffix": ""
},
{
"first": "Ruhi",
"middle": [],
"last": "Sarikaya",
"suffix": ""
},
{
"first": "Hong-Kwang Jeff",
"middle": [],
"last": "Kuo",
"suffix": ""
},
{
"first": "Laurent",
"middle": [],
"last": "Besacier",
"suffix": ""
},
{
"first": "Yuqing",
"middle": [],
"last": "Gao",
"suffix": ""
}
],
"year": 2006,
"venue": "Ninth International Conference on Spoken Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mohamed Afify, Ruhi Sarikaya, Hong-Kwang Jeff Kuo, Laurent Besacier, and Yuqing Gao. 2006. On the use of morphological analysis for dialectal ara- bic speech recognition. In Ninth International Con- ference on Spoken Language Processing.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Typological distinctions in word-formation. Language typology and syntactic description",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Alexandra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Aikhenvald",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "3",
"issue": "",
"pages": "1--65",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexandra Y Aikhenvald et al. 2007. Typological dis- tinctions in word-formation. Language typology and syntactic description, 3:1-65.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Morphology in machine translation systems: Efficient integration of finite state transducers and feature structure descriptions",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jan",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Amtrup",
"suffix": ""
}
],
"year": 2003,
"venue": "Machine Translation",
"volume": "18",
"issue": "3",
"pages": "217--238",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jan W Amtrup. 2003. Morphology in machine trans- lation systems: Efficient integration of finite state transducers and feature structure descriptions. Ma- chine Translation, 18(3):217-238.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Neural machine translation by jointly learning to align and translate",
"authors": [
{
"first": "Dzmitry",
"middle": [],
"last": "Bahdanau",
"suffix": ""
},
{
"first": "Kyung",
"middle": [
"Hyun"
],
"last": "Cho",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2015,
"venue": "3rd International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In 3rd Inter- national Conference on Learning Representations, ICLR 2015.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Complex predicates and agreement in polysynthetic languages. Complex predicates",
"authors": [
{
"first": "C",
"middle": [],
"last": "Mark",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Baker",
"suffix": ""
}
],
"year": 1997,
"venue": "",
"volume": "",
"issue": "",
"pages": "247--288",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mark C Baker. 1997. Complex predicates and agree- ment in polysynthetic languages. Complex predi- cates, pages 247-288.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "The Seneca Verb: Labeling the Ancient Voice. Seneca Nation Education Department",
"authors": [
{
"first": "Phyllis",
"middle": [
"E"
],
"last": "Wms",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Bardeau",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Phyllis E. Wms. Bardeau. 2007. The Seneca Verb: La- beling the Ancient Voice. Seneca Nation Education Department, Cattaraugus Territory.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Arabic finite-state morphological analysis and generation",
"authors": [
{
"first": "R",
"middle": [],
"last": "Kenneth",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Beesley",
"suffix": ""
}
],
"year": 1996,
"venue": "The 16th International Conference on Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenneth R Beesley. 1996. Arabic finite-state morpho- logical analysis and generation. In COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Data statements for natural language processing: Toward mitigating system bias and enabling better science",
"authors": [
{
"first": "Emily",
"middle": [
"M"
],
"last": "Bender",
"suffix": ""
},
{
"first": "Batya",
"middle": [],
"last": "Friedman",
"suffix": ""
}
],
"year": 2018,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "6",
"issue": "",
"pages": "587--604",
"other_ids": {
"DOI": [
"10.1162/tacl_a_00041"
]
},
"num": null,
"urls": [],
"raw_text": "Emily M. Bender and Batya Friedman. 2018. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587-604.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "A Grammar of the Seneca Language",
"authors": [
{
"first": "",
"middle": [],
"last": "Wallace L Chafe",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "149",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wallace L Chafe. 2015. A Grammar of the Seneca Lan- guage, volume 149. University of California Press.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Combining morpheme-based machine translation with postprocessing morpheme prediction",
"authors": [
{
"first": "Ann",
"middle": [],
"last": "Clifton",
"suffix": ""
},
{
"first": "Anoop",
"middle": [],
"last": "Sarkar",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "32--42",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ann Clifton and Anoop Sarkar. 2011. Combin- ing morpheme-based machine translation with post- processing morpheme prediction. In Proceedings of the 49th Annual Meeting of the Association for Com- putational Linguistics: Human Language Technolo- gies, pages 32-42, Portland, Oregon, USA. Associa- tion for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Morphological segmentation inside-out",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Cotterell",
"suffix": ""
},
{
"first": "Arun",
"middle": [],
"last": "Kumar",
"suffix": ""
},
{
"first": "Hinrich",
"middle": [],
"last": "Sch\u00fctze",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2325--2330",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1256"
]
},
"num": null,
"urls": [],
"raw_text": "Ryan Cotterell, Arun Kumar, and Hinrich Sch\u00fctze. 2016a. Morphological segmentation inside-out. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2325-2330, Austin, Texas. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Labeled morphological segmentation with semi-Markov models",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Cotterell",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "M\u00fcller",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Fraser",
"suffix": ""
},
{
"first": "Hinrich",
"middle": [],
"last": "Sch\u00fctze",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the Nineteenth Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "164--174",
"other_ids": {
"DOI": [
"10.18653/v1/K15-1017"
]
},
"num": null,
"urls": [],
"raw_text": "Ryan Cotterell, Thomas M\u00fcller, Alexander Fraser, and Hinrich Sch\u00fctze. 2015. Labeled morphological seg- mentation with semi-Markov models. In Proceed- ings of the Nineteenth Conference on Computational Natural Language Learning, pages 164-174, Bei- jing, China. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "A joint model of orthography and morphological segmentation",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Cotterell",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Vieira",
"suffix": ""
},
{
"first": "Hinrich",
"middle": [],
"last": "Sch\u00fctze",
"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": "664--669",
"other_ids": {
"DOI": [
"10.18653/v1/N16-1080"
]
},
"num": null,
"urls": [],
"raw_text": "Ryan Cotterell, Tim Vieira, and Hinrich Sch\u00fctze. 2016b. A joint model of orthography and morpho- logical segmentation. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 664-669, San Diego, California. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Unsupervised discovery of morphemes",
"authors": [
{
"first": "Mathias",
"middle": [],
"last": "Creutz",
"suffix": ""
},
{
"first": "Krista",
"middle": [],
"last": "Lagus",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning",
"volume": "",
"issue": "",
"pages": "21--30",
"other_ids": {
"DOI": [
"10.3115/1118647.1118650"
]
},
"num": null,
"urls": [],
"raw_text": "Mathias Creutz and Krista Lagus. 2002. Unsupervised discovery of morphemes. In Proceedings of the ACL-02 Workshop on Morphological and Phonolog- ical Learning, pages 21-30. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Highperformance, language-independent morphological segmentation",
"authors": [
{
"first": "Sajib",
"middle": [],
"last": "Dasgupta",
"suffix": ""
},
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2007,
"venue": "Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sajib Dasgupta and Vincent Ng. 2007. High- performance, language-independent morphological segmentation. In Human Language Technologies 2007: The Conference of the North American Chap- ter of the Association for Computational Linguistics;",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Association for Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "155--163",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Proceedings of the Main Conference, pages 155- 163, Rochester, New York. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "N\u00e1huatl de Acaxochitl\u00e1n (Hidalgo)",
"authors": [
{
"first": "Yolanda",
"middle": [],
"last": "Lastra De Su\u00e1rez",
"suffix": ""
}
],
"year": 1980,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yolanda Lastra de Su\u00e1rez. 1980. N\u00e1huatl de Acaxo- chitl\u00e1n (Hidalgo). El Colegio de M\u00e9xico.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "An introduction to the bootstrap",
"authors": [
{
"first": "Bradley",
"middle": [],
"last": "Efron",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Robert",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Tibshirani",
"suffix": ""
}
],
"year": 1994,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bradley Efron and Robert J Tibshirani. 1994. An intro- duction to the bootstrap. CRC press.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Finding structure in time",
"authors": [
{
"first": "",
"middle": [],
"last": "Jeffrey L Elman",
"suffix": ""
}
],
"year": 1990,
"venue": "Cognitive science",
"volume": "14",
"issue": "",
"pages": "179--211",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey L Elman. 1990. Finding structure in time. Cog- nitive science, 14(2):179-211.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Mayo de Los Capomos",
"authors": [
{
"first": "",
"middle": [],
"last": "Ray A Freeze",
"suffix": ""
}
],
"year": 1989,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ray A Freeze. 1989. Mayo de Los Capomos, Sinaloa. El Colegio de M\u00e9xico.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Semitic morphological analysis and generation using finite state transducers with feature structures",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Gasser",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 12th Conference of the European Chapter",
"volume": "",
"issue": "",
"pages": "309--317",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Gasser. 2009. Semitic morphological analy- sis and generation using finite state transducers with feature structures. In Proceedings of the 12th Con- ference of the European Chapter of the ACL (EACL 2009), pages 309-317.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Hornmorpho: a system for morphological processing of amharic, oromo, and tigrinya",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Gasser",
"suffix": ""
}
],
"year": 2011,
"venue": "Conference on Human Language Technology for Development",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Gasser. 2011. Hornmorpho: a system for morphological processing of amharic, oromo, and tigrinya. In Conference on Human Language Tech- nology for Development, Alexandria, Egypt.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Datasheets for datasets",
"authors": [
{
"first": "Timnit",
"middle": [],
"last": "Gebru",
"suffix": ""
},
{
"first": "Jamie",
"middle": [],
"last": "Morgenstern",
"suffix": ""
},
{
"first": "Briana",
"middle": [],
"last": "Vecchione",
"suffix": ""
},
{
"first": "Jennifer",
"middle": [
"Wortman"
],
"last": "Vaughan",
"suffix": ""
},
{
"first": "Hanna",
"middle": [],
"last": "Wallach",
"suffix": ""
},
{
"first": "Hal",
"middle": [],
"last": "Daum\u00e9",
"suffix": ""
},
{
"first": "Iii",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Kate",
"middle": [],
"last": "Crawford",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 5th Workshop on Fairness, Accountability, and Transparency in Machine Learning",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daum\u00e9 III, and Kate Crawford. 2018. Datasheets for datasets. In Proceedings of the 5th Workshop on Fairness, Accountability, and Transparency in Ma- chine Learning.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Unsupervised learning of the morphology of a natural language",
"authors": [
{
"first": "John",
"middle": [],
"last": "Goldsmith",
"suffix": ""
}
],
"year": 2001,
"venue": "Computational linguistics",
"volume": "27",
"issue": "2",
"pages": "153--198",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John Goldsmith. 2001. Unsupervised learning of the morphology of a natural language. Computational linguistics, 27(2):153-198.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "A bayesian framework for word segmentation: Exploring the effects of context",
"authors": [
{
"first": "Sharon",
"middle": [],
"last": "Goldwater",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Thomas",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Griffiths",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2009,
"venue": "Cognition",
"volume": "112",
"issue": "1",
"pages": "21--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sharon Goldwater, Thomas L Griffiths, and Mark John- son. 2009. A bayesian framework for word segmen- tation: Exploring the effects of context. Cognition, 112(1):21-54.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Huichol de",
"authors": [
{
"first": "Paula",
"middle": [],
"last": "G\u00f3mez",
"suffix": ""
},
{
"first": "Paula",
"middle": [
"G\u00f3mez"
],
"last": "L\u00f3pez",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Paula G\u00f3mez and Paula G\u00f3mez L\u00f3pez. 1999. Huichol de San Andr\u00e9s Cohamiata, Jalisco. El Colegio de M\u00e9xico.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "We need to talk about standard splits",
"authors": [
{
"first": "Kyle",
"middle": [],
"last": "Gorman",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bedrick",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "2786--2791",
"other_ids": {
"DOI": [
"10.18653/v1/P19-1267"
]
},
"num": null,
"urls": [],
"raw_text": "Kyle Gorman and Steven Bedrick. 2019. We need to talk about standard splits. In Proceedings of the 57th Annual Meeting of the Association for Com- putational Linguistics, pages 2786-2791, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "A quantitative approach to the morphological typology of language",
"authors": [
{
"first": "H",
"middle": [],
"last": "Joseph",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Greenberg",
"suffix": ""
}
],
"year": 1960,
"venue": "International journal of American linguistics",
"volume": "26",
"issue": "3",
"pages": "178--194",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joseph H Greenberg. 1960. A quantitative approach to the morphological typology of language. Inter- national journal of American linguistics, 26(3):178- 194.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Word segmentation by letter successor varieties. Information storage and retrieval",
"authors": [
{
"first": "A",
"middle": [],
"last": "Margaret",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hafer",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Stephen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Weiss",
"suffix": ""
}
],
"year": 1974,
"venue": "",
"volume": "10",
"issue": "",
"pages": "371--385",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Margaret A Hafer and Stephen F Weiss. 1974. Word segmentation by letter successor varieties. Informa- tion storage and retrieval, 10(11-12):371-385.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Unsupervised learning of morphology",
"authors": [
{
"first": "Harald",
"middle": [],
"last": "Hammarstr\u00f6m",
"suffix": ""
},
{
"first": "Lars",
"middle": [],
"last": "Borin",
"suffix": ""
}
],
"year": 2011,
"venue": "Computational Linguistics",
"volume": "37",
"issue": "2",
"pages": "309--350",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Harald Hammarstr\u00f6m and Lars Borin. 2011. Unsuper- vised learning of morphology. Computational Lin- guistics, 37(2):309-350.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "From phoneme to morpheme",
"authors": [
{
"first": "S",
"middle": [],
"last": "Zellig",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Harris",
"suffix": ""
}
],
"year": 1955,
"venue": "Language",
"volume": "31",
"issue": "2",
"pages": "190--222",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zellig S Harris. 1955. From phoneme to morpheme. Language, 31(2):190-222.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Long short-term memory",
"authors": [
{
"first": "Sepp",
"middle": [],
"last": "Hochreiter",
"suffix": ""
},
{
"first": "J\u00fcrgen",
"middle": [],
"last": "Schmidhuber",
"suffix": ""
}
],
"year": 1997,
"venue": "Neural computation",
"volume": "9",
"issue": "8",
"pages": "1735--1780",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "ASR for documenting acutely under-resourced indigenous languages",
"authors": [
{
"first": "Robbie",
"middle": [],
"last": "Jimerson",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Prud",
"suffix": ""
},
{
"first": "'",
"middle": [],
"last": "Hommeaux",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robbie Jimerson and Emily Prud'hommeaux. 2018. ASR for documenting acutely under-resourced in- digenous languages. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. Eu- ropean Language Resources Association (ELRA).",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Towards realistic practices in lowresource natural language processing: The development set",
"authors": [
{
"first": "Katharina",
"middle": [],
"last": "Kann",
"suffix": ""
},
{
"first": "Kyunghyun",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "Samuel",
"middle": [
"R"
],
"last": "Bowman",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "3342--3349",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1329"
]
},
"num": null,
"urls": [],
"raw_text": "Katharina Kann, Kyunghyun Cho, and Samuel R. Bow- man. 2019. Towards realistic practices in low- resource natural language processing: The develop- ment set. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP-IJCNLP), pages 3342-3349, Hong Kong, China. Association for Computational Linguistics.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Neural morphological analysis: Encodingdecoding canonical segments",
"authors": [
{
"first": "Katharina",
"middle": [],
"last": "Kann",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Cotterell",
"suffix": ""
},
{
"first": "Hinrich",
"middle": [],
"last": "Sch\u00fctze",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "961--967",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1097"
]
},
"num": null,
"urls": [],
"raw_text": "Katharina Kann, Ryan Cotterell, and Hinrich Sch\u00fctze. 2016. Neural morphological analysis: Encoding- decoding canonical segments. In Proceedings of the 2016 Conference on Empirical Methods in Nat- ural Language Processing, pages 961-967, Austin, Texas. Association for Computational Linguistics.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Fortification of neural morphological segmentation models for polysynthetic minimal-resource languages",
"authors": [
{
"first": "Katharina",
"middle": [],
"last": "Kann",
"suffix": ""
},
{
"first": "Jesus",
"middle": [],
"last": "Manuel Mager",
"suffix": ""
},
{
"first": "Ivan",
"middle": [
"Vladimir"
],
"last": "Hois",
"suffix": ""
},
{
"first": "Hinrich",
"middle": [],
"last": "Meza-Ruiz",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sch\u00fctze",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "47--57",
"other_ids": {
"DOI": [
"10.18653/v1/N18-1005"
]
},
"num": null,
"urls": [],
"raw_text": "Katharina Kann, Jesus Manuel Mager Hois, Ivan Vladimir Meza-Ruiz, and Hinrich Sch\u00fctze. 2018. Fortification of neural morphological segmen- tation models for polysynthetic minimal-resource languages. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 47-57, New Orleans, Louisiana. Association for Computational Linguistics.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Regular models of phonological rule systems",
"authors": [
{
"first": "M",
"middle": [],
"last": "Ronald",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Kaplan",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kay",
"suffix": ""
}
],
"year": 1994,
"venue": "Computational linguistics",
"volume": "20",
"issue": "3",
"pages": "331--378",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronald M Kaplan and Martin Kay. 1994. Regular mod- els of phonological rule systems. Computational lin- guistics, 20(3):331-378.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "OpenNMT: Opensource toolkit for neural machine translation",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Yuntian",
"middle": [],
"last": "Deng",
"suffix": ""
},
{
"first": "Jean",
"middle": [],
"last": "Senellart",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Rush",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of ACL 2017, System Demonstrations",
"volume": "",
"issue": "",
"pages": "67--72",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senel- lart, and Alexander Rush. 2017. OpenNMT: Open- source toolkit for neural machine translation. In Proceedings of ACL 2017, System Demonstrations, pages 67-72, Vancouver, Canada. Association for Computational Linguistics.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Semi-supervised learning of concatenative morphology",
"authors": [
{
"first": "Oskar",
"middle": [],
"last": "Kohonen",
"suffix": ""
},
{
"first": "Sami",
"middle": [],
"last": "Virpioja",
"suffix": ""
},
{
"first": "Krista",
"middle": [],
"last": "Lagus",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphology and Phonology",
"volume": "",
"issue": "",
"pages": "78--86",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oskar Kohonen, Sami Virpioja, and Krista Lagus. 2010. Semi-supervised learning of concatenative morphol- ogy. In Proceedings of the 11th Meeting of the ACL Special Interest Group on Computational Morphol- ogy and Phonology, pages 78-86.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Binary codes capable of correcting deletions, insertions, and reversals",
"authors": [
{
"first": "",
"middle": [],
"last": "Vladimir I Levenshtein",
"suffix": ""
}
],
"year": 1966,
"venue": "Soviet physics doklady",
"volume": "10",
"issue": "",
"pages": "707--710",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vladimir I Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707-710. Soviet Union.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Weighted finite-state morphological analysis of Finnish compounding with hfst-lexc",
"authors": [
{
"first": "Tommi",
"middle": [],
"last": "Krister Lind\u00e9n",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Pirinen",
"suffix": ""
}
],
"year": 2009,
"venue": "NEALT Proceedings Series",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Krister Lind\u00e9n, Tommi Pirinen, et al. 2009. Weighted finite-state morphological analysis of Finnish com- pounding with hfst-lexc. In NEALT Proceedings Se- ries.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Effective approaches to attention-based neural machine translation",
"authors": [
{
"first": "Thang",
"middle": [],
"last": "Luong",
"suffix": ""
},
{
"first": "Hieu",
"middle": [],
"last": "Pham",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1412--1421",
"other_ids": {
"DOI": [
"10.18653/v1/D15-1166"
]
},
"num": null,
"urls": [],
"raw_text": "Thang Luong, Hieu Pham, and Christopher D. Man- ning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natu- ral Language Processing, pages 1412-1421, Lis- bon, Portugal. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "Memory-based morphological analysis generation and part-of-speech tagging of Arabic",
"authors": [
{
"first": "Erwin",
"middle": [],
"last": "Marsi",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Van Den",
"suffix": ""
},
{
"first": "Abdelhadi",
"middle": [],
"last": "Bosch",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Soudi",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erwin Marsi, Antal van den Bosch, and Abdelhadi Soudi. 2005. Memory-based morphological analy- sis generation and part-of-speech tagging of Arabic. In Proceedings of the ACL Workshop on Computa- tional Approaches to Semitic Languages, pages 1- 8, Ann Arbor, Michigan. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "When is self-training effective for parsing",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
},
{
"first": "Eugene",
"middle": [],
"last": "Charniak",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 22nd International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "561--568",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David McClosky, Eugene Charniak, and Mark Johnson. 2008. When is self-training effective for parsing? In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 561-568, Manchester, UK. Coling 2008 Organizing Committee.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "We are our language: An ethnography of language revitalization in a Northern Athabaskan community",
"authors": [
{
"first": "A",
"middle": [],
"last": "Barbra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Meek",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Barbra A Meek. 2012. We are our language: An ethnography of language revitalization in a North- ern Athabaskan community. University of Arizona Press.",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "Improving morphology induction by learning spelling rules",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Naradowsky",
"suffix": ""
},
{
"first": "Sharon",
"middle": [],
"last": "Goldwater",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 21st international jont conference on Artifical intelligence",
"volume": "",
"issue": "",
"pages": "1531--1536",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Naradowsky and Sharon Goldwater. 2009. Im- proving morphology induction by learning spelling rules. In Proceedings of the 21st international jont conference on Artifical intelligence, pages 1531- 1536.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "Morphological segmentation for keyword spotting",
"authors": [
{
"first": "Karthik",
"middle": [],
"last": "Narasimhan",
"suffix": ""
},
{
"first": "Damianos",
"middle": [],
"last": "Karakos",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Schwartz",
"suffix": ""
},
{
"first": "Stavros",
"middle": [],
"last": "Tsakalidis",
"suffix": ""
},
{
"first": "Regina",
"middle": [],
"last": "Barzilay",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "880--885",
"other_ids": {
"DOI": [
"10.3115/v1/D14-1095"
]
},
"num": null,
"urls": [],
"raw_text": "Karthik Narasimhan, Damianos Karakos, Richard Schwartz, Stavros Tsakalidis, and Regina Barzilay. 2014. Morphological segmentation for keyword spotting. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 880-885, Doha, Qatar. Association for Computational Linguistics.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "A summary of the first workshop on language technology for language documentation and revitalization",
"authors": [
{
"first": "Roshan",
"middle": [
"S"
],
"last": "Xia",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Sharma",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Littell",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
"volume": "",
"issue": "",
"pages": "342--351",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xia, Roshan S Sharma, and Patrick Littell. 2020. A summary of the first workshop on language technol- ogy for language documentation and revitalization. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced lan- guages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 342-351, Marseille, France. European Language Re- sources association.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Unsupervised morphological segmentation with log-linear models",
"authors": [
{
"first": "Hoifung",
"middle": [],
"last": "Poon",
"suffix": ""
},
{
"first": "Colin",
"middle": [],
"last": "Cherry",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"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": "209--217",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hoifung Poon, Colin Cherry, and Kristina Toutanova. 2009. Unsupervised morphological segmentation with log-linear models. In Proceedings of Human Language Technologies: The 2009 Annual Confer- ence of the North American Chapter of the Associa- tion for Computational Linguistics, pages 209-217.",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "Stochastic complexity in statistical inquiry",
"authors": [
{
"first": "Jorma",
"middle": [],
"last": "Rissanen",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "15",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jorma Rissanen. 1998. Stochastic complexity in statis- tical inquiry, volume 15. World scientific.",
"links": null
},
"BIBREF51": {
"ref_id": "b51",
"title": "A stochastic approximation method. The annals of mathematical statistics",
"authors": [
{
"first": "Herbert",
"middle": [],
"last": "Robbins",
"suffix": ""
},
{
"first": "Sutton",
"middle": [],
"last": "Monro",
"suffix": ""
}
],
"year": 1951,
"venue": "",
"volume": "",
"issue": "",
"pages": "400--407",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. The annals of mathematical statistics, pages 400-407.",
"links": null
},
"BIBREF52": {
"ref_id": "b52",
"title": "A comparative study of minimally supervised morphological segmentation",
"authors": [
{
"first": "Oskar",
"middle": [],
"last": "Teemu Ruokolainen",
"suffix": ""
},
{
"first": "Kairit",
"middle": [],
"last": "Kohonen",
"suffix": ""
},
{
"first": "Stig-Arne",
"middle": [],
"last": "Sirts",
"suffix": ""
},
{
"first": "Mikko",
"middle": [],
"last": "Gr\u00f6nroos",
"suffix": ""
},
{
"first": "Sami",
"middle": [],
"last": "Kurimo",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Virpioja",
"suffix": ""
}
],
"year": 2016,
"venue": "Computational Linguistics",
"volume": "42",
"issue": "1",
"pages": "91--120",
"other_ids": {
"DOI": [
"10.1162/COLI_a_00243"
]
},
"num": null,
"urls": [],
"raw_text": "Teemu Ruokolainen, Oskar Kohonen, Kairit Sirts, Stig- Arne Gr\u00f6nroos, Mikko Kurimo, and Sami Virpioja. 2016. A comparative study of minimally supervised morphological segmentation. Computational Lin- guistics, 42(1):91-120.",
"links": null
},
"BIBREF53": {
"ref_id": "b53",
"title": "Supervised morphological segmentation in a low-resource learning setting using conditional random fields",
"authors": [
{
"first": "Oskar",
"middle": [],
"last": "Teemu Ruokolainen",
"suffix": ""
},
{
"first": "Sami",
"middle": [],
"last": "Kohonen",
"suffix": ""
},
{
"first": "Mikko",
"middle": [],
"last": "Virpioja",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kurimo",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "29--37",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Teemu Ruokolainen, Oskar Kohonen, Sami Virpioja, and Mikko Kurimo. 2013. Supervised morpholog- ical segmentation in a low-resource learning setting using conditional random fields. In Proceedings of the Seventeenth Conference on Computational Nat- ural Language Learning, pages 29-37, Sofia, Bul- garia. Association for Computational Linguistics.",
"links": null
},
"BIBREF54": {
"ref_id": "b54",
"title": "Painless semi-supervised morphological segmentation using conditional random fields",
"authors": [
{
"first": "Oskar",
"middle": [],
"last": "Teemu Ruokolainen",
"suffix": ""
},
{
"first": "Sami",
"middle": [],
"last": "Kohonen",
"suffix": ""
},
{
"first": "Mikko",
"middle": [],
"last": "Virpioja",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kurimo",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "84--89",
"other_ids": {
"DOI": [
"10.3115/v1/E14-4017"
]
},
"num": null,
"urls": [],
"raw_text": "Teemu Ruokolainen, Oskar Kohonen, Sami Virpioja, and Mikko Kurimo. 2014. Painless semi-supervised morphological segmentation using conditional ran- dom fields. In Proceedings of the 14th Conference of the European Chapter of the Association for Compu- tational Linguistics, volume 2: Short Papers, pages 84-89, Gothenburg, Sweden. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF55": {
"ref_id": "b55",
"title": "A graphbased lattice dependency parser for joint morphological segmentation and syntactic analysis. Transactions of the Association for",
"authors": [
{
"first": "Wolfgang",
"middle": [],
"last": "Seeker",
"suffix": ""
},
{
"first": "\u00d6zlem",
"middle": [],
"last": "\u00c7etinoglu",
"suffix": ""
}
],
"year": 2015,
"venue": "Computational Linguistics",
"volume": "3",
"issue": "",
"pages": "359--373",
"other_ids": {
"DOI": [
"10.1162/tacl_a_00144"
]
},
"num": null,
"urls": [],
"raw_text": "Wolfgang Seeker and \u00d6zlem \u00c7etinoglu. 2015. A graph- based lattice dependency parser for joint morpholog- ical segmentation and syntactic analysis. Transac- tions of the Association for Computational Linguis- tics, 3:359-373.",
"links": null
},
"BIBREF56": {
"ref_id": "b56",
"title": "Handling unknown words in Arabic FST morphology",
"authors": [
{
"first": "Khaled",
"middle": [],
"last": "Shaalan",
"suffix": ""
},
{
"first": "Mohammed",
"middle": [],
"last": "Attia",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 10th International Workshop on Finite State Methods and Natural Language Processing",
"volume": "",
"issue": "",
"pages": "20--24",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Khaled Shaalan and Mohammed Attia. 2012. Handling unknown words in Arabic FST morphology. In Pro- ceedings of the 10th International Workshop on Fi- nite State Methods and Natural Language Process- ing, pages 20-24, Donostia-San Sebasti\u00e1n. Associa- tion for Computational Linguistics.",
"links": null
},
"BIBREF57": {
"ref_id": "b57",
"title": "Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?",
"authors": [
{
"first": "Alexey",
"middle": [],
"last": "Sorokin",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 16th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexey Sorokin. 2019. Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art? In Proceedings of the 16th",
"links": null
},
"BIBREF58": {
"ref_id": "b58",
"title": "Workshop on Computational Research in Phonetics, Phonology, and Morphology",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "154--159",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 154-159.",
"links": null
},
"BIBREF59": {
"ref_id": "b59",
"title": "Deep convolutional networks for supervised morpheme segmentation of Russian language",
"authors": [
{
"first": "Alexey",
"middle": [],
"last": "Sorokin",
"suffix": ""
},
{
"first": "Anastasia",
"middle": [],
"last": "Kravtsova",
"suffix": ""
}
],
"year": 2018,
"venue": "Conference on Artificial Intelligence and Natural Language",
"volume": "",
"issue": "",
"pages": "3--10",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexey Sorokin and Anastasia Kravtsova. 2018. Deep convolutional networks for supervised morpheme segmentation of Russian language. In Conference on Artificial Intelligence and Natural Language, pages 3-10. Springer.",
"links": null
},
"BIBREF60": {
"ref_id": "b60",
"title": "The composition and use of the universal morphological feature schema (unimorph schema)",
"authors": [
{
"first": "John",
"middle": [],
"last": "Sylak-Glassman",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John Sylak-Glassman. 2016. The composition and use of the universal morphological feature schema (uni- morph schema). Johns Hopkins University.",
"links": null
},
"BIBREF61": {
"ref_id": "b61",
"title": "Synthetic data augmentation for improving low-resource asr",
"authors": [
{
"first": "Bao",
"middle": [],
"last": "Thai",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Jimerson",
"suffix": ""
},
{
"first": "Dominic",
"middle": [],
"last": "Arcoraci",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Prud'hommeaux",
"suffix": ""
},
{
"first": "Raymond",
"middle": [],
"last": "Ptucha",
"suffix": ""
}
],
"year": 2019,
"venue": "2019 IEEE Western New York Image and Signal Processing Workshop (WNYISPW)",
"volume": "",
"issue": "",
"pages": "1--9",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bao Thai, Robert Jimerson, Dominic Arcoraci, Emily Prud'hommeaux, and Raymond Ptucha. 2019. Syn- thetic data augmentation for improving low-resource asr. In 2019 IEEE Western New York Image and Sig- nal Processing Workshop (WNYISPW), pages 1-9. IEEE.",
"links": null
},
"BIBREF62": {
"ref_id": "b62",
"title": "Fully convolutional ASR for less-resourced endangered languages",
"authors": [
{
"first": "Bao",
"middle": [],
"last": "Thai",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Jimerson",
"suffix": ""
},
{
"first": "Raymond",
"middle": [],
"last": "Ptucha",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Prud",
"suffix": ""
},
{
"first": "'",
"middle": [],
"last": "Hommeaux",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)",
"volume": "",
"issue": "",
"pages": "126--130",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bao Thai, Robert Jimerson, Raymond Ptucha, and Emily Prud'hommeaux. 2020. Fully convolutional ASR for less-resourced endangered languages. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced lan- guages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 126-130, Marseille, France. European Language Re- sources association.",
"links": null
},
"BIBREF63": {
"ref_id": "b63",
"title": "Mexicanero de la sierra madre occidental",
"authors": [
{
"first": "Canger",
"middle": [],
"last": "Una",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Canger Una. 2001. Mexicanero de la sierra madre oc- cidental. El Colegio de M\u00e9xico.",
"links": null
},
"BIBREF64": {
"ref_id": "b64",
"title": "Memory-based morphological analysis",
"authors": [
{
"first": "Antal",
"middle": [],
"last": "Van Den",
"suffix": ""
},
{
"first": "Walter",
"middle": [],
"last": "Bosch",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Daelemans",
"suffix": ""
}
],
"year": 1999,
"venue": "Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "285--292",
"other_ids": {
"DOI": [
"10.3115/1034678.1034726"
]
},
"num": null,
"urls": [],
"raw_text": "Antal van den Bosch and Walter Daelemans. 1999. Memory-based morphological analysis. In Proceed- ings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 285-292, Col- lege Park, Maryland, USA. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF65": {
"ref_id": "b65",
"title": "Adadelta: an adaptive learning rate method",
"authors": [
{
"first": "D",
"middle": [],
"last": "Matthew",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zeiler",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1212.5701"
]
},
"num": null,
"urls": [],
"raw_text": "Matthew D Zeiler. 2012. Adadelta: an adaptive learn- ing rate method. arXiv preprint arXiv:1212.5701.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"num": null,
"type_str": "figure",
"uris": null,
"text": "Model testing results given different evaluation designs; error bars indicate 95% CI after bootstrapping."
},
"TABREF1": {
"content": "<table/>",
"num": null,
"type_str": "table",
"html": null,
"text": "Descriptive information of the Seneca language and data."
},
"TABREF3": {
"content": "<table><tr><td>Grammar book</td><td>Models</td><td>Accuracy</td><td>F1</td><td colspan=\"3\">Avg. Distance better than Morfessor? Selected?</td></tr><tr><td>In-domain</td><td>naive baseline</td><td>11.43</td><td>40.32</td><td>5.90</td><td>Yes</td></tr><tr><td/><td>less naive baseline</td><td>12.35</td><td>40.77</td><td>4.01</td><td>Yes</td></tr><tr><td>Cross-domain</td><td>self-training</td><td>13.38</td><td>42.96</td><td>3.77</td><td>Yes</td></tr><tr><td/><td>multi-task learning</td><td>14.66</td><td>42.97</td><td>3.24</td><td>Yes</td><td>\u2713</td></tr><tr><td>Cross-linguistic</td><td>multi-task learning</td><td>12.54</td><td>41.63</td><td>3.28</td><td>Yes</td></tr><tr><td/><td>transfer learning</td><td>15.12</td><td>40.89</td><td>3.40</td><td>Yes</td></tr><tr><td/><td>fine-tuning</td><td>15.52</td><td>41.15</td><td>3.40</td><td/></tr><tr><td>Informal sources</td><td/><td/><td/><td/><td/></tr><tr><td>In-domain</td><td>naive baseline</td><td>10.18</td><td>44.16</td><td>4.58</td><td>Yes</td></tr><tr><td/><td>less naive baseline</td><td>12.97</td><td>45.38</td><td>3.66</td><td/></tr><tr><td>Cross-domain</td><td>self-training</td><td>12.92</td><td>45.08</td><td>3.31</td><td>Yes</td></tr><tr><td/><td>multi-task learning</td><td>16.59</td><td>47.79</td><td>2.97</td><td>Yes</td><td>\u2713</td></tr><tr><td>Cross-linguistic</td><td>multi-task learning</td><td>14.65</td><td>45.91</td><td>3.15</td><td>Yes</td></tr><tr><td/><td>transfer learning</td><td>13.61</td><td>45.07</td><td>3.07</td><td>Yes</td></tr><tr><td/><td>fine-tuning</td><td>13.61</td><td>45.24</td><td>3.06</td><td/></tr></table>",
"num": null,
"type_str": "table",
"html": null,
"text": "Model training and evaluation with the development set. The value of each metric for every model was compared to those of the two baselines; boldface indicates significant differences from both baselines, derived by comparing their respective 95% CI after bootstrapping. Selected training setting for model testing is checkmarked."
},
"TABREF4": {
"content": "<table/>",
"num": null,
"type_str": "table",
"html": null,
"text": "Model training and evaluation with the development domain. The value of each metric for every model was compared to those of the two baselines; boldface indicates significant differences from both baselines, derived by comparing their respective 95% CI after bootstrapping. Selected training setting for model testing is checkmarked."
}
}
}
} |