File size: 106,684 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 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T01:47:13.904521Z"
},
"title": "TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing",
"authors": [
{
"first": "Ziqing",
"middle": [],
"last": "Yang",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "zqyang5@iflytek.com"
},
{
"first": "Yiming",
"middle": [],
"last": "Cui",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "ymcui@iflytek.com"
},
{
"first": "Zhipeng",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "zpchen@iflytek.com"
},
{
"first": "Wanxiang",
"middle": [],
"last": "Che",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "tliu@ir.hit.edu.cn"
},
{
"first": "Shijin",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "sjwang3@iflytek.com"
},
{
"first": "Guoping",
"middle": [],
"last": "Hu",
"suffix": "",
"affiliation": {
"laboratory": "State Key Laboratory of Cognitive Intelligence",
"institution": "Harbin Institute of Technology",
"location": {
"settlement": "Harbin, Langfang",
"country": "China, China"
}
},
"email": "gphu@iflytek.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 1",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Large pre-trained language models, such as GPT (Radford, 2018) , BERT (Devlin et al., 2019) , RoBERTa and XLNet have achieved great success in many NLP tasks and greatly contributed to the progress of NLP research. However, one big issue of these models is the high demand for computing resources -they usually have hundreds of millions of parameters, and take several gigabytes of memory to train and inference -which makes it impractical to deploy them on mobile devices or online systems. From a research point of view, we are tempted to ask: is it necessary to have such a big model that contains hundreds of millions of parameters to achieve a high performance? Motivated by the above considerations, recently, some researchers in the NLP community have tried to design lite models (Lan et al., 2019) , or resort to knowledge 1 TextBrewer: http://textbrewer.hfl-rc.com distillation (KD) technique to compress large pretrained models to small models.",
"cite_spans": [
{
"start": 47,
"end": 62,
"text": "(Radford, 2018)",
"ref_id": "BIBREF12"
},
{
"start": 70,
"end": 91,
"text": "(Devlin et al., 2019)",
"ref_id": "BIBREF4"
},
{
"start": 787,
"end": 805,
"text": "(Lan et al., 2019)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "KD is a technique of transferring knowledge from a teacher model to a student model, which is usually smaller than the teacher. The student model is trained to mimic the outputs of the teacher model. Before the birth of BERT, KD had been applied to several specific tasks like machine translation (Kim and Rush, 2016; Tan et al., 2019) in NLP. While the recent studies of distilling large pre-trained models focus on finding general distillation methods that work on various tasks and are receiving more and more attention (Sanh et al., 2019; Jiao et al., 2019; Sun et al., 2019a; Tang et al., 2019; Clark et al., 2019; .",
"cite_spans": [
{
"start": 297,
"end": 317,
"text": "(Kim and Rush, 2016;",
"ref_id": "BIBREF7"
},
{
"start": 318,
"end": 335,
"text": "Tan et al., 2019)",
"ref_id": "BIBREF19"
},
{
"start": 523,
"end": 542,
"text": "(Sanh et al., 2019;",
"ref_id": "BIBREF14"
},
{
"start": 543,
"end": 561,
"text": "Jiao et al., 2019;",
"ref_id": "BIBREF6"
},
{
"start": 562,
"end": 580,
"text": "Sun et al., 2019a;",
"ref_id": "BIBREF16"
},
{
"start": 581,
"end": 599,
"text": "Tang et al., 2019;",
"ref_id": "BIBREF20"
},
{
"start": 600,
"end": 619,
"text": "Clark et al., 2019;",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Though various distillation methods have been proposed, they usually share a common workflow: firstly, train a teacher model, then optimize the student model by minimizing some losses that are calculated between the outputs of the teacher and the student. Therefore it is desirable to have a reusable distillation workflow framework and treat different distillation strategies and tricks as plugins so that they could be easily and arbitrarily added to the framework. In this way, we could also achieve great flexibility in experimenting with different combinations of distillation strategies and comparing their effects.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we introduce TextBrewer, a PyTorch-based distillation toolkit for NLP that aims to provide a unified distillation workflow, save the effort of setting up experiments and help users to distill more effective models. TextBrewer provides simple-to-use APIs, a collection of distillation methods, and highly customizable configurations. It has also been proved able to distill BERT models efficiently and reproduce the state-of-theart results on typical NLP tasks. The main features of TextBrewer are:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 Versatility in tasks and models. It works with a wide range of models, from the RNNbased model to the transformer-based model, and works on typical natural language understanding tasks. Its usability in tasks like text classification, reading comprehension, and sequence labeling has been fully tested.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 Flexibility in configurations. The distillation process is configured by configuration objects, which can be initialized from JSON files and contain many tunable hyperparameters. Users can extend the configurations with new custom losses, schedulers, etc., if the presets do not meet their requirements.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 Including various distillation methods and strategies. KD has been studied extensively in computer vision (CV) and has achieved great success. It would be worthwhile to introduce these studies to the NLP community as some of the methods in these studies could also be applied to texts. TextBrewer includes a set of methods from both CV and NLP, such as flow of solution procedure (FSP) matrix loss (Yim et al., 2017) , neuron selectivity transfer (NST) (Huang and Wang, 2017) , probability shift and dynamic temperature (Wen et al., 2019) , attention matrix loss, multi-task distillation . In our experiments, we will show the effectiveness of applying methods from CV on NLP tasks.",
"cite_spans": [
{
"start": 400,
"end": 418,
"text": "(Yim et al., 2017)",
"ref_id": "BIBREF27"
},
{
"start": 455,
"end": 477,
"text": "(Huang and Wang, 2017)",
"ref_id": "BIBREF5"
},
{
"start": 522,
"end": 540,
"text": "(Wen et al., 2019)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 Being non-intrusive and simple to use. Nonintrusive means there is no need to modify the existing code that defines the models. Users can re-use the most parts of their existing training scripts, such as model definition and initialization, data preprocessing and task evaluation. Only some preparatory work (see Section 3.3) are additionally required to use TextBrewer to perform the distillation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "TextBrewer also provides some useful utilities such as model size analysis and data augmentation to help model design and distillation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Recently some distilled BERT models have been released, such as DistilBERT (Sanh et al., 2019) , TinyBERT (Jiao et al., 2019) , and ERNIE Slim 2 . DistilBERT performs distillation on the pretraining task, i.e., masked language modeling.",
"cite_spans": [
{
"start": 75,
"end": 94,
"text": "(Sanh et al., 2019)",
"ref_id": "BIBREF14"
},
{
"start": 106,
"end": 125,
"text": "(Jiao et al., 2019)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "2 https://github.com/PaddlePaddle/ERNIE TinyBERT performs transformer distillation at both the pre-training and task-specific learning stages. ERNIE Slim distills ERNIE (Sun et al., 2019b ,c)on a sentiment classification task. Their distillation code is publicly available, and users can replicate their experiments easily. However, it is laborious and error-prone to change the distillation method or adapt the distillation code for some other models and tasks, since the code is not written for general distillation purposes.",
"cite_spans": [
{
"start": 169,
"end": 187,
"text": "(Sun et al., 2019b",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "There also exist some libraries for general model compression. Distiller (Zmora et al., 2018) and PaddleSlim 3 are two versatile libraries supporting pruning, quantization and knowledge distillation. They focus on models and tasks in computer vision. In comparison, TextBrewer is more focused on knowledge distillation on NLP tasks, more flexible, and offers more functionalities. Based on PyTorch, It provides simple APIs and rich customization for fast and clean implementations of experiments.",
"cite_spans": [
{
"start": 73,
"end": 93,
"text": "(Zmora et al., 2018)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "3 Architecture and Design Figure 1 shows an overview of the main functionalities and architecture of TextBrewer. To support different models and different tasks and meanwhile stay flexible and extensible, TextBrewer provides distillers to conduct the actual experiments and configuration classes to configure the behaviors of the distillers.",
"cite_spans": [],
"ref_spans": [
{
"start": 26,
"end": 34,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Distillers are the cores of TextBrewer. They automatically train and save models and support custom evaluation functions. Five distillers have been implemented: BasicDistiller is used for single-task single-teacher distillation; GeneralDistiller in addition supports more advanced intermediate loss functions; MultiTeacherDistiller distills an ensemble of teacher models into a single student model; MultiTaskDistiller distills multiple teacher models of different tasks into a single multi-task student model (Clark et al., 2019; . We also have implemented BasicTrainer for training teachers on labeled data to unify the workflows of supervised learning and distillation. All the distillers share the same interface and usage. They can be replaced by each other easily. ",
"cite_spans": [
{
"start": 510,
"end": 530,
"text": "(Clark et al., 2019;",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Distillers",
"sec_num": "3.1"
},
{
"text": "The general training settings and the distillation method settings of a distiller are specified by two configurations: TrainingConfig and DistillationConfig.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "TrainingConfig defines the settings that are general to deep learning experiments, including the directory where logs and student model are stored (log dir, output dir), the device to use (device), the frequency of storing and evaluating student model (ckpt frequencey), etc.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "DistillationConfig defines the settings that are pertinent to distillation, where various distillation methods could be configured or enabled.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "It includes the type of KD loss (kd loss type), the temperature and weight of KD loss (temperature and kd loss weight), the weight of hard-label loss (hard label weight), probability shift switch, schedulers and intermediate losses, etc. Intermediate losses are used for computing the losses between the intermediate states of teacher and student, and they could be freely combined and added to the distillers. Schedulers are used to adjust loss weight or temperature dynamically.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "The available values of configuration options such as loss functions and schedulers are defined as dictionaries in presets. For example, the loss function dictionary includes hidden state loss, cosine similarity loss, FSP loss, NST loss, etc.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "All the configurations can be initialized from JSON files. In Figure 3 we show an example of DistillationConfig for distilling BERT BASE , to a 4-layer transformers. See Section 4 for more details. ",
"cite_spans": [],
"ref_spans": [
{
"start": 62,
"end": 70,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Configurations and Presets",
"sec_num": "3.2"
},
{
"text": "Before distilling a teacher model using TextBrewer, some preparatory works have to be done:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "1. Train a teacher model on a labeled dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "Users usually train the teacher model with their own training scripts. TextBrewer also provides BasicTrainer for supervised training on a labeled dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "2. Define and initialize the student model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "3. Build a dataloader of the dataset for distillation and initialize the optimizer and learning rate scheduler.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "The above steps are usually common to all deep learning experiments. To perform distillation, take the following additional steps:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "1. Initialize training and distillation configurations, and construct a distiller.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "2. Define adaptors and a callback function.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "3. Call the train method of the distiller.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "A code snippet that shows the minimal workflow is presented in Figure 2 . The concepts of callback and adaptor will be explained below.",
"cite_spans": [],
"ref_spans": [
{
"start": 63,
"end": 71,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Workflow",
"sec_num": "3.3"
},
{
"text": "An example of distillation configuration. This configuration is used to distill a 12-layer BERT BASE to a 4-layer T4-tiny.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 3:",
"sec_num": null
},
{
"text": "To monitor the performance of the student model during training, people usually evaluate the student model on a development set at some checkpoints besides logging the loss curve. For example, in the early stopping strategy, users choose the best model weights checkpoint based on the performance of the student model on the development set at the end of each epoch. TextBrewer supports such functionality by providing the callback function argument in the train method, as shown in line 24 of Figure 2 . The callback function takes two arguments: the student model and the current training step. At each checkpoint step (determined by num train epochs and ckpt frequencey), the distiller saves the student model and then calls the callback function.",
"cite_spans": [],
"ref_spans": [
{
"start": 494,
"end": 502,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Callback Function",
"sec_num": "3.3.1"
},
{
"text": "Since it is impractical to implement evaluation metrics and evaluation procedures for all NLP tasks, we encourage users to implement their own evaluation functions as the callbacks for the best practice.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Callback Function",
"sec_num": "3.3.1"
},
{
"text": "The distiller is model-agnostic. It needs a translator to translate the model outputs into meaningful data. Adaptor plays the role of translator. An Adaptor is an interface and responsible for explaining the inputs and outputs of the teacher and student for the distiller.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adaptor",
"sec_num": "3.3.2"
},
{
"text": "Adaptor takes two arguments: the model inputs and the model outputs. It is expected to return a dictionary with some specific keys. Each key explains the meaning of the corresponding value, as shown in Figure 1 (b) . For example, logits is the logits of final outputs, hidden is intermediate hidden states, attention is the attention matrices, inputs mask is used to mask padding positions. The distiller only takes necessary elements from the outputs of adaptors according to its distillation configurations. A minimal adaptor only needs to explain logits, as shown in lines 11-14 of Figure 2 .",
"cite_spans": [],
"ref_spans": [
{
"start": 202,
"end": 214,
"text": "Figure 1 (b)",
"ref_id": "FIGREF0"
},
{
"start": 585,
"end": 593,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Adaptor",
"sec_num": "3.3.2"
},
{
"text": "TextBrewer also works with users' custom modules. New loss functions and schedulers can be easily added to the toolkit. For example, to use a custom loss function, one first implements the loss function with a compatible interface, then adds it to the loss function dictionary in the presets with a custom name, so that the new loss function becomes available as a new option value of the configuration and can be recognized by distillers.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Extensibility",
"sec_num": "3.4"
},
{
"text": "In this section, we conduct several experiments to show TextBrewer's ability to distill large pretrained models on different NLP tasks and achieve results are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 4",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "Datasets and tasks. We conduct experiments on both English and Chinese datasets. For English datasets, We use MNLI for text classification task, SQuAD1.1 (Rajpurkar et al., 2016) for span-extraction machine reading comprehension (MRC) task and CoNLL-2003 (Tjong Kim Sang and De Meulder, 2003) for named entity recognition (NER) task. For Chinese datasets, we use the Chinese part of XNLI (Conneau et al., 2018) , LCQMC , CMRC 2018 (Cui et al., 2019b) and DRCD (Shao et al., 2018) . XNLI is the multilingual version of MNLI. LCQMC is a large-scale Chinese question matching corpus. CMRC 2018 and DRCD are two span-extraction machine reading comprehension datasets similar to SQuAD. The statistics of the datasets are listed in Table 1 . Models. All the teachers are BERT BASE -based models. For English tasks, teachers are initialized with the weights released by Google 5 and converted into PyTorch format via Transformers 6 . For Chinese tasks, teacher is initialized with the pre-trained RoBERTa-wwm-ext 7 (Cui et al., 2019a) . We test the performance of the following student models:",
"cite_spans": [
{
"start": 154,
"end": 178,
"text": "(Rajpurkar et al., 2016)",
"ref_id": "BIBREF13"
},
{
"start": 255,
"end": 292,
"text": "(Tjong Kim Sang and De Meulder, 2003)",
"ref_id": "BIBREF21"
},
{
"start": 388,
"end": 410,
"text": "(Conneau et al., 2018)",
"ref_id": "BIBREF1"
},
{
"start": 431,
"end": 450,
"text": "(Cui et al., 2019b)",
"ref_id": "BIBREF3"
},
{
"start": 460,
"end": 479,
"text": "(Shao et al., 2018)",
"ref_id": "BIBREF15"
},
{
"start": 1008,
"end": 1027,
"text": "(Cui et al., 2019a)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [
{
"start": 726,
"end": 733,
"text": "Table 1",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "\u2022 T6 and T3 are BERT BASE with fewer layers of transformers. Especially, T6 has the same structure as DistilBERT (Sanh et al., 2019) .",
"cite_spans": [
{
"start": 113,
"end": 132,
"text": "(Sanh et al., 2019)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "\u2022 T3-small is a 3-layer BERT with half BERTbase's hidden size and feed-forward size.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "\u2022 T4-tiny is the same as TinyBERT, a 4-layer model with an even smaller hidden size and feedforward size.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "\u2022 BiGRU is a single-layer bidirectional GRU. Its word embeddings are taken from BERT BASE .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "T3-small and T4-tiny are initialized randomly. The model structures of the teacher and students are summarized in Table 3 . Training settings. To keep experiments simple, we directly distill the teacher model that has been trained on the task, while we do not perform task-irrelevant language modeling distillation in advance. The number of epochs ranges from 30 to 60, and the learning rate of students is 1e-4 for all distillation experiments. Distillation settings. Temperature is set to 8 for all experiments. We add intermediate losses uniformly distributed among all the layers between teacher and student (except BiGRU). The loss functions we choose are hidden mse loss which computes the mean square loss between two hidden states, and NST loss which is an effective method in CV. In Figure 3 we show an example of distillation configuration for distilling BERT BASE to a T4-tiny. Since their hidden sizes are different, we use proj option to add linear layers to match the dimensions. The linear layers will be trained together with the student automatically. We experiment with two kinds of distillers: GeneralDistiller and MultiTeacherDistiller .",
"cite_spans": [],
"ref_spans": [
{
"start": 114,
"end": 121,
"text": "Table 3",
"ref_id": "TABREF6"
},
{
"start": 792,
"end": 800,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Settings",
"sec_num": "4.1"
},
{
"text": "We list the public results (DistilBERT and Tiny-BERT) and our distillation results obtained by GeneralDistiller in Table 2 . We have the following observations.",
"cite_spans": [],
"ref_spans": [
{
"start": 115,
"end": 122,
"text": "Table 2",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Results on English Datasets",
"sec_num": "4.2"
},
{
"text": "First, teachers can be distilled to T6 models with minor losses in performance. All the T6 models achieve 99% performance of the teachers, higher than the DistilBERT.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results on English Datasets",
"sec_num": "4.2"
},
{
"text": "Second, T4-tiny outperforms TinyBERT though they share the same structure. This is attributed to the NST losses in the distillation configuration. This result proves the effectiveness of applying KD method developed in CV on NLP tasks.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results on English Datasets",
"sec_num": "4.2"
},
{
"text": "Third, although T4-tiny has less parameters than T3-small, T4-tiny outperforms T3-small in most T6 6 768 3072 65M 60% T3 3 768 3072 44M 41% T3-small 3 384 1536 17M 16% T4-tiny 4 312 1200 14M 13% BiGRU 1 768 -31M 29% cases. It may be a hint that narrow-and-deep models are better than wide-and-shallow models. Finally, data augmentation (DA) is critical. For the experiments in the last line in Table 2 , we use additional datasets during distillation: a subset of NewsQA (Trischler et al., 2017) training set is used in SQuAD; passages from the HotpotQA (Yang et al., 2018) training set is used in CoNLL-2003. The augmentation datasets significantly improve the performance, especially when the size of the training set is small, like CoNLL-2003. We next show the effectiveness of MultiTeacherDistiller, which distills an ensemble of teachers to a single student model. For each task, we train three BERT BASE teacher models with different seeds. The student is also a BERT BASE model. The temperature is set to 8, and intermediate losses are not used. As Table 4 shows, for each task, the student achieves the best performance, even higher than the ensemble result.",
"cite_spans": [
{
"start": 499,
"end": 523,
"text": "(Trischler et al., 2017)",
"ref_id": "BIBREF22"
},
{
"start": 582,
"end": 601,
"text": "(Yang et al., 2018)",
"ref_id": "BIBREF26"
},
{
"start": 763,
"end": 774,
"text": "CoNLL-2003.",
"ref_id": null
}
],
"ref_spans": [
{
"start": 96,
"end": 243,
"text": "T6 6 768 3072 65M 60% T3 3 768 3072 44M 41% T3-small 3 384 1536 17M 16% T4-tiny 4 312 1200 14M 13% BiGRU 1 768 -31M 29%",
"ref_id": "TABREF2"
},
{
"start": 422,
"end": 429,
"text": "Table 2",
"ref_id": "TABREF4"
},
{
"start": 1084,
"end": 1091,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Results on English Datasets",
"sec_num": "4.2"
},
{
"text": "The results on Chinese datasets are presented in Table 5 . We notice that T4-tiny still outperforms T3-small on all tasks, which is consistent with their performance on English tasks. In the experiments with DA, CMRC 2018 and DRCD take each other's dataset as data augmentation. We observe that since CMRC 2018 has a relatively small training set, DA has a much more significant effect.",
"cite_spans": [],
"ref_spans": [
{
"start": 49,
"end": 56,
"text": "Table 5",
"ref_id": "TABREF9"
}
],
"eq_spans": [],
"section": "Results on Chinese Datasets",
"sec_num": "5"
},
{
"text": "In this paper, we present TextBrewer, a flexible PyTorch-based distillation toolkit for NLP research and applications. TextBrewer provides rich customization options for users to compare different distillation methods and build their strategies. We have conducted a series of experiments. The results show that the distilled models can achieve state-of-the-art results with simple settings. TextBrewer also has its limitations. For example, its usability in generation tasks such as machine translation has not been tested. We will keep adding more examples and tests to expand TextBrewer's scope of application.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "6"
},
{
"text": "Apart from the distillation strategies, the model structure also affects the performance. In the future, we aim to integrate neural architecture search into the toolkit to automate the searching for model structures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "6"
},
{
"text": "https://github.com/PaddlePaddle/PaddleSlim",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "More results are presented in the online documentation: https://textbrewer.readthedocs.io",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://github.com/google-research/bert 6 https://github.com/huggingface/transformers 7 https://github.com/ymcui/Chinese-BERT-wwm",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We would like to thank all anonymous reviewers for their valuable comments on our work. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61976072, 61632011, and 61772153.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "BAM! born-again multi-task networks for natural language understanding",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "Minh-Thang",
"middle": [],
"last": "Luong",
"suffix": ""
},
{
"first": "Urvashi",
"middle": [],
"last": "Khandelwal",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Quoc",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Le",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "5931--5937",
"other_ids": {
"DOI": [
"10.18653/v1/P19-1595"
]
},
"num": null,
"urls": [],
"raw_text": "Kevin Clark, Minh-Thang Luong, Urvashi Khandel- wal, Christopher D. Manning, and Quoc V. Le. 2019. BAM! born-again multi-task networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computa- tional Linguistics, pages 5931-5937, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "XNLI: Evaluating cross-lingual sentence representations",
"authors": [
{
"first": "Alexis",
"middle": [],
"last": "Conneau",
"suffix": ""
},
{
"first": "Ruty",
"middle": [],
"last": "Rinott",
"suffix": ""
},
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Adina",
"middle": [],
"last": "Williams",
"suffix": ""
},
{
"first": "Samuel",
"middle": [],
"last": "Bowman",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Stoyanov",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2475--2485",
"other_ids": {
"DOI": [
"10.18653/v1/D18-1269"
]
},
"num": null,
"urls": [],
"raw_text": "Alexis Conneau, Ruty Rinott, Guillaume Lample, Ad- ina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. XNLI: Evaluating cross-lingual sentence representations. In Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2475-2485, Brussels, Belgium. Association for Computational Linguistics.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Pre-training with whole word masking for chinese",
"authors": [
{
"first": "Yiming",
"middle": [],
"last": "Cui",
"suffix": ""
},
{
"first": "Wanxiang",
"middle": [],
"last": "Che",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Ziqing",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Shijin",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Guoping",
"middle": [],
"last": "Hu",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. 2019a. Pre-training with whole word masking for chinese BERT. CoRR, abs/1906.08101.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "A span-extraction dataset for Chinese machine reading comprehension",
"authors": [
{
"first": "Yiming",
"middle": [],
"last": "Cui",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Wanxiang",
"middle": [],
"last": "Che",
"suffix": ""
},
{
"first": "Li",
"middle": [],
"last": "Xiao",
"suffix": ""
},
{
"first": "Zhipeng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Wentao",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Shijin",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Guoping",
"middle": [],
"last": "Hu",
"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": "5886--5891",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1600"
]
},
"num": null,
"urls": [],
"raw_text": "Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, and Guop- ing Hu. 2019b. A span-extraction dataset for Chi- nese machine reading comprehension. In Proceed- ings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 5886-5891, Hong Kong, China. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "BERT: Pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "4171--4186",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1423"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Like what you like: Knowledge distill via neuron selectivity transfer",
"authors": [
{
"first": "Zehao",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Naiyan",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zehao Huang and Naiyan Wang. 2017. Like what you like: Knowledge distill via neuron selectivity trans- fer. CoRR, abs/1707.01219.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Tinybert: Distilling BERT for natural language understanding",
"authors": [
{
"first": "Xiaoqi",
"middle": [],
"last": "Jiao",
"suffix": ""
},
{
"first": "Yichun",
"middle": [],
"last": "Yin",
"suffix": ""
},
{
"first": "Lifeng",
"middle": [],
"last": "Shang",
"suffix": ""
},
{
"first": "Xin",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Xiao",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Linlin",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Fang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Qun",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2019. Tinybert: Distilling BERT for natural lan- guage understanding. CoRR, abs/1909.10351.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Sequencelevel knowledge distillation",
"authors": [
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Alexander",
"middle": [
"M"
],
"last": "Rush",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1317--1327",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1139"
]
},
"num": null,
"urls": [],
"raw_text": "Yoon Kim and Alexander M. Rush. 2016. Sequence- level knowledge distillation. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 1317-1327, Austin, Texas. Association for Computational Linguistics.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "ALBERT: A lite BERT for selfsupervised learning of language representations",
"authors": [
{
"first": "Zhenzhong",
"middle": [],
"last": "Lan",
"suffix": ""
},
{
"first": "Mingda",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Goodman",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Gimpel",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Sori- cut. 2019. ALBERT: A lite BERT for self- supervised learning of language representations. CoRR, abs/1909.11942.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Improving multi-task deep neural networks via knowledge distillation for natural language understanding",
"authors": [
{
"first": "Xiaodong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Pengcheng",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Weizhu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jian- feng Gao. 2019a. Improving multi-task deep neural networks via knowledge distillation for natural lan- guage understanding. CoRR, abs/1904.09482.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "LCQMC:a large-scale Chinese question matching corpus",
"authors": [
{
"first": "Xin",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Qingcai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Chong",
"middle": [],
"last": "Deng",
"suffix": ""
},
{
"first": "Huajun",
"middle": [],
"last": "Zeng",
"suffix": ""
},
{
"first": "Jing",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Dongfang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Buzhou",
"middle": [],
"last": "Tang",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 27th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "1952--1962",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, and Buzhou Tang. 2018. LCQMC:a large-scale Chinese question matching corpus. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1952-1962, Santa Fe, New Mexico, USA. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Roberta: A robustly optimized BERT pretraining approach",
"authors": [
{
"first": "Yinhan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Myle",
"middle": [],
"last": "Ott",
"suffix": ""
},
{
"first": "Naman",
"middle": [],
"last": "Goyal",
"suffix": ""
},
{
"first": "Jingfei",
"middle": [],
"last": "Du",
"suffix": ""
},
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Danqi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Stoyanov",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b. Roberta: A robustly optimized BERT pretraining ap- proach. CoRR, abs/1907.11692.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Improving language understanding by generative pre-training",
"authors": [
{
"first": "Alec",
"middle": [],
"last": "Radford",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alec Radford. 2018. Improving language understand- ing by generative pre-training.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "SQuAD: 100,000+ questions for machine comprehension of text",
"authors": [
{
"first": "Pranav",
"middle": [],
"last": "Rajpurkar",
"suffix": ""
},
{
"first": "Jian",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Konstantin",
"middle": [],
"last": "Lopyrev",
"suffix": ""
},
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2383--2392",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1264"
]
},
"num": null,
"urls": [],
"raw_text": "Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 2383-2392, Austin, Texas. Association for Computational Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter",
"authors": [
{
"first": "Victor",
"middle": [],
"last": "Sanh",
"suffix": ""
},
{
"first": "Lysandre",
"middle": [],
"last": "Debut",
"suffix": ""
},
{
"first": "Julien",
"middle": [],
"last": "Chaumond",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Wolf",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR, abs/1910.01108.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "DRCD: a chinese machine reading comprehension dataset",
"authors": [
{
"first": "Chih-Chieh",
"middle": [],
"last": "Shao",
"suffix": ""
},
{
"first": "Trois",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Yuting",
"middle": [],
"last": "Lai",
"suffix": ""
},
{
"first": "Yiying",
"middle": [],
"last": "Tseng",
"suffix": ""
},
{
"first": "Sam",
"middle": [],
"last": "Tsai",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chih-Chieh Shao, Trois Liu, Yuting Lai, Yiying Tseng, and Sam Tsai. 2018. DRCD: a chinese machine reading comprehension dataset. CoRR, abs/1806.00920.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Patient knowledge distillation for BERT model compression",
"authors": [
{
"first": "Siqi",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Yu",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "Zhe",
"middle": [],
"last": "Gan",
"suffix": ""
},
{
"first": "Jingjing",
"middle": [],
"last": "Liu",
"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": "4323--4332",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1441"
]
},
"num": null,
"urls": [],
"raw_text": "Siqi Sun, Yu Cheng, Zhe Gan, and Jingjing Liu. 2019a. Patient knowledge distillation for BERT model com- pression. 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 4323-4332, Hong Kong, China. Association for Computational Linguistics.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "ERNIE: enhanced representation through knowledge integration",
"authors": [
{
"first": "Yu",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Shuohuan",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Yu-Kun",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Shikun",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "Xuyi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Han",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xin",
"middle": [],
"last": "Tian",
"suffix": ""
},
{
"first": "Danxiang",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Hao Tian",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Wu",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019b. ERNIE: en- hanced representation through knowledge integra- tion. CoRR, abs/1904.09223.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "ERNIE 2.0: A continual pre-training framework for language understanding",
"authors": [
{
"first": "Yu",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Shuohuan",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Yu-Kun",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Shikun",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Hao Tian",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2019c. ERNIE 2.0: A continual pre-training framework for language understanding. CoRR, abs/1907.12412.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Multilingual neural machine translation with knowledge distillation",
"authors": [
{
"first": "Xu",
"middle": [],
"last": "Tan",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Ren",
"suffix": ""
},
{
"first": "Di",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Zhou",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Tie-Yan",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2019,
"venue": "7th International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xu Tan, Yi Ren, Di He, Tao Qin, Zhou Zhao, and Tie-Yan Liu. 2019. Multilingual neural machine translation with knowledge distillation. In 7th Inter- national Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Natural language generation for effective knowledge distillation",
"authors": [
{
"first": "Raphael",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Yao",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Jimmy",
"middle": [],
"last": "Lin",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP",
"volume": "",
"issue": "",
"pages": "202--208",
"other_ids": {
"DOI": [
"10.18653/v1/D19-6122"
]
},
"num": null,
"urls": [],
"raw_text": "Raphael Tang, Yao Lu, and Jimmy Lin. 2019. Natu- ral language generation for effective knowledge dis- tillation. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 202-208, Hong Kong, China. Association for Computational Linguistics.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "Tjong",
"suffix": ""
},
{
"first": "Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Fien",
"middle": [],
"last": "De Meulder",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003",
"volume": "",
"issue": "",
"pages": "142--147",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natu- ral Language Learning at HLT-NAACL 2003, pages 142-147.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "NewsQA: A machine comprehension dataset",
"authors": [
{
"first": "Adam",
"middle": [],
"last": "Trischler",
"suffix": ""
},
{
"first": "Tong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Xingdi",
"middle": [],
"last": "Yuan",
"suffix": ""
},
{
"first": "Justin",
"middle": [],
"last": "Harris",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Sordoni",
"suffix": ""
},
{
"first": "Philip",
"middle": [],
"last": "Bachman",
"suffix": ""
},
{
"first": "Kaheer",
"middle": [],
"last": "Suleman",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2nd Workshop on Representation Learning for NLP",
"volume": "",
"issue": "",
"pages": "191--200",
"other_ids": {
"DOI": [
"10.18653/v1/W17-2623"
]
},
"num": null,
"urls": [],
"raw_text": "Adam Trischler, Tong Wang, Xingdi Yuan, Justin Har- ris, Alessandro Sordoni, Philip Bachman, and Ka- heer Suleman. 2017. NewsQA: A machine compre- hension dataset. In Proceedings of the 2nd Work- shop on Representation Learning for NLP, pages 191-200, Vancouver, Canada. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "GLUE: A multi-task benchmark and analysis platform for natural language understanding",
"authors": [
{
"first": "Alex",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Amanpreet",
"middle": [],
"last": "Singh",
"suffix": ""
},
{
"first": "Julian",
"middle": [],
"last": "Michael",
"suffix": ""
},
{
"first": "Felix",
"middle": [],
"last": "Hill",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Samuel",
"middle": [
"R"
],
"last": "Bowman",
"suffix": ""
}
],
"year": 2019,
"venue": "7th International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. GLUE: A multi-task benchmark and analysis plat- form for natural language understanding. In 7th International Conference on Learning Representa- tions, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Preparing lessons: Improve knowledge distillation with better supervision",
"authors": [
{
"first": "Tiancheng",
"middle": [],
"last": "Wen",
"suffix": ""
},
{
"first": "Shenqi",
"middle": [],
"last": "Lai",
"suffix": ""
},
{
"first": "Xueming",
"middle": [],
"last": "Qian",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tiancheng Wen, Shenqi Lai, and Xueming Qian. 2019. Preparing lessons: Improve knowledge distillation with better supervision. CoRR, abs/1911.07471.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Xlnet: Generalized autoregressive pretraining for language understanding",
"authors": [
{
"first": "Zhilin",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Zihang",
"middle": [],
"last": "Dai",
"suffix": ""
},
{
"first": "Yiming",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Jaime",
"middle": [
"G"
],
"last": "Carbonell",
"suffix": ""
},
{
"first": "Ruslan",
"middle": [],
"last": "Salakhutdinov",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Quoc",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Le",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Car- bonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. CoRR, abs/1906.08237.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "HotpotQA: A dataset for diverse, explainable multi-hop question answering",
"authors": [
{
"first": "Zhilin",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Peng",
"middle": [],
"last": "Qi",
"suffix": ""
},
{
"first": "Saizheng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "William",
"middle": [],
"last": "Cohen",
"suffix": ""
},
{
"first": "Ruslan",
"middle": [],
"last": "Salakhutdinov",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2369--2380",
"other_ids": {
"DOI": [
"10.18653/v1/D18-1259"
]
},
"num": null,
"urls": [],
"raw_text": "Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answer- ing. In Proceedings of the 2018 Conference on Em- pirical Methods in Natural Language Processing, pages 2369-2380, Brussels, Belgium. Association for Computational Linguistics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "A gift from knowledge distillation: Fast optimization, network minimization and transfer learning",
"authors": [
{
"first": "Junho",
"middle": [],
"last": "Yim",
"suffix": ""
},
{
"first": "Donggyu",
"middle": [],
"last": "Joo",
"suffix": ""
},
{
"first": "Ji-Hoon",
"middle": [],
"last": "Bae",
"suffix": ""
},
{
"first": "Junmo",
"middle": [],
"last": "Kim",
"suffix": ""
}
],
"year": 2017,
"venue": "2017 IEEE Conference on Computer Vision and Pattern Recognition",
"volume": "",
"issue": "",
"pages": "7130--7138",
"other_ids": {
"DOI": [
"10.1109/CVPR.2017.754"
]
},
"num": null,
"urls": [],
"raw_text": "Junho Yim, Donggyu Joo, Ji-Hoon Bae, and Junmo Kim. 2017. A gift from knowledge distillation: Fast optimization, network minimization and trans- fer learning. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Hon- olulu, HI, USA, July 21-26, 2017, pages 7130-7138.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Extreme language model compression with optimal subwords and shared projections",
"authors": [
{
"first": "Sanqiang",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Raghav",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": "Yang",
"middle": [],
"last": "Song",
"suffix": ""
},
{
"first": "Denny",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sanqiang Zhao, Raghav Gupta, Yang Song, and Denny Zhou. 2019. Extreme language model compres- sion with optimal subwords and shared projections. CoRR, abs/1909.11687.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Bar Elharar, and Gal Novik",
"authors": [
{
"first": "Neta",
"middle": [],
"last": "Zmora",
"suffix": ""
},
{
"first": "Guy",
"middle": [],
"last": "Jacob",
"suffix": ""
},
{
"first": "Lev",
"middle": [],
"last": "Zlotnik",
"suffix": ""
}
],
"year": 2018,
"venue": "Neural network distiller",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.5281/zenodo.1297430"
]
},
"num": null,
"urls": [],
"raw_text": "Neta Zmora, Guy Jacob, Lev Zlotnik, Bar Elharar, and Gal Novik. 2018. Neural network distiller.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "(a) An overview of the main functionalities of TextBrewer. (b) A sketch that shows the function of adaptors inside a distiller.",
"type_str": "figure",
"num": null,
"uris": null
},
"FIGREF1": {
"text": "A code snippet that demonstrates the minimal TextBrewer workflow.",
"type_str": "figure",
"num": null,
"uris": null
},
"TABREF2": {
"num": null,
"text": "A summary of the datasets used in experiments. The size of CoNLL-2003 is measured in number of entities.",
"type_str": "table",
"html": null,
"content": "<table/>"
},
"TABREF4": {
"num": null,
"text": "Performance of BERT BASE (teacher) and various students on the development sets of MNLI and SQuAD, and the test set of CoNLL-2003. m and mm under MNLI denote the accuracies on matched and mismatched sections respectively.",
"type_str": "table",
"html": null,
"content": "<table/>"
},
"TABREF6": {
"num": null,
"text": "Model sizes of teacher and students. The number of parameters includes embeddings but does not include output layers.",
"type_str": "table",
"html": null,
"content": "<table><tr><td>Model</td><td>MNLI m mm EM SQuAD F1</td><td>CoNLL-2003 F1</td></tr><tr><td colspan=\"2\">Teacher 1 83.6 84.0 81.1 88.6</td><td>91.2</td></tr><tr><td colspan=\"2\">Teacher 2 83.6 84.2 81.2 88.5</td><td>90.8</td></tr><tr><td colspan=\"2\">Teacher 3 83.7 83.8 81.2 88.7</td><td>91.3</td></tr><tr><td colspan=\"2\">Ensemble 84.3 84.7 82.3 89.4</td><td>91.5</td></tr><tr><td>Student</td><td>84.8 85.3 83.5 90.0</td><td>91.6</td></tr></table>"
},
"TABREF7": {
"num": null,
"text": "Results of multi-teacher distillation. All the models are BERT BASE . Different teachers are trained with different random seeds. For each task, the ensemble is the average of three teachers' results.",
"type_str": "table",
"html": null,
"content": "<table/>"
},
"TABREF9": {
"num": null,
"text": "Development set results for the teacher and various students on Chinese tasks.",
"type_str": "table",
"html": null,
"content": "<table/>"
}
}
}
} |