File size: 111,323 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 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T01:28:59.918204Z"
},
"title": "Label Noise in Context",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Desmond",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IBM Research AI",
"location": {
"addrLine": "1101 Kitchawan Rd",
"postCode": "10598",
"settlement": "Yorktown Heights",
"region": "NY",
"country": "USA"
}
},
"email": "mdesmond@us.ibm.com"
},
{
"first": "Catherine",
"middle": [],
"last": "Finegan-Dollak",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IBM Research AI",
"location": {
"addrLine": "1101 Kitchawan Rd",
"postCode": "10598",
"settlement": "Yorktown Heights",
"region": "NY",
"country": "USA"
}
},
"email": ""
},
{
"first": "Jeff",
"middle": [],
"last": "Boston",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IBM Research AI",
"location": {
"addrLine": "1101 Kitchawan Rd",
"postCode": "10598",
"settlement": "Yorktown Heights",
"region": "NY",
"country": "USA"
}
},
"email": ""
},
{
"first": "Matthew",
"middle": [],
"last": "Arnold",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IBM Research AI",
"location": {
"addrLine": "1101 Kitchawan Rd",
"postCode": "10598",
"settlement": "Yorktown Heights",
"region": "NY",
"country": "USA"
}
},
"email": "marnold@us.ibm.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Label noise-incorrectly or ambiguously labeled training examples-can negatively impact model performance. Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process. Examples incorrectly flagged as noise waste reviewers' time, and correcting label noise without guidance can be difficult. We propose LNIC, a noise-detection method that uses an example's neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the example was flagged as noise. We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F 0.5-score. We also show how LNIC's training set context helps a reviewer to understand and correct label noise in a dataset. The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "Label noise-incorrectly or ambiguously labeled training examples-can negatively impact model performance. Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process. Examples incorrectly flagged as noise waste reviewers' time, and correcting label noise without guidance can be difficult. We propose LNIC, a noise-detection method that uses an example's neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the example was flagged as noise. We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F 0.5-score. We also show how LNIC's training set context helps a reviewer to understand and correct label noise in a dataset. The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Label noise-examples with incorrect or ambiguous labels in a training set-degrades the performance of the learned model, resulting in inaccurate predictions (Fr\u00e9nay and Verleysen, 2014) . Automated data collection risks generating noisy datasets, and human annotators may introduce noise through a lack of attention or expertise.",
"cite_spans": [
{
"start": 157,
"end": 185,
"text": "(Fr\u00e9nay and Verleysen, 2014)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Automatic noise-detection algorithms analyze a training set and flag \"suspicious\" examples that are likely mislabeled (Brodley and Friedl, 1999; Fr\u00e9nay and Verleysen, 2014) . Suspicious examples can be deleted, automatically corrected by an algorithm, or reviewed by a human. Human review is the most effective of these mitigation options but is comparatively expensive. * The first two authors contributed equally. Two problems contribute to making human review time consuming: false positives and a lack of explanation. False positives are examples that are incorrectly flagged as noise; reviewing such examples wastes the annotator's time. Showing a reviewer a suspicious example without an explanation is effective in the simplest cases, but is likely to cause difficulty and frustration in the more common case of non-obvious noise that requires a deeper comprehension of the data.",
"cite_spans": [
{
"start": 118,
"end": 144,
"text": "(Brodley and Friedl, 1999;",
"ref_id": null
},
{
"start": 145,
"end": 172,
"text": "Fr\u00e9nay and Verleysen, 2014)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To date, few noise-detection algorithms have been designed with human review in mind. Sluban et al. (2010) is the only work we are aware of that recognized that a noise-detection algorithm for use in a human review process should emphasize precision (i.e., reduce the proportion of false positives). However, we are unaware of any existing work that addresses the explainability of detected label noise.",
"cite_spans": [
{
"start": 86,
"end": 106,
"text": "Sluban et al. (2010)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We propose the Label Noise in Context system, or LNIC, which uses the neighborhood surrounding a suspicious example in the training set to improve both precision and explainability. By calculating a similarity matrix for the dataset, we are able to identify a suspicious example's neighborhood and use a method similar to a nearest-neighbors classifier to filter out false positives. Applying a set of simple heuristics to the same similarity matrix allows us to construct a training set context, like that in Table 1 . Seen in isolation, an example about running ability labeled as belonging to the sports class is not obviously wrong; however, once the annotator understands that she is seeing it because there are more similar examples in the fitness class, it becomes apparent that there is a better label.",
"cite_spans": [],
"ref_spans": [
{
"start": 510,
"end": 517,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main contributions of this work are \u2022 We describe LNIC's nearest-neighbors-based algorithm to improve precision and explainability of automatically detected label noise (Sec. 3). \u2022 We show that neighborhood-based filtering after noise-detection improves precision and F 0.5 over the state of the art for five short-text classification datasets (Sec. 4 and 5). \u2022 We present the LNIC tool for reviewing noise in context, demonstrating the value of explanations for understanding and fixing label noise (Sec. 6).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "A demo video is available at https://www. youtube.com/watch?v=20cigQaCc_k, and a live web demo is at http://lnic.mybluemix.net/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Noise Detection. Fr\u00e9nay and Verleysen (2014) conducted a comprehensive survey of the various approaches to detecting and remediating label noise. Many works advocate removing label noise to improve model performance (Brodley and Friedl, 1999; S\u00e1nchez et al., 2003; Smith and Martinez, 2011) . Teng (2000) advocates automatic relabeling, while others present the case for human-inthe-loop (Ekambaram et al., 2016; Fefilatyev et al., 2012; Matic et al., 1992; Sluban et al., 2010) and hybrid techniques (Miranda et al., 2009) . In work contemporaneous with ours, Northcutt et al. (2019) remove examples where a classifier's confidence is low.",
"cite_spans": [
{
"start": 216,
"end": 242,
"text": "(Brodley and Friedl, 1999;",
"ref_id": null
},
{
"start": 243,
"end": 264,
"text": "S\u00e1nchez et al., 2003;",
"ref_id": "BIBREF11"
},
{
"start": 265,
"end": 290,
"text": "Smith and Martinez, 2011)",
"ref_id": "BIBREF15"
},
{
"start": 293,
"end": 304,
"text": "Teng (2000)",
"ref_id": "BIBREF16"
},
{
"start": 388,
"end": 412,
"text": "(Ekambaram et al., 2016;",
"ref_id": null
},
{
"start": 413,
"end": 437,
"text": "Fefilatyev et al., 2012;",
"ref_id": null
},
{
"start": 438,
"end": 457,
"text": "Matic et al., 1992;",
"ref_id": "BIBREF3"
},
{
"start": 458,
"end": 478,
"text": "Sluban et al., 2010)",
"ref_id": "BIBREF14"
},
{
"start": 501,
"end": 523,
"text": "(Miranda et al., 2009)",
"ref_id": "BIBREF4"
},
{
"start": 561,
"end": 584,
"text": "Northcutt et al. (2019)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The most directly related work is Brodley and Friedl (1999) , describing a noise detection method using predictions from an ensemble of classifiers, and Sluban et al. (2010) , proposing the High Agreement Random Forest (HARF) system; both systems are described in detail in Section 3.1. Brodley and Friedl (1999) dropped suspicious examples but propose correction instead as future work. Sluban et al. (2010) note that precision of noise-detection is important when a human will review all suspicious examples. Garcia et al. (2016) 's experiments show that HARF also achieved stateof-the-art F 1 scores on a variety of datasets.",
"cite_spans": [
{
"start": 34,
"end": 59,
"text": "Brodley and Friedl (1999)",
"ref_id": null
},
{
"start": 153,
"end": 173,
"text": "Sluban et al. (2010)",
"ref_id": "BIBREF14"
},
{
"start": 287,
"end": 312,
"text": "Brodley and Friedl (1999)",
"ref_id": null
},
{
"start": 388,
"end": 408,
"text": "Sluban et al. (2010)",
"ref_id": "BIBREF14"
},
{
"start": 511,
"end": 531,
"text": "Garcia et al. (2016)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Active Learning Similar to label noise remediation, active learning (Settles, 2014) seeks to minimize the effort a human needs to expend on data labeling activities in order to improve model performance. However, active learning aims to select the most informative unlabeled data to label next, while label noise detection identifies alreadylabeled data that may require additional labeling effort. We consider active learning and label noise detection as complimentary technologies, that might be woven together within a robust model improvement flow.",
"cite_spans": [
{
"start": 68,
"end": 83,
"text": "(Settles, 2014)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "At a technical level, some active learning and label noise detection techniques are based on similar foundations. Query By Committee (QBC) (Seung et al., 1992) active learning uses an ensemble of classifiers, selecting examples on which the ensemble disagrees for labeling. Similarly ensemblebased noise detection algorithms select examples where the ensemble agrees (but disagrees with the given label). Model uncertainty, which underpins many effective active learning strategies such as least confident, margin, and entropy, is also the basis of label noise detection methods such as cleanlab (Northcutt et al., 2019) .",
"cite_spans": [
{
"start": 139,
"end": 159,
"text": "(Seung et al., 1992)",
"ref_id": null
},
{
"start": 596,
"end": 620,
"text": "(Northcutt et al., 2019)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Explainability. With the rise of increasingly complex classification models, explaining classifier predictions has received a great deal of attention. Perhaps the most well-known system is LIME (Ribeiro et al., 2016) . The LIME authors noted that explaining classifier predictions increases human trust and provides insights that can be used to improve the model. To explain a classifier's prediction on a particular example, the algorithm collects nearby examples and the model's predictions for them. It trains a linear model on a simpler representation of this data, allowing it to indicate which words or super-pixels are important in the classifier's decision.",
"cite_spans": [
{
"start": 194,
"end": 216,
"text": "(Ribeiro et al., 2016)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Numerous recent works in NLP and machine learning emphasize explainability. Dhurandhar et al. (2018) explained classifier predictions with positive features that push an example towards its assigned class and negative features whose absence prevent an example from being placed in a different class. Lei et al. (2016) jointly trained a generator and an encoder in order to generate rationales for sentiment prediction and a similar-questionretrieval task. Mullenbach et al. (2018) used a convolutional neural network to predict codes describing the diagnosis and treatment of patients given the clinical notes on the patent encounter. Their attention mechanism not only improved the system's precision and F 1 , but also highlighted the text that was most relevant to each code. Chiyah Garcia et al. (2018)'s system used an expert-generated decision tree and a set of templates to generate natural language explanations of what an autonomous underwater vehicle was doing and why.",
"cite_spans": [
{
"start": 300,
"end": 317,
"text": "Lei et al. (2016)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Despite the interest in explainable models, no work that we are aware of has attempted to make detected label noise explainable.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "LNIC uses a three-step process. First, a noisedetection algorithm flags suspicious examples. Second, a neighborhood-based filter decides which of these examples to ignore and which to flag for human review. Finally, we generate a context, using rules to select neighbors to present to the user.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithms",
"sec_num": "3"
},
{
"text": "LNIC's noise-detection phase can use any noisedetection algorithm. Here, we report on three ensemble algorithms derived from the literature: consensus (Brodley and Friedl, 1999) , agreed correction, and HARF (Sluban et al., 2010) . 1 Ensemble noise detection algorithms train several classifiers on cross-validation splits of the train set. Each classifier predicts labels for the left-out examples. The predicted label is the classifier's \"vote\" for that example. If it matches the current label, the classifier voted that the example is not suspicious; otherwise, the classifier voted that it is. In Brodley and Friedl (1999)'s consensus algorithm, if all votes agree that an example is suspicious, the algorithm flags that example as suspicious. Our agreed correction variant requires all votes from the ensemble to agree not only that an example is mislabeled, but also on what the correct label would be. HARF (Sluban et al., 2010) relies on the fact that a random forest is an ensemble of decision trees; it flags an example as suspicious if a super-majority of trees vote that it is.",
"cite_spans": [
{
"start": 151,
"end": 177,
"text": "(Brodley and Friedl, 1999)",
"ref_id": null
},
{
"start": 208,
"end": 229,
"text": "(Sluban et al., 2010)",
"ref_id": "BIBREF14"
},
{
"start": 232,
"end": 233,
"text": "1",
"ref_id": null
},
{
"start": 915,
"end": 936,
"text": "(Sluban et al., 2010)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Noise-Detection Algorithms",
"sec_num": "3.1"
},
{
"text": "Neighborhood filtering reduces the number of examples that are incorrectly flagged as noise. If a majority of neighbors of an example have the same label as that example, it suggests that the example is correctly labeled, so LNIC filters it out of the list of suspicious examples.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Neighborhood Filtering",
"sec_num": "3.2"
},
{
"text": "The neighborhood filter calculates the pairwise cosine similarity of all examples in the training 1 Models and hyperparameters are listed in Appendix A data, then finds the k neighbors closest to each suspicious example s, where k is a tunable hyperparameter. If s's current label y c is also the most common among those neighbors, s is filtered from the pool of suspicious examples as a false positive, otherwise s is flagged for human review. 2 LNIC supports filtering on the feature neighborhood or the activation neighborhood. The feature neighborhood represents each example using its original feature vector (here, USE embeddings (Cer et al., 2018) ). The activation neighborhood represents each example in the training set using final layer activations from a neural classifier trained on the entire data set, the idea being to project training examples into a classification space.",
"cite_spans": [
{
"start": 445,
"end": 446,
"text": "2",
"ref_id": null
},
{
"start": 614,
"end": 654,
"text": "(here, USE embeddings (Cer et al., 2018)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Neighborhood Filtering",
"sec_num": "3.2"
},
{
"text": "The final step of the LNIC algorithm is to apply heuristics to the neighborhood to generate a training set context. This context acts as an explanation, showing (a) which classes the noise-detection ensemble proposed as a better label for the suspicious example, and (b) the most similar examples from the current class and those proposed classes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Context Generation",
"sec_num": "3.3"
},
{
"text": "The ensembles in the noise-detection algorithms generate a list of predicted labels for each suspicious example. These labels plus the example's current label comprise the permitted labels for that example. The heuristic selects the example from each permitted label that is closest to the suspicious example. If there are fewer than k permitted labels (where k is the desired context size), the balance of the context is filled out by selecting the remaining k \u2212 n nearest neighbors from the permitted labels.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Context Generation",
"sec_num": "3.3"
},
{
"text": "We build the explanation based on both the activation neighborhood and the feature neighborhood; an example that already appears in the activation context is omitted from the feature context and replaced by the next-nearest neighbor. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Context Generation",
"sec_num": "3.3"
},
{
"text": "We hypothesize that adding a neighborhood-based filter after noise detection reduces the rates of false positives while retaining true noisy examples. We test this by injecting noise into datasets, running algorithms over them, and measuring the correctly and incorrectly flagged suspicious examples.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "We evaluate on the short-text classification datasets listed in Table 2 . 3 Phase one of the evaluation introduces label noise-effectively \"corrupting\" the datasets. The amount of introduced label noise was controlled by an error-rate parameter, interpreted as the fraction of the training set to mislabel.",
"cite_spans": [
{
"start": 74,
"end": 75,
"text": "3",
"ref_id": null
}
],
"ref_spans": [
{
"start": 64,
"end": 71,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "We used two strategies to introduce label noise: random and next-best. Both selected a random sample of the training data to mislabel. The random strategy assigned a random incorrect label to each selected example. The next-best strategy assigned the \"next-best\" incorrect label, as predicted by a classifier trained on the entire train set; this simulates a best effort but incorrect labeling, as might be performed by a confused human labeler.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "Because the goal of the algorithm is to avoid wasting human time, our evaluation should heavily punish false positives. We therefore measure the precision of each algorithm. We also follow Sluban et al. (2010) in reporting F 0.5 , an F -score that values precision twice as much as recall.",
"cite_spans": [
{
"start": 189,
"end": 209,
"text": "Sluban et al. (2010)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Metrics",
"sec_num": "4.2"
},
{
"text": "precision \u2022 recall F 0.5 = (1 + 0.5 2 ) (0.5 2 \u2022 precision) + recall",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Metrics",
"sec_num": "4.2"
},
{
"text": "(1) Not every situation calls for precision to be valued twice as much as recall. Therefore, we also report F \u03b2 (Rijsbergen, 1979) for \u03b2 \u2208 {1.0, 0.2, 0.1} to reflect the preferences of users who value precision and recall equally, precision five times more than recall, and precision ten times more.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Metrics",
"sec_num": "4.2"
},
{
"text": "Figure 1 shows average precision and F 0.5 scores across the five datasets, and Table 3 further summarizes by averaging across error rates. Appendix B shows results split by dataset and error rate. Table 3 shows that, averaged across datasets and error rates, adding neighborhood filtering of any kind improves precision of all of the underlying algorithms. For randomly generated noise, this is true for F 0.5 as well. Figure 1a also shows that the neighborhood activation filter gives a large boost to precision over all three noise-detection algorithms, and the feature neighborhood filter gives a smaller but still observable benefit. For next-best noise, adding the feature neighborhood filtering improves F 0.5 , but activation neighborhood filtering slightly worsens F 0.5 . From the graph in Figure 1d , it is apparent that activation neighborhood filtering has a benefit to F 0.5 at low error rates but declines relative to the other systems as the error rate increases, crossing at error rates near 15%. Addition of too much next-best noise negatively impacts the neural network trained on the uncorrected data, distorting the activation space. While this distortion does not harm precision, it is detrimental to recall.",
"cite_spans": [],
"ref_spans": [
{
"start": 80,
"end": 87,
"text": "Table 3",
"ref_id": "TABREF5"
},
{
"start": 198,
"end": 205,
"text": "Table 3",
"ref_id": "TABREF5"
},
{
"start": 420,
"end": 429,
"text": "Figure 1a",
"ref_id": "FIGREF2"
},
{
"start": 800,
"end": 809,
"text": "Figure 1d",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "For both random and next-best noise, agreed correction with activation neighborhood filtering achieves the best average precision. For random noise, HARF with activation-neighborhood filtering gives the best F 0.5 across noise rates. However, for next-best noise, HARF suffered a dramatic loss in recall when error rates exceeded about 12% (Figure 1d) , leading it to have low overall F 0.5 . This may be due to the random forest's use of bagging: if a subset of trees trains on samples with a great deal of non-random noise, those trees could learn to misclassify systematically. Agreed correction with feature neighborhood filtering gave the highest average F 0.5 for next-best noise.",
"cite_spans": [],
"ref_spans": [
{
"start": 340,
"end": 351,
"text": "(Figure 1d)",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "The upward trend in precision as error rates increase suggests that the same core of false positives are consistently detected. As the number of true positives increases with higher error rates, the core of false positives makes up a smaller fraction of the total number of examples flagged as suspicious. Table 4 lists F \u03b2 scores. As expected, using a neighborhood filter, which reduces the number of suspicious examples shown to a user, is particularly advantageous when precision is valued more than recall (F 0.2 and F 0.1 ), but often extracts a cost when recall and precision are equally important (F 1.0 ). Thus, agreed correction with no neighborhood filter is the best system to optimize F 1.0 when using next-best noise. Nevertheless, the strongest system for F 1.0 on random noise is still HARF with activation neighborhood filtering, followed closely by consensus with activation neighborhood filtering.",
"cite_spans": [],
"ref_spans": [
{
"start": 306,
"end": 313,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "The LNIC tool implements the algorithms described above and provides a web interface to review label noise in context. The interface visually summarizes the overall label noise within a dataset and links to groups of suspicious examples in con- (Xu et al., 2015) Subset of www.j-archive.com (Price, 1990; Hakkani-Tur et al., 2016) https://github.com/snipsco/ nlu-benchmark/ text. LNIC's representation of the noise summary column. 4 Clicking on a cell brings the user to a (Figure 2 ) is similar to a confusion matrix. In the list of examples flagged as noise. Each of these label noise matrix each cell indicates the number examples can be expanded to show the context, as of noisy examples discovered where the context includes the classes specified by the row and the Data from Stack Exchange illustrates how context helps a reviewer understand problems in a dataset. Sometimes, context shows that an example is mislabeled. Without context, it is easy for an annotator to be uncertain of whether a question about the existence of a myth belongs in the history class; it is a question about a historical civilization, after all. However, from the context in Figure 4 , it is clear that even questions about the history of myths are categorized as mythology, and so the example's label should be changed to maintain consistency. Other times, context can reveal more complex issues with the class structure of the data. Figure 5 shows a suspicious example from the health class that the noise detection algorithm suggests may belong in the fitness class. The context shows that in fact, both classes include questions about the timing of meals with regard to exercise. A human reviewer should make a decision about where the boundary between these two classes should lie and assign these utterances consistently to one class.",
"cite_spans": [
{
"start": 245,
"end": 262,
"text": "(Xu et al., 2015)",
"ref_id": "BIBREF17"
},
{
"start": 291,
"end": 304,
"text": "(Price, 1990;",
"ref_id": "BIBREF8"
},
{
"start": 305,
"end": 330,
"text": "Hakkani-Tur et al., 2016)",
"ref_id": "BIBREF1"
},
{
"start": 431,
"end": 432,
"text": "4",
"ref_id": null
}
],
"ref_spans": [
{
"start": 473,
"end": 482,
"text": "(Figure 2",
"ref_id": "FIGREF4"
},
{
"start": 1160,
"end": 1168,
"text": "Figure 4",
"ref_id": "FIGREF6"
},
{
"start": 1420,
"end": 1428,
"text": "Figure 5",
"ref_id": "FIGREF7"
}
],
"eq_spans": [],
"section": "The LNIC Tool",
"sec_num": "6"
},
{
"text": "Although NLP practitioners know that label noise harms performance, and noise detection algorithms have long been available, this technology is not being applied in practice, perhaps because human review of detected errors is difficult and time consuming. LNIC makes human review of possible label noise easier and more efficient. It reduces the number of false positive examples that the reviewer must look at, providing state-of-the-art precision and F 0.5 across several short text datasets. And by providing an explanation of why the model flagged an example as suspicious, it makes the output of label noise detectors understandable and actionable. A Appendix: Model Details",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "For ease of replication, this appendix specifies the details of the models used in our experiments. For consensus and agreed-only noise detection, our ensemble consisted of three classifiers from Scikit Learn (Pedregosa et al., 2011) : LogisticRegression, RandomForestClassifier, and MLPClassifier. We used default parameters, except that we set MLPClassifier's max iter parameter to 1000 to speed up experiments.",
"cite_spans": [
{
"start": 209,
"end": 233,
"text": "(Pedregosa et al., 2011)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "For HARF, we used a RandomForestClassifier model with 500 trees and required 90% agreement. Sluban et al. (2010) reported on models requiring lower levels of agreement, but preliminary testing demonstrated that 90% improved results on our datasets.",
"cite_spans": [
{
"start": 92,
"end": 112,
"text": "Sluban et al. (2010)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "For the neighborhood filter, we set k = 5.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "Our raw vector representation of all utterances was USE (Cer et al., 2018) . The activations for activation-based filtering and context generation were generated using an MLPClassifier with hidden layer sizes = [100, 512].",
"cite_spans": [
{
"start": 52,
"end": 74,
"text": "USE (Cer et al., 2018)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "Results were summarized in the body of the paper for conciseness. In this appendix, we present precision and F 0.5 for each of the five datasets and for each of the error rates. This appendix contains the same images as the body of the paper, enlarged to improve accessibility. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "B Appendix: Detailed Results",
"sec_num": null
},
{
"text": "When using raw features, this filter acts like a k-nearest neighbors classifier with veto power over the ensemble. Experiments with a vote by weighted cosine similarity correlated closely with this simpler technique, and we did not pursue it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "All data is publicly available. Lists of the exact subsets we used for Stack Exchange, Stack Overflow, and Jeopardy are available at https://github.com/cfd-01/ LNiC_data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "The agreed-correction algorithm guarantees that each context contains exactly two classes. When using larger contexts, the summary can be a list of class tuples.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We thank Evelyn Duesterwald as well as the anonymous reviewers for helpful feedback. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Noise detection in the metalearning level",
"authors": [
{
"first": "P",
"middle": [
"F"
],
"last": "Lu\u00eds",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Garcia",
"suffix": ""
},
{
"first": "C",
"middle": [
"P L F"
],
"last": "Andr\u00e9",
"suffix": ""
},
{
"first": "Ana",
"middle": [
"C"
],
"last": "De Carvalho",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lorena",
"suffix": ""
}
],
"year": 2016,
"venue": "Neurocomputing",
"volume": "176",
"issue": "",
"pages": "14--25",
"other_ids": {
"DOI": [
"10.1016/j.neucom.2014.12.100"
]
},
"num": null,
"urls": [],
"raw_text": "Lu\u00eds P.F. Garcia, Andr\u00e9 C.P.L.F. de Carvalho, and Ana C. Lorena. 2016. Noise detection in the meta- learning level. Neurocomputing, 176:14-25.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM",
"authors": [
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-Tur",
"suffix": ""
},
{
"first": "Gokhan",
"middle": [],
"last": "Tur",
"suffix": ""
},
{
"first": "Asli",
"middle": [],
"last": "Celikyilmaz",
"suffix": ""
},
{
"first": "Yun-Nung",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Ye-Yi",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of Interspeech",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dilek Hakkani-Tur, Gokhan Tur, Asli Celikyilmaz, Yun-Nung Chen, Jianfeng Gao, and Ye-Yi Wang. 2016. Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM. In Proceedings of Interspeech.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Rationalizing Neural Predictions",
"authors": [
{
"first": "Tao",
"middle": [],
"last": "Lei",
"suffix": ""
},
{
"first": "Regina",
"middle": [],
"last": "Barzilay",
"suffix": ""
},
{
"first": "Tommi",
"middle": [],
"last": "Jaakkola",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "107--117",
"other_ids": {
"DOI": [
"10.18653/v1/D16-1011"
]
},
"num": null,
"urls": [],
"raw_text": "Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing Neural Predictions. In Proceedings of the 2016 Conference on Empirical Methods in Nat- ural Language Processing, pages 107-117, Austin, Texas. Association for Computational Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Computer aided cleaning of large databases for character recognition",
"authors": [
{
"first": "N",
"middle": [],
"last": "Matic",
"suffix": ""
},
{
"first": "I",
"middle": [],
"last": "Guyon",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Denker",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Vapnik",
"suffix": ""
}
],
"year": 1992,
"venue": "Proceedings., 11th IAPR International Conference on Pattern Recognition",
"volume": "II",
"issue": "",
"pages": "330--333",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "N. Matic, I. Guyon, L. Bottou, J. Denker, and V. Vapnik. 1992. Computer aided cleaning of large databases for character recognition. In Proceedings., 11th IAPR International Conference on Pattern Recog- nition. Vol. II. Conference B: Pattern Recognition Methodology and Systems, pages 330-333. IEEE.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Use of classification algorithms in noise detection and elimination",
"authors": [
{
"first": "L",
"middle": [
"B"
],
"last": "Andr\u00e9",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Miranda",
"suffix": ""
},
{
"first": "Paulo",
"middle": [
"F"
],
"last": "Lu\u00eds",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Garcia",
"suffix": ""
},
{
"first": "C",
"middle": [
"P L F"
],
"last": "Andr\u00e9",
"suffix": ""
},
{
"first": "Ana",
"middle": [
"C"
],
"last": "Carvalho",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lorena",
"suffix": ""
}
],
"year": 2009,
"venue": "International Conference on Hybrid Artificial Intelligence Systems",
"volume": "",
"issue": "",
"pages": "417--424",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andr\u00e9 L.B. Miranda, Lu\u00eds Paulo F. Garcia, Andr\u00e9 C.P.L.F. Carvalho, and Ana C. Lorena. 2009. Use of classification algorithms in noise detection and elimination. In International Con- ference on Hybrid Artificial Intelligence Systems, pages 417-424. Springer.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Explainable Prediction of Medical Codes from Clinical Text",
"authors": [
{
"first": "James",
"middle": [],
"last": "Mullenbach",
"suffix": ""
},
{
"first": "Sarah",
"middle": [],
"last": "Wiegreffe",
"suffix": ""
},
{
"first": "Jon",
"middle": [],
"last": "Duke",
"suffix": ""
},
{
"first": "Jimeng",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Jacob",
"middle": [],
"last": "Eisenstein",
"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": "1101--1111",
"other_ids": {
"DOI": [
"10.18653/v1/N18-1100"
]
},
"num": null,
"urls": [],
"raw_text": "James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. 2018. Explainable Pre- diction of Medical Codes from Clinical Text. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1101-1111, New Orleans, Louisiana. Association for Computational Linguistics.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Confident learning: Estimating uncertainty in dataset labels",
"authors": [
{
"first": "G",
"middle": [],
"last": "Curtis",
"suffix": ""
},
{
"first": "Lu",
"middle": [],
"last": "Northcutt",
"suffix": ""
},
{
"first": "Isaac",
"middle": [
"L"
],
"last": "Jiang",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Chuang",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1911.00068"
]
},
"num": null,
"urls": [],
"raw_text": "Curtis G Northcutt, Lu Jiang, and Isaac L Chuang. 2019. Confident learning: Estimating uncertainty in dataset labels. arXiv preprint arXiv:1911.00068.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Scikit-learn: Machine learning in Python",
"authors": [
{
"first": "F",
"middle": [],
"last": "Pedregosa",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Varoquaux",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Gramfort",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Michel",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Thirion",
"suffix": ""
},
{
"first": "O",
"middle": [],
"last": "Grisel",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Blondel",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Prettenhofer",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Weiss",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Dubourg",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Vanderplas",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Passos",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Cournapeau",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Brucher",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Perrot",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Duchesnay",
"suffix": ""
}
],
"year": 2011,
"venue": "Journal of Machine Learning Research",
"volume": "12",
"issue": "",
"pages": "2825--2830",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duch- esnay. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Evaluation of spoken language systems: The ATIS domain",
"authors": [
{
"first": "P",
"middle": [
"J"
],
"last": "Price",
"suffix": ""
}
],
"year": 1990,
"venue": "Proceedings of the Workshop on Speech and Natural Language, HLT '90",
"volume": "",
"issue": "",
"pages": "91--95",
"other_ids": {
"DOI": [
"10.3115/116580.116612"
]
},
"num": null,
"urls": [],
"raw_text": "P. J. Price. 1990. Evaluation of spoken language sys- tems: The ATIS domain. In Proceedings of the Workshop on Speech and Natural Language, HLT '90, pages 91-95, Stroudsburg, PA, USA. Associa- tion for Computational Linguistics.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Explaining the Predictions of Any Classifier Marco",
"authors": [
{
"first": "Sameer",
"middle": [],
"last": "Marco Tulio Ribeiro",
"suffix": ""
},
{
"first": "Carlos",
"middle": [],
"last": "Singh",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Guestrin",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
"volume": "",
"issue": "",
"pages": "1135--1144",
"other_ids": {
"DOI": [
"10.1145/2939672.2939778"
]
},
"num": null,
"urls": [],
"raw_text": "Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. \"Why Should I Trust You?\" Ex- plaining the Predictions of Any Classifier Marco. In Proceedings of the 22nd ACM SIGKDD Inter- national Conference on Knowledge Discovery and Data Mining, pages 1135-1144, San Francisco, Cal- ifornia, USA. ACM.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Analysis of new techniques to obtain quality training sets",
"authors": [
{
"first": "Salvador",
"middle": [],
"last": "Jos\u00e9",
"suffix": ""
},
{
"first": "Ricardo",
"middle": [],
"last": "S\u00e1nchez",
"suffix": ""
},
{
"first": "Ana",
"middle": [
"I"
],
"last": "Barandela",
"suffix": ""
},
{
"first": "Roberto",
"middle": [],
"last": "Marqu\u00e9s",
"suffix": ""
},
{
"first": "Jorge",
"middle": [],
"last": "Alejo",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Badenas",
"suffix": ""
}
],
"year": 2003,
"venue": "Pattern Recognition Letters",
"volume": "24",
"issue": "7",
"pages": "1015--1022",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jos\u00e9 Salvador S\u00e1nchez, Ricardo Barandela, Ana I. Marqu\u00e9s, Roberto Alejo, and Jorge Badenas. 2003. Analysis of new techniques to obtain quality training sets. Pattern Recognition Letters, 24(7):1015-1022.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Active learning literature survey",
"authors": [
{
"first": "Burr",
"middle": [],
"last": "Settles",
"suffix": ""
}
],
"year": 2010,
"venue": "Computer Sciences Technical Report",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Burr Settles. 2014. Active learning literature survey. 2010. Computer Sciences Technical Report, 1648.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Manfred Opper, and Haim Sompolinsky. 1992. Query by committee",
"authors": [
{
"first": "",
"middle": [],
"last": "H Sebastian Seung",
"suffix": ""
}
],
"year": null,
"venue": "Proceedings of the fifth annual workshop on Computational learning theory",
"volume": "",
"issue": "",
"pages": "287--294",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H Sebastian Seung, Manfred Opper, and Haim Som- polinsky. 1992. Query by committee. In Proceed- ings of the fifth annual workshop on Computational learning theory, pages 287-294.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Advances in class noise detection",
"authors": [
{
"first": "Borut",
"middle": [],
"last": "Sluban",
"suffix": ""
},
{
"first": "Dragan",
"middle": [],
"last": "Gamberger",
"suffix": ""
},
{
"first": "Nada",
"middle": [],
"last": "Lavra",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 19th European Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "1105--1106",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Borut Sluban, Dragan Gamberger, and Nada Lavra. 2010. Advances in class noise detection. In Pro- ceedings of the 19th European Conference on Artifi- cial Intelligence, pages 1105-1106. IOS Press.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Improving classification accuracy by identifying and removing instances that should be misclassified",
"authors": [
{
"first": "R",
"middle": [],
"last": "Michael",
"suffix": ""
},
{
"first": "Tony",
"middle": [],
"last": "Smith",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Martinez",
"suffix": ""
}
],
"year": 2011,
"venue": "The 2011 International Joint Conference on Neural Networks",
"volume": "",
"issue": "",
"pages": "2690--2697",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael R. Smith and Tony Martinez. 2011. Improving classification accuracy by identifying and removing instances that should be misclassified. In The 2011 International Joint Conference on Neural Networks, pages 2690-2697. IEEE.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Evaluating noise correction",
"authors": [
{
"first": "Teng",
"middle": [],
"last": "Choh Man",
"suffix": ""
}
],
"year": 2000,
"venue": "Pacific Rim International Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "188--198",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Choh Man Teng. 2000. Evaluating noise correction. In Pacific Rim International Conference on Artificial Intelligence, pages 188-198. Springer.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Short text clustering via convolutional neural networks",
"authors": [
{
"first": "Jiaming",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Peng",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Guanhua",
"middle": [],
"last": "Tian",
"suffix": ""
},
{
"first": "Bo",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Fangyuan",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Hongwei",
"middle": [],
"last": "Hao",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing",
"volume": "",
"issue": "",
"pages": "62--69",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiaming Xu, Peng Wang, Guanhua Tian, Bo Xu, Jun Zhao, Fangyuan Wang, and Hongwei Hao. 2015. Short text clustering via convolutional neural net- works. In Proceedings of the 1st Workshop on Vec- tor Space Modeling for Natural Language Process- ing, pages 62-69, Denver, Colorado. Association for Computational Linguistics.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Precision of noise detection for randomly generated noise (b) Precision of noise detection for next-best noise",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": ") Precision of noise detection for randomly generated noise (b) Precision of noise detection for next-best noise.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Precision at various noise levels, averaged across the five datasets. (c) F0.5 of noise detection for randomly generated noise (d) F0.5 of noise detection for next",
"authors": [],
"year": null,
"venue": "Figure",
"volume": "6",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Figure 6: Precision at various noise levels, averaged across the five datasets. (c) F0.5 of noise detection for randomly generated noise (d) F0.5 of noise detection for next-best noise.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Figures 4 and 5 show a examples of this contextual explanation.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF2": {
"text": "Precision of noise detection for randomly generated noise (b) Precision of noise detection for next-best noise.(c) F0.5 of noise detection for randomly generated noise (d) F0.5 of noise detection for next-best noise.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF3": {
"text": "Precision and F 0.5 at various noise levels, averaged across the five datasets.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF4": {
"text": "The label noise matrix summarizing noise discovered in approximately 30k examples from the Stack Exchange dataset.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF5": {
"text": "Suspicious examples at the intersection of history and mythology classes without context.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF6": {
"text": "An example fromFigure 3, with context. In red is the suspicious example. Examples in the white box are its context from activation space, and those in the blue box are context from raw embedding space. Numbers in parentheses indicate cosine similarity.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF7": {
"text": "Context shows overlapping class definitions.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF8": {
"text": "A summary of noise discovered in approximately 30k examples from Stack Exchange.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF9": {
"text": "Suspicious examples at the intersection of history and mythology classes without context.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF10": {
"text": "An example fromFigure 3, with context. In red is the suspicious example. Examples in the white box are its context from activation space, and those in the blue box are context from raw embedding space. Numbers in parentheses indicate cosine similarity.",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF11": {
"text": "Context shows overlapping class definitions.",
"uris": null,
"num": null,
"type_str": "figure"
},
"TABREF1": {
"content": "<table/>",
"num": null,
"type_str": "table",
"text": "Training set context can help an annotator decide if the highlighted suspicious training example is correctly labeled sports or should be labeled fitness.",
"html": null
},
"TABREF3": {
"content": "<table/>",
"num": null,
"type_str": "table",
"text": "Dataset details.",
"html": null
},
"TABREF5": {
"content": "<table/>",
"num": null,
"type_str": "table",
"text": "Mean precision and F 0.5 for the five datasets, averaged across all error rates. The top row in each section is a baseline system with no filtering.",
"html": null
},
"TABREF7": {
"content": "<table/>",
"num": null,
"type_str": "table",
"text": "Average F-scores across the datasets valuing precision to different degrees.",
"html": null
},
"TABREF10": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr></table>",
"num": null,
"type_str": "table",
"text": "ATIS, Next-Best Noise",
"html": null
},
"TABREF11": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr><tr><td/><td/><td>0.01</td><td>0.169058</td><td>0.197610</td></tr><tr><td/><td/><td>0.05</td><td>0.438177</td><td>0.479689</td></tr><tr><td/><td/><td>0.10</td><td>0.591796</td><td>0.626953</td></tr><tr><td/><td/><td>0.15</td><td>0.678183</td><td>0.704604</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.741275</td><td>0.758822</td></tr><tr><td/><td/><td>0.25</td><td>0.787061</td><td>0.794762</td></tr><tr><td/><td/><td>0.30</td><td>0.818512</td><td>0.818545</td></tr><tr><td/><td/><td>0.35</td><td>0.847556</td><td>0.837087</td></tr><tr><td/><td/><td>0.40</td><td>0.865010</td><td>0.843633</td></tr><tr><td/><td/><td>0.01</td><td>0.340710</td><td>0.337163</td></tr><tr><td/><td/><td>0.05</td><td>0.581698</td><td>0.591143</td></tr><tr><td/><td/><td>0.10</td><td>0.702483</td><td>0.699962</td></tr><tr><td/><td/><td>0.15</td><td>0.765191</td><td>0.752932</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.811529</td><td>0.785388</td></tr><tr><td/><td/><td>0.25</td><td>0.843820</td><td>0.806149</td></tr><tr><td/><td/><td>0.30</td><td>0.864403</td><td>0.817224</td></tr><tr><td/><td/><td>0.35</td><td>0.882211</td><td>0.818990</td></tr><tr><td>Consensus</td><td/><td>0.40 0.01</td><td>0.895110 0.191324</td><td>0.814209 0.221609</td></tr><tr><td/><td/><td>0.05</td><td>0.472302</td><td>0.511817</td></tr><tr><td/><td/><td>0.10</td><td>0.624910</td><td>0.655624</td></tr><tr><td/><td/><td>0.15</td><td>0.707260</td><td>0.727099</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.766436</td><td>0.776426</td></tr><tr><td/><td/><td>0.25</td><td>0.806752</td><td>0.805997</td></tr><tr><td/><td/><td>0.30</td><td>0.834342</td><td>0.824318</td></tr><tr><td/><td/><td>0.35</td><td>0.862185</td><td>0.838768</td></tr><tr><td/><td/><td>0.40</td><td>0.879229</td><td>0.843028</td></tr><tr><td/><td/><td>0.01</td><td>0.185901</td><td>0.215997</td></tr><tr><td/><td/><td>0.05</td><td>0.463941</td><td>0.504450</td></tr><tr><td/><td/><td>0.10</td><td>0.614485</td><td>0.646574</td></tr><tr><td/><td/><td>0.15</td><td>0.701349</td><td>0.722953</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.762975</td><td>0.773727</td></tr><tr><td/><td/><td>0.25</td><td>0.804869</td><td>0.804780</td></tr><tr><td/><td/><td>0.30</td><td>0.833155</td><td>0.824230</td></tr><tr><td/><td/><td>0.35</td><td>0.859147</td><td>0.835885</td></tr><tr><td/><td/><td>0.40</td><td>0.875436</td><td>0.839302</td></tr></table>",
"num": null,
"type_str": "table",
"text": "ATIS, Random Noise",
"html": null
},
"TABREF12": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Jeopardy, Next-Best Noise",
"html": null
},
"TABREF13": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr><tr><td/><td/><td>0.01</td><td>0.169058</td><td>0.197610</td></tr><tr><td/><td/><td>0.05</td><td>0.438177</td><td>0.479689</td></tr><tr><td/><td/><td>0.10</td><td>0.591796</td><td>0.626953</td></tr><tr><td/><td/><td>0.15</td><td>0.678183</td><td>0.704604</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.741275</td><td>0.758822</td></tr><tr><td/><td/><td>0.25</td><td>0.787061</td><td>0.794762</td></tr><tr><td/><td/><td>0.30</td><td>0.818512</td><td>0.818545</td></tr><tr><td/><td/><td>0.35</td><td>0.847556</td><td>0.837087</td></tr><tr><td/><td/><td>0.40</td><td>0.865010</td><td>0.843633</td></tr><tr><td/><td/><td>0.01</td><td>0.340710</td><td>0.337163</td></tr><tr><td/><td/><td>0.05</td><td>0.581698</td><td>0.591143</td></tr><tr><td/><td/><td>0.10</td><td>0.702483</td><td>0.699962</td></tr><tr><td/><td/><td>0.15</td><td>0.765191</td><td>0.752932</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.811529</td><td>0.785388</td></tr><tr><td/><td/><td>0.25</td><td>0.843820</td><td>0.806149</td></tr><tr><td/><td/><td>0.30</td><td>0.864403</td><td>0.817224</td></tr><tr><td/><td/><td>0.35</td><td>0.882211</td><td>0.818990</td></tr><tr><td>Consensus</td><td/><td>0.40 0.01</td><td>0.895110 0.191324</td><td>0.814209 0.221609</td></tr><tr><td/><td/><td>0.05</td><td>0.472302</td><td>0.511817</td></tr><tr><td/><td/><td>0.10</td><td>0.624910</td><td>0.655624</td></tr><tr><td/><td/><td>0.15</td><td>0.707260</td><td>0.727099</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.766436</td><td>0.776426</td></tr><tr><td/><td/><td>0.25</td><td>0.806752</td><td>0.805997</td></tr><tr><td/><td/><td>0.30</td><td>0.834342</td><td>0.824318</td></tr><tr><td/><td/><td>0.35</td><td>0.862185</td><td>0.838768</td></tr><tr><td/><td/><td>0.40</td><td>0.879229</td><td>0.843028</td></tr><tr><td/><td/><td>0.01</td><td>0.185901</td><td>0.215997</td></tr><tr><td/><td/><td>0.05</td><td>0.463941</td><td>0.504450</td></tr><tr><td/><td/><td>0.10</td><td>0.614485</td><td>0.646574</td></tr><tr><td/><td/><td>0.15</td><td>0.701349</td><td>0.722953</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.762975</td><td>0.773727</td></tr><tr><td/><td/><td>0.25</td><td>0.804869</td><td>0.804780</td></tr><tr><td/><td/><td>0.30</td><td>0.833155</td><td>0.824230</td></tr><tr><td/><td/><td>0.35</td><td>0.859147</td><td>0.835885</td></tr><tr><td/><td/><td>0.40</td><td>0.875436</td><td>0.839302</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Jeopardy, Random Noise",
"html": null
},
"TABREF14": {
"content": "<table><tr><td/><td colspan=\"3\">: SNIPS, Next-Best Noise</td><td/></tr><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr></table>",
"num": null,
"type_str": "table",
"text": "",
"html": null
},
"TABREF15": {
"content": "<table><tr><td>: SNIPS, Random Noise</td></tr></table>",
"num": null,
"type_str": "table",
"text": "",
"html": null
},
"TABREF16": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Stack Exchange, Next-Best Noise",
"html": null
},
"TABREF17": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr><tr><td/><td/><td>0.01</td><td>0.169058</td><td>0.197610</td></tr><tr><td/><td/><td>0.05</td><td>0.438177</td><td>0.479689</td></tr><tr><td/><td/><td>0.10</td><td>0.591796</td><td>0.626953</td></tr><tr><td/><td/><td>0.15</td><td>0.678183</td><td>0.704604</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.741275</td><td>0.758822</td></tr><tr><td/><td/><td>0.25</td><td>0.787061</td><td>0.794762</td></tr><tr><td/><td/><td>0.30</td><td>0.818512</td><td>0.818545</td></tr><tr><td/><td/><td>0.35</td><td>0.847556</td><td>0.837087</td></tr><tr><td/><td/><td>0.40</td><td>0.865010</td><td>0.843633</td></tr><tr><td/><td/><td>0.01</td><td>0.340710</td><td>0.337163</td></tr><tr><td/><td/><td>0.05</td><td>0.581698</td><td>0.591143</td></tr><tr><td/><td/><td>0.10</td><td>0.702483</td><td>0.699962</td></tr><tr><td/><td/><td>0.15</td><td>0.765191</td><td>0.752932</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.811529</td><td>0.785388</td></tr><tr><td/><td/><td>0.25</td><td>0.843820</td><td>0.806149</td></tr><tr><td/><td/><td>0.30</td><td>0.864403</td><td>0.817224</td></tr><tr><td/><td/><td>0.35</td><td>0.882211</td><td>0.818990</td></tr><tr><td>Consensus</td><td/><td>0.40 0.01</td><td>0.895110 0.191324</td><td>0.814209 0.221609</td></tr><tr><td/><td/><td>0.05</td><td>0.472302</td><td>0.511817</td></tr><tr><td/><td/><td>0.10</td><td>0.624910</td><td>0.655624</td></tr><tr><td/><td/><td>0.15</td><td>0.707260</td><td>0.727099</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.766436</td><td>0.776426</td></tr><tr><td/><td/><td>0.25</td><td>0.806752</td><td>0.805997</td></tr><tr><td/><td/><td>0.30</td><td>0.834342</td><td>0.824318</td></tr><tr><td/><td/><td>0.35</td><td>0.862185</td><td>0.838768</td></tr><tr><td/><td/><td>0.40</td><td>0.879229</td><td>0.843028</td></tr><tr><td/><td/><td>0.01</td><td>0.185901</td><td>0.215997</td></tr><tr><td/><td/><td>0.05</td><td>0.463941</td><td>0.504450</td></tr><tr><td/><td/><td>0.10</td><td>0.614485</td><td>0.646574</td></tr><tr><td/><td/><td>0.15</td><td>0.701349</td><td>0.722953</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.762975</td><td>0.773727</td></tr><tr><td/><td/><td>0.25</td><td>0.804869</td><td>0.804780</td></tr><tr><td/><td/><td>0.30</td><td>0.833155</td><td>0.824230</td></tr><tr><td/><td/><td>0.35</td><td>0.859147</td><td>0.835885</td></tr><tr><td/><td/><td>0.40</td><td>0.875436</td><td>0.839302</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Stack Exchange, Random Noise",
"html": null
},
"TABREF18": {
"content": "<table><tr><td>Underlying Algorithm</td><td>Context Filter</td><td>error rate</td><td>precision</td><td>F0.5</td></tr><tr><td/><td/><td>0.01</td><td>0.203559</td><td>0.235175</td></tr><tr><td/><td/><td>0.05</td><td>0.516995</td><td>0.551899</td></tr><tr><td/><td/><td>0.10</td><td>0.678281</td><td>0.692148</td></tr><tr><td/><td/><td>0.15</td><td>0.758825</td><td>0.754214</td></tr><tr><td/><td>(baseline)</td><td>0.20</td><td>0.815761</td><td>0.792989</td></tr><tr><td/><td/><td>0.25</td><td>0.855683</td><td>0.815505</td></tr><tr><td/><td/><td>0.30</td><td>0.874819</td><td>0.821995</td></tr><tr><td/><td/><td>0.35</td><td>0.902361</td><td>0.829439</td></tr><tr><td/><td/><td>0.40</td><td>0.914755</td><td>0.823054</td></tr><tr><td/><td/><td>0.01</td><td>0.379626</td><td>0.374314</td></tr><tr><td/><td/><td>0.05</td><td>0.653646</td><td>0.647463</td></tr><tr><td/><td/><td>0.10</td><td>0.777705</td><td>0.745331</td></tr><tr><td/><td/><td>0.15</td><td>0.831916</td><td>0.782190</td></tr><tr><td/><td>activation</td><td>0.20</td><td>0.873480</td><td>0.801011</td></tr><tr><td/><td/><td>0.25</td><td>0.899718</td><td>0.810401</td></tr><tr><td/><td/><td>0.30</td><td>0.910957</td><td>0.806536</td></tr><tr><td/><td/><td>0.35</td><td>0.925808</td><td>0.796353</td></tr><tr><td>Agreed correction</td><td/><td>0.40 0.01</td><td>0.936250 0.228381</td><td>0.779562 0.261363</td></tr><tr><td/><td/><td>0.05</td><td>0.551033</td><td>0.582140</td></tr><tr><td/><td/><td>0.10</td><td>0.709730</td><td>0.716853</td></tr><tr><td/><td/><td>0.15</td><td>0.785696</td><td>0.772165</td></tr><tr><td/><td>feature</td><td>0.20</td><td>0.836971</td><td>0.804862</td></tr><tr><td/><td/><td>0.25</td><td>0.871780</td><td>0.821373</td></tr><tr><td/><td/><td>0.30</td><td>0.887976</td><td>0.823381</td></tr><tr><td/><td/><td>0.35</td><td>0.912542</td><td>0.825164</td></tr><tr><td/><td/><td>0.40</td><td>0.925177</td><td>0.816177</td></tr><tr><td/><td/><td>0.01</td><td>0.219619</td><td>0.252413</td></tr><tr><td/><td/><td>0.05</td><td>0.541191</td><td>0.573870</td></tr><tr><td/><td/><td>0.10</td><td>0.700703</td><td>0.710048</td></tr><tr><td/><td/><td>0.15</td><td>0.777494</td><td>0.767097</td></tr><tr><td/><td>nonsuspicious</td><td>0.20</td><td>0.832716</td><td>0.802072</td></tr><tr><td/><td/><td>0.25</td><td>0.870279</td><td>0.820193</td></tr><tr><td/><td/><td>0.30</td><td>0.885262</td><td>0.822268</td></tr><tr><td/><td/><td>0.35</td><td>0.909316</td><td>0.823756</td></tr><tr><td/><td/><td>0.40</td><td>0.922156</td><td>0.814744</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Stack Overflow, Next-Best Noise",
"html": null
},
"TABREF19": {
"content": "<table><tr><td>C Appendix: Enlarged Figures</td></tr></table>",
"num": null,
"type_str": "table",
"text": "Stack Overflow, Random Noise",
"html": null
}
}
}
} |