File size: 94,302 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 | {
"paper_id": "2022",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T16:31:22.490968Z"
},
"title": "Low-Resource Adaptation of Open-Domain Generative Chatbots Greyson Gerhard-Young",
"authors": [
{
"first": "Raviteja",
"middle": [],
"last": "Anantha",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Brown University \u2660 Amazon",
"location": {}
},
"email": ""
},
{
"first": "Srinivas",
"middle": [],
"last": "Chappidi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Brown University \u2660 Amazon",
"location": {}
},
"email": ""
},
{
"first": "Bj\u00f6rn",
"middle": [],
"last": "Hoffmeister",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Brown University \u2660 Amazon",
"location": {}
},
"email": ""
},
{
"first": "\u2660",
"middle": [],
"last": "Apple",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Brown University \u2660 Amazon",
"location": {}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Recent work building open-domain chatbots has demonstrated that increasing model size improves performance (Adiwardana et al., 2020; Roller et al., 2020). On the other hand, latency and connectivity considerations dictate the move of digital assistants on the device (Verge, 2021). Giving a digital assistant like Siri, Alexa, or Google Assistant the ability to discuss just about anything leads to the need for reducing the chatbot model size such that it fits on the user's device. We demonstrate that low parameter models can simultaneously retain their general knowledge conversational abilities while improving in a specific domain. Additionally, we propose a generic framework that accounts for variety in question types, tracks reference throughout multiturn conversations, and removes inconsistent and potentially toxic responses. Our framework seamlessly transitions between chatting and performing transactional tasks, which will ultimately make interactions with digital assistants more human-like. We evaluate our framework on 1 internal and 4 public benchmark datasets using both automatic (Perplexity) and human (SSA-Sensibleness and Specificity Average) evaluation metrics and establish comparable performance while reducing model parameters by 90%.",
"pdf_parse": {
"paper_id": "2022",
"_pdf_hash": "",
"abstract": [
{
"text": "Recent work building open-domain chatbots has demonstrated that increasing model size improves performance (Adiwardana et al., 2020; Roller et al., 2020). On the other hand, latency and connectivity considerations dictate the move of digital assistants on the device (Verge, 2021). Giving a digital assistant like Siri, Alexa, or Google Assistant the ability to discuss just about anything leads to the need for reducing the chatbot model size such that it fits on the user's device. We demonstrate that low parameter models can simultaneously retain their general knowledge conversational abilities while improving in a specific domain. Additionally, we propose a generic framework that accounts for variety in question types, tracks reference throughout multiturn conversations, and removes inconsistent and potentially toxic responses. Our framework seamlessly transitions between chatting and performing transactional tasks, which will ultimately make interactions with digital assistants more human-like. We evaluate our framework on 1 internal and 4 public benchmark datasets using both automatic (Perplexity) and human (SSA-Sensibleness and Specificity Average) evaluation metrics and establish comparable performance while reducing model parameters by 90%.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Recent progress on end-to-end neural approaches for building open-domain chatbots (Zhang et al., 2020; Adiwardana et al., 2020; Roller et al., 2020) has demonstrated that large-scale pre-training using heavy-weight models combined with careful selection of datasets for fine-tuning to acquire specific skills can deliver superior performance. However, for one model to perform several tasks -such as dialogue state tracking or reference resolution, response generation, mitigating toxic responses, avoiding in-turn contradictions, and avoiding incorrect or \"I don't know\" responses due to lack of knowledge -in a reliable fashion, there is still a long way to go. Despite much research, these limitations from the recently proposed approaches prevent practical adoption. In addition, due to huge model sizes, these approaches lack practical utility in a low-resource setting. Some complex frameworks (Serban et al., 2017; Worswick, 2018; use a mix of templates and dialogue managers with rulebased systems. These complex frameworks often have problems: the produced responses are vague and generic, and they lack engagingness (Adiwardana et al., 2020) . Other complex frameworks address this issue by employing modularizing design assigning each conversational task to a specific component, which can help improve overall performance of the dialogue systems (Fang et al., 2017; Yu et al., 2019) . Prior works have shown that generative neural response models outperform template-based or hybrid response generation methods as measured using various human evaluation techniques (Adiwardana et al., 2020; Roller et al., 2020) .",
"cite_spans": [
{
"start": 82,
"end": 102,
"text": "(Zhang et al., 2020;",
"ref_id": "BIBREF18"
},
{
"start": 103,
"end": 127,
"text": "Adiwardana et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 128,
"end": 148,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
},
{
"start": 900,
"end": 921,
"text": "(Serban et al., 2017;",
"ref_id": null
},
{
"start": 922,
"end": 937,
"text": "Worswick, 2018;",
"ref_id": "BIBREF15"
},
{
"start": 1126,
"end": 1151,
"text": "(Adiwardana et al., 2020)",
"ref_id": "BIBREF0"
},
{
"start": 1358,
"end": 1377,
"text": "(Fang et al., 2017;",
"ref_id": "BIBREF6"
},
{
"start": 1378,
"end": 1394,
"text": "Yu et al., 2019)",
"ref_id": "BIBREF16"
},
{
"start": 1577,
"end": 1602,
"text": "(Adiwardana et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 1603,
"end": 1623,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this work, we propose a generic, modular and light-weight framework that blends the desired characteristics of both classes of methods. A snippet of sample dialogue with our proposed framework is shown in Figure 1 . Our contributions are as follows: (1) demonstrating that a light-weight response generation model in a modular framework achieves comparable performance to recent models (Adiwardana et al., 2020; Roller et al., 2020) that have billions of parameters; (2) providing evidence that adding a reference resolution component improves the quality of the generated response for multi-turn conversations, compared to previous approaches that state track conversational context explicitly or use latent representations (Cervone et al., 2019; Roller et al., 2020) ; (3) providing a generic end-to-end framework that can process both objective (factual) and subjective questions.",
"cite_spans": [
{
"start": 389,
"end": 414,
"text": "(Adiwardana et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 415,
"end": 435,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
},
{
"start": 728,
"end": 750,
"text": "(Cervone et al., 2019;",
"ref_id": "BIBREF2"
},
{
"start": 751,
"end": 771,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 208,
"end": 216,
"text": "Figure 1",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Lightweight Entertainment Domain (LED) chatbot interacts with the user through a pipeline of models. The LED chatbot architecture is illustrated in Figure 2 . Each module in our pipeline architecture handles a specific conversational task and passes the output for further processing to the downstream modules. In the following subsections, we describe these modules with their respective tasks and training details.",
"cite_spans": [],
"ref_spans": [
{
"start": 148,
"end": 156,
"text": "Figure 2",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Lightweight Entertainment Domain Chatbot",
"sec_num": "2"
},
{
"text": "In a multi-turn dialogue, the follow-up questions often contain implicit or explicit references to the entities from the previous turns. It is well established that providing self-contained questions by resolving references improves the efficiency of the language understanding systems (Elgohary et al., 2019; Anantha et al., 2021) . A illustration of reference resolution where the entity reference (in bold) in the question (Q) is disambiguated (Skyfall song vs Skyfall movie) by adding the entity type (song). The rewritten question (R) is a self-contained version of the follow-up question, that will be used for answering (A), where both the coreferences and ellipses (in bold) are resolved.",
"cite_spans": [
{
"start": 286,
"end": 309,
"text": "(Elgohary et al., 2019;",
"ref_id": "BIBREF5"
},
{
"start": 310,
"end": 331,
"text": "Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Reference Resolution",
"sec_num": "2.1"
},
{
"text": "The input to the reference resolution component is the current turn query along with the conversation context, i.e., previous queries and responses. We follow the implementation of the CopyTransformer model (Anantha et al., 2021) . Our reference resolution model consists of 90M parameters. A sample of input and output is shown in Figure 3 .",
"cite_spans": [
{
"start": 207,
"end": 229,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [
{
"start": 332,
"end": 340,
"text": "Figure 3",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Reference Resolution",
"sec_num": "2.1"
},
{
"text": "One of the goals in a low-latency setting is to process a maximum amount of information on the device, and only send to server if it is absolutely needed. This design approach provides faster responses by avoiding unnecessary round trips to the server. In order to determine if the query can be processed on the device it is important to predict if the query needs information from external knowledge sources, such as the world wide web. We refer to the questions that require general knowledge and are of type objective as \"Factual Questions,\" and the questions that are of type chit-chat as \"Subjective Questions.\" We refer to the on-device classifier that predicts if a question is factual or not (subjective) as \"Factual Classifier\".",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Factual Classifier",
"sec_num": "2.2"
},
{
"text": "We use ALBERT as our factual classifier. We initialize the factual classifier weights using HuggingFace pre-trained ALBERT 1 model and train using binary labels from our Internal Media dataset, where 1 represents a factual question and 0 a subjective question. Our factual classifier consists of 11M parameters. We observed the optimal value for the threshold to be 0.8.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Factual Classifier",
"sec_num": "2.2"
},
{
"text": "The subjective response generation component of our pipeline is a 90M parameter model with a conventional Seq2Seq Transformer architecture. Our work uses the optimized setup discussed in Blender to convert input sequences of dialogue to an output response (Roller et al., 2020) . However, there are a couple core differences. Our dialogue model was fine-tuned for a particular use case: subjective entertainment-domain questions. Additionally, our model has been trained on rewritten inputs (given our reference resolver in a prior portion of the pipeline).",
"cite_spans": [
{
"start": 256,
"end": 277,
"text": "(Roller et al., 2020)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Subjective Response Generation",
"sec_num": "2.3"
},
{
"text": "The core response generation model was trained using the ParlAI 2 framework, a platform designed specifically for dialogue models. We build upon the 1 https://huggingface.co/albert-base-v2 2 https://github.com/facebookresearch/ ParlAI work of Blender's 90M generative model included in the broader ParlAI zoo (Roller et al., 2020) . The critical objective for this portion of the pipeline was to maintain general-domain performance while concurrently improving in our target domains: music and movies. As described in Section 3, our datasets contain human rewritten questions where anaphoric references are resolved, and we use the rewritten questions as input for the response generation. Our experimentation uses a variety of different techniques, with the methodology behind each tactic covered in this section. In order to understand how our fine-tuned model performed on both explicit and implicit inputs, we run all trials on original and rewritten questions (before comparing performance). The tests draw upon common tactics in transfer learning and dialogue models: comparisons on freezing different numbers of layers, retaining the original datasets, and selecting a decoding algorithm. In all experiments, we freeze the encoder portion of Blender's architecture to maintain their well-tuned representation. We compare results between training on the entire decoder and locking its first four layers. In separate automatic evaluation, we contrast using only internal media data to simply adding it as a fifth dataset. Finally, we look at the relative effect of the beam search and Top-K decoding algorithms on human evaluation. The validation perplexity and loss curves of the best run are shown in Figures 4 and 5 respectively.",
"cite_spans": [
{
"start": 309,
"end": 330,
"text": "(Roller et al., 2020)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Subjective Response Generation",
"sec_num": "2.3"
},
{
"text": "In principle, any generative response module is bound to fail when a knowledge-based question is presented and if the response module does not have access to factual information. In our architecture, we route factual questions to the Extract-NParaphrase module, which extracts the answer spans and paraphrases the relevant text to generate a natural and engaging response. The response path for Turn-1 in Figure 2 illustrates the processing of the question.",
"cite_spans": [],
"ref_spans": [
{
"start": 405,
"end": 413,
"text": "Figure 2",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "ExtractNParaphrase",
"sec_num": "2.4"
},
{
"text": "ExtractNParaphrase consists of three stages: (1) Passage Retrieval, (2) Reading Comprehension and (3) Paraphrasing. The first two steps follow Anantha et al.; and for the third step, paraphrasing, we take motivation from the refine step of . We use BM25 to retrieve Top-K passages and a light-weight BERT-based model to extract answer spans. The scores obtained from passage retrieval and answer span extraction are combined to produce the final score. Passage retrieval and answer extraction models are comprised of 50M parameters. We refer to (Anantha et al., 2021) for more details. Finally, we train a sentence paraphraser model based on Transformer, which is comprised of 24M parameters. The paraphrased labels are provided as part of internal media dataset, which is described in Section 3.",
"cite_spans": [
{
"start": 545,
"end": 567,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "ExtractNParaphrase",
"sec_num": "2.4"
},
{
"text": "Logical consistency in dialogue and avoiding unnecessary or potentially toxic responses are critical factors to consider when developing open-domain chatbots. When interacting with chatbots, people expect coherent responses that at least do not contradict the chatbot's earlier responses in the same conversation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Inconsistency/Toxicity Predictor",
"sec_num": "2.5"
},
{
"text": "We train a classifier that can detect inconsistent responses given the conversation context. We follow the training procedure described in (Nie et al., 2020 ) using DECODE 3 dataset and internal media dataset. We use the ALBERT model for inconsistency/toxicity predictor.",
"cite_spans": [
{
"start": 139,
"end": 156,
"text": "(Nie et al., 2020",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Inconsistency/Toxicity Predictor",
"sec_num": "2.5"
},
{
"text": "We use various datasets for training and evaluation focused on different tasks. In this section, we describe each dataset along with the corresponding modules that use the dataset for training.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "QReCC (Anantha et al., 2021) contains around 81,000 conversation turns. Every turn contains a question which may have anaphoric references, a rewritten version of the question with references resolved, an answer span to the question and a corresponding web URL. QReCC data is used to train the reference resolution, passage retrieval and answer span extraction models.",
"cite_spans": [
{
"start": 6,
"end": 28,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "Wizard of Wikipedia (Dinan et al., 2019b) (WoW) contains 194,000 turns of dialogue distributed over 1,250 topics. Each conversation is predicated on discussing the relevant topic in depth, with the goal of displaying expert knowledge in that subject. Note that in our pipeline framework, we refer objective questions to the ExtractNParaphrase component, so the subjective response generation model is not required to answer factual questions with a high degree of accuracy. Still, the WoW dataset helps our generative model maintain a breadth of knowledge to provide pertinent answers to subjective inputs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "ConvAI2 is based off of the work of Per-sonaChat (Zhang et al., 2018; Dinan et al., 2019a) and was used at the NeurIPS 2018 ConvAI competition. This dataset is made up of 140,000 turns where gatherers are given a persona and tasked with learning about their counterpart. This helps opendomain agents ask questions, and perhaps more relevantly in our use case, respond in an engaging manner. We use the ConvAI2 dataset to train the subjective response generation model. Empathetic Dialogues (Rashkin et al., 2019) (ED) is a library of 50,000 turns where one speaker plays the role of sympathetic listener. These skills translate well to our needs, as the subjective model must account for previous dialogue history and attempt to match their chosen response to the appropriate tone.",
"cite_spans": [
{
"start": 49,
"end": 69,
"text": "(Zhang et al., 2018;",
"ref_id": "BIBREF17"
},
{
"start": 70,
"end": 90,
"text": "Dinan et al., 2019a)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "Blended Skill Talk (Smith et al., 2020) (BST) is a 76,000 turn compilation of the previous three datasets: WoW, ConvAI2, and ED. Guided human speakers were given the option to select between outputs from models trained on each of the individual tasks, which produces data that can teach the bot when a certain class of response should be used.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "DECODE (Nie et al., 2020 ) is a conversational dataset made up of 40,280 turns from human to human and human to bot contradictory dialogues. We use DECODE to train the inconsistency/toxicity detector model based off of the ALBERT model, along with our internal media dataset.",
"cite_spans": [
{
"start": 7,
"end": 24,
"text": "(Nie et al., 2020",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "Internal Media dataset is composed of 100,000 movie themed turns. Each turn contains a natural question without explicit reference to the movie being discussed, as well as rewritten questions that convert those references to specifics (akin to the reference resolution component of our pipeline). Answer span along with web URL as well as paraphrased variation that is natural and engaging is also provided.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "The dataset is collected using crowd-sourced annotators. The goal of the annotators is to mimic the flow of a natural human conversation while maintaining a neutral persona. The responses were validated against guidelines to be non-controversial, eliminate profanity, be neutral, engaging and concise (with an upper bound of 30 words). Every conversation consists of 10 turns, and we collect 10,000 conversations. We give instructions to explicitly add anaphoric references in follow up turns.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training Data",
"sec_num": "3"
},
{
"text": "We categorize our evaluation metrics based on component-wise vs end-to-end evaluation. QReCC and DECODE datasets are only used for taskspecific model training and are not used in establishing a chatbot's end-to-end metrics: Perplexity and Sensibleness and Specificity Average (SSA). We establish a human evaluation metric, SSA, on our internal media dataset only, due to limited human annotators. We establish the automatic evaluation metric, perplexity, on all 5 datasets: WoW, ConvAI2, ED, BT, and our internal media dataset. Below we discuss the intrinsic (component-wise) and extrinsic (end-to-end) metrics used to evaluate our LED framework.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Metrics and Results",
"sec_num": "4"
},
{
"text": "Excluding the subjective response generation model, all other components in LED have their own task-specific evaluation metrics. For reference resolution model using query rewriting and paraphraser in ExtractNParaphrase module, we use ROUGE, USE and Recall@10 as described in (Anantha et al., 2021) . For factual classifier and inconsistency/toxicity predictor, we use F1 as the evaluation metric and obtain 0.94 and 0.61 respectively. For passage retrieval of ExtractNParaphrase module we use MRR and Recall@k; similarly for answer-span extraction we use F1 and exact match as described in (Anantha et al., 2021) . For the subjective response generation model we use perplexity, which is also our extrinsic metric.",
"cite_spans": [
{
"start": 276,
"end": 298,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
},
{
"start": 591,
"end": 613,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Intrinsic Metrics",
"sec_num": "4.1"
},
{
"text": "Our chatbot framework uses perplexity as its extrinsic metric for automatic evaluation. While there are a number of evaluation metrics that can serve to measure the quality of responses (see the other components of our pipeline), perplexity correlates well with human judgement (Adiwardana et al., 2020) . We build on the work of Meena (Adiwardana et al., 2020) that proposed SSA, Sensibleness and Specificity Average. We use SSA as another extrinsic metric for human evaluation. Adiwardana et al. subsequently demonstrated a strong correlation between perplexity and SSA among numerous state-of-the-art chatbots. Table 1 shows perplexity metrics of Blender models, both 90M and 2.7B parameter models; and LED framework, both with and without reference resolution, across all 5 datasets: 1 internal media dataset and 4 public dataset. Table 2 shows SSA metrics of Blender models (both 90M and 2.7B parameter models) and LED framework (both with and without reference resolution) on internal media dataset.",
"cite_spans": [
{
"start": 278,
"end": 303,
"text": "(Adiwardana et al., 2020)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [
{
"start": 614,
"end": 621,
"text": "Table 1",
"ref_id": "TABREF0"
},
{
"start": 835,
"end": 842,
"text": "Table 2",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Extrinsic Metrics",
"sec_num": "4.2"
},
{
"text": "Our work follows the objective of combining opendomain chatbot and transactional digital assistants. The factual classifier component of LED serves (Zhang et al., 2020; Adiwardana et al., 2020; Roller et al., 2020) have shown that end-toend neural approaches, where the responses are produced in a generative fashion, can result in engaging dialogue. However, the resultant models from these approaches are huge -multiple billions of parameters -and are not on-device friendly. It has also been shown that end-to-end generative chatbots frequently generate responses with inconsistencies (Adiwardana et al., 2020; Roller et al., 2020) . It is obvious that there is need for an additional module that can correct, or at least detect, these inconsistencies. Generalizing this approach where we assign a specific task to a module, modularization can lead to overall improvement in dialogue systems (Fang et al., 2017; Yu et al., 2019) . We adopt the modularization approach to opendomain generative chatbot to minimize the total number of parameters while tackling some of the shortcomings in the end-to-end neural approaches.",
"cite_spans": [
{
"start": 148,
"end": 168,
"text": "(Zhang et al., 2020;",
"ref_id": "BIBREF18"
},
{
"start": 169,
"end": 193,
"text": "Adiwardana et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 194,
"end": 214,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
},
{
"start": 588,
"end": 613,
"text": "(Adiwardana et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 614,
"end": 634,
"text": "Roller et al., 2020)",
"ref_id": "BIBREF10"
},
{
"start": 895,
"end": 914,
"text": "(Fang et al., 2017;",
"ref_id": "BIBREF6"
},
{
"start": 915,
"end": 931,
"text": "Yu et al., 2019)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Blender (Roller et al., 2020) showed non-trivial improvement in response generation when evaluated using human side-by-side comparison. We adopt the Blender model as a basis for the core response generation functionality in subjective cases. We follow the Blender methodology of experimenting with multiple decoding algorithms for optimal human performance. However we also differ from Blender's approach. Firstly, we place a larger emphasis on model size for better on-device compatibility. Secondly, we account for a wider variety of cases where we use answer extraction and paraphrasing to accurately answer factual questions.",
"cite_spans": [
{
"start": 8,
"end": 29,
"text": "(Roller et al., 2020)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "And finally, we use the reference resolution component to track dialogue state since it is helpful for multi-turn conversations (Anantha et al., 2021) , along with providing our fine-tuned model with a wider variety of training data (multi-turn conversations where questions are either rewritten or preserved).",
"cite_spans": [
{
"start": 128,
"end": 150,
"text": "(Anantha et al., 2021)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Meena (Adiwardana et al., 2020) proposed a new metric, Sensibleness and Specificity Average (SSA), which captures key elements of a humanlike multi-turn conversation. Additionally, they also show perplexity is the best automatic metric that correlates well with human judgement. We borrow SSA to evaluate human performance. It is good for our use case, where the model is required not just to answer logically but should also be rewarded for referencing context from earlier in the conversation. One of the differences between our work and Meena is we do not use Evolved Transformer layers, though that may be basis for future work. One difference of our work compared to both Blender and Meena is we follow a modularized approach, instead of a single parameter-heavy model.",
"cite_spans": [
{
"start": 6,
"end": 31,
"text": "(Adiwardana et al., 2020)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Although we reduce the number of parameters by 90% and achieve comparable performance, we still notice shortcomings which can be possibly mitigated by the inconsistency/toxicity classifier.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Limitations",
"sec_num": "6.1"
},
{
"text": "LED, often, is in agreement with the user which might cause the user to feel non-engaging. This behavior stems from the inclusion of the Empathetic Dialogues (Rashkin et al., 2019) dataset in the Subjective Response Generation component. Utilized in both the pre-trained Blender model and our finetuning process, Empathetic Dialgoues data incentivize the model to choose agreeable responses. An example of this behavior is shown in Figure 6 .",
"cite_spans": [],
"ref_spans": [
{
"start": 432,
"end": 440,
"text": "Figure 6",
"ref_id": "FIGREF6"
}
],
"eq_spans": [],
"section": "Consistent Agreement",
"sec_num": "6.1.1"
},
{
"text": "LED responds to controversial questions with a non-neutral persona. These are instances where the inconsistency/toxicity predictor failed. While this class of responses was frequently present in the Subjective Response Generation component, we were able to significantly mitigate overall prevalence through the inclusion of the inconsis- ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sensitive Issues",
"sec_num": "6.1.2"
},
{
"text": "LED provides unnecessary or questionable advice to questions seeking advice. The root cause of these outputs are examples from the Wizard of Wikipedia (Dinan et al., 2019b) dataset, where the model is taught to display expert knowledge in a particular area. An example of unnecessary financial advice is shown in Figure 8 . ",
"cite_spans": [
{
"start": 151,
"end": 172,
"text": "(Dinan et al., 2019b)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [
{
"start": 313,
"end": 321,
"text": "Figure 8",
"ref_id": "FIGREF8"
}
],
"eq_spans": [],
"section": "Questionable Advice",
"sec_num": "6.1.3"
},
{
"text": "We plan to investigate solutions to mitigate the undesired patterns noticed in Section 6.1 by improving the inconsistency/toxicity predictor, as well as, investigate the feasibility of a common embedding layer for all modules in our framework in an effort to further minimize the number of parameters with minimum or no-drop in performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Future Work",
"sec_num": "6.2"
},
{
"text": "Also, transactional requests have a stronger user feedback signal (e.g. if playing the wrong movie, then the user will stop the movie), which can help to learn whether a conversation was successful. The conversational models (i.e., natural language understanding) can learn from user feedback signals. We plan to investigate incorporating such feedback signals to improve task completion rate in a conversation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Future Work",
"sec_num": "6.2"
},
{
"text": "https://parl.ai/projects/ contradiction/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We would like to thank Barry Theobald, Russ Webb, Alex Acero and John Giannandrea for their insightful comments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Towards a human-like opendomain chatbot",
"authors": [
{
"first": "Daniel",
"middle": [],
"last": "Adiwardana",
"suffix": ""
},
{
"first": "Minh-Thang",
"middle": [],
"last": "Luong",
"suffix": ""
},
{
"first": "David",
"middle": [
"R"
],
"last": "So",
"suffix": ""
},
{
"first": "Jamie",
"middle": [],
"last": "Hall",
"suffix": ""
},
{
"first": "Noah",
"middle": [],
"last": "Fiedel",
"suffix": ""
},
{
"first": "Romal",
"middle": [],
"last": "Thoppilan",
"suffix": ""
},
{
"first": "Zi",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Apoorv",
"middle": [],
"last": "Kulshreshtha",
"suffix": ""
},
{
"first": "Gaurav",
"middle": [],
"last": "Nemade",
"suffix": ""
},
{
"first": "Yifeng",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Quoc",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Le",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2001.09977"
]
},
"num": null,
"urls": [],
"raw_text": "Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V. Le. 2020. Towards a human-like open- domain chatbot. arXiv:2001.09977.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Open-domain question answering goes conversational via question rewriting",
"authors": [
{
"first": "Raviteja",
"middle": [],
"last": "Anantha",
"suffix": ""
},
{
"first": "Svitlana",
"middle": [],
"last": "Vakulenko",
"suffix": ""
},
{
"first": "Zhucheng",
"middle": [],
"last": "Tu",
"suffix": ""
},
{
"first": "Shayne",
"middle": [],
"last": "Longpre",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Pulman",
"suffix": ""
},
{
"first": "Srinivas",
"middle": [],
"last": "Chappidi",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "520--534",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. Open-domain question answering goes conversational via question rewriting. In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, page 520-534.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Natural language generation at scale: A case study for open domain question answering",
"authors": [
{
"first": "Alessandra",
"middle": [],
"last": "Cervone",
"suffix": ""
},
{
"first": "Chandra",
"middle": [],
"last": "Khatri",
"suffix": ""
},
{
"first": "Rahul",
"middle": [],
"last": "Goel",
"suffix": ""
},
{
"first": "Behnam",
"middle": [],
"last": "Hedayatnia",
"suffix": ""
},
{
"first": "Anu",
"middle": [],
"last": "Venkatesh",
"suffix": ""
},
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-T\u00fcr",
"suffix": ""
},
{
"first": "Raefer",
"middle": [],
"last": "Gabriel",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 12th International Conference on Natural Language Generation",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alessandra Cervone, Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Anu Venkatesh, Dilek Hakkani-T\u00fcr, and Raefer Gabriel. 2019. Natural language generation at scale: A case study for open domain question answering. In Proceedings of the 12th International Conference on Natural Language Generation.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "The second conversational intelligence challenge (convai2)",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Varvara",
"middle": [],
"last": "Logacheva",
"suffix": ""
},
{
"first": "Valentin",
"middle": [],
"last": "Malykh",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Miller",
"suffix": ""
},
{
"first": "Kurt",
"middle": [],
"last": "Shuster",
"suffix": ""
},
{
"first": "Jack",
"middle": [],
"last": "Urbanek",
"suffix": ""
},
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
},
{
"first": "Arthur",
"middle": [],
"last": "Szlam",
"suffix": ""
},
{
"first": "Iulian",
"middle": [],
"last": "Serban",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Lowe",
"suffix": ""
},
{
"first": "Shrimai",
"middle": [],
"last": "Prabhumoye",
"suffix": ""
},
{
"first": "Alan",
"middle": [
"W"
],
"last": "Black",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Rudnicky",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Williams",
"suffix": ""
},
{
"first": "Joelle",
"middle": [],
"last": "Pineau",
"suffix": ""
},
{
"first": "Mikhail",
"middle": [],
"last": "Burtsev",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1902.00098"
]
},
"num": null,
"urls": [],
"raw_text": "Emily Dinan, Varvara Logacheva, Valentin Ma- lykh, Alexander Miller, Kurt Shuster, Jack Ur- banek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W Black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Mikhail Burtsev, and Jason Weston. 2019a. The sec- ond conversational intelligence challenge (convai2). arXiv:1902.00098.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Wizard of wikipedia: Knowledge-powered conversational agents",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Roller",
"suffix": ""
},
{
"first": "Kurt",
"middle": [],
"last": "Shuster",
"suffix": ""
},
{
"first": "Angela",
"middle": [],
"last": "Fan",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Auli",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1811.01241"
]
},
"num": null,
"urls": [],
"raw_text": "Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019b. Wiz- ard of wikipedia: Knowledge-powered conversa- tional agents. arXiv:1811.01241.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Can you unpack that? learning to rewrite questions-in-context",
"authors": [
{
"first": "Ahmed",
"middle": [],
"last": "Elgohary",
"suffix": ""
},
{
"first": "Denis",
"middle": [],
"last": "Peskov",
"suffix": ""
},
{
"first": "Jordan",
"middle": [],
"last": "Boyd-Graber",
"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",
"volume": "",
"issue": "",
"pages": "5920--5926",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ahmed Elgohary, Denis Peskov, and Jordan Boyd- Graber. 2019. Can you unpack that? learning to rewrite questions-in-context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 5920-5926.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Sounding board -university of washington's alexa prize submission",
"authors": [
{
"first": "Hao",
"middle": [],
"last": "Fang",
"suffix": ""
},
{
"first": "Hao",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "Elizabeth",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "Ariel",
"middle": [],
"last": "Holtzman",
"suffix": ""
},
{
"first": "Maarten",
"middle": [],
"last": "Sap",
"suffix": ""
},
{
"first": "Mari",
"middle": [],
"last": "Ostendorf",
"suffix": ""
},
{
"first": "Yejin",
"middle": [],
"last": "Choi",
"suffix": ""
},
{
"first": "Noah A",
"middle": [],
"last": "Smith",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hao Fang, Hao Cheng, Elizabeth Clark, Ariel Holtz- man, Maarten Sap, Mari Ostendorf, Yejin Choi, and Noah A Smith. 2017. Sounding board -university of washington's alexa prize submission.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Albert: A lite bert for self-supervised 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": ""
},
{
"first": "Piyush",
"middle": [],
"last": "Sharma",
"suffix": ""
},
{
"first": "Radu",
"middle": [],
"last": "Soricut",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1909.11942"
]
},
"num": null,
"urls": [],
"raw_text": "Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. Albert: A lite bert for self-supervised learn- ing of language representations. arXiv:1909.11942.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "2020. I like fish, especially dolphins: Addressing contradictions in dialogue modelling",
"authors": [
{
"first": "Yixin",
"middle": [],
"last": "Nie",
"suffix": ""
},
{
"first": "Mary",
"middle": [],
"last": "Williamson",
"suffix": ""
},
{
"first": "Mohit",
"middle": [],
"last": "Bansal",
"suffix": ""
},
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yixin Nie, Mary Williamson, Mohit Bansal, Douwe Kiela, and Jason Weston. 2020. I like fish, espe- cially dolphins: Addressing contradictions in dia- logue modelling.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Towards empathetic opendomain conversation models: a new benchmark and dataset",
"authors": [
{
"first": "Eric",
"middle": [
"Michael"
],
"last": "Hannah Rashkin",
"suffix": ""
},
{
"first": "Margaret",
"middle": [],
"last": "Smith",
"suffix": ""
},
{
"first": "Y-Lan",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Boureau",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "5370--5381",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2019. Towards empathetic open- domain conversation models: a new benchmark and dataset. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguistics, page 5370-5381.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Recipes for building an opendomain chatbot",
"authors": [
{
"first": "Stephen",
"middle": [],
"last": "Roller",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Naman",
"middle": [],
"last": "Goyal",
"suffix": ""
},
{
"first": "Da",
"middle": [],
"last": "Ju",
"suffix": ""
},
{
"first": "Mary",
"middle": [],
"last": "Williamson",
"suffix": ""
},
{
"first": "Yinhan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Jing",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Myle",
"middle": [],
"last": "Ott",
"suffix": ""
},
{
"first": "Kurt",
"middle": [],
"last": "Shuster",
"suffix": ""
},
{
"first": "Eric",
"middle": [
"M"
],
"last": "Smith",
"suffix": ""
},
{
"first": "Y-Lan",
"middle": [],
"last": "Boureau",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2004.13637"
]
},
"num": null,
"urls": [],
"raw_text": "Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, and Jason Weston. 2020. Recipes for building an open- domain chatbot. arXiv:2004.13637.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "A deep reinforcement learning chatbot",
"authors": [
{
"first": "V",
"middle": [],
"last": "Iulian",
"suffix": ""
},
{
"first": "Chinnadhurai",
"middle": [],
"last": "Serban",
"suffix": ""
},
{
"first": "Mathieu",
"middle": [],
"last": "Sankar",
"suffix": ""
},
{
"first": "Saizheng",
"middle": [],
"last": "Germain",
"suffix": ""
},
{
"first": "Zhouhan",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Sandeep",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Taesup",
"middle": [],
"last": "Subramanian",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Sarath",
"middle": [],
"last": "Pieper",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Chandar",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1709.02349"
]
},
"num": null,
"urls": [],
"raw_text": "Iulian V. Serban, Chinnadhurai Sankar, Mathieu Ger- main, Saizheng Zhang, Zhouhan Lin, Sandeep Sub- ramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeshwar, Alexan- dre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, and Yoshua Bengio. 2017. A deep reinforcement learning chatbot. arXiv:1709.02349.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Can you put it all together: Evaluating conversational agents' ability to blend skills",
"authors": [
{
"first": "Eric",
"middle": [
"Michael"
],
"last": "Smith",
"suffix": ""
},
{
"first": "Mary",
"middle": [],
"last": "Williamson",
"suffix": ""
},
{
"first": "Kurt",
"middle": [],
"last": "Shuster",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "Y-Lan",
"middle": [],
"last": "Boureau",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "2021--2030",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eric Michael Smith, Mary Williamson, Kurt Shuster, Jason Weston, and Y-Lan Boureau. 2020. Can you put it all together: Evaluating conversational agents' ability to blend skills. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, page 2021-2030.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Apple's siri will finally work without an internet connection with on-device speech recognition",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "The Verge. 2021. Apple's siri will finally work without an internet connection with on-device speech recog- nition.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Retrieve and refine: Improved sequence generation models for dialogue",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Alexander",
"middle": [
"H"
],
"last": "Miller",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI",
"volume": "",
"issue": "",
"pages": "978--979",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Weston, Emily Dinan, and Alexander H. Miller. 2018. Retrieve and refine: Improved sequence gen- eration models for dialogue. In Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd Interna- tional Workshop on Search-Oriented Conversational AI 978-1-948087-75-9.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Mitsuku wins loebner",
"authors": [
{
"first": "Steve",
"middle": [],
"last": "Worswick",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Steve Worswick. 2018. Mitsuku wins loebner prize 2018!",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Gunrock: A social bot for complex and engaging long conversations",
"authors": [
{
"first": "Dian",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Michelle",
"middle": [],
"last": "Cohn",
"suffix": ""
},
{
"first": "Yi",
"middle": [
"Mang"
],
"last": "Yang",
"suffix": ""
},
{
"first": "Chun-Yen",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Weiming",
"middle": [],
"last": "Wen",
"suffix": ""
},
{
"first": "Jiaping",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Mingyang",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Jesse",
"suffix": ""
},
{
"first": "Austin",
"middle": [],
"last": "Chau",
"suffix": ""
},
{
"first": "Antara",
"middle": [],
"last": "Bhowmick",
"suffix": ""
},
{
"first": "Shreenath",
"middle": [],
"last": "Iyer",
"suffix": ""
},
{
"first": "Giritheja",
"middle": [],
"last": "Sreenivasulu",
"suffix": ""
},
{
"first": "Sam",
"middle": [],
"last": "Davidson",
"suffix": ""
},
{
"first": "Ashwin Bhandare Andd Zhou",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 EMNLP and the 9th IJCNLP (System Demonstrations)",
"volume": "",
"issue": "",
"pages": "79--84",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dian Yu, Michelle Cohn, Yi Mang Yang, Chun-Yen Chen, Weiming Wen, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, Shreenath Iyer, Giritheja Sreenivasulu, Sam David- son, and Ashwin Bhandare andd Zhou Yu. 2019. Gunrock: A social bot for complex and engaging long conversations. In Proceedings of the 2019 EMNLP and the 9th IJCNLP (System Demonstra- tions), page 79-84.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Personalizing dialogue agents: I have a dog",
"authors": [
{
"first": "Saizheng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Jack",
"middle": [],
"last": "Urbanek",
"suffix": ""
},
{
"first": "Arthur",
"middle": [],
"last": "Szlam",
"suffix": ""
},
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1801.07243v5"
]
},
"num": null,
"urls": [],
"raw_text": "Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Per- sonalizing dialogue agents: I have a dog, do you have pets too? arXiv:1801.07243v5.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Dialogpt : Large-scale generative pre-training for conversational response generation",
"authors": [
{
"first": "Yizhe",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Siqi",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Michel",
"middle": [],
"last": "Galley",
"suffix": ""
},
{
"first": "Yen-Chun",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Brockett",
"suffix": ""
},
{
"first": "Xiang",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Jingjing",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Bill",
"middle": [],
"last": "Dolan",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and Bill Dolan. 2020. Dialogpt : Large-scale generative pre-training for conversational response generation. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics: System Demonstrations.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "The design and implementation of xiaoice, an empathetic social chatbot",
"authors": [
{
"first": "Li",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Di",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Heung-Yeung",
"middle": [],
"last": "Shum",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1812.08989"
]
},
"num": null,
"urls": [],
"raw_text": "Li Zhou, Jianfeng Gao, Di Li, and Heung-Yeung Shum. 2019. The design and implementation of xiaoice, an empathetic social chatbot. arXiv:1812.08989.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"text": "Equal contribution. \u2665 Work done while at Apple.",
"type_str": "figure",
"num": null
},
"FIGREF1": {
"uris": null,
"text": "A sample dialogue of paper author (left) conversing with our LED chatbot framework (right). The responses are from the pipeline of models: Reference Resolution, Factual Classifier, Subjective Response Generator, ExtractNParaphrase, Inconsistency/Toxicity Module.",
"type_str": "figure",
"num": null
},
"FIGREF2": {
"uris": null,
"text": "LED Pipeline illustrating end-to-end processing of multi-turn requests and response generation.",
"type_str": "figure",
"num": null
},
"FIGREF3": {
"uris": null,
"text": "Figure 3: A illustration of reference resolution where the entity reference (in bold) in the question (Q) is disambiguated (Skyfall song vs Skyfall movie) by adding the entity type (song). The rewritten question (R) is a self-contained version of the follow-up question, that will be used for answering (A), where both the coreferences and ellipses (in bold) are resolved.",
"type_str": "figure",
"num": null
},
"FIGREF4": {
"uris": null,
"text": "Validation perplexity of subjective response generation model using all five datasets: Wizard of Wiki, ConvAI2, Empathetic Dialogues, Blended Skill Talk, and our internal media dataset with rewritten questions as input.",
"type_str": "figure",
"num": null
},
"FIGREF5": {
"uris": null,
"text": "Validation loss of subjective response generation model using all five datasets: Wizard of Wiki, ConvAI2, Empathetic Dialogues, Blended Skill Talk, and our internal media dataset with rewritten questions as input.",
"type_str": "figure",
"num": null
},
"FIGREF6": {
"uris": null,
"text": "LED in agreement with user the majority of the time. tency/toxicity predictor component. An example of such an instance is shown in Figure 7.",
"type_str": "figure",
"num": null
},
"FIGREF7": {
"uris": null,
"text": "LED responding to controversial question in a non-neutral manner.",
"type_str": "figure",
"num": null
},
"FIGREF8": {
"uris": null,
"text": "LED providing unnecessary or questionable financial advice.",
"type_str": "figure",
"num": null
},
"TABREF0": {
"text": "Comparison of Perplexity metric across various datasets of Blender and LED chatbot frameworks with different parameter size.",
"num": null,
"type_str": "table",
"html": null,
"content": "<table><tr><td>LED without LED with</td></tr></table>"
},
"TABREF1": {
"text": "Comparison of SSA metric and number of model parameters of Blender and LED chatbot frameworks on internal media dataset.",
"num": null,
"type_str": "table",
"html": null,
"content": "<table><tr><td colspan=\"4\">Model/Metric Parameters Sensibleness Specificity</td><td>SSA</td></tr><tr><td>Blender</td><td>90M</td><td>72.60</td><td colspan=\"2\">83.10 77.85</td></tr><tr><td>Blender LED</td><td>2.7B 186M</td><td>80.42 78.28</td><td colspan=\"2\">92.70 86.56 89.12 83.70</td></tr><tr><td>LED</td><td>276M</td><td>80.38</td><td colspan=\"2\">91.95 86.17</td></tr><tr><td colspan=\"5\">as the gatekeeper between these two categories,</td></tr><tr><td colspan=\"5\">sending objective asks through the ExtractNPara-</td></tr><tr><td colspan=\"5\">phrase model and subjective inputs through our</td></tr><tr><td colspan=\"5\">fine-tuned open domain model. While our work</td></tr><tr><td colspan=\"5\">broadly falls under the category of open-domain</td></tr><tr><td colspan=\"5\">generative chatbots, because of the variety of mod-</td></tr><tr><td colspan=\"5\">els and their corresponding tasks, our work also</td></tr><tr><td colspan=\"5\">covers multiple key areas in language understand-</td></tr><tr><td colspan=\"5\">ing with a focus on low-resource adaptation design.</td></tr><tr><td>Prior works</td><td/><td/><td/></tr></table>"
}
}
}
} |