File size: 82,388 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 |
{
"paper_id": "2022",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T02:09:37.739045Z"
},
"title": "BIT-Xiaomi's Simultaneous Translation System for AutoSimTrans 2022",
"authors": [
{
"first": "Mengge",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "liumengge@bit.edu.cn"
},
{
"first": "Xiang",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {},
"email": "lixiang21@xiaomi.com"
},
{
"first": "Bao",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "chenbao@bit.edu.cn"
},
{
"first": "Yanzhi",
"middle": [],
"last": "Tian",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "tianyanzhi@bit.edu.cn"
},
{
"first": "Tianwei",
"middle": [],
"last": "Lan",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "lantianwei@bit.edu.cn"
},
{
"first": "Silin",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {},
"email": "lisilin@bit.edu.cn"
},
{
"first": "Yuhang",
"middle": [],
"last": "Guo",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "guoyuhang@bit.edu.cn"
},
{
"first": "Jian",
"middle": [],
"last": "Luan",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Beijing Institute of Technology",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "luanjian@xiaomi.com"
},
{
"first": "Bin",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {},
"email": "wangbin11@xiaomi.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track, and the En-Es test-to-text track. In our system, wait-k is utilized to train prefix-to-prefix translation models. We integrate streaming chunking to detect segmentation boundaries as the source streaming reading in. We further improve our system with data selection, data augmentation, and R-Drop training methods. Results show that our wait-k implementation outperforms the organizer's baseline by at most 8 BLEU score and our proposed streaming chunking method further improves by about 2 BLEU score in the low latency regime. * The work was done during the author's internship at Xiaomi.",
"pdf_parse": {
"paper_id": "2022",
"_pdf_hash": "",
"abstract": [
{
"text": "This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge. We participated in three tracks: the Zh-En text-to-text track, the Zh-En audio-to-text track, and the En-Es test-to-text track. In our system, wait-k is utilized to train prefix-to-prefix translation models. We integrate streaming chunking to detect segmentation boundaries as the source streaming reading in. We further improve our system with data selection, data augmentation, and R-Drop training methods. Results show that our wait-k implementation outperforms the organizer's baseline by at most 8 BLEU score and our proposed streaming chunking method further improves by about 2 BLEU score in the low latency regime. * The work was done during the author's internship at Xiaomi.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Simultaneous translation (Cho and Esipova, 2016; Yarmohammadi et al., 2013; Ma et al., 2019) , is a task in Machine Translation (MT), which intends to provide low latency translation in real-time scenarios. To achieve low latency translation, the translation system needs to begin translating before the end of source sentences, which can be viewed as prefix-toprefix translation (Ma et al., 2019) . Simultaneous translation is widely used in real-time translation scenarios such as simultaneous interpretation, online subtitles, and live broadcasting. In these scenarios, low latency may have equal or even higher priority than translation quality.",
"cite_spans": [
{
"start": 25,
"end": 48,
"text": "(Cho and Esipova, 2016;",
"ref_id": "BIBREF1"
},
{
"start": 49,
"end": 75,
"text": "Yarmohammadi et al., 2013;",
"ref_id": "BIBREF17"
},
{
"start": 76,
"end": 92,
"text": "Ma et al., 2019)",
"ref_id": "BIBREF10"
},
{
"start": 380,
"end": 397,
"text": "(Ma et al., 2019)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In simultaneous translation, the most challenge is the balance of translation quality and latency. Low latency translation requires beginning translation with insufficient source information, which may cause incorrect translation results. How to find a simultaneous policy to balance quality and latency is the most challenging question. On another hand, in most cases, the standard machine translation model is trained on full sentences, which can achieve good performance in full-sentence evaluation. But for prefix-to-prefix inference, which is crucial for simultaneous translation, the standard machine translation model always perform poorly.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Previous methods for simultaneous translation can be classified as the fixed policy and the adaptive policy according to different simultaneous policies. Fixed policy uses fixedlatency simultaneous strategy, for example, set value K, and forces the translation to lag behind source for K tokens (Ma et al., 2019) . The adaptive policy needs an agent module to perform adaptive simultaneous translation. The agent will consider the current translation state, including the source prefix and the hypothesis prefix, to decide whether to output new tokens at the current state (Gu et al., 2017; Arivazhagan et al., 2019; Ma et al., 2020) . Chunk-base Zhang et al., 2020 ) simultaneous translation is a special adaptive policy, which makes a decision only based on the source prefix.",
"cite_spans": [
{
"start": 295,
"end": 312,
"text": "(Ma et al., 2019)",
"ref_id": "BIBREF10"
},
{
"start": 573,
"end": 590,
"text": "(Gu et al., 2017;",
"ref_id": "BIBREF4"
},
{
"start": 591,
"end": 616,
"text": "Arivazhagan et al., 2019;",
"ref_id": "BIBREF0"
},
{
"start": 617,
"end": 633,
"text": "Ma et al., 2020)",
"ref_id": "BIBREF11"
},
{
"start": 647,
"end": 665,
"text": "Zhang et al., 2020",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In our system, we propose a streaming chunking method that can be combined with a fixed wait-k policy. The streaming chunking method can significantly improve translation quality with little latency increase in low latency regions. We train a segmentation model to detect boundaries in streaming sources and employ a wait-k policy to decide output token numbers. We pre-train transformer models with multi-path wait-k on a",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "#Sentence Pairs",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus",
"sec_num": null
},
{
"text": "Zh-En BSTC CWMT 38K 9M En-Es UN Parallel 22M",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus",
"sec_num": null
},
{
"text": "Zh ASR BSTC AIshell 68h 150h Table 1 : Data statistics. Parallel corpus is counted by sentence pairs. ASR corpus is counted by audio time (hour).",
"cite_spans": [],
"ref_spans": [
{
"start": 29,
"end": 36,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Corpus",
"sec_num": null
},
{
"text": "large general corpus and fine-tune with single k on a small domain corpus. We augment the general corpus and domain corpus with Back-Translation (BT) and Front-Translation (FT), and further augment the domain corpus with character-level pseudo ASR error. In training we incorporate R-Drop (liang et al., 2021) method to improve translation quality. In text-to-text tracks, we use text streaming input provided by the organizer. In the audio-totext track, we train our ASR system to transcript audio into the streaming text as translation input. The remainder of this paper is organized as follows. We describe the techniques employed in our system and the methods we propose in Section 2. In Section 3 we show our experiment settings and results, including data and model. Finally, we conclude this paper.",
"cite_spans": [
{
"start": 282,
"end": 309,
"text": "R-Drop (liang et al., 2021)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus",
"sec_num": null
},
{
"text": "In this section, we describe the data, the utilized prefix-to-prefix translation model, and the proposed streaming chunking method.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methods",
"sec_num": "2"
},
{
"text": "We describe the data used in our system from the following aspects: statistics, preprocessing, filtering and data-augmentation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "All allowed bilingual training sets are employed, including the BSTC and the CWMT21 for the Zh-En track, the UN Parallel Corpus for the En-Es track. For the ASR model in the Zh-En audioto-text track, we use the BSTC and the AIshell (Hui Bu, 2017) corpus for training. Data statistics are shown in Table 1 . Pre-processing. Sacremoses 1 is conducted to normalize and tokenize English and Span-1 https://github.com/alvations/sacremoses ish sentences. Jieba 2 is used to segment Chinese sentences. And redundant spaces in the text are removed. After tokenization, we apply Subword-nmt 3 to learn byte-pair encoding with 32K operations. Data filtering. The noises in the original data may bring a negative impact on translation quality, so we filter the training set as following steps:",
"cite_spans": [],
"ref_spans": [
{
"start": 297,
"end": 304,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "\u2022 First, the parallel corpus is filtered by hand-crafted rules. Sentences that contain less than 30% linguistic words will be viewed as noise sentences. When any sentence in a sentence pair is judged as noise, this pair is discarded. For Chinese sentences, we consider Chinese characters as linguistic words. For En or Es, we consider words only containing alphabet characters as linguistic words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "\u2022 Second, we utilize fast_align 4 to filter out poorly aligned sentence pairs. We calculate align scores for each sentence pair and filter out sentence pairs with low scores. Align score threshold is set as \u22127.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "\u2022 Third, language identification is applied with langid 5 . Sentences in the wrong languages are viewed as low-quality samples and removed.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "\u2022 Finally, we discard duplicate pairs and remove the pair with a length ratio greater than 3.0 or the sentence with a length more than 200.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "Data selection Because the bilingual corpus utilized in training is not all from the speech domain, we use a language-model-based data selection method select domain data, which is similar to methods proposed by Moore and Lewis (2010). We train two 5-gram language model on source sentences with KenLM 6 , one on the BSTC corpus (denoted as lm in ), another on the CWMT corpus (denoted as lm out ). Than for each sentence in the CWMT corpus, we compute the perplexity distances with two language model, which denoted as domain score for the sentence ppl_score = \u2212(ppl in \u2212 ppl out ). We sort the corpus by domain score and remove the pair with a large domain distance. Data augmentation As the training corpus is limited, we utilize back-translation (BT) and front-translation (FT) to augment the training corpus. We first train two translation models in two directions: Zh-En and En-Zh, then generate pseudo training corpus in two directions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "2.1"
},
{
"text": "R-Drop 7 is a method to improve translation quality in machine translation, which can be easily incorporated with our translation model. All models in our system are trained with the R-Drop algorithm proposed by liang et al. (2021).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "R-Drop",
"sec_num": "2.2"
},
{
"text": "Wait-k is a simple and effective method for fixed-policy simultaneous translation, which can train prefix-to-prefix translation ability for transformer models. We build our system based on fairseq, which provides a wait-k baseline similar to efficient wait-k (Elbayad et al., 2020) . Two-stage training is employed to achieve better performance in the speech domain. Model is firstly trained on large scale parallel corpus with multi-path wait-k, which randomly selects a value of k within the interval (for example, [k, k+n]) for each training batch (denoted as wait(k)-(k+n)). Secondly, we fine-tune the model with a small speech domain parallel corpus with simple wait-k (denoted as wait(k)) or multi-path wait-k.",
"cite_spans": [
{
"start": 259,
"end": 281,
"text": "(Elbayad et al., 2020)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Wait-k",
"sec_num": "2.3"
},
{
"text": "In a streaming translation system, the source is received token by token. The wait-k policy will try to translate each time source is ahead of target for k tokens, which may bring some mistakes when the source stops at a partial phrase. Especially for Chinese streaming input, in which source streaming is growing by character. So some source prefixes may contain incomplete word pieces which may cause misunderstanding and incorrect translation. A stream case with error source prefixes is shown in Table 2 . We propose a streaming chunking method, which employs a streaming segmentation model to detect word boundaries on-the-fly in streaming input.",
"cite_spans": [],
"ref_spans": [
{
"start": 500,
"end": 507,
"text": "Table 2",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Streaming Chunking",
"sec_num": "2.4"
},
{
"text": "We build our streaming segmentation model base on chinese-roberta-wwm-ext 8 proposed by Cui et al. (2021) . Compared with a vanilla Chinese word segmentation model, the streaming segmentation model does not need to obtain the complete sentence and can segment words without introducing an additional delay. We treat the streaming word segmentation task as a sequence classification task and use the final hidden state of the classification token ([CLS]) to perform binary classification through a 3-layer fully connected network to determine whether the current source sentence prefix end with complete words. We construct training data using transcribed sentences from the BSTC training set. The complete sentences in the training data are segmented using pkuseg (Luo et al., 2019) . The source sentence prefixes ending with word boundaries are considered positive examples, while the rest of the source sentence prefixes are negative examples.",
"cite_spans": [
{
"start": 88,
"end": 105,
"text": "Cui et al. (2021)",
"ref_id": "BIBREF2"
},
{
"start": 764,
"end": 782,
"text": "(Luo et al., 2019)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Streaming Segmentation Model",
"sec_num": "2.4.1"
},
{
"text": "We utilize the streaming segmentation model to detect word boundaries and only enable the wait-k policy at the word boundaries to determine word numbers that need to translate. Then the prefix-to-prefix translation is performed, which can avoid translating on source prefix containing incomplete words. Algorithm 1 gives the pseudo code of our proposed method. And Figure 1 shows how the streaming segmentation model works with the wait-k inference.",
"cite_spans": [],
"ref_spans": [
{
"start": 365,
"end": 373,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Combine with wait-k",
"sec_num": "2.4.2"
},
{
"text": "We evaluate our simultaneous translation model in two aspects. First is translation quality, we compute BLEU (Papineni et al., 2002) score with merged document translation results. Second, for latency, we utilize Average Lagging (AL) (Ma et al., 2019) Target: ideal simultaneous interpretation, which is calculated in the following equation:",
"cite_spans": [
{
"start": 109,
"end": 132,
"text": "(Papineni et al., 2002)",
"ref_id": "BIBREF13"
},
{
"start": 234,
"end": 251,
"text": "(Ma et al., 2019)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "2.5"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "AL = 1 \u03c4 \u03c4 \u2211 j=1 g(j) \u2212 j \u2212 1 \u03b3 where \u03c4 = arg min t [ g(j) = |X| ] \u03b3 = |Y |/|X|",
"eq_num": "(1)"
}
],
"section": "Evaluation",
"sec_num": "2.5"
},
{
"text": "In this section, we describe our experiment settings and results on all the three tracks we participate in.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "3"
},
{
"text": "For the Zh-En text-to-text track, we introduce our experiments in detail, including model configurations, data, as well as results of a strong wait-k baseline and streaming chunking method.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Zh-En text-to-text track",
"sec_num": "3.1"
},
{
"text": "In our experiment, we train transformer-big models with the same parameters in Vaswani et al. (2017) . The token-level batch size is about 100k on 8 GPUs for pre-training in all experiments. The learning rate is set as 5e-4 for pre-training and 5e-6 for fine-tuning, controlled by Adam optimizer (Kingma and Ba, 2015). We pre-train the model for 100000 steps and save the model every 2000 steps. We fine-tune the model for 10000 steps and save every 200 steps (batch size is about 30k).",
"cite_spans": [
{
"start": 79,
"end": 100,
"text": "Vaswani et al. (2017)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Model Configurations",
"sec_num": "3.1.1"
},
{
"text": "We filter the BSTC corpus and the CWMT corpus with methods described in Section 2.1 and apply language-model-based data selection to the CWMT corpus. For the first edition standard transformer model, we mix the BSTC corpus and the CWMT corpus for pre-training, using the BSTC corpus for fine-tuning (denoted as M1). And following is the detail of the M1 model. For the pre-training stage, we show our results in each filtering step in Table 3 . We directly mix the CWMT and the BSTC parallel data as the D0 corpus. The rules-filter discards noise data containing few linguistic words, which improves about 1.3 BLEU. In align-langid-filter, we drop sentence pairs with a align score less than \u22127 and sentences in the wrong languages. In PPL-selection, we use ppl_score computed by the language model to sort sentence pairs and drop sentence pairs with a ppl_score larger than 8000. With alignlangid-filter and PPL-selection, 1.5M sentence pairs are dropped and nearly no BLEU descend is observed. We get the D1 corpus after all the filtering and selection. Further, we up-sample the BSTC corpus 5 times to enlarge the proportion of domain data. The R-Drop method is incorporated and we choose a larger dropout value (default dropout 0.1). Results in 4 show that the R-Drop (\u03b1 = 5) method significantly improves BLEU, and more increase is observed as we employ these methods together. For fine-tuning, we filter the BSTC corpus by hand-crafted rules and train with the consistent R-Drop method in the pretraining. Finally, we integrate the pre-training and the fine-tuning to train the M1 model, and the performance on the development set is shown in Table 7 .",
"cite_spans": [],
"ref_spans": [
{
"start": 435,
"end": 442,
"text": "Table 3",
"ref_id": "TABREF3"
},
{
"start": 1649,
"end": 1656,
"text": "Table 7",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Data",
"sec_num": "3.1.2"
},
{
"text": "As the training corpus is limited, we utilize data augmentation methods. We perform data augmentation with the M1 model, containing forward-translation (FT) and backward- translation (BT) on the pre-training and the fine-tuning corpus. For the pre-training corpus, we leverage the M1 model to perform FT and BT on the D1 corpus, mixed with D1 corpus as the augmented pre-training corpus. Results in Table 5 show FT has better performance than BT. For fine-tuning corpus, we employ the M1 model to translate BSTC corpus in forward and backward paths and add all 5 beam results to the fine-tuning corpus. What's more, to strengthen the robustness of the model, we add char-level augmentation into the fine-tuning corpus, which contains insertion, deletion, duplication, and homophone substitution. For homophone substitution, we use python-pinyin 9 to extract homophone dictionary and substitute homophone characters according to character frequency. Results on the fine-tuning corpus are shown in Table 6 , which indicates that each augmentation method is useful. Finally, we add FT augmentation in pretraining, add FT, and BT as well as character augmentation in fine-tuning. The model trained with augmented pre-training and finetuning is denoted as the M2 models. Significant improvement of the M2 model against the M1 model could be observed in Table 7 .",
"cite_spans": [],
"ref_spans": [
{
"start": 399,
"end": 406,
"text": "Table 5",
"ref_id": null
},
{
"start": 996,
"end": 1003,
"text": "Table 6",
"ref_id": "TABREF6"
},
{
"start": 1348,
"end": 1355,
"text": "Table 7",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Data",
"sec_num": "3.1.2"
},
{
"text": "To improve prefix-to-prefix translation quality, we use wait-k training described in Sec-9 https://github.com/mozillazg/python-pinyin tion 2.3. Using the same training data of the M2 model, we pre-train the model with multipath wait-k and fine-tune with simple wait-k or multi-path wait-k. We report the results of our model on the BSTC development set. All trained model is listed in Table 8 , and we show the AL-BLEU curve of several models. We achieve good performance according to Figure 2 , in which our M2_wait1-9_wait5 model exceeds the PaddlePaddle wait-5 model by at most 8 BLEU. The model trained with small k may achieve better performance in the low-latency regime, but not perform well in the high-latency regime. What's more, we ensemble the top-3 model in each inference k, which shows benefits across all latency regimes. Same as Guo et al. (2022) , standard beam-search is utilized after the source stream is finished. Our models achieve almost consistent performance in high latency regime. Table 8 . PaddlePaddle_wait5 is wait-k model provided by organizer.",
"cite_spans": [
{
"start": 846,
"end": 863,
"text": "Guo et al. (2022)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 385,
"end": 392,
"text": "Table 8",
"ref_id": null
},
{
"start": 485,
"end": 493,
"text": "Figure 2",
"ref_id": "FIGREF1"
},
{
"start": 1009,
"end": 1016,
"text": "Table 8",
"ref_id": null
}
],
"eq_spans": [],
"section": "Wait-k Baseline",
"sec_num": "3.1.3"
},
{
"text": "Pre-training (Augmentation) Data statistic (Pre-training) dev (SacreBleu) M1 (only pre-train) 6.34M 21.48 +FT pre-train 10.95M 22.32 +BT pre-train 11.03M 19.90 Table 5 : Results of data augmentation in the pre-training stage. We use the M1 model to generate the FT and BT augment data and mixed with the D1 corpus for pre-training. 8: Our wait-k models are pre-trained and finetuned on the same data of the M2 model in Section 3.1. We show the K value settings in pre-training and finetuning wait-k training for all M2 wait-k models. Take M2_wait5-15_wait5 for example, we use multi-path wait-k training with K \u2208 [5, 15] for pre-training and use simple wait-k with K = 5 for fine-tuning.",
"cite_spans": [],
"ref_spans": [
{
"start": 160,
"end": 167,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Wait-k Baseline",
"sec_num": "3.1.3"
},
{
"text": "K = 1 M2_wait1-9_wait3 K \u2208 [1, 9] K = 3 M2_wait1-9_wait5 K \u2208 [1, 9] K = 5 M2_wait1-9_wait1-9 K \u2208 [1, 9] K \u2208 [1, 9]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Wait-k Baseline",
"sec_num": "3.1.3"
},
{
"text": "In this section, we add streaming chunking methods. We first fine-tune our segmentation model based on chinese-roberta-wwm-ext on BSTC train set and get 92.0% accuracy and 93.7% F-score on the BSTC development set. Then we employ our segmentation to perform online source chunking to detect word boundaries. The results in Figure 3 show about 2 BLEU improvements in the low-latency regime with a little increase in AL. M2_ensemble_chunk add streaming segmentation model compare to M2_ensemble.",
"cite_spans": [],
"ref_spans": [
{
"start": 323,
"end": 331,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Streaming Chunking",
"sec_num": "3.1.4"
},
{
"text": "For En-Es text-to-text track, we use the same data filtering rules on the UN-parallel corpus. Because of lacking speech corpus, we didn't perform data selection and augmentation. Standard and wait1-11 transformers are trained and we report our results on the devel-opment set in Figure 4 . ",
"cite_spans": [],
"ref_spans": [
{
"start": 279,
"end": 287,
"text": "Figure 4",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "En-Es text-to-text track",
"sec_num": "3.2"
},
{
"text": "In Zh-En audio-to-text track, we train a simple transformer ASR model 10 with audio from BSTC and AIshell. The audio wav files are segmented by Silero-VAD(Team, 2021) and we achieve 0.38 WER on development and 0.28 WER on the test. And we perform simultaneous decoding on the ASR transcriptions with the same model and settings in the text-to-text track. Results show on development Figure 5 shows that the translation BLEU dropped by about 10 BLEU on audio input. ",
"cite_spans": [],
"ref_spans": [
{
"start": 383,
"end": 391,
"text": "Figure 5",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Zh-En audio-to-text track",
"sec_num": "3.3"
},
{
"text": "We elaborate on the BIT-Xiaomi simultaneous translation system in this paper. We investigate data filtering and augmentation to enlarge high-quality corpus and utilize the R-Drop method to improve translation quality. We train our simultaneous translation models 10 https://github.com/facebookresearch/fairseq /blob/main/examples/speech_to_text/docs/ mustc_example.md based on the wait-k strategy, and the streaming chunking method is employed to avoid segmentation errors in the source stream. The results on Zh-En text-to-text track indicate that the streaming chunking method can be integrated with the streaming decoding and improves translation quality. The slightly worse quality on the audio track suggests that the ASR error may affect translation quality much. In the future, we will explore better streaming ASR models and try more interesting simultaneous policies to get better latency and quality.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "4"
},
{
"text": "https://github.com/fxsjy/jieba 3 https://github.com/rsennrich/subword-nmt 4 https://github.com/clab/fast_align 5 https://github.com/saffsd/langid.py 6 https://github.com/kpu/kenlm",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://github.com/dropreg/R-Drop",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://huggingface.co/hfl/chinese-roberta-wwmext",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work is supported by the National Key RD Program of China (No. 2020AAA0106600).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Monotonic infinite lookback attention for simultaneous machine translation",
"authors": [
{
"first": "Naveen",
"middle": [],
"last": "Arivazhagan",
"suffix": ""
},
{
"first": "Colin",
"middle": [],
"last": "Cherry",
"suffix": ""
},
{
"first": "Wolfgang",
"middle": [],
"last": "Macherey",
"suffix": ""
},
{
"first": "Chung-Cheng",
"middle": [],
"last": "Chiu",
"suffix": ""
},
{
"first": "Semih",
"middle": [],
"last": "Yavuz",
"suffix": ""
},
{
"first": "Ruoming",
"middle": [],
"last": "Pang",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Colin",
"middle": [],
"last": "Raffel",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. of ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, and Colin Raffel. 2019. Monotonic infinite lookback attention for simul- taneous machine translation. In Proc. of ACL.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Can neural machine translation do simultaneous translation?",
"authors": [
{
"first": "Kyunghyun",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "Masha",
"middle": [],
"last": "Esipova",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1606.02012"
]
},
"num": null,
"urls": [],
"raw_text": "Kyunghyun Cho and Masha Esipova. 2016. Can neural machine translation do simultaneous translation? arXiv preprint arXiv:1606.02012.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Pre-training with whole word masking for chinese bert",
"authors": [
{
"first": "Yiming",
"middle": [],
"last": "Cui",
"suffix": ""
},
{
"first": "Wanxiang",
"middle": [],
"last": "Che",
"suffix": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Ziqing",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Shijin",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Guoping",
"middle": [],
"last": "Hu",
"suffix": ""
}
],
"year": 2021,
"venue": "Speech, and Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, and Guoping Hu. 2021. Pre-training with whole word masking for chinese bert. IEEE/ACM Transactions on Au- dio, Speech, and Language Processing.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Efficient Wait-k Models for Simultaneous Machine Translation",
"authors": [
{
"first": "Maha",
"middle": [],
"last": "Elbayad",
"suffix": ""
},
{
"first": "Laurent",
"middle": [],
"last": "Besacier",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Verbeek",
"suffix": ""
}
],
"year": 2020,
"venue": "Proc. of Interspeech",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Maha Elbayad, Laurent Besacier, and Jakob Ver- beek. 2020. Efficient Wait-k Models for Simul- taneous Machine Translation. In Proc. of Inter- speech.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Learning to translate in real-time with neural machine translation",
"authors": [
{
"first": "Jiatao",
"middle": [],
"last": "Gu",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
},
{
"first": "Kyunghyun",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "O",
"middle": [
"K"
],
"last": "Victor",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2017,
"venue": "Proc. of EACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiatao Gu, Graham Neubig, Kyunghyun Cho, and Victor O.K. Li. 2017. Learning to translate in real-time with neural machine translation. In Proc. of EACL.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "The xiaomi text-totext simultaneous speech translation system for IWSLT 2022",
"authors": [
{
"first": "Bao",
"middle": [],
"last": "Guo",
"suffix": ""
},
{
"first": "Mengge",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Wen",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Hexuan",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Chang",
"middle": [],
"last": "Mu",
"suffix": ""
},
{
"first": "Xiang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jianwei",
"middle": [],
"last": "Cui",
"suffix": ""
},
{
"first": "Bin",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Yuhang",
"middle": [],
"last": "Guo",
"suffix": ""
}
],
"year": 2022,
"venue": "Proceedings of the 19th International Conference on Spoken Language Translation",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bao Guo, Mengge Liu, Wen Zhang, Hexuan Chen, Chang Mu, Xiang Li, Jianwei Cui, Bin Wang, and Yuhang Guo. 2022. The xiaomi text-to- text simultaneous speech translation system for IWSLT 2022. In Proceedings of the 19th Inter- national Conference on Spoken Language Trans- lation (IWSLT 2022).",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline",
"authors": [],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xingyu Na Bengu Wu Hao Zheng Hui Bu, Ji- ayu Du. 2017. Aishell-1: An open-source man- darin speech corpus and a speech recognition baseline. In Oriental COCOSDA 2017.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Adam: A method for stochastic optimization",
"authors": [
{
"first": "P",
"middle": [],
"last": "Diederick",
"suffix": ""
},
{
"first": "Jimmy",
"middle": [],
"last": "Kingma",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ba",
"suffix": ""
}
],
"year": 2015,
"venue": "Proc. of ICLR",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Diederick P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proc. of ICLR.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "R-drop: Regularized dropout for neural networks",
"authors": [
{
"first": "Lijun",
"middle": [],
"last": "Xiaobo Liang",
"suffix": ""
},
{
"first": "Juntao",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Yue",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Qi",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Meng",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Min",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Tie-Yan",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2021,
"venue": "Proc. of NeurIPS",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "xiaobo liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, and Tie-Yan Liu. 2021. R-drop: Regular- ized dropout for neural networks. In Proc. of NeurIPS.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Pkuseg: A toolkit for multi-domain chinese word segmentation",
"authors": [
{
"first": "Ruixuan",
"middle": [],
"last": "Luo",
"suffix": ""
},
{
"first": "Jingjing",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xuancheng",
"middle": [],
"last": "Ren",
"suffix": ""
},
{
"first": "Xu",
"middle": [],
"last": "Sun",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruixuan Luo, Jingjing Xu, Yi Zhang, Xuancheng Ren, and Xu Sun. 2019. Pkuseg: A toolkit for multi-domain chinese word segmentation. CoRR.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework",
"authors": [
{
"first": "Mingbo",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Hao",
"middle": [],
"last": "Xiong",
"suffix": ""
},
{
"first": "Renjie",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Kaibo",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Baigong",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hairong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Xing",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. of ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, and Haifeng Wang. 2019. STACL: Simulta- neous translation with implicit anticipation and controllable latency using prefix-to-prefix frame- work. In Proc. of ACL.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Monotonic multihead attention",
"authors": [
{
"first": "Xutai",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Juan",
"middle": [
"Miguel"
],
"last": "Pino",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Cross",
"suffix": ""
},
{
"first": "Liezl",
"middle": [],
"last": "Puzon",
"suffix": ""
},
{
"first": "Jiatao",
"middle": [],
"last": "Gu",
"suffix": ""
}
],
"year": 2020,
"venue": "Proc. of ICLR",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xutai Ma, Juan Miguel Pino, James Cross, Liezl Puzon, and Jiatao Gu. 2020. Monotonic multi- head attention. In Proc. of ICLR.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Intelligent selection of language model training data",
"authors": [
{
"first": "C",
"middle": [],
"last": "Robert",
"suffix": ""
},
{
"first": "William",
"middle": [],
"last": "Moore",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lewis",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the ACL 2010 conference short papers",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robert C Moore and William Lewis. 2010. Intelli- gent selection of language model training data. In Proceedings of the ACL 2010 conference short papers.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Bleu: a method for automatic evaluation of machine translation",
"authors": [
{
"first": "Kishore",
"middle": [],
"last": "Papineni",
"suffix": ""
},
{
"first": "Salim",
"middle": [],
"last": "Roukos",
"suffix": ""
},
{
"first": "Todd",
"middle": [],
"last": "Ward",
"suffix": ""
},
{
"first": "Wei-Jing",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2002,
"venue": "Proc. of ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for au- tomatic evaluation of machine translation. In Proc. of ACL.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Silero vad: pre-trained enterprise-grade voice activity detector (vad), number detector and language classifier",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Silero Team. 2021. Silero vad: pre-trained enterprise-grade voice activity detector (vad), number detector and language classifier.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Attention is all you need",
"authors": [
{
"first": "Ashish",
"middle": [],
"last": "Vaswani",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Aidan",
"middle": [
"N"
],
"last": "Gomez",
"suffix": ""
},
{
"first": "\u0141ukasz",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "Illia",
"middle": [],
"last": "Polosukhin",
"suffix": ""
}
],
"year": 2017,
"venue": "Proc. of NeurIPS",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. At- tention is all you need. Proc. of NeurIPS.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Dutongchuan: Context-aware translation model for simultaneous interpreting",
"authors": [
{
"first": "Hao",
"middle": [],
"last": "Xiong",
"suffix": ""
},
{
"first": "Ruiqing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1907.12984"
]
},
"num": null,
"urls": [],
"raw_text": "Hao Xiong, Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. 2019. Dutongchuan: Context-aware transla- tion model for simultaneous interpreting. arXiv preprint arXiv:1907.12984.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Incremental segmentation and decoding strategies for simultaneous translation",
"authors": [
{
"first": "Mahsa",
"middle": [],
"last": "Yarmohammadi",
"suffix": ""
},
{
"first": "Vivek",
"middle": [],
"last": "Kumar Rangarajan",
"suffix": ""
},
{
"first": "Srinivas",
"middle": [],
"last": "Sridhar",
"suffix": ""
},
{
"first": "Baskaran",
"middle": [],
"last": "Bangalore",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sankaran",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the Sixth International Joint Conference on Natural Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mahsa Yarmohammadi, Vivek Kumar Rangara- jan Sridhar, Srinivas Bangalore, and Baskaran Sankaran. 2013. Incremental segmentation and decoding strategies for simultaneous translation. In Proceedings of the Sixth International Joint Conference on Natural Language Processing.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "BSTC: A large-scale Chinese-English speech translation dataset",
"authors": [
{
"first": "Ruiqing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xiyang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Zhi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Ying",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Qinfei",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the Second Workshop on Automatic Simultaneous Translation",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruiqing Zhang, Xiyang Wang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Zhi Li, Haifeng Wang, Ying Chen, and Qinfei Li. 2021. BSTC: A large-scale Chinese-English speech translation dataset. In Proceedings of the Second Workshop on Automatic Simultaneous Translation.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Learning adaptive segmentation policy for simultaneous translation",
"authors": [
{
"first": "Ruiqing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Chuanqiang",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhongjun",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Haifeng",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2020,
"venue": "Proc. of EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruiqing Zhang, Chuanqiang Zhang, Zhongjun He, Hua Wu, and Haifeng Wang. 2020. Learning adaptive segmentation policy for simultaneous translation. In Proc. of EMNLP.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "This example shows how the streaming segmentation model works with a wait-k model. The solid lines are the translation points of our proposed method, and the dashed lines are the additional possible translation points of the wait-k model.Algorithm 1: Wait-k decoding with the streaming chunking method Input: the translation model M t , the chunking model M c , the source sequence x, wait-k lagging K Output: The translated sentence\u0177 1 Initialization: the read token sequenc\u00ea x = [], the output sentence\u0177 = [],the incomplete word read x p = \u2032\u2032 2 while |\u0177| \u0338 = '</s>' x p + x.next_char() // x p is a complete word 8 if M c (x, x p ) then 9",
"num": null,
"uris": null
},
"FIGREF1": {
"type_str": "figure",
"text": "Results of M2 wait-k models. Models are list in",
"num": null,
"uris": null
},
"FIGREF2": {
"type_str": "figure",
"text": "Results of streaming chunking method.",
"num": null,
"uris": null
},
"FIGREF3": {
"type_str": "figure",
"text": "Results of En-Es text-to-text track. BLEU is computed in document level with Mteval-v13a.",
"num": null,
"uris": null
},
"FIGREF4": {
"type_str": "figure",
"text": "Results of Zh-En audio-to-text track. BLEU is computed in document level with Mteval-v13a.",
"num": null,
"uris": null
},
"TABREF1": {
"type_str": "table",
"num": null,
"html": null,
"text": "",
"content": "<table/>"
},
"TABREF2": {
"type_str": "table",
"num": null,
"html": null,
"text": "",
"content": "<table><tr><td>Orig BSTC+CWMT (D0)</td><td>9.1M</td><td>16.82</td></tr><tr><td>+rules-filter</td><td>7.7M</td><td>18.09</td></tr><tr><td>+align-langid-filter</td><td>7.2M</td><td>18.04</td></tr><tr><td>+PPL-selection (D1)</td><td>6.2M</td><td>17.99</td></tr></table>"
},
"TABREF3": {
"type_str": "table",
"num": null,
"html": null,
"text": "Data filtering and selection in the pre-training stage. BLEU is computed by ScareBleu in sentence-level.Filtering and selection methods are applied incrementally.",
"content": "<table><tr><td>Pre-training (method)</td><td colspan=\"2\">Data statistic dev (SacreBleu)</td></tr><tr><td>BSTC+CWMT (D1)</td><td>6.2M</td><td>17.99</td></tr><tr><td>+up-sampling</td><td>6.34M</td><td>18.40</td></tr><tr><td>+dropout 0.25</td><td>6.2M</td><td>18.59</td></tr><tr><td>+R-Drop (\u03b1 = 5)</td><td>6.2M</td><td>19.72</td></tr><tr><td>+up-sampling + dropout 0.25 + R-Drop</td><td>6.34M</td><td>21.48</td></tr></table>"
},
"TABREF4": {
"type_str": "table",
"num": null,
"html": null,
"text": "Data statistic and BLEU on the development of our pre-training methods. BLEU is computed by ScareBleu in sentence-level.",
"content": "<table/>"
},
"TABREF6": {
"type_str": "table",
"num": null,
"html": null,
"text": "Results of data augmentation in the fine-tuning stage. The M1 model is leveraged to generate FT and BT augment data, and beam 5 results are saved. For the char-aug, we use character-level augmentations including insertion, deletion, duplication, and homophone substitution. The models in this table are all based on the same pre-trained model.",
"content": "<table><tr><td colspan=\"3\">Model dev (SacreBleu) dev (Mteval-v13a)</td></tr><tr><td>M1</td><td>22.43</td><td>27.26</td></tr><tr><td>M2</td><td>23.62</td><td>28.96</td></tr></table>"
},
"TABREF7": {
"type_str": "table",
"num": null,
"html": null,
"text": "Results of data augmentation on standard transformer model. The M1 model is trained with pretraining and fine-tuning. The M2 model leverage data augmentation in both the pre-training and the finetuning stage.",
"content": "<table><tr><td>Model name</td><td>Pre-train Fine-tune</td></tr><tr><td colspan=\"2\">M2_wait5-15_wait5 M2_wait5-15_wait7 M2_wait5-15_wait9 M2_wait5-15_wait11 M2_wait5-15_wait13 M2_wait5-15_wait15 M2_wait5-15_wait5-15 K \u2208 [5, 15] K \u2208 [5, 15] K \u2208 [5, 15] K = 5 K \u2208 [5, 15] K = 7 K \u2208 [5, 15] K = 9 K \u2208 [5, 15] K = 11 K \u2208 [5, 15] K = 13 K \u2208 [5, 15] K = 15 M2_wait1-9_wait1 K \u2208 [1, 9]</td></tr></table>"
},
"TABREF8": {
"type_str": "table",
"num": null,
"html": null,
"text": "",
"content": "<table/>"
}
}
}
} |