File size: 112,965 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 | {
"paper_id": "P13-1017",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:33:32.500680Z"
},
"title": "Word Alignment Modeling with Context Dependent Deep Neural Network",
"authors": [
{
"first": "Nan",
"middle": [],
"last": "Yang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Science and Technology of China",
"location": {
"settlement": "Hefei",
"country": "China"
}
},
"email": "v-nayang@microsoft.com"
},
{
"first": "Shujie",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Microsoft Research Asia",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": ""
},
{
"first": "Mu",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Microsoft Research Asia",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "muli@microsoft.com"
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Microsoft Research Asia",
"location": {
"settlement": "Beijing",
"country": "China"
}
},
"email": "mingzhou@microsoft.com"
},
{
"first": "Nenghai",
"middle": [],
"last": "Yu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Science and Technology of China",
"location": {
"settlement": "Hefei",
"country": "China"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNN-HMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale English-Chinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.",
"pdf_parse": {
"paper_id": "P13-1017",
"_pdf_hash": "",
"abstract": [
{
"text": "In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNN-HMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale English-Chinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Recent years research communities have seen a strong resurgent interest in modeling with deep (multi-layer) neural networks. This trending topic, usually referred under the name Deep Learning, is started by ground-breaking papers such as (Hinton et al., 2006) , in which innovative training procedures of deep structures are proposed. Unlike shallow learning methods, such as Support Vector Machine, Conditional Random Fields, and Maximum Entropy, which need hand-craft features as input, DNN can learn suitable features (representations) automatically with raw input data, given a training objective.",
"cite_spans": [
{
"start": 238,
"end": 259,
"text": "(Hinton et al., 2006)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "DNN did not achieve expected success until 2006, when researchers discovered a proper way to intialize and train the deep architectures, which contains two phases: layer-wise unsupervised pretraining and supervised fine tuning. For pretraining, Restricted Boltzmann Machine (RBM) (Hinton et al., 2006) , auto-encoding (Bengio et al., 2007) and sparse coding (Lee et al., 2007) are proposed and popularly used. The unsupervised pretraining trains the network one layer at a time, and helps to guide the parameters of the layer towards better regions in parameter space (Bengio, 2009) . Followed by fine tuning in this region, DNN is shown to be able to achieve state-of-the-art performance in various area, or even better (Dahl et al., 2012) (Kavukcuoglu et al., 2010) . DNN also achieved breakthrough results on the ImageNet dataset for objective recognition (Krizhevsky et al., 2012) . For speech recognition, (Dahl et al., 2012) proposed context-dependent neural network with large vocabulary, which achieved 16.0% relative error reduction.",
"cite_spans": [
{
"start": 280,
"end": 301,
"text": "(Hinton et al., 2006)",
"ref_id": "BIBREF13"
},
{
"start": 318,
"end": 339,
"text": "(Bengio et al., 2007)",
"ref_id": null
},
{
"start": 358,
"end": 376,
"text": "(Lee et al., 2007)",
"ref_id": "BIBREF19"
},
{
"start": 568,
"end": 582,
"text": "(Bengio, 2009)",
"ref_id": "BIBREF3"
},
{
"start": 721,
"end": 740,
"text": "(Dahl et al., 2012)",
"ref_id": "BIBREF9"
},
{
"start": 741,
"end": 767,
"text": "(Kavukcuoglu et al., 2010)",
"ref_id": "BIBREF14"
},
{
"start": 859,
"end": 884,
"text": "(Krizhevsky et al., 2012)",
"ref_id": "BIBREF16"
},
{
"start": 911,
"end": 930,
"text": "(Dahl et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "DNN has also been applied in Natural Language Processing (NLP) field. Most works convert atomic lexical entries into a dense, low dimensional, real-valued representation, called word embedding; Each dimension represents a latent aspect of a word, capturing its semantic and syntactic properties (Bengio et al., 2006) . Word embedding is usually first learned from huge amount of monolingual texts, and then fine-tuned with taskspecific objectives. (Collobert et al., 2011) and (Socher et al., 2011) further apply Recursive Neural Networks to address the structural prediction tasks such as tagging and parsing, and (Socher et al., 2012) explores the compositional aspect of word representations.",
"cite_spans": [
{
"start": 295,
"end": 316,
"text": "(Bengio et al., 2006)",
"ref_id": "BIBREF0"
},
{
"start": 448,
"end": 472,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF8"
},
{
"start": 477,
"end": 498,
"text": "(Socher et al., 2011)",
"ref_id": "BIBREF27"
},
{
"start": 615,
"end": 636,
"text": "(Socher et al., 2012)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Inspired by successful previous works, we propose a new DNN-based word alignment method, which exploits contextual and semantic similarities between words. As shown in example (a) of Figure 1 , in word pair {\"juda\" \u21d2\"mammoth\"}, the Chinese word \"juda\" is a common word, but the English word \"mammoth\" is not, so it is very hard to align them correctly. If we know that \"mammoth\" has the similar meaning with \"big\", or \"huge\", it would be easier to find the corresponding word in the Chinese sentence. As we mentioned in the last paragraph, word embedding (trained with huge monolingual texts) has the ability to map a word into a vector space, in which, similar words are near each other. For example (b) in Figure 1 , for the word pair {\"yibula\" \u21d2 \"Yibula\"}, both the Chinese word \"yibula\" and English word \"Yibula\" are rare name entities, but the words around them are very common, which are {\"nongmin\", \"shuo\"} for Chinese side and {\"farmer\", \"said\"} for the English side. The pattern of the context {\"nongmin X shuo\" \u21d2 \"farmer X said\"} may help to align the word pair which fill the variable X, and also, the pattern {\"yixiang X gongcheng\" \u21d2 \"a X job\"} is helpful to align the word pair {\"juda\" \u21d2\"mammoth\"} for example (a).",
"cite_spans": [],
"ref_spans": [
{
"start": 183,
"end": 191,
"text": "Figure 1",
"ref_id": "FIGREF0"
},
{
"start": 708,
"end": 716,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Based on the above analysis, in this paper, both the words in the source and target sides are firstly mapped to a vector via a discriminatively trained word embeddings, and word pairs are scored by a multi-layer neural network which takes rich contexts (surrounding words on both source and target sides) into consideration; and a HMM-like distortion model is applied on top of the neural network to characterize structural aspect of bilingual sentences.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In the rest of this paper, related work about DNN and word alignment are first reviewed in Section 2, followed by a brief introduction of DNN in Section 3. We then introduce the details of leveraging DNN for word alignment, including the details of our network structure in Section 4 and the training method in Section 5. The merits of our approach are illustrated with the experiments described in Section 6, and we conclude our paper in Section 7.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "DNN with unsupervised pre-training was firstly introduced by (Hinton et al., 2006) for MNIST digit image classification problem, in which, RBM was introduced as the layer-wise pre-trainer. The layer-wise pre-training phase found a better local maximum for the multi-layer network, thus led to improved performance. (Krizhevsky et al., 2012) proposed to apply DNN to do object recognition task (ImageNet dataset), which brought down the state-of-the-art error rate from 26.1% to 15.3%. (Seide et al., 2011) and (Dahl et al., 2012) apply Context-Dependent Deep Neural Network with HMM (CD-DNN-HMM) to speech recognition task, which significantly outperforms traditional models.",
"cite_spans": [
{
"start": 61,
"end": 82,
"text": "(Hinton et al., 2006)",
"ref_id": "BIBREF13"
},
{
"start": 315,
"end": 340,
"text": "(Krizhevsky et al., 2012)",
"ref_id": "BIBREF16"
},
{
"start": 485,
"end": 505,
"text": "(Seide et al., 2011)",
"ref_id": "BIBREF25"
},
{
"start": 510,
"end": 529,
"text": "(Dahl et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Most methods using DNN in NLP start with a word embedding phase, which maps words into a fixed length, real valued vectors. (Bengio et al., 2006) proposed to use multi-layer neural network for language modeling task. (Collobert et al., 2011) applied DNN on several NLP tasks, such as part-of-speech tagging, chunking, name entity recognition, semantic labeling and syntactic parsing, where they got similar or even better results than the state-of-the-art on these tasks. (Niehues and Waibel, 2012) shows that machine translation results can be improved by combining neural language model with n-gram traditional language. (Son et al., 2012) improves translation quality of n-gram translation model by using a bilingual neural language model. (Titov et al., 2012 ) learns a context-free cross-lingual word embeddings to facilitate cross-lingual information retrieval.",
"cite_spans": [
{
"start": 124,
"end": 145,
"text": "(Bengio et al., 2006)",
"ref_id": "BIBREF0"
},
{
"start": 217,
"end": 241,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF8"
},
{
"start": 472,
"end": 498,
"text": "(Niehues and Waibel, 2012)",
"ref_id": "BIBREF23"
},
{
"start": 623,
"end": 641,
"text": "(Son et al., 2012)",
"ref_id": "BIBREF29"
},
{
"start": 743,
"end": 762,
"text": "(Titov et al., 2012",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "For the related works of word alignment, the most popular methods are based on generative models such as IBM Models (Brown et al., 1993) and HMM (Vogel et al., 1996) . Discriminative approaches are also proposed to use hand crafted features to improve word alignment. Among them, (Liu et al., 2010) proposed to use phrase and rule pairs to model the context information in a loglinear framework. Unlike previous discriminative methods, in this work, we do not resort to any hand crafted features, but use DNN to induce \"features\" from raw words.",
"cite_spans": [
{
"start": 116,
"end": 136,
"text": "(Brown et al., 1993)",
"ref_id": "BIBREF6"
},
{
"start": 145,
"end": 165,
"text": "(Vogel et al., 1996)",
"ref_id": "BIBREF32"
},
{
"start": 280,
"end": 298,
"text": "(Liu et al., 2010)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The most important and prevalent features available in NLP are the words themselves. To apply DNN to NLP task, the first step is to transform a discrete word into its word embedding, a low dimensional, dense, real-valued vector (Bengio et al., 2006) . Word embeddings often implicitly encode syntactic or semantic knowledge of the words. Assuming a finite sized vocabulary V , word embeddings form a (L\u00d7|V |)-dimension embedding matrix W V , where L is a pre-determined embedding length; mapping words to embeddings is done by simply looking up their respective columns in the embedding matrix W V . The lookup process is called a lookup layer LT , which is usually the first layer after the input layer in neural network.",
"cite_spans": [
{
"start": 228,
"end": 249,
"text": "(Bengio et al., 2006)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "After words have been transformed to their embeddings, they can be fed into subsequent classical network layers to model highly non-linear relations:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "z l = f l (M l z l\u22121 + b l )",
"eq_num": "(1)"
}
],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "where",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "z l is the output of lth layer, M l is a |z l | \u00d7 |z l\u22121 | matrix, b",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "l is a |z l |-length vector, and f l is an activation function. Except for the last layer, f l must be non-linear. Common choices for f l include sigmoid function, hyperbolic function, \"hard\" hyperbolic function etc. Following (Collobert et al., 2011), we choose \"hard\" hyperbolic function as our activation function in this work:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "htanh(x) = \uf8f1 \uf8f2 \uf8f3 1 if x is greater than 1 \u22121",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "if x is less than -1 x otherwise (2) If probabilistic interpretation is desired, a softmax layer (Bridle, 1990) can be used to do normalization:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "z l i = e z l\u22121 i |z l | j=1 e z l\u22121 j",
"eq_num": "(3)"
}
],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "The above layers can only handle fixed sized input and output. If input must be of variable length, convolution layer and max layer can be used, (Collobert et al., 2011) which transform variable length input to fixed length vector for further processing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "Multi-layer neural networks are trained with the standard back propagation algorithm (LeCun, 1985) . As the networks are non-linear and the task specific objectives usually contain many local maximums, special care must be taken in the optimization process to obtain good parameters. Techniques such as layerwise pre-training (Bengio et al., 2007) and many tricks (LeCun et al., 1998) have been developed to train better neural networks. Besides that, neural network training also involves some hyperparameters such as learning rate, the number of hidden layers. We will address these issues in section 4.",
"cite_spans": [
{
"start": 85,
"end": 98,
"text": "(LeCun, 1985)",
"ref_id": "BIBREF18"
},
{
"start": 326,
"end": 347,
"text": "(Bengio et al., 2007)",
"ref_id": null
},
{
"start": 364,
"end": 384,
"text": "(LeCun et al., 1998)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "DNN structures for NLP",
"sec_num": "3"
},
{
"text": "Our DNN word alignment model extends classic HMM word alignment model (Vogel et al., 1996) . Given a sentence pair (e, f), HMM word alignment takes the following form:",
"cite_spans": [
{
"start": 70,
"end": 90,
"text": "(Vogel et al., 1996)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "P (a, e|f) = |e| i=1 P lex (e i |f a i )P d (a i \u2212 a i\u22121 ) (4)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "where P lex is the lexical translation probability and P d is the jump distance distortion probability.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "One straightforward way to integrate DNN into HMM is to use neural network to compute the emission (lexical translation) probability P lex . Such approach requires a softmax layer in the neural network to normalize over all words in source vocabulary. As vocabulary for natural languages is usually very large, it is prohibitively expensive to do the normalization. Hence we give up the probabilistic interpretation and resort to a nonprobabilistic, discriminative view:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "s N N (a|e, f) = |e| i=1 t lex (e i , f a i |e, f)t d (a i , a i\u22121 |e, f) (5)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "where t lex is a lexical translation score computed by neural network, and t d is a distortion score.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "In the classic HMM word alignment model, context is not considered in the lexical translation probability. Although we can rewrite P lex (e i |f a i ) to P lex (e i |context of f a i ) to model context, it introduces too many additional parameters and leads to serious over-fitting problem due to data sparseness. As a matter of fact, even without any contexts, the lexical translation table in HMM already contains O(|V e | * |V f |) parameters, where |V e | and V f denote source and target vocabulary sizes. In contrast, our model does not maintain a separate translation score parameters for every source-target word pair, but computes t lex through a multi-layer network, which naturally handles contexts on both sides without explosive growth of number of parameters.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "DNN for word alignment",
"sec_num": "4"
},
{
"text": "Target window f",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": ") ( 3 2 3 b z M \uf02b \uf0b4 ) ( 2 1 2 b z M \uf02b \uf0b4 i i-1 i+1 j-1 j j+1 Lookup LT 0 z Layer f1 1 z Layer f2 2 z \u519c\u6c11 \u4f0a\u5e03\u62c9 \u8bf4 farmer yibula said ) ( 1 0 1 b z M \uf02b \uf0b4 htanh htanh Layer f3 ) , | , ( f e f e t j i lex",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "Figure 2: Network structure for computing context dependent lexical translation scores. The example computes translation score for word pair (yibula, yibulayin) given its surrounding context. Figure 2 shows the neural network we used to compute context dependent lexical translation score t lex . For word pair (e i , f j ), we take fixed length windows surrounding both e i and f j as input:",
"cite_spans": [],
"ref_spans": [
{
"start": 192,
"end": 200,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "(e i\u2212 sw 2 , . . . , e i+ sw 2 , f j\u2212 tw 2 , . . . , f j+ tw 2 )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": ", where sw, tw stand window sizes on source and target side respectively. Words are converted to embeddings using the lookup table LT , and the catenation of embeddings are fed to a classic neural network with two hidden-layers, and the output of the network is the our lexical translation score:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "t lex (e i , f j |e, f) = f 3 \u2022 f 2 \u2022 f 1 \u2022 LT (window(e i ), window(f j ))",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "(6) f 1 and f 2 layers use htanh as activation functions, while f 3 is only a linear transformation with no activation function.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "For the distortion t d , we could use a lexicalized distortion model:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "t d (a i , a i\u22121 |e, f) = t d (a i \u2212 a i\u22121 |window(f a i\u22121 ))",
"eq_num": "(7)"
}
],
"section": "Source window e",
"sec_num": null
},
{
"text": "which can be computed by a neural network similar to the one used to compute lexical translation scores. If we map jump distance (a i \u2212 a i\u22121 ) to B buckets, we can change the length of the output layer to B, where each dimension in the output stands for a different bucket of jump distances. But we found in our initial experiments on small scale data, lexicalized distortion does not produce better alignment over the simple jumpdistance based model. So we drop the lexicalized distortion and reverse to the simple version:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "t d (a i , a i\u22121 |e, f) = t d (a i \u2212 a i\u22121 )",
"eq_num": "(8)"
}
],
"section": "Source window e",
"sec_num": null
},
{
"text": "Vocabulary V of our alignment model consists of a source vocabulary V e and a target vocabulary V f . As in (Collobert et al., 2011) , in addition to real words, each vocabulary contains a special unknown word symbol unk to handle unseen words; two sentence boundary symbols s and /s , which are filled into surrounding window when necessary; furthermore, to handle null alignment, we must also include a special null symbol null . When f j is null word, we simply fill the surrounding window with the identical null symbols.",
"cite_spans": [
{
"start": 108,
"end": 132,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "To decode our model, the lexical translation scores are computed for each source-target word pair in the sentence pair, which requires going through the neural network (|e| \u00d7 |f|) times; after that, the forward-backward algorithm can be used to find the viterbi path as in the classic HMM model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "The majority of tunable parameters in our model resides in the lookup table LT , which is a (L \u00d7 (|V e | + |V f |))-dimension matrix. For a reasonably large vocabulary, the number is much smaller than the number of parameters in classic HMM model, which is in the order of (|V e |\u00d7|V f |).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "1 The ability to model context is not unique to our model. In fact, discriminative word alignment can model contexts by deploying arbitrary features (Moore, 2005) . Different from previous discriminative word alignment, our model does not use manually engineered features, but learn \"features\" automatically from raw words by the neural network. (Berger et al., 1996) use a maximum entropy model to model the bag-of-words context for word alignment, but their model treats each word as a distinct feature, which can not leverage the similarity between words as our model.",
"cite_spans": [
{
"start": 149,
"end": 162,
"text": "(Moore, 2005)",
"ref_id": "BIBREF22"
},
{
"start": 346,
"end": 367,
"text": "(Berger et al., 1996)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Source window e",
"sec_num": null
},
{
"text": "Although unsupervised training technique such as Contrastive Estimation as in (Smith and Eisner, 2005) , (Dyer et al., 2011) can be adapted to train our model from raw sentence pairs, they are too computational demanding as the lexical translation probabilities must be computed from neural networks. Hence, we opt for a simpler supervised approach, which learns the model from sentence pairs with word alignment. As we do not have a large manually word aligned corpus, we use traditional word alignment models such as HMM and IBM model 4 to generate word alignment on a large parallel corpus. We obtain bidirectional alignment by running the usual growdiag-final heuristics (Koehn et al., 2003) on unidirectional results from both directions, and use the results as our training data. Similar approach has been taken in speech recognition task (Dahl et al., 2012) , where training data for neural network model is generated by forced decoding with traditional Gaussian mixture models.",
"cite_spans": [
{
"start": 78,
"end": 102,
"text": "(Smith and Eisner, 2005)",
"ref_id": "BIBREF26"
},
{
"start": 105,
"end": 124,
"text": "(Dyer et al., 2011)",
"ref_id": "BIBREF11"
},
{
"start": 675,
"end": 695,
"text": "(Koehn et al., 2003)",
"ref_id": "BIBREF15"
},
{
"start": 845,
"end": 864,
"text": "(Dahl et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "5"
},
{
"text": "Tunable parameters in neural network alignment model include: word embeddings in lookup table LT , parameters W l , b l for linear transformations in the hidden layers of the neural network, and distortion parameters s d of jump distance. We take the following ranking loss with margin as our training criteria:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "5"
},
{
"text": "loss(\u03b8) = every (e,f) max{0, 1 \u2212 s \u03b8 (a + |e, f) + s \u03b8 (a \u2212 |e, f)} (9)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "5"
},
{
"text": "where \u03b8 denotes all tunable parameters, a + is the gold alignment path, a \u2212 is the highest scoring incorrect alignment path under \u03b8, and s \u03b8 is model score for alignment path defined in Eq. 5 . One nuance here is that the gold alignment after grow-diag-final contains many-to-many links, which cannot be generated by any path. Our solution is that for each source word alignment multiple target, we randomly choose one link among all candidates as the golden link.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "5"
},
{
"text": "Because our multi-layer neural network is inherently non-linear and is non-convex, directly training against the above criteria is unlikely to yield good results. Instead, we take the following steps to train our model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "5"
},
{
"text": "Most parameters reside in the word embeddings.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Pre-training initial word embedding with monolingual data",
"sec_num": "5.1"
},
{
"text": "To get a good initial value, the usual approach is to pre-train the embeddings on a large monolingual corpus. We replicate the work in (Collobert et al., 2011) and train word embeddings for source and target languages from their monolingual corpus respectively. Our vocabularies V s and V t contain the most frequent 100,000 words from each side of the parallel corpus, and all other words are treated as unknown words. We set word embedding length to 20, window size to 5, and the length of the only hidden layer to 40. Follow (Turian et al., 2010) , we randomly initialize all parameters to [-0.1, 0.1], and use stochastic gradient descent to minimize the ranking loss with a fixed learning rate 0.01. Note that embedding for null word in either V e and V f cannot be trained from monolingual corpus, and we simply leave them at the initial value untouched. Word embeddings from monolingual corpus learn strong syntactic knowledge of each word, which is not always desirable for word alignment between some language pairs like English and Chinese. For example, many Chinese words can act as a verb, noun and adjective without any change, while their English counter parts are distinct words with quite different word embeddings due to their different syntactic roles. Thus we have to modify the word embeddings in subsequent steps according to bilingual data.",
"cite_spans": [
{
"start": 135,
"end": 159,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF8"
},
{
"start": 528,
"end": 549,
"text": "(Turian et al., 2010)",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Pre-training initial word embedding with monolingual data",
"sec_num": "5.1"
},
{
"text": "Training the network against the sentence level criteria Eq. 5 directly is not efficient. Instead, we first ignore the distortion parameters and train neural networks for lexical translation scores against the following local pairwise loss:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training neural network based on local criteria",
"sec_num": "5.2"
},
{
"text": "max{0, 1 \u2212 t \u03b8 ((e, f ) + |e, f) + t \u03b8 ((e, f ) \u2212 |e, f)} (10)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training neural network based on local criteria",
"sec_num": "5.2"
},
{
"text": "where (e, f ) + is a correct word pair, (e, f ) \u2212 is a wrong word pair in the same sentence, and t \u03b8 is as defined in Eq. 6 . This training criteria essentially means our model suffers loss unless it gives correct word pairs a higher score than random pairs from the same sentence pair with some margin.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training neural network based on local criteria",
"sec_num": "5.2"
},
{
"text": "We initialize the lookup table with embeddings obtained from monolingual training, and randomly initialize all W l and b l in linear layers to [-0.1, 0.1] . We minimize the loss using stochastic gradient descent as follows. We randomly cycle through all sentence pairs in training data; for each correct word pair (including null alignment), we generate a positive example, and generate two negative examples by randomly corrupting either side of the pair with another word in the sentence pair. We set learning rate to 0.01. As there is no clear stopping criteria, we simply run the stochastic optimizer through parallel corpus for N iterations. In this work, N is set to 50.",
"cite_spans": [
{
"start": 143,
"end": 154,
"text": "[-0.1, 0.1]",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Training neural network based on local criteria",
"sec_num": "5.2"
},
{
"text": "To make our model concrete, there are still hyper-parameters to be determined: the window size sw and tw, the length of each hidden layer L l . We empirically set sw and tw to 11, L 1 to 120, and L 2 to 10, which achieved a minimal loss on a small held-out data among several settings we tested.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training neural network based on local criteria",
"sec_num": "5.2"
},
{
"text": "We fix neural network parameters obtained from the last step, and tune the distortion parameters s d with respect to the sentence level loss using standard stochastic gradient descent. We use a separate parameter for jump distance from -7 and 7, and another two parameters for longer forward/backward jumps. We initialize all parameters in s d to 0, set the learning rate for the stochastic optimizer to 0.001. As there are only 17 parameters in s d , we only need to run the optimizer over a small portion of the parallel corpus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training distortion parameters",
"sec_num": "5.3"
},
{
"text": "Up-to-now, parameters in the lexical translation neural network have not been trained against the sentence level criteria Eq. 5. We could achieve this by re-using the same online training method used to train distortion parameters, except that we now fix the distortion parameters and let the loss back-propagate through the neural networks. Sentence level training does not take larger context in modeling word translations, but only to optimize the parameters regarding to the sentence level loss. This tuning is quite slow, and it did not improve alignment on an initial small scale experiment; so, we skip this step in all subsequent experiment in this work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tuning neural network based on sentence level criteria",
"sec_num": "5.4"
},
{
"text": "We conduct our experiment on Chinese-to-English word alignment task. We use the manually aligned Chinese-English alignment corpus (Haghighi et al., 2009) which contains 491 sentence pairs as test set. We adapt the segmentation on the Chinese side to fit our word segmentation standard.",
"cite_spans": [
{
"start": 130,
"end": 153,
"text": "(Haghighi et al., 2009)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6"
},
{
"text": "Our parallel corpus contains about 26 million unique sentence pairs in total which are mined from web. The monolingual corpus to pre-train word embeddings are also crawled from web, which amounts to about 1.1 billion unique sentences for English and about 300 million unique sentences for Chinese. As pre-processing, we lowercase all English words, and map all numbers to one special token; and we also map all email addresses and URLs to another special token.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data",
"sec_num": "6.1"
},
{
"text": "We use classic HMM and IBM model 4 as our baseline, which are generated by Giza++ (Och and Ney, 2000) . We train our proposed model from results of classic HMM and IBM model 4 separately. Since classic HMM, IBM model 4 and our model are all uni-directional, we use the standard growdiag-final to generate bi-directional results for all models.",
"cite_spans": [
{
"start": 75,
"end": 101,
"text": "Giza++ (Och and Ney, 2000)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "6.2"
},
{
"text": "Models are evaluated on the manually aligned test set using standard metric: precision, recall and F1-score.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Settings",
"sec_num": "6.2"
},
{
"text": "It can be seen from Table 1 : Word alignment result. The first row and third row show baseline results obtained by classic HMM and IBM4 model. The second row and fourth row show results of the proposed model trained from HMM and IBM4 respectively. results of our model also depends on the quality of baseline results, which is used as training data of our model. In future we would like to explore whether our method can improve other word alignment models.",
"cite_spans": [],
"ref_spans": [
{
"start": 20,
"end": 27,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Alignment Result",
"sec_num": "6.3"
},
{
"text": "We also conduct experiment to see the effect on end-to-end SMT performance. We train hier-archical phrase model (Chiang, 2007) from different word alignments. Despite different alignment scores, we do not obtain significant difference in translation performance. In our C-E experiment, we tuned on NIST-03, and tested on NIST-08. Case-insensitive BLEU-4 scores on NIST-08 test are 0.305 and 0.307 for models trained from IBM-4 and NN alignment results. The result is not surprising considering our parallel corpus is quite large, and similar observations have been made in previous work as ( DeNero and Macherey, 2011) that better alignment quality does not necessarily lead to better end-to-end result.",
"cite_spans": [
{
"start": 112,
"end": 126,
"text": "(Chiang, 2007)",
"ref_id": "BIBREF7"
},
{
"start": 592,
"end": 618,
"text": "DeNero and Macherey, 2011)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Alignment Result",
"sec_num": "6.3"
},
{
"text": "From Table 1 we can see higher F-1 score of our model mainly comes from higher precision, with recall similar to baseline. By analyzing the results, we found out that for both baseline and our model, a large part of missing alignment links involves stop words like English words \"the\", \"a\", \"it\" and Chinese words \"de\". Stop words are inherently hard to align, which often requires grammatical judgment unavailable to our models; as they are also extremely frequent, our model fully learns their alignment patterns of the baseline models, including errors. On the other hand, our model performs better on low-frequency words, especially proper nouns. Take person names for example. Most names are low-frequency words, on which baseline HMM and IBM4 models show the \"garbage collector\" phenomenon. In our model, different person names have very similar word embeddings on both English side and Chinese side, due to monolingual pre-training; what is more, different person names often appear in similar contexts. As our model considers both word embeddings and contexts, it learns that English person names should be aligned to Chinese person names, which corrects errors of baseline models and leads to better precision.",
"cite_spans": [],
"ref_spans": [
{
"start": 5,
"end": 12,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Error Analysis",
"sec_num": "6.4.1"
},
{
"text": "To examine how context contribute to alignment quality, we re-train our model with different window size, all from result of IBM model 4. From Figure 3 , we can see introducing context increase the quality of the learned alignment, but the benefit is diminished for window size over 5. On the other hand, the results are quite stable even with large window size 13, without noticeable over- fitting problem. This is not surprising considering that larger window size only requires slightly more parameters in the linear layers. Lastly, it is worth noticing that our model with no context (window size 1) performs much worse than settings with larger window size and baseline IBM4. Our explanation is as follows. Our model uses the simple jump distance based distortion, which is weaker than the more sophisticated distortions in IBM model 4; thus without context, it does not perform well compared to IBM model 4. With larger window size, our model is able to produce more accurate translation scores based on more contexts, which leads to better alignment despite the simpler distortions.",
"cite_spans": [],
"ref_spans": [
{
"start": 143,
"end": 151,
"text": "Figure 3",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Effect of context",
"sec_num": "6.4.2"
},
{
"text": "F-1 1-hidden-layer 0.834 2-hidden-layer 0.847 3-hidden-layer 0.843 Table 3 : Effect of different number of hidden layers. Two hidden layers outperform one hidden layer, while three hidden layers do not bring further improvement.",
"cite_spans": [],
"ref_spans": [
{
"start": 67,
"end": 74,
"text": "Table 3",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "IBM4+NN",
"sec_num": null
},
{
"text": "Our neural network contains two hidden layers besides the lookup layer. It is natural to ask whether adding more layers would be beneficial. To answer this question, we train models with 1, 2 and 3 layers respectively, all from result of IBM model 4. For 1-hidden-layer setting, we set the hidden layer length to 120; and for 3-hidden-layer setting, we set hidden layer lengths to 120, 100, 10 respectively. As can be seen from improvement. Due to time constraint, we have not tuned the hyper-parameters such as length of hidden layers in 1 and 3-hidden-layer settings, nor have we tested settings with more hidden-layers. It would be wise to test more settings to verify whether more layers would help.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Effect of number of hidden layers",
"sec_num": "6.4.3"
},
{
"text": "Following (Collobert et al., 2011) , we show some words together with its nearest neighbors using the Euclidean distance between their embeddings. As we can see from Table 2 , after bilingual training, \"bad\" is no longer in the nearest neighborhood of \"good\" as they hold opposite semantic meanings; the nearest neighbor of \"history\" is now changed to its related adjective \"historical\". Neighbors of proper nouns such as person names are relatively unchanged. For example, neighbors of word \"zetian\" are all Chinese names in both settings. As Chinese language lacks morphology, the single form and plural form of a noun in English often correspond to the same Chinese word, thus it is desirable that the two English words should have similar word embeddings. While this is true for relatively frequent nouns such as \"lab\" and \"labs\", rarer nouns still remain near their monolingual embeddings as they are only modified a few times during the bilingual training. As shown in last column, neighborhood of \"laggards\" still consists of other plural forms even after bilingual training.",
"cite_spans": [
{
"start": 10,
"end": 34,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [
{
"start": 166,
"end": 173,
"text": "Table 2",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Word Embedding",
"sec_num": "6.4.4"
},
{
"text": "In this paper, we explores applying deep neural network for word alignment task. Our model integrates a multi-layer neural network into an HMM-like framework, where context dependent lexical translation score is computed by neural network, and distortion is modeled by a simple jump-distance scheme. Our model is discriminatively trained on bilingual corpus, while huge monolingual data is used to pre-train wordembeddings. Experiments on large-scale Chineseto-English task show that the proposed method produces better word alignment results, compared with both classic HMM model and IBM model 4. For future work, we will investigate more settings of different hyper-parameters in our model. Secondly, we want to explore the possibility of unsupervised training of our neural word alignment model, without reliance of alignment result of other models. Furthermore, our current model use rather simple distortions; it might be helpful to use more sophisticated model such as ITG (Wu, 1997) , which can be modeled by Recursive Neural Networks (Socher et al., 2011) .",
"cite_spans": [
{
"start": 979,
"end": 989,
"text": "(Wu, 1997)",
"ref_id": "BIBREF33"
},
{
"start": 1042,
"end": 1063,
"text": "(Socher et al., 2011)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "7"
},
{
"text": "In practice, the number of non-zero parameters in classic HMM model would be much smaller, as many words do not co-occur in bilingual sentence pairs. In our experiments, the number of non-zero parameters in classic HMM model is about 328 millions, while the NN model only has about 4 millions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We thank anonymous reviewers for insightful comments. We also thank Dongdong Zhang, Lei Cui, Chunyang Wu and Zhenyan He for fruitful discussions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Neural probabilistic language models. Innovations in Machine Learning",
"authors": [
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
},
{
"first": "Jean-S\u00e9bastien",
"middle": [],
"last": "Sen\u00e9cal",
"suffix": ""
},
{
"first": "Fr\u00e9deric",
"middle": [],
"last": "Morin",
"suffix": ""
},
{
"first": "Jean-Luc",
"middle": [],
"last": "Gauvain",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "137--186",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoshua Bengio, Holger Schwenk, Jean-S\u00e9bastien Sen\u00e9cal, Fr\u00e9deric Morin, and Jean-Luc Gauvain. 2006. Neural probabilistic language models. Inno- vations in Machine Learning, pages 137-186.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Greedy layer-wise training of deep networks",
"authors": [
{
"first": "Hugo",
"middle": [],
"last": "Larochelle",
"suffix": ""
}
],
"year": 2007,
"venue": "Advances in neural information processing systems",
"volume": "19",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hugo Larochelle. 2007. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19:153.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Learning deep architectures for ai. Foundations and Trends R in Machine Learning",
"authors": [
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2009,
"venue": "",
"volume": "2",
"issue": "",
"pages": "1--127",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoshua Bengio. 2009. Learning deep architectures for ai. Foundations and Trends R in Machine Learning, 2(1):1-127.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "A maximum entropy approach to natural language processing",
"authors": [
{
"first": "Adam",
"middle": [
"L"
],
"last": "Berger",
"suffix": ""
},
{
"first": "Vincent",
"middle": [
"J"
],
"last": "Della Pietra",
"suffix": ""
},
{
"first": "Stephen",
"middle": [
"A"
],
"last": "Della Pietra",
"suffix": ""
}
],
"year": 1996,
"venue": "Comput. Linguist",
"volume": "22",
"issue": "1",
"pages": "39--71",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Adam L. Berger, Vincent J. Della Pietra, and Stephen A. Della Pietra. 1996. A maximum entropy ap- proach to natural language processing. Comput. Linguist., 22(1):39-71, March.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Neurocomputing: Algorithms, architectures and applications, chapter probabilistic interpretation of feedforward classification network outputs",
"authors": [
{
"first": "",
"middle": [],
"last": "Js Bridle",
"suffix": ""
}
],
"year": 1990,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "JS Bridle. 1990. Neurocomputing: Algorithms, archi- tectures and applications, chapter probabilistic inter- pretation of feedforward classification network out- puts, with relationships to statistical pattern recogni- tion.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "The mathematics of statistical machine translation: Parameter estimation",
"authors": [
{
"first": "Vincent J Della",
"middle": [],
"last": "Peter F Brown",
"suffix": ""
},
{
"first": "Stephen A Della",
"middle": [],
"last": "Pietra",
"suffix": ""
},
{
"first": "Robert L",
"middle": [],
"last": "Pietra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mercer",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational linguistics",
"volume": "19",
"issue": "2",
"pages": "263--311",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter F Brown, Vincent J Della Pietra, Stephen A Della Pietra, and Robert L Mercer. 1993. The mathemat- ics of statistical machine translation: Parameter esti- mation. Computational linguistics, 19(2):263-311.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Hierarchical phrase-based translation. computational linguistics",
"authors": [
{
"first": "David",
"middle": [],
"last": "Chiang",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "33",
"issue": "",
"pages": "201--228",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Chiang. 2007. Hierarchical phrase-based trans- lation. computational linguistics, 33(2):201-228.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Natural language processing (almost) from scratch",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "L\u00e9on",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Karlen",
"suffix": ""
},
{
"first": "Koray",
"middle": [],
"last": "Kavukcuoglu",
"suffix": ""
},
{
"first": "Pavel",
"middle": [],
"last": "Kuksa",
"suffix": ""
}
],
"year": 2011,
"venue": "The Journal of Machine Learning Research",
"volume": "12",
"issue": "",
"pages": "2493--2537",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert, Jason Weston, L\u00e9on Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. The Journal of Machine Learning Re- search, 12:2493-2537.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing",
"authors": [
{
"first": "E",
"middle": [],
"last": "George",
"suffix": ""
},
{
"first": "Dong",
"middle": [],
"last": "Dahl",
"suffix": ""
},
{
"first": "Li",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Deng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Acero",
"suffix": ""
}
],
"year": 2012,
"venue": "IEEE Transactions on",
"volume": "20",
"issue": "1",
"pages": "30--42",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "George E Dahl, Dong Yu, Li Deng, and Alex Acero. 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1):30-42.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Modelbased aligner combination using dual decomposition",
"authors": [
{
"first": "John",
"middle": [],
"last": "Denero",
"suffix": ""
},
{
"first": "Klaus",
"middle": [],
"last": "Macherey",
"suffix": ""
}
],
"year": 2011,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John DeNero and Klaus Macherey. 2011. Model- based aligner combination using dual decomposi- tion. In Proc. ACL.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Unsupervised word alignment with arbitrary features",
"authors": [
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "Alon",
"middle": [],
"last": "Lavie",
"suffix": ""
},
{
"first": "Noah A",
"middle": [],
"last": "Smith",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "409--419",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chris Dyer, Jonathan Clark, Alon Lavie, and Noah A Smith. 2011. Unsupervised word alignment with ar- bitrary features. In Proceedings of the 49th Annual Meeting of the Association for Computational Lin- guistics: Human Language Technologies-Volume 1, pages 409-419. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Better word alignments with supervised itg models",
"authors": [
{
"first": "Aria",
"middle": [],
"last": "Haghighi",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Blitzer",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Denero",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP",
"volume": "2",
"issue": "",
"pages": "923--931",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aria Haghighi, John Blitzer, John DeNero, and Dan Klein. 2009. Better word alignments with su- pervised itg models. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natu- ral Language Processing of the AFNLP: Volume 2- Volume 2, pages 923-931. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "A fast learning algorithm for deep belief nets",
"authors": [
{
"first": "Geoffrey",
"middle": [
"E"
],
"last": "Hinton",
"suffix": ""
},
{
"first": "Simon",
"middle": [],
"last": "Osindero",
"suffix": ""
},
{
"first": "Yee-Whye",
"middle": [],
"last": "Teh",
"suffix": ""
}
],
"year": 2006,
"venue": "Neural computation",
"volume": "18",
"issue": "7",
"pages": "1527--1554",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep be- lief nets. Neural computation, 18(7):1527-1554.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Learning convolutional feature hierarchies for visual recognition",
"authors": [
{
"first": "Koray",
"middle": [],
"last": "Kavukcuoglu",
"suffix": ""
},
{
"first": "Pierre",
"middle": [],
"last": "Sermanet",
"suffix": ""
},
{
"first": "Y-Lan",
"middle": [],
"last": "Boureau",
"suffix": ""
},
{
"first": "Karol",
"middle": [],
"last": "Gregor",
"suffix": ""
},
{
"first": "Micha\u00ebl",
"middle": [],
"last": "Mathieu",
"suffix": ""
},
{
"first": "Yann",
"middle": [],
"last": "Lecun",
"suffix": ""
}
],
"year": 2010,
"venue": "Advances in Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "1090--1098",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Koray Kavukcuoglu, Pierre Sermanet, Y-Lan Boureau, Karol Gregor, Micha\u00ebl Mathieu, and Yann LeCun. 2010. Learning convolutional feature hierarchies for visual recognition. Advances in Neural Information Processing Systems, pages 1090-1098.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Statistical phrase-based translation",
"authors": [
{
"first": "Philipp",
"middle": [],
"last": "Koehn",
"suffix": ""
},
{
"first": "Franz",
"middle": [
"Josef"
],
"last": "Och",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Marcu",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology",
"volume": "1",
"issue": "",
"pages": "48--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003. Statistical phrase-based translation. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computa- tional Linguistics on Human Language Technology- Volume 1, pages 48-54. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Imagenet classification with deep convolutional neural networks",
"authors": [
{
"first": "Alex",
"middle": [],
"last": "Krizhevsky",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Geoff",
"middle": [],
"last": "Hinton",
"suffix": ""
}
],
"year": 2012,
"venue": "Advances in Neural Information Processing Systems",
"volume": "25",
"issue": "",
"pages": "1106--1114",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton. 2012. Imagenet classification with deep convolu- tional neural networks. In Advances in Neural Infor- mation Processing Systems 25, pages 1106-1114.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Gradient-based learning applied to document recognition",
"authors": [
{
"first": "Yann",
"middle": [],
"last": "Lecun",
"suffix": ""
},
{
"first": "L\u00e9on",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Haffner",
"suffix": ""
}
],
"year": 1998,
"venue": "Proceedings of the IEEE",
"volume": "86",
"issue": "11",
"pages": "2278--2324",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "A learning scheme for asymmetric threshold networks",
"authors": [
{
"first": "Yann",
"middle": [],
"last": "Lecun",
"suffix": ""
}
],
"year": 1985,
"venue": "Proceedings of Cognitiva",
"volume": "85",
"issue": "",
"pages": "599--604",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yann LeCun. 1985. A learning scheme for asymmet- ric threshold networks. Proceedings of Cognitiva, 85:599-604.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Efficient sparse coding algorithms",
"authors": [
{
"first": "Honglak",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Alexis",
"middle": [],
"last": "Battle",
"suffix": ""
},
{
"first": "Rajat",
"middle": [],
"last": "Raina",
"suffix": ""
},
{
"first": "Andrew Y",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2007,
"venue": "Advances in neural information processing systems",
"volume": "19",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Honglak Lee, Alexis Battle, Rajat Raina, and An- drew Y Ng. 2007. Efficient sparse coding algo- rithms. Advances in neural information processing systems, 19:801.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Discriminative pruning for discriminative itg alignment",
"authors": [
{
"first": "Shujie",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Chi-Ho",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL",
"volume": "10",
"issue": "",
"pages": "316--324",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shujie Liu, Chi-Ho Li, and Ming Zhou. 2010. Dis- criminative pruning for discriminative itg alignment. In Proceedings of the 48th Annual Meeting of the As- sociation for Computational Linguistics, ACL, vol- ume 10, pages 316-324.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Sparse feature learning for deep belief networks",
"authors": [
{
"first": "Lan",
"middle": [],
"last": "Y Marcaurelio Ranzato",
"suffix": ""
},
{
"first": "Yann",
"middle": [],
"last": "Boureau",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Le-Cun",
"suffix": ""
}
],
"year": 2007,
"venue": "Advances in neural information processing systems",
"volume": "20",
"issue": "",
"pages": "1185--1192",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y MarcAurelio Ranzato, Lan Boureau, and Yann Le- Cun. 2007. Sparse feature learning for deep belief networks. Advances in neural information process- ing systems, 20:1185-1192.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "A discriminative framework for bilingual word alignment",
"authors": [
{
"first": "C",
"middle": [],
"last": "Robert",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Moore",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "81--88",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robert C Moore. 2005. A discriminative framework for bilingual word alignment. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Process- ing, pages 81-88. Association for Computational Linguistics.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Continuous space language models using restricted boltzmann machines",
"authors": [
{
"first": "Jan",
"middle": [],
"last": "Niehues",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Waibel",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the nineth International Workshop on Spoken Language Translation (IWSLT)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jan Niehues and Alex Waibel. 2012. Continuous space language models using restricted boltzmann machines. In Proceedings of the nineth Interna- tional Workshop on Spoken Language Translation (IWSLT).",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Giza++: Training of statistical translation models",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Franz",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Och",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ney",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franz Josef Och and Hermann Ney. 2000. Giza++: Training of statistical translation models.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Conversational speech transcription using context-dependent deep neural networks",
"authors": [
{
"first": "Frank",
"middle": [],
"last": "Seide",
"suffix": ""
},
{
"first": "Gang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Dong",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2011,
"venue": "Proc. Interspeech",
"volume": "",
"issue": "",
"pages": "437--440",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frank Seide, Gang Li, and Dong Yu. 2011. Conversa- tional speech transcription using context-dependent deep neural networks. In Proc. Interspeech, pages 437-440.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Contrastive estimation: Training log-linear models on unlabeled data",
"authors": [
{
"first": "A",
"middle": [],
"last": "Noah",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Smith",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Eisner",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "354--362",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Noah A Smith and Jason Eisner. 2005. Contrastive estimation: Training log-linear models on unlabeled data. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 354-362. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Parsing natural scenes and natural language with recursive neural networks",
"authors": [
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Cliff",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Andrew",
"suffix": ""
},
{
"first": "Christopher D",
"middle": [],
"last": "Ng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 26th International Conference on Machine Learning (ICML)",
"volume": "2",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard Socher, Cliff C Lin, Andrew Y Ng, and Christopher D Manning. 2011. Parsing natural scenes and natural language with recursive neu- ral networks. In Proceedings of the 26th Inter- national Conference on Machine Learning (ICML), volume 2, page 7.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Semantic compositionality through recursive matrix-vector spaces",
"authors": [
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Brody",
"middle": [],
"last": "Huval",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Christopher",
"suffix": ""
},
{
"first": "Andrew Y",
"middle": [],
"last": "Manning",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "1201--1211",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard Socher, Brody Huval, Christopher D Manning, and Andrew Y Ng. 2012. Semantic compositional- ity through recursive matrix-vector spaces. In Pro- ceedings of the 2012 Joint Conference on Empiri- cal Methods in Natural Language Processing and Computational Natural Language Learning, pages 1201-1211. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Continuous space translation models with neural networks",
"authors": [
{
"first": "Le",
"middle": [],
"last": "Hai Son",
"suffix": ""
},
{
"first": "Alexandre",
"middle": [],
"last": "Allauzen",
"suffix": ""
},
{
"first": "Fran\u00e7ois",
"middle": [],
"last": "Yvon",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 conference of the north american chapter of the association for computational linguistics: Human language technologies",
"volume": "",
"issue": "",
"pages": "39--48",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Le Hai Son, Alexandre Allauzen, and Fran\u00e7ois Yvon. 2012. Continuous space translation models with neural networks. In Proceedings of the 2012 confer- ence of the north american chapter of the associa- tion for computational linguistics: Human language technologies, pages 39-48. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Inducing crosslingual distributed representations of words",
"authors": [
{
"first": "Ivan",
"middle": [],
"last": "Titov",
"suffix": ""
},
{
"first": "Alexandre",
"middle": [],
"last": "Klementiev",
"suffix": ""
},
{
"first": "Binod",
"middle": [],
"last": "Bhattarai",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ivan Titov, Alexandre Klementiev, and Binod Bhat- tarai. 2012. Inducing crosslingual distributed rep- resentations of words.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Word representations: a simple and general method for semi-supervised learning",
"authors": [
{
"first": "Joseph",
"middle": [],
"last": "Turian",
"suffix": ""
},
{
"first": "Lev",
"middle": [],
"last": "Ratinov",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2010,
"venue": "",
"volume": "51",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. Urbana, 51:61801.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Hmm-based word alignment in statistical translation",
"authors": [
{
"first": "Stephan",
"middle": [],
"last": "Vogel",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Ney",
"suffix": ""
},
{
"first": "Christoph",
"middle": [],
"last": "Tillmann",
"suffix": ""
}
],
"year": 1996,
"venue": "Proceedings of the 16th conference on Computational linguistics",
"volume": "2",
"issue": "",
"pages": "836--841",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephan Vogel, Hermann Ney, and Christoph Tillmann. 1996. Hmm-based word alignment in statistical translation. In Proceedings of the 16th conference on Computational linguistics-Volume 2, pages 836- 841. Association for Computational Linguistics.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Stochastic inversion transduction grammars and bilingual parsing of parallel corpora",
"authors": [
{
"first": "Dekai",
"middle": [],
"last": "Wu",
"suffix": ""
}
],
"year": 1997,
"venue": "Computational linguistics",
"volume": "23",
"issue": "3",
"pages": "377--403",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dekai Wu. 1997. Stochastic inversion transduction grammars and bilingual parsing of parallel corpora. Computational linguistics, 23(3):377-403.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"num": null,
"uris": null,
"text": "Two examples of word alignment",
"type_str": "figure"
},
"FIGREF1": {
"num": null,
"uris": null,
"text": "Effect of different window sizes on word alignment F-score.",
"type_str": "figure"
},
"TABREF0": {
"content": "<table><tr><td/><td colspan=\"2\">, the proposed model</td></tr><tr><td colspan=\"3\">consistently outperforms its corresponding base-</td></tr><tr><td colspan=\"3\">line whether it is trained from alignment of classic</td></tr><tr><td colspan=\"3\">HMM or IBM model 4. It is also clear that the</td></tr><tr><td>setting</td><td>prec. recall</td><td>F-1</td></tr><tr><td>HMM</td><td colspan=\"2\">0.768 0.786 0.777</td></tr><tr><td colspan=\"3\">HMM+NN 0.810 0.790 0.798</td></tr><tr><td>IBM4</td><td colspan=\"2\">0.839 0.805 0.822</td></tr><tr><td colspan=\"3\">IBM4+NN 0.885 0.812 0.847</td></tr></table>",
"type_str": "table",
"num": null,
"html": null,
"text": ""
},
"TABREF1": {
"content": "<table><tr><td>, 2-</td></tr></table>",
"type_str": "table",
"num": null,
"html": null,
"text": ""
},
"TABREF2": {
"content": "<table/>",
"type_str": "table",
"num": null,
"html": null,
"text": "Nearest neighbors of several words according to their embedding distance. LM shows neighbors of word embeddings trained by monolingual language model method; WA shows neighbors of word embeddings trained by our word alignment model."
}
}
}
} |