File size: 192,568 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186 4187 | {
"paper_id": "P19-1007",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:28:40.042547Z"
},
"title": "Semantic Parsing with Dual Learning",
"authors": [
{
"first": "Ruisheng",
"middle": [],
"last": "Cao",
"suffix": "",
"affiliation": {
"laboratory": "MoE Key Lab of Artificial Intelligence SpeechLab",
"institution": "Jiao Tong University",
"location": {
"settlement": "Shanghai",
"country": "China"
}
},
"email": ""
},
{
"first": "Su",
"middle": [],
"last": "Zhu",
"suffix": "",
"affiliation": {
"laboratory": "MoE Key Lab of Artificial Intelligence SpeechLab",
"institution": "Jiao Tong University",
"location": {
"settlement": "Shanghai",
"country": "China"
}
},
"email": ""
},
{
"first": "Chen",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "MoE Key Lab of Artificial Intelligence SpeechLab",
"institution": "Jiao Tong University",
"location": {
"settlement": "Shanghai",
"country": "China"
}
},
"email": ""
},
{
"first": "Jieyu",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "MoE Key Lab of Artificial Intelligence SpeechLab",
"institution": "Jiao Tong University",
"location": {
"settlement": "Shanghai",
"country": "China"
}
},
"email": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": "",
"affiliation": {
"laboratory": "MoE Key Lab of Artificial Intelligence SpeechLab",
"institution": "Jiao Tong University",
"location": {
"settlement": "Shanghai",
"country": "China"
}
},
"email": "kai.yu@sjtu.edu.cn"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.",
"pdf_parse": {
"paper_id": "P19-1007",
"_pdf_hash": "",
"abstract": [
{
"text": "Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game. This game between a primal model (semantic parsing) and a dual model (logical form to query) forces them to regularize each other, and can achieve feedback signals from some prior-knowledge. By utilizing the prior-knowledge of logical form structures, we propose a novel reward signal at the surface and semantic levels which tends to generate complete and reasonable logical forms. Experimental results show that our approach achieves new state-of-the-art performance on ATIS dataset and gets competitive performance on OVERNIGHT dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Semantic parsing is the task of mapping a natural language query into a logical form (Zelle and Mooney, 1996; Wong and Mooney, 2007; Zettlemoyer and Collins, 2007; Lu et al., 2008; Zettlemoyer and Collins, 2005) . A logical form is one type of meaning representation understood by computers, which usually can be executed by an executor to obtain the answers.",
"cite_spans": [
{
"start": 85,
"end": 109,
"text": "(Zelle and Mooney, 1996;",
"ref_id": "BIBREF54"
},
{
"start": 110,
"end": 132,
"text": "Wong and Mooney, 2007;",
"ref_id": "BIBREF44"
},
{
"start": 133,
"end": 163,
"text": "Zettlemoyer and Collins, 2007;",
"ref_id": "BIBREF55"
},
{
"start": 164,
"end": 180,
"text": "Lu et al., 2008;",
"ref_id": "BIBREF24"
},
{
"start": 181,
"end": 211,
"text": "Zettlemoyer and Collins, 2005)",
"ref_id": "BIBREF56"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The successful application of recurrent neural networks (RNN) in a variety of NLP tasks (Bahdanau et al., 2014; Sutskever et al., 2014; Vinyals et al., 2015) has provided strong impetus to treat semantic parsing as a sequence-tosequence (Seq2seq) problem (Jia and Liang, 2016; Dong and Lapata, 2016) . This approach generates a logical form based on the input query in an endto-end manner but still leaves two main issues: (1) lack of labeled data and (2) constrained decoding.",
"cite_spans": [
{
"start": 88,
"end": 111,
"text": "(Bahdanau et al., 2014;",
"ref_id": "BIBREF0"
},
{
"start": 112,
"end": 135,
"text": "Sutskever et al., 2014;",
"ref_id": "BIBREF35"
},
{
"start": 136,
"end": 157,
"text": "Vinyals et al., 2015)",
"ref_id": "BIBREF41"
},
{
"start": 255,
"end": 276,
"text": "(Jia and Liang, 2016;",
"ref_id": "BIBREF15"
},
{
"start": 277,
"end": 299,
"text": "Dong and Lapata, 2016)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Firstly, semantic parsing relies on sufficient labeled data. However, data annotation of semantic parsing is a labor-intensive and time-consuming task. Especially, the logical form is unfriendly for human annotation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Secondly, unlike natural language sentences, a logical form is strictly structured. For example, the lambda expression of \"show flight from ci0 to ci1\" is ( lambda $0 e ( and ( from $0 ci0 ) ( to $0 ci1 ) ( flight $0 ) ) ). If we make no constraint on decoding, the generated logical form may be invalid or incomplete at surface and semantic levels.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Surface The generated sequence should be structured as a complete logical form. For example, left and right parentheses should be matched to force the generated sequence to be a valid tree.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Semantic Although the generated sequence is a legal logical form at surface level, it may be meaningless or semantically ill-formed. For example, the predefined binary predicate from takes no more than two arguments. The first argument must represent a flight and the second argument should be a city.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To avoid producing incomplete or semantically illformed logical forms, the output space must be constrained.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we introduce a semantic parsing framework (see Figure 1 ) by incorporating dual learning (He et al., 2016) to tackle the problems mentioned above. In this framework, we have a primal task (query to logical form) and a dual task (logical form to query). They can form a closed loop, and generate informative feedback signals to train the primal and dual models even without supervision. In this loop, the primal and dual models restrict or regularize each other by generating intermediate output in one model and then checking it in the other. Actually, it can be viewed as a method of data augmentation. Thus it can leverage unlabeled data (queries or synthesized logical forms) in a more effective way which helps alleviate the problem of lack of annotated data.",
"cite_spans": [
{
"start": 104,
"end": 121,
"text": "(He et al., 2016)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [
{
"start": 62,
"end": 70,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In the dual learning framework, the primal and dual models are represented as two agents and they teach each other through a reinforcement learning process. To force the generated logical form complete and well-formed, we newly propose a validity reward by checking the output of the primal model at the surface and semantic levels.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We evaluate our approach on two standard datasets: ATIS and OVERNIGHT. The results show that our method can obtain significant improvements over strong baselines on both datasets with fully labeled data, and even outperforms state-of-the-art results on ATIS. With additional logical forms synthesized from rules or templates, our method is competitive with state-ofthe-art systems on OVERNIGHT. Furthermore, our method is compatible with various semantic parsing models. We also conduct extensive experiments to further investigate our framework in semi-supervised settings, trying to figure out why it works.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main contributions of this paper are summarized as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 An innovative semantic parsing framework based on dual learning is introduced, which can fully exploit data (labeled or unlabeled) and incorporate various prior-knowledge as feedback signals. We are the first to introduce dual learning in semantic parsing to the best of our knowledge.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We further propose a novel validity reward focusing on the surface and semantics of logical forms, which is a feedback signal indicating whether the generated logical form is well-formed. It involves the prior-knowledge about structures of logical forms predefined in a domain.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We conduct extensive experiments on ATIS and OVERNIGHT benchmarks. The results show that our method achieves new stateof-the-art performance (test accuracy 89.1%) on ATIS dataset and gets competitive performance on OVERNIGHT dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Before discussing the dual learning algorithm for semantic parsing, we first present the primal and dual tasks (as mentioned before) in detail. The primal and dual tasks are modeled on the attention-based Encoder-Decoder architectures (i.e. Seq2seq) which have been successfully applied in neural semantic parsing (Jia and Liang, 2016; Dong and Lapata, 2016) . We also include copy mechanism See et al., 2017) to tackle unknown tokens.",
"cite_spans": [
{
"start": 314,
"end": 335,
"text": "(Jia and Liang, 2016;",
"ref_id": "BIBREF15"
},
{
"start": 336,
"end": 358,
"text": "Dong and Lapata, 2016)",
"ref_id": "BIBREF7"
},
{
"start": 392,
"end": 409,
"text": "See et al., 2017)",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Primal and Dual Tasks of Semantic Parsing",
"sec_num": "2"
},
{
"text": "The primal task is semantic parsing which converts queries into logical forms (Q2LF ). Let",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "x = x 1 \u2022 \u2022 \u2022",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "x |x| denote the query, and y = y 1 \u2022 \u2022 \u2022 y |y| denote the logical form. An encoder is exploited to encode the query x into vector representations, and a decoder learns to generate logical form y depending on the encoding vectors.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "Encoder Each word x i is mapped to a fixeddimensional vector by a word embedding function \u03c8(\u2022) and then fed into a bidirectional LSTM (Hochreiter and Schmidhuber, 1997) . The hidden vectors are recursively computed at the i-th time step via:",
"cite_spans": [
{
"start": 134,
"end": 168,
"text": "(Hochreiter and Schmidhuber, 1997)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u2212 \u2192 h i =f LSTM (\u03c8(x i ), \u2212 \u2192 h i\u22121 ), i = 1, \u2022 \u2022 \u2022 , |x| (1) \u2190 \u2212 h i =f LSTM (\u03c8(x i ), \u2190 \u2212 h i+1 ), i = |x|, \u2022 \u2022 \u2022 , 1 (2) h i =[ \u2212 \u2192 h i ; \u2190 \u2212 h i ]",
"eq_num": "(3)"
}
],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "where",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "[\u2022; \u2022] denotes vector concatenation, h i \u2208 R 2n",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": ", n is the number of hidden cells and f LSTM is the LSTM function.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "Decoder Decoder is an unidirectional LSTM with the attention mechanism (Luong et al., 2015) . The hidden vector at the t-th time step is computed by",
"cite_spans": [
{
"start": 71,
"end": 91,
"text": "(Luong et al., 2015)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "s t = f LSTM (\u03c6(y t\u22121 ), s t\u22121 ), where \u03c6(\u2022)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "is the token embedding function for logical forms and s t \u2208 R n . The hidden vector of the first time step is initialized as s 0 = \u2190 \u2212 h 1 . The attention weight for the current step t of the decoder, with the i-th step in the encoder is a t",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "i = exp(u t i ) |x| j=1 exp(u t j )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "and",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "u t i =v T tanh(W 1 h i + W 2 s t + b a )",
"eq_num": "(4)"
}
],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "Figure 1: An overview of dual semantic parsing framework. The primal model (Q2LF ) and dual model (LF 2Q) can form a closed cycle. But there are two different directed loops, depending on whether they start from a query or logical form. Validity reward is used to estimate the quality of the middle generation output, and reconstruction reward is exploited to avoid information loss. The primal and dual models can be pre-trained and fine-tuned with labeled data to keep the models effective.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "where v, b a \u2208 R n , and W 1 \u2208 R n\u00d72n , W 2 \u2208 R n\u00d7n are parameters. Then we compute the vocabulary distribution P gen (y t |y <t , x) by",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "c t = |x| i=1 a t i h i (5) P gen (y t |y <t , x) =softmax(W o [s t ; c t ] + b o ) (6)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "where",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "W o \u2208 R |Vy|\u00d73n , b o \u2208 R |Vy| and |V y |",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "is the output vocabulary size. Generation ends once an end-of-sequence token \"EOS\" is emitted. Copy Mechanism We also include copy mechanism to improve model generalization following the implementation of See et al. (2017) , a hybrid between and pointer network . The predicted token is from either a fixed output vocabulary V y or raw input words x. We use sigmoid gate function \u03c3 to make a soft decision between generation and copy at each step t.",
"cite_spans": [
{
"start": 205,
"end": 222,
"text": "See et al. (2017)",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "g t =\u03c3(v T g [s t ; c t ; \u03c6(y t\u22121 )] + b g ) (7) P (y t |y <t , x) =g t P gen (y t |y <t , x) + (1 \u2212 g t )P copy (y t |y <t , x)",
"eq_num": "(8)"
}
],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "where g t \u2208 [0, 1] is the balance score, v g is a weight vector and b g is a scalar bias. Distribution P copy (y t |y <t , x) will be described as follows. Entity Mapping Although the copy mechanism can deal with unknown words, many raw words can not be directly copied to be part of a logical form. For example, kobe bryant is represented as en.player.kobe_bryant in OVERNIGHT (Wang et al., 2015) . It is common that entities are identified by Uniform Resource Identifier (URI, Klyne and Carroll, 2006) in a knowledge base. Thus, a mapping from raw words to URI is included after copying. Mathematically, P copy in Eq.8 is calculated by:",
"cite_spans": [
{
"start": 378,
"end": 397,
"text": "(Wang et al., 2015)",
"ref_id": "BIBREF43"
},
{
"start": 479,
"end": 503,
"text": "Klyne and Carroll, 2006)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "P copy (y t = w|y <t , x) = i,j: KB(x i:j )=w j k=i a t k",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "where i < j, a t k is the attention weight of position k at decoding step t, KB(\u2022) is a dictionarylike function mapping a specific noun phrase to the corresponding entity token in the vocabulary of logical forms.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Primal Task",
"sec_num": "2.1"
},
{
"text": "The dual task (LF 2Q) is an inverse of the primal task, which aims to generate a natural language query given a logical form. We can also exploit the attention-based Encoder-Decoder architecture (with copy mechanism or not) to build the dual model. Reverse Entity Mapping Different with the primal task, we reversely map every possible KB entity y t of a logical form to the corresponding noun phrase before query generation, KB \u22121 (y t ) 1 . Since each KB entity may have multiple aliases in the real world, e.g. kobe bryant has a nickname the black mamba, we make different selections in two cases:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "\u2022 For paired data, we select the noun phrase from KB \u22121 (y t ), which exists in the query.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "\u2022 For unpaired data, we randomly select one from KB \u22121 (y t ).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "3 Dual learning for Semantic Parsing",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "In this section, we present a semantic parsing framework with dual learning. We use one agent to represent the model of the primal task (Q2LF ) and another agent to represent the model of the dual task (LF 2Q), then design a two-agent game in a closed loop which can provide quality feedback to the primal and dual models even if only queries or logical forms are available. As the feedback reward may be non-differentiable, reinforcement learning algorithm (Sutton and Barto, 2018) based on policy gradient (Sutton et al., 2000) is applied for optimization. Two agents, Q2LF and LF 2Q, participate in the collaborative game with two directed loops as illustrated in Figure 1 .",
"cite_spans": [
{
"start": 508,
"end": 529,
"text": "(Sutton et al., 2000)",
"ref_id": "BIBREF37"
}
],
"ref_spans": [
{
"start": 667,
"end": 675,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "One loop query->logical_form->query starts from a query, generates possible logical forms by agent Q2LF and tries to reconstruct the original query by LF 2Q.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "The other loop logical_form->query->logical_form starts from the opposite side. Each agent will obtain quality feedback depending on reward functions defined in the directed loops.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dual Model",
"sec_num": "2.2"
},
{
"text": "Suppose we have fully labeled dataset T = { x, y }, unlabeled dataset Q with only queries if available, and unlabeled dataset LF with only logical forms if available. We firstly pre-train the primal model Q2LF and the dual model LF 2Q on T by maximum likelihood estimation (MLE). Let \u0398 Q2LF and \u0398 LF 2Q denote all the parameters of Q2LF and LF 2Q respectively. Our learning algorithm in each iteration consists of three parts:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Learning algorithm",
"sec_num": "3.1"
},
{
"text": "As shown in Figure 1 (a), we sample a query x from Q \u222a T randomly. Given x, Q2LF model could generate k possible logical forms y 1 , y 2 , \u2022 \u2022 \u2022 , y k via beam search (k is beam size). For each y i , we can obtain a validity reward R val q (y i ) (a scalar) computed by a specific reward function which will be discussed in Section 3.2.1. After feeding y i into LF 2Q, we finally get a reconstruction reward R rec q (x, y i ) which forces the generated query as similar to x as possible and will be discussed in Section 3.2.2.",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 20,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Loop starts from a query",
"sec_num": "3.1.1"
},
{
"text": "A hyper-parameter \u03b1 is exploited to balance these two rewards in r q",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a query",
"sec_num": "3.1.1"
},
{
"text": "i = \u03b1R val q (y i ) + (1 \u2212 \u03b1)R rec q (x, y i ), where \u03b1 \u2208 [0, 1].",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a query",
"sec_num": "3.1.1"
},
{
"text": "By utilizing policy gradient (Sutton et al., 2000) , the stochastic gradients of \u0398 Q2LF and \u0398 LF 2Q are computed as:",
"cite_spans": [
{
"start": 29,
"end": 50,
"text": "(Sutton et al., 2000)",
"ref_id": "BIBREF37"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a query",
"sec_num": "3.1.1"
},
{
"text": "\u2207\u0398 Q2LF\u00ca [r] = 1 k k i=1 r q i \u2207\u0398 Q2LF log P (yi|x; \u0398Q2LF ) \u2207\u0398 LF 2Q\u00ca [r] = 1 \u2212 \u03b1 k k i=1 \u2207\u0398 LF 2Q log P (x|yi; \u0398LF 2Q)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a query",
"sec_num": "3.1.1"
},
{
"text": "As shown in Figure 1 (b), we sample a logical form y from LF \u222a T randomly. Given y, LF 2Q model generates k possible queries",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 20,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "x 1 , x 2 , \u2022 \u2022 \u2022 , x k via beam search.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "For each x i , we can obtain a validity reward R val lf (x i ) (a scalar) which will also be discussed in Section 3.2.1. After feeding x i into Q2LF , we can also get a reconstruction reward R rec lf (y, x i ), which forces the generated logical form as similar to y as possible and will be discussed in Section 3.2.2.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "A hyper-parameter \u03b2 is also exploited to balance these two rewards by",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "r lf i = \u03b2R val lf (x i )+(1\u2212 \u03b2)R rec lf (y, x i ), where \u03b2 \u2208 [0, 1]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": ". By utilizing policy gradient, the stochastic gradients of \u0398 Q2LF and \u0398 LF 2Q are computed as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "\u2207\u0398 LF 2Q\u00ca [r] = 1 k k i=1 r lf i \u2207\u0398 LF 2Q log P (xi|y; \u0398LF 2Q) \u2207\u0398 Q2LF\u00ca [r] = 1 \u2212 \u03b2 k k i=1 \u2207\u0398 Q2LF log P (y|xi; \u0398Q2LF )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Loop starts from a logical form",
"sec_num": "3.1.2"
},
{
"text": "The previous two stages are unsupervised learning processes, which need no labeled data. If there is no supervision for the primal and dual models after pre-training, these two models would be rotten especially when T is limited. To keep the learning process stable and prevent the models from crashes, we randomly select samples from T to fine-tune the primal and dual models by maximum likelihood estimation (MLE). Details about the dual learning algorithm for semantic parsing are provided in Appendix A.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Supervisor guidance",
"sec_num": "3.1.3"
},
{
"text": "As mentioned in Section 3.1, there are two types of reward functions in each loop: validity reward (R val q , R val lf ) and reconstruction reward (R rec q , R rec lf ). But each type of reward function may be different in different loops.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Reward design",
"sec_num": "3.2"
},
{
"text": "Validity reward is used to evaluate the quality of intermediate outputs in a loop (see Figure 1 ). In the loop starts from a query, it indicates whether the generated logical forms are well-formed at the surface and semantic levels. While in the loop starts from a logical form, it indicates how natural and fluent the intermediate queries are. Loop starts from a query: We estimate the quality of the generated logical forms at two levels, i.e. surface and semantics. Firstly, we check whether the logical form is a complete tree without parentheses mismatching. As for semantics, we check whether the logical form is understandable without errors like type inconsistency. It can be formulated as R val q (y) = grammar_error_indicator(y) 9which returns 1 when y has no error at the surface and semantic levels, and returns 0 otherwise. If there exists an executing program or search engine for logical form y, e.g. dataset OVERNIGHT (Wang et al., 2015) , grammar_error_indicator(\u2022) has been included.",
"cite_spans": [
{
"start": 934,
"end": 953,
"text": "(Wang et al., 2015)",
"ref_id": "BIBREF43"
}
],
"ref_spans": [
{
"start": 87,
"end": 95,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "Otherwise, we should construct a grammar error indicator based on the ontology of the corresponding dataset. For example, a specification of ATIS can be extracted by clarifying all (1) entities paired with corresponding types, (2) unary/binary predicates with argument constraints (see Table 1 ). Accordingly, Algorithm 1 abstracts the procedure of checking the surface and semantics for a logical form candidate y based on the specification. Loop starts from a logical form: A language model (LM) is exploited to evaluate the quality of intermediate queries (Mikolov et al., 2010) . We",
"cite_spans": [
{
"start": 559,
"end": 581,
"text": "(Mikolov et al., 2010)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [
{
"start": 286,
"end": 293,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "Algorithm 1 Grammar error indicator on ATIS Input: Logical form string y; specification D Output: 1/0, whether y is valid 1: if to_lisp_tree(y) succeed then 2:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "lispT ree \u2190 to_lisp_tree(y) using Depth-First-Search for lispT ree 3:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "if type_consistent(lispT ree, D) then 4:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "return 1 5:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "end if 6: end if 7: return 0 apply length-normalization (Wu et al., 2016) to make a fair competition between short and long queries.",
"cite_spans": [
{
"start": 56,
"end": 73,
"text": "(Wu et al., 2016)",
"ref_id": "BIBREF46"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "R val lf (x) = log LM q (x)/Length(x),",
"eq_num": "(10)"
}
],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "where LM q (\u2022) is a language model pre-trained on all the queries of Q \u222a T (referred in Section 3.1).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Validity reward",
"sec_num": "3.2.1"
},
{
"text": "Reconstruction reward is used to estimate how similar the output of one loop is compared with the input. We take log likelihood as reconstruction rewards for the loop starts from a query and the loop starts from a logical form. Thus,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Reconstruction reward",
"sec_num": "3.2.2"
},
{
"text": "R rec q (x, y i ) = log P (x|y i ; \u0398 LF 2Q ) R rec lf (y, x i ) = log P (y|x i ; \u0398 Q2LF )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Reconstruction reward",
"sec_num": "3.2.2"
},
{
"text": "where y i and x i are intermediate outputs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Reconstruction reward",
"sec_num": "3.2.2"
},
{
"text": "In this section, we evaluate our framework on the ATIS and OVERNIGHT datasets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment",
"sec_num": "4"
},
{
"text": "ATIS We use the preprocessed version provided by Dong and Lapata (2018) , where natural language queries are lowercased and stemmed with NLTK (Loper and Bird, 2002) , and entity mentions are replaced by numbered markers. We also leverage an external lexicon that maps word phrases (e.g., first class) to entities (e.g., first:cl) like what Jia and Liang (2016) did.",
"cite_spans": [
{
"start": 49,
"end": 71,
"text": "Dong and Lapata (2018)",
"ref_id": "BIBREF8"
},
{
"start": 142,
"end": 164,
"text": "(Loper and Bird, 2002)",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset",
"sec_num": "4.1"
},
{
"text": "OVERNIGHT It contains natural language paraphrases paired with logical forms across eight domains. We follow the traditional 80%/20% train/valid splits as Wang et al. (2015) to choose the best model during training.",
"cite_spans": [
{
"start": 155,
"end": 173,
"text": "Wang et al. (2015)",
"ref_id": "BIBREF43"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset",
"sec_num": "4.1"
},
{
"text": "ATIS and OVERNIGHT never provide unlabeled queries. To test our method in semi-supervised learning, we keep a part of the training set as fully labeled data and leave the rest as unpaired queries and logical forms which simulate unlabeled data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset",
"sec_num": "4.1"
},
{
"text": "Although there is no unlabeled query provided in most semantic parsing benchmarks, it should be easy to synthesize logical forms. Since a logical form is strictly structured and can be modified from the existing one or created from simple grammars, it is much cheaper than query collection. Our synthesized logical forms are public 2 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Synthesis of logical forms",
"sec_num": "4.2"
},
{
"text": "On ATIS, we randomly sample a logical form from the training set, and select one entity or predicate for replacement according to the specification in Table 1 . If the new logical form after replacement is valid and never seen, it is added to the unsupervised set. 4592 new logical forms are created for ATIS. An example is shown in Figure 2 . ",
"cite_spans": [],
"ref_spans": [
{
"start": 151,
"end": 158,
"text": "Table 1",
"ref_id": "TABREF1"
},
{
"start": 333,
"end": 341,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Modification based on ontology",
"sec_num": "4.2.1"
},
{
"text": "Wang et al. 2015proposed an underlying grammar to generate logical forms along with their corresponding canonical utterances on OVERNIGHT, which can be found in SEMPRE 3 . We reorder the entity instances (e.g., ENTITYNP) of one type (e.g., TYPENP) in grammar files to generate new logical forms. We could include new entity instances if we want more unseen logical forms, but we didn't do that actually. Finally, we get about 500 new logical forms for each domain on average. More examples can be found in Appendix B.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Generation based on grammar",
"sec_num": "4.2.2"
},
{
"text": "We use 200 hidden units and 100-dimensional word vectors for all encoders and decoders of Q2LF and LF 2Q models. The LSTMs used are in single-layer. Word embeddings on query side are initialized by Glove6B (Pennington et al., 2014) . Out-of-vocabulary words are replaced with a special token unk . Other parameters are initialized by uniformly sampling within the interval [\u22120.2, 0.2]. The language model we used is also a single-layer LSTM with 200 hidden units and 100-dim word embedding layer.",
"cite_spans": [
{
"start": 206,
"end": 231,
"text": "(Pennington et al., 2014)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Base models",
"sec_num": "4.3.1"
},
{
"text": "We individually pre-train Q2LF /LF 2Q models using only labeled data and language model LM q using both labeled and unlabeled queries. The language model is fixed for calculating reward. The hyper-parameters \u03b1 and \u03b2 are selected according to validation set (0.5 is used), and beam size k is selected from {3, 5}. The batch size is selected from {10, 20}. We use optimizer Adam (Kingma and Ba, 2014) with learning rate 0.001 for all experiments. Finally, we evaluate the primal model (Q2LF , semantic parsing) and report test accuracy on each dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training and decoding",
"sec_num": "4.3.2"
},
{
"text": "We perform a PSEUDO baseline following the setup in Sennrich et al. (2016) and Guo et al. (2018) . The pre-trained LF 2Q or Q2LF model is used to generate pseudo query, logical f orm pairs from unlabeled logical forms or unlabeled queries, which extends the training set. The pseudo-labeled data is used carefully with a discount factor (0.5) in loss function (Lee, 2013) , when we train Q2LF by supervised training.",
"cite_spans": [
{
"start": 52,
"end": 74,
"text": "Sennrich et al. (2016)",
"ref_id": "BIBREF32"
},
{
"start": 79,
"end": 96,
"text": "Guo et al. (2018)",
"ref_id": "BIBREF11"
},
{
"start": 360,
"end": 371,
"text": "(Lee, 2013)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Results and analysis",
"sec_num": "4.4"
},
{
"text": "The results are illustrated in Table 2 and 3. ATT and ATTPTR represent that the primal/dual models are attention-based Seq2seq and attention-based Seq2seq with copy mechanism respectively. We train models with the dual learning algorithm if DUAL is included, otherwise we only train the primal model by supervised training. LF refers to the synthesized logical forms. PSEUDO uses the (Wang et al., 2015) 46.3 41.9 74.4 54.0 59.0 70.8 75.9 48.2 58.8 DSP-C (Xiao et al., 2016) 80 Method ATIS ZC07 (Zettlemoyer and Collins, 2007) 84.6 FUBL (Kwiatkowski et al., 2011) 82.8 GUSP++ (Poon, 2013) 83.5 TISP (Zhao and Huang, 2015) 84.2 SEQ2TREE (Dong and Lapata, 2016) 84.6 ASN+SUPATT (Rabinovich et al., 2017) 85.9 TRANX 86.2 COARSE2FINE (Dong and Lapata, 2018) 87 LF 2Q model and LF to generate pseudo-labeled data. From the overall results, we can see that:",
"cite_spans": [
{
"start": 384,
"end": 403,
"text": "(Wang et al., 2015)",
"ref_id": "BIBREF43"
},
{
"start": 455,
"end": 474,
"text": "(Xiao et al., 2016)",
"ref_id": "BIBREF48"
},
{
"start": 495,
"end": 526,
"text": "(Zettlemoyer and Collins, 2007)",
"ref_id": "BIBREF55"
},
{
"start": 537,
"end": 563,
"text": "(Kwiatkowski et al., 2011)",
"ref_id": "BIBREF20"
},
{
"start": 576,
"end": 588,
"text": "(Poon, 2013)",
"ref_id": "BIBREF29"
},
{
"start": 599,
"end": 621,
"text": "(Zhao and Huang, 2015)",
"ref_id": "BIBREF57"
},
{
"start": 636,
"end": 659,
"text": "(Dong and Lapata, 2016)",
"ref_id": "BIBREF7"
},
{
"start": 676,
"end": 701,
"text": "(Rabinovich et al., 2017)",
"ref_id": "BIBREF30"
},
{
"start": 730,
"end": 753,
"text": "(Dong and Lapata, 2018)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [
{
"start": 31,
"end": 38,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Main results",
"sec_num": "4.4.1"
},
{
"text": "Bas. Blo. Cal. Hou. Pub. Rec. Res. Soc. Avg. SPO",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Main results",
"sec_num": "4.4.1"
},
{
"text": "1) Even without the additional logical forms by synthesizing, the dual learning based semantic parser can outperform our baselines with supervised training, e.g., \"ATT + DUAL\" gets much better performances than \"ATT + PSEUDO(LF)\" in Table 2 and 3. We think the Q2LF and LF 2Q models can teach each other in dual learning: one model sends informative signals to help regularize the other model. Actually, it can also be explained as a data augmentation procedure, e.g., Q2LF can generate samples utilized by LF 2Q and vice versa. While the PSEUDO greatly depends on the quality of pseudo-samples even if a discount factor is considered.",
"cite_spans": [],
"ref_spans": [
{
"start": 233,
"end": 240,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Main results",
"sec_num": "4.4.1"
},
{
"text": "2) By involving the synthesized logical forms LF in the dual learning for each domain respectively, the performances are improved further. We achieve state-of-the-art performance (89.1%) 4 on ATIS as shown in Table 3 . On OVERNIGHT dataset, we achieve a competitive performance on average (80.2%). The best average accuracy is from Su and Yan (2017) , which benefits from cross-domain training. We believe our method could get more improvements with stronger primal models (e.g. with domain adaptation). Our method would be compatible with various models.",
"cite_spans": [
{
"start": 332,
"end": 349,
"text": "Su and Yan (2017)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [
{
"start": 209,
"end": 216,
"text": "Table 3",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Main results",
"sec_num": "4.4.1"
},
{
"text": "3) Copy mechanism can remarkably improve accuracy on ATIS, while not on OVERNIGHT. The average accuracy even decreases from 80.2% to 79.9% when using the copy mechanism. We argue that OVERNIGHT dataset contains a very small number of distinct entities that copy is not essential, and it contains less training samples than ATIS. This phenomenon also exists in Jia and Liang (2016) .",
"cite_spans": [
{
"start": 360,
"end": 380,
"text": "Jia and Liang (2016)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Main results",
"sec_num": "4.4.1"
},
{
"text": "Semi-supervised learning We keep a part of the training set as labeled data T randomly and leave the rest as unpaired queries (Q) and logical forms (LF) to validate our method in a semi-supervised setting. The ratio of labeled data is 50%. PSEUDO here uses the Q2LF model and Q to generate pseudo-labeled data, as well as LF 2Q model and LF. From Table 4 , we can see that the dual learning method outperforms the PSEUDO baseline in two datasets dramatically. The dual learning method is more efficient to exploit unlabeled data. In general, both unpaired queries and logi- Table 4 : Semi-supervised learning experiments. We keep 50% of the training set as labeled data randomly, and leave the rest as unpaired queries(Q) and logical forms(LF) to simulate unsupervised dataset. cal forms could boost the performance of semantic parsers with dual learning.",
"cite_spans": [],
"ref_spans": [
{
"start": 347,
"end": 354,
"text": "Table 4",
"ref_id": null
},
{
"start": 574,
"end": 581,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "Different ratios To investigate the efficiency of our method in semi-supervised learning, we vary the ratio of labeled data kept on ATIS from 1% to 90%. In Figure 3 , we can see that dual learning strategy enhances semantic parsing over all proportions. The prominent gap happens when the ratio is between 0.2 and 0.4. Generally, the more unlabeled data we have, the more remarkable the leap is. However, if the labeled data is really limited, less supervision can be exploited to keep the primal and dual models reasonable. For example, when the ratio of labeled data is from only 1% to 10%, the improvement is not that obvious. Does more unlabeled data give better result?",
"cite_spans": [],
"ref_spans": [
{
"start": 156,
"end": 164,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "We also fix the ratio of labeled data as 30%, and change the ratio of unlabeled samples to the rest data on ATIS, as illustrated in Figure 4 . Even Figure 4 : Test accuracies on ATIS. It fixes the ratio of labeled data as 30%, and varies the ratio of unlabeled samples to the rest data.",
"cite_spans": [],
"ref_spans": [
{
"start": 132,
"end": 140,
"text": "Figure 4",
"ref_id": null
},
{
"start": 148,
"end": 156,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "without unlabeled data (i.e. the ratio of unlabeled data is zero), the dual learning based semantic parser can outperform our baselines. However, the performance of our method doesn't improve constantly, when the amount of unlabeled data is increased. We think the power of the primal and dual models is constrained by the limited amount of labeled data. When some complex queries or logical forms come, the two models may converge to an equilibrium where the intermediate value loses some implicit semantic information, but the rewards are high.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "Choice for validity reward We conduct another experiment by changing validity reward in Eq.9 with length-normalized LM score (i.e. language model of logical forms) like Eq.10. Results (Table 5) show that \"hard\" surface/semantic check is more suitable than \"soft\" probability of logical Table 5 : Test accuracies on ATIS and OVERNIGHT in semi-supervised learning setting (the ratio of labeled data is 50%). On OVERNIGHT, we average across all eight domains. LM lf means using a logical form language model for validity reward, while \"grammar check\" means using the surface and semantic check.",
"cite_spans": [],
"ref_spans": [
{
"start": 286,
"end": 293,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "form LM. We think that simple language models may suffer from long-dependency and data imbalance issues, and it is hard to capture inner structures of logical forms from a sequential model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Ablation study",
"sec_num": "4.4.2"
},
{
"text": "Lack of data A semantic parser can be trained from labeled logical forms or weakly supervised samples (Krishnamurthy and Mitchell, 2012; Berant et al., 2013; Liang et al., 2017; Goldman et al., 2018) . Yih et al. 2016demonstrate logical forms can be collected efficiently and more useful than merely answers to queries. Wang et al. (2015) construct a semantic parsing dataset starting from grammar rules to crowdsourcing paraphrase. Jia and Liang (2016) induces synchronous contextfree grammar (SCFG) and creates new \"recombinant\" examples accordingly. Su and Yan (2017) use multiple source domains to reduce the cost of collecting data for the target domain. Guo et al. (2018) pre-train a question generation model to produce pseudo-labeled data as a supplement. In this paper, we introduce the dual learning to make full use of data (both labeled and unlabeled). introduce a variational auto-encoding model for semi-supervised semantic parsing. Beyond semantic parsing, the semisupervised and adaptive learnings are also typical in natural language understanding (Tur et al., 2005; Bapna et al., 2017; Zhu et al., 2014 .",
"cite_spans": [
{
"start": 102,
"end": 136,
"text": "(Krishnamurthy and Mitchell, 2012;",
"ref_id": "BIBREF19"
},
{
"start": 137,
"end": 157,
"text": "Berant et al., 2013;",
"ref_id": "BIBREF2"
},
{
"start": 158,
"end": 177,
"text": "Liang et al., 2017;",
"ref_id": "BIBREF22"
},
{
"start": 178,
"end": 199,
"text": "Goldman et al., 2018)",
"ref_id": "BIBREF9"
},
{
"start": 320,
"end": 338,
"text": "Wang et al. (2015)",
"ref_id": "BIBREF43"
},
{
"start": 553,
"end": 570,
"text": "Su and Yan (2017)",
"ref_id": "BIBREF33"
},
{
"start": 660,
"end": 677,
"text": "Guo et al. (2018)",
"ref_id": "BIBREF11"
},
{
"start": 1065,
"end": 1083,
"text": "(Tur et al., 2005;",
"ref_id": "BIBREF40"
},
{
"start": 1084,
"end": 1103,
"text": "Bapna et al., 2017;",
"ref_id": "BIBREF1"
},
{
"start": 1104,
"end": 1120,
"text": "Zhu et al., 2014",
"ref_id": "BIBREF59"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Constrained decoding To avoid invalid parses, additional restrictions must be considered in the decoding. Dong and Lapata (2016) propose SEQ2TREE method to ensure the matching of parentheses, which can generate syntactically valid output. Cheng et al. (2017) and Dong and Lapata (2018) both try to decode in two steps, from a coarse rough sketch to a finer structure hierarchically. Krishnamurthy et al. (2017) define a grammar of production rules such that only welltyped logical forms can be generated. Yin and Neubig (2017) and Chen et al. (2018a) both transform the generation of logical forms into query graph construction. Zhao et al. (2019) propose a hierarchical parsing model following the structure of semantic representations, which is predefined by domain developers. We introduce a validity reward at the surface and semantic levels in the dual learning algorithm as a constraint signal. Dual learning Dual learning framework is first proposed to improve neural machine translation (NMT) (He et al., 2016) . Actually, the primal and dual tasks are symmetric in NMT, while not in semantic parsing. The idea of dual learning has been applied in various tasks (Xia et al., 2017) , such as Question Answering/Generation (Tang et al., 2017 , Image-to-Image Translation (Yi et al., 2017) and Open-domain Information Extraction/Narration . We are the first to introduce dual learning in semantic parsing to the best of our knowledge.",
"cite_spans": [
{
"start": 106,
"end": 128,
"text": "Dong and Lapata (2016)",
"ref_id": "BIBREF7"
},
{
"start": 239,
"end": 258,
"text": "Cheng et al. (2017)",
"ref_id": "BIBREF6"
},
{
"start": 263,
"end": 285,
"text": "Dong and Lapata (2018)",
"ref_id": "BIBREF8"
},
{
"start": 383,
"end": 410,
"text": "Krishnamurthy et al. (2017)",
"ref_id": "BIBREF18"
},
{
"start": 505,
"end": 526,
"text": "Yin and Neubig (2017)",
"ref_id": "BIBREF51"
},
{
"start": 531,
"end": 550,
"text": "Chen et al. (2018a)",
"ref_id": "BIBREF3"
},
{
"start": 629,
"end": 647,
"text": "Zhao et al. (2019)",
"ref_id": "BIBREF58"
},
{
"start": 1001,
"end": 1018,
"text": "(He et al., 2016)",
"ref_id": "BIBREF12"
},
{
"start": 1170,
"end": 1188,
"text": "(Xia et al., 2017)",
"ref_id": "BIBREF47"
},
{
"start": 1229,
"end": 1247,
"text": "(Tang et al., 2017",
"ref_id": "BIBREF38"
},
{
"start": 1277,
"end": 1294,
"text": "(Yi et al., 2017)",
"ref_id": "BIBREF49"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "In this paper, we develop a semantic parsing framework based on dual learning algorithm, which enables a semantic parser to fully utilize labeled and even unlabeled data through a duallearning game between the primal and dual models. We also propose a novel reward function at the surface and semantic levels by utilizing the prior-knowledge of logical form structures. Thus, the primal model tends to generate complete and reasonable semantic representation. Experimental results show that semantic parsing based on dual learning improves performance across datasets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "In the future, we want to incorporate this framework with much refined primal and dual models, and design more informative reward signals to make the training more efficient. It would be appealing to apply graph neural networks (Chen et al., 2018b (Chen et al., , 2019 for each possible logical form y i do",
"cite_spans": [
{
"start": 228,
"end": 247,
"text": "(Chen et al., 2018b",
"ref_id": "BIBREF5"
},
{
"start": 248,
"end": 268,
"text": "(Chen et al., , 2019",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Obtain validity reward for y i as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "R val q (y i ) = grammar_error_indicator(y i ) 12:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "Get reconstruction reward for y i as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "R rec q (x, y i ) = log P (x|y i ; \u0398 LF 2Q )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "13:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "Compute total reward for y i as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "r q i = \u03b1R val q (y i ) + (1 \u2212 \u03b1)R rec q (x, y i ) 14:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "end for",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "11:",
"sec_num": null
},
{
"text": "Compute stochastic gradient of \u0398 Q2LF :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "\u2207\u0398 Q2LF\u00ca [r] = 1 k k i=1 r q i \u2207\u0398 Q2LF log P (yi|x; \u0398Q2LF )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "16:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "Compute stochastic gradient of \u0398 LF 2Q :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "\u2207\u0398 LF 2Q\u00ca [r] = 1 \u2212 \u03b1 k k i=1 \u2207\u0398 LF 2Q log P (x|yi; \u0398LF 2Q)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "17:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "Model updates:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "\u0398 Q2LF \u2190\u0398 Q2LF + \u03b7 1 \u2022 \u2207 \u0398 Q2LF\u00ca [r] \u0398 LF 2Q \u2190\u0398 LF 2Q + \u03b7 2 \u2022 \u2207 \u0398 LF 2Q\u00ca [r]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "18:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "Sample a logical form y from LF \u222a T ;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "19:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "LF 2Q model generates k queries x 1 , x 2 , \u2022 \u2022 \u2022 , x k via beam search; 20: for each possible query x i do 21: Obtain validity reward for x i as R val lf (x i ) = log LM q (x i )/Length(x i ) 22: Get reconstruction reward for x i as R rec lf (y, x i ) = log P (y|x i ; \u0398 Q2LF ) 23:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "Compute total reward for x i as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "r lf i = \u03b2R val lf (x i ) + (1 \u2212 \u03b2)R rec lf (y, x i ) 24:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "end for",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "15:",
"sec_num": null
},
{
"text": "Compute stochastic gradient of \u0398 LF 2Q :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "25:",
"sec_num": null
},
{
"text": "\u2207\u0398 LF 2Q\u00ca [r] = 1 k k i=1 r lf i \u2207\u0398 LF 2Q log P (xi|y; \u0398LF 2Q)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "25:",
"sec_num": null
},
{
"text": "Compute stochastic gradient of \u0398 Q2LF :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "\u2207\u0398 Q2LF\u00ca [r] = 1 \u2212 \u03b2 k k i=1 \u2207\u0398 Q2LF log P (y|xi; \u0398Q2LF )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "27:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "Model updates:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "\u0398 LF 2Q \u2190\u0398 LF 2Q + \u03b7 2 \u2022 \u2207 \u0398 LF 2Q\u00ca [r] \u0398 Q2LF \u2190\u0398 Q2LF + \u03b7 1 \u2022 \u2207 \u0398 Q2LF\u00ca [r]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "After reinforcement learning process, use labeled data to fine-tune models ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "Update \u0398 Q2LF by \u0398 Q2LF \u2190 \u0398 Q2LF + \u03b7 1 \u2022 \u2207 \u0398 Q2LF log P (y|x; \u0398 Q2LF ) 30: Update \u0398 LF 2Q by \u0398 LF 2Q \u2190 \u0398 LF 2Q + \u03b7 2 \u2022 \u2207 \u0398 LF 2Q log P (x|y; \u0398 LF 2Q ) 31: until Q2LF model converges B Examples of synthesized logical forms Original",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "After Modification Entity Replacement ( lambda $0 e ( and ( flight $0 ) ( meal $0 lunch:me ) ( from $0 ci0 ) ( to $0 ci1 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( meal $0 dinner:me ) ( from $0 ci0 ) ( to $0 ci1 ) ) ) ( = al0 ( abbrev delta:al ) ) ( = al0 ( abbrev usair:al ) ) ( lambda $0 e ( and ( flight $0 ) ( class_type $0 thrift:cl ) ( from $0 ci1 ) ( to $0 ci0 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( class_type $0 business:cl ) ( from $0 ci1 ) ( to $0 ci0 ) ) ) Unary Replacement ( lambda $0 e ( exists $1 ( and ( round_trip $1 ) ( from $1 ci0 ) ( to $1 ci1 ) ( = ( fare $1 ) $0 ) ) ) ) ( lambda $0 e ( exists $1 ( and ( oneway $1 ) ( from $1 ci0 ) ( to $1 ci1 ) ( = ( fare $1 ) $0 ) ) ) ) ( lambda $0 e ( and ( ground_transport $0 ) ( to_city $0 ci0 ) ) ) ( lambda $0 e ( and ( has_meal $0 ) ( to_city $0 ci0 ) ) ) ( lambda $0 e ( and ( taxi $0 ) ( to_city $0 ci0 ) ( from_airport $0 ap0 ) ) ) ( lambda $0 e ( and ( limousine $0 ) ( to_city $0 ci0 ) ( from_airport $0 ap0 ) ) ) Binary Replacement ( lambda $0 e ( and ( flight $0 ) ( airline $0 al0 ) ( approx_departure_time $0 ti0 ) ( from $0 ci0 ) ( to $0 ci1 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( airline $0 al0 ) ( approx_arrival_time $0 ti0 ) ( from $0 ci0 ) ( to $0 ci1 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( from $0 ci0 ) ( to $0 ci1 ) ( day_return $0 da0 ) ( day_number_return $0 dn0 ) ( month_return $0 mn0 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( from $0 ci0 ) ( to $0 ci1 ) ( day_arrival $0 da0 ) ( day_number_arrival $0 dn0 ) ( month_arrival $0 mn0 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( airline $0 al0 ) ( stop $0 ci0 ) ) ) ( lambda $0 e ( and ( flight $0 ) ( airline $0 al0 ) ( from $0 ci0 ) ) ) = ) en.field.history ) ) ( call SW.domain ( string student ) ) ) ( string student ) ) ) Table 7 : Examples of synthesized logical forms on OVERNIGHT.",
"cite_spans": [],
"ref_spans": [
{
"start": 1680,
"end": 1683,
"text": "= )",
"ref_id": null
},
{
"start": 1768,
"end": 1775,
"text": "Table 7",
"ref_id": null
}
],
"eq_spans": [],
"section": "26:",
"sec_num": null
},
{
"text": "KB \u22121 (\u2022) is the inverse operation of KB(\u2022), which returns the set of all corresponding noun phrases given a KB entity.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://github.com/RhythmCao/ Synthesized-Logical-Forms 3 https://github.com/percyliang/sempre",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Although there is another published result that achieved better performance by using word class information from Wiktionary(Wang et al., 2014), it is unfair to compare it with our results and other previous systems which only exploit data resources of ATIS.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work has been supported by the National Key Research and Development Program of China (Grant No.2017YFB1002102) and the China NSFC projects (No. 61573241).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Neural machine translation by jointly learning to align and translate",
"authors": [
{
"first": "Dzmitry",
"middle": [],
"last": "Bahdanau",
"suffix": ""
},
{
"first": "Kyunghyun",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1409.0473"
]
},
"num": null,
"urls": [],
"raw_text": "Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Towards zero-shot frame semantic parsing for domain scaling",
"authors": [
{
"first": "Ankur",
"middle": [],
"last": "Bapna",
"suffix": ""
},
{
"first": "G\u00f6khan",
"middle": [],
"last": "T\u00fcr",
"suffix": ""
},
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-T\u00fcr",
"suffix": ""
},
{
"first": "Larry",
"middle": [
"P"
],
"last": "Heck",
"suffix": ""
}
],
"year": 2017,
"venue": "18th Annual Conference of the International Speech Communication Association",
"volume": "",
"issue": "",
"pages": "2476--2480",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ankur Bapna, G\u00f6khan T\u00fcr, Dilek Hakkani-T\u00fcr, and Larry P. Heck. 2017. Towards zero-shot frame se- mantic parsing for domain scaling. In Interspeech 2017, 18th Annual Conference of the International Speech Communication Association, pages 2476- 2480.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Semantic parsing on freebase from question-answer pairs",
"authors": [
{
"first": "Jonathan",
"middle": [],
"last": "Berant",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Chou",
"suffix": ""
},
{
"first": "Roy",
"middle": [],
"last": "Frostig",
"suffix": ""
},
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1533--1544",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013. Semantic parsing on freebase from question-answer pairs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Lan- guage Processing, pages 1533-1544.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Sequenceto-action: End-to-end semantic graph generation for semantic parsing",
"authors": [
{
"first": "Bo",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Le",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Xianpei",
"middle": [],
"last": "Han",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "766--777",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bo Chen, Le Sun, and Xianpei Han. 2018a. Sequence- to-action: End-to-end semantic graph generation for semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 766-777.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Agentgraph: Towards universal dialogue management with structured deep reinforcement learning",
"authors": [
{
"first": "Lu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Zhi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Bowen",
"middle": [],
"last": "Tan",
"suffix": ""
},
{
"first": "Sishan",
"middle": [],
"last": "Long",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Gasic",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1905.11259"
]
},
"num": null,
"urls": [],
"raw_text": "Lu Chen, Zhi Chen, Bowen Tan, Sishan Long, Mil- ica Gasic, and Kai Yu. 2019. Agentgraph: To- wards universal dialogue management with struc- tured deep reinforcement learning. arXiv preprint arXiv:1905.11259.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Structured dialogue policy with graph neural networks",
"authors": [
{
"first": "Lu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Bowen",
"middle": [],
"last": "Tan",
"suffix": ""
},
{
"first": "Sishan",
"middle": [],
"last": "Long",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 27th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "1257--1268",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lu Chen, Bowen Tan, Sishan Long, and Kai Yu. 2018b. Structured dialogue policy with graph neu- ral networks. In Proceedings of the 27th Inter- national Conference on Computational Linguistics, pages 1257-1268, Santa Fe, New Mexico, USA.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Learning structured natural language representations for semantic parsing",
"authors": [
{
"first": "Jianpeng",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "Siva",
"middle": [],
"last": "Reddy",
"suffix": ""
},
{
"first": "Vijay",
"middle": [],
"last": "Saraswat",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "44--55",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jianpeng Cheng, Siva Reddy, Vijay Saraswat, and Mirella Lapata. 2017. Learning structured natural language representations for semantic parsing. In Proceedings of the 55th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 44-55.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Language to logical form with neural attention",
"authors": [
{
"first": "Li",
"middle": [],
"last": "Dong",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "33--43",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Li Dong and Mirella Lapata. 2016. Language to logi- cal form with neural attention. In Proceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 33-43.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Coarse-to-fine decoding for neural semantic parsing",
"authors": [
{
"first": "Li",
"middle": [],
"last": "Dong",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "731--742",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Li Dong and Mirella Lapata. 2018. Coarse-to-fine de- coding for neural semantic parsing. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers), pages 731-742.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Weakly supervised semantic parsing with abstract examples",
"authors": [
{
"first": "Omer",
"middle": [],
"last": "Goldman",
"suffix": ""
},
{
"first": "Veronica",
"middle": [],
"last": "Latcinnik",
"suffix": ""
},
{
"first": "Ehud",
"middle": [],
"last": "Nave",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Globerson",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Berant",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1809--1819",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Omer Goldman, Veronica Latcinnik, Ehud Nave, Amir Globerson, and Jonathan Berant. 2018. Weakly su- pervised semantic parsing with abstract examples. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1809-1819.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Pointing the unknown words",
"authors": [
{
"first": "Caglar",
"middle": [],
"last": "Gulcehre",
"suffix": ""
},
{
"first": "Sungjin",
"middle": [],
"last": "Ahn",
"suffix": ""
},
{
"first": "Ramesh",
"middle": [],
"last": "Nallapati",
"suffix": ""
},
{
"first": "Bowen",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "140--149",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, Bowen Zhou, and Yoshua Bengio. 2016. Pointing the unknown words. In Proceedings of the 54th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 140- 149.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Question generation from sql queries improves neural semantic parsing",
"authors": [
{
"first": "Daya",
"middle": [],
"last": "Guo",
"suffix": ""
},
{
"first": "Yibo",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Duyu",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Nan",
"middle": [],
"last": "Duan",
"suffix": ""
},
{
"first": "Jian",
"middle": [],
"last": "Yin",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Chi",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Cao",
"suffix": ""
},
{
"first": "Peng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1597--1607",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, and Ming Zhou. 2018. Question generation from sql queries im- proves neural semantic parsing. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing, pages 1597-1607.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Dual learning for machine translation",
"authors": [
{
"first": "Di",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Yingce",
"middle": [],
"last": "Xia",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Liwei",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Nenghai",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Tie-Yan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Wei-Ying",
"middle": [],
"last": "Ma",
"suffix": ""
}
],
"year": 2016,
"venue": "Advances in Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "820--828",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Di He, Yingce Xia, Tao Qin, Liwei Wang, Nenghai Yu, Tie-Yan Liu, and Wei-Ying Ma. 2016. Dual learn- ing for machine translation. In Advances in Neural Information Processing Systems, pages 820-828.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Neural semantic parsing over multiple knowledge-bases",
"authors": [
{
"first": "Jonathan",
"middle": [],
"last": "Herzig",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Berant",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "623--628",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jonathan Herzig and Jonathan Berant. 2017. Neural semantic parsing over multiple knowledge-bases. In Proceedings of the 55th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers), pages 623-628.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Long short-term memory",
"authors": [
{
"first": "Sepp",
"middle": [],
"last": "Hochreiter",
"suffix": ""
},
{
"first": "J\u00fcrgen",
"middle": [],
"last": "Schmidhuber",
"suffix": ""
}
],
"year": 1997,
"venue": "Neural computation",
"volume": "9",
"issue": "8",
"pages": "1735--1780",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Data recombination for neural semantic parsing",
"authors": [
{
"first": "Robin",
"middle": [],
"last": "Jia",
"suffix": ""
},
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "12--22",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robin Jia and Percy Liang. 2016. Data recombination for neural semantic parsing. In Proceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 12-22.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Adam: A method for stochastic optimization",
"authors": [
{
"first": "P",
"middle": [],
"last": "Diederik",
"suffix": ""
},
{
"first": "Jimmy",
"middle": [],
"last": "Kingma",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ba",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1412.6980"
]
},
"num": null,
"urls": [],
"raw_text": "Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Resource description framework (rdf): Concepts and abstract syntax",
"authors": [
{
"first": "Graham",
"middle": [],
"last": "Klyne",
"suffix": ""
},
{
"first": "Jeremy",
"middle": [
"J"
],
"last": "Carroll",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Graham Klyne and Jeremy J Carroll. 2006. Resource description framework (rdf): Concepts and abstract syntax.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Neural semantic parsing with type constraints for semi-structured tables",
"authors": [
{
"first": "Jayant",
"middle": [],
"last": "Krishnamurthy",
"suffix": ""
},
{
"first": "Pradeep",
"middle": [],
"last": "Dasigi",
"suffix": ""
},
{
"first": "Matt",
"middle": [],
"last": "Gardner",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1516--1526",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gard- ner. 2017. Neural semantic parsing with type con- straints for semi-structured tables. In Proceedings of the 2017 Conference on Empirical Methods in Nat- ural Language Processing, pages 1516-1526.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Weakly supervised training of semantic parsers",
"authors": [
{
"first": "Jayant",
"middle": [],
"last": "Krishnamurthy",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Tom",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mitchell",
"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": "754--765",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jayant Krishnamurthy and Tom M Mitchell. 2012. Weakly supervised training of semantic parsers. In Proceedings of the 2012 Joint Conference on Empir- ical Methods in Natural Language Processing and Computational Natural Language Learning, pages 754-765.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Lexical generalization in ccg grammar induction for semantic parsing",
"authors": [
{
"first": "Tom",
"middle": [],
"last": "Kwiatkowski",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Sharon",
"middle": [],
"last": "Goldwater",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Steedman",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the conference on empirical methods in natural language processing",
"volume": "",
"issue": "",
"pages": "1512--1523",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom Kwiatkowski, Luke Zettlemoyer, Sharon Goldwa- ter, and Mark Steedman. 2011. Lexical generaliza- tion in ccg grammar induction for semantic pars- ing. In Proceedings of the conference on empiri- cal methods in natural language processing, pages 1512-1523.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks",
"authors": [
{
"first": "Dong-Hyun",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 2013,
"venue": "Workshop on Challenges in Representation Learning, ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dong-Hyun Lee. 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning, ICML.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Neural symbolic machines: Learning semantic parsers on freebase with weak supervision",
"authors": [
{
"first": "Chen",
"middle": [],
"last": "Liang",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Berant",
"suffix": ""
},
{
"first": "Quoc",
"middle": [],
"last": "Le",
"suffix": ""
},
{
"first": "Kenneth",
"middle": [
"D"
],
"last": "Forbus",
"suffix": ""
},
{
"first": "Ni",
"middle": [],
"last": "Lao",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "23--33",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. 2017. Neural symbolic ma- chines: Learning semantic parsers on freebase with weak supervision. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23-33.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "NLTK: The natural language toolkit",
"authors": [
{
"first": "Edward",
"middle": [],
"last": "Loper",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bird",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Edward Loper and Steven Bird. 2002. NLTK: The nat- ural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Compu- tational Linguistics.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "A generative model for parsing natural language to meaning representations",
"authors": [
{
"first": "Wei",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Wee",
"middle": [],
"last": "Hwee Tou Ng",
"suffix": ""
},
{
"first": "Luke",
"middle": [
"S"
],
"last": "Sun Lee",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "783--792",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wei Lu, Hwee Tou Ng, Wee Sun Lee, and Luke S Zettlemoyer. 2008. A generative model for pars- ing natural language to meaning representations. In Proceedings of the Conference on Empirical Meth- ods in Natural Language Processing, pages 783- 792.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Effective approaches to attention-based neural machine translation",
"authors": [
{
"first": "Thang",
"middle": [],
"last": "Luong",
"suffix": ""
},
{
"first": "Hieu",
"middle": [],
"last": "Pham",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1412--1421",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thang Luong, Hieu Pham, and Christopher D. Man- ning. 2015. Effective approaches to attention-based neural machine translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412-1421.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Recurrent neural network based language model",
"authors": [
{
"first": "Tom\u00e1\u0161",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Karafi\u00e1t",
"suffix": ""
},
{
"first": "Luk\u00e1\u0161",
"middle": [],
"last": "Burget",
"suffix": ""
},
{
"first": "Ja\u0148",
"middle": [],
"last": "Cernock\u1ef3",
"suffix": ""
},
{
"first": "Sanjeev",
"middle": [],
"last": "Khudanpur",
"suffix": ""
}
],
"year": 2010,
"venue": "Eleventh annual conference of the international speech communication association",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom\u00e1\u0161 Mikolov, Martin Karafi\u00e1t, Luk\u00e1\u0161 Burget, Ja\u0148 Cernock\u1ef3, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Eleventh annual conference of the international speech com- munication association.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Abstractive text summarization using sequence-tosequence rnns and beyond",
"authors": [
{
"first": "Ramesh",
"middle": [],
"last": "Nallapati",
"suffix": ""
},
{
"first": "Bowen",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Caglar",
"middle": [],
"last": "Cicero Dos Santos",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Gulcehre",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Xiang",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "280--290",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016. Ab- stractive text summarization using sequence-to- sequence rnns and beyond. In Proceedings of The 20th SIGNLL Conference on Computational Natu- ral Language Learning, pages 280-290.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Glove: Global vectors for word representation",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Pennington",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "1532--1543",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 confer- ence on empirical methods in natural language pro- cessing (EMNLP), pages 1532-1543.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Grounded unsupervised semantic parsing",
"authors": [
{
"first": "Hoifung",
"middle": [],
"last": "Poon",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "933--943",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hoifung Poon. 2013. Grounded unsupervised seman- tic parsing. In Proceedings of the 51st Annual Meet- ing of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 933-943.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Abstract syntax networks for code generation and semantic parsing",
"authors": [
{
"first": "Maxim",
"middle": [],
"last": "Rabinovich",
"suffix": ""
},
{
"first": "Mitchell",
"middle": [],
"last": "Stern",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1139--1149",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017. Abstract syntax networks for code genera- tion and semantic parsing. In Proceedings of the 55th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 1139-1149.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Get to the point: Summarization with pointergenerator networks",
"authors": [
{
"first": "Abigail",
"middle": [],
"last": "See",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Peter",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Liu",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1073--1083",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer- generator networks. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073- 1083.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Improving neural machine translation models with monolingual data",
"authors": [
{
"first": "Rico",
"middle": [],
"last": "Sennrich",
"suffix": ""
},
{
"first": "Barry",
"middle": [],
"last": "Haddow",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Birch",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "86--96",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving neural machine translation mod- els with monolingual data. In Proceedings of the 54th Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 86-96.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Cross-domain semantic parsing via paraphrasing",
"authors": [
{
"first": "Yu",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "Xifeng",
"middle": [],
"last": "Yan",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1235--1246",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Su and Xifeng Yan. 2017. Cross-domain seman- tic parsing via paraphrasing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1235-1246.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Logician and orator: Learning from the duality between language and knowledge in open domain",
"authors": [
{
"first": "Mingming",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Xu",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Ping",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2119--2130",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mingming Sun, Xu Li, and Ping Li. 2018. Logician and orator: Learning from the duality between lan- guage and knowledge in open domain. In Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2119-2130.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Sequence to sequence learning with neural networks",
"authors": [
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Oriol",
"middle": [],
"last": "Vinyals",
"suffix": ""
},
{
"first": "Quoc V",
"middle": [],
"last": "Le",
"suffix": ""
}
],
"year": 2014,
"venue": "Advances in neural information processing systems",
"volume": "",
"issue": "",
"pages": "3104--3112",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural net- works. In Advances in neural information process- ing systems, pages 3104-3112.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Reinforcement learning: An introduction",
"authors": [
{
"first": "S",
"middle": [],
"last": "Richard",
"suffix": ""
},
{
"first": "Andrew",
"middle": [
"G"
],
"last": "Sutton",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Barto",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard S Sutton and Andrew G Barto. 2018. Rein- forcement learning: An introduction. MIT press.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Policy gradient methods for reinforcement learning with function approximation",
"authors": [
{
"first": "S",
"middle": [],
"last": "Richard",
"suffix": ""
},
{
"first": "David",
"middle": [
"A"
],
"last": "Sutton",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mcallester",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Satinder",
"suffix": ""
},
{
"first": "Yishay",
"middle": [],
"last": "Singh",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mansour",
"suffix": ""
}
],
"year": 2000,
"venue": "Advances in neural information processing systems",
"volume": "",
"issue": "",
"pages": "1057--1063",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour. 2000. Policy gradi- ent methods for reinforcement learning with func- tion approximation. In Advances in neural informa- tion processing systems, pages 1057-1063.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Question answering and question generation as dual tasks",
"authors": [
{
"first": "Duyu",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Nan",
"middle": [],
"last": "Duan",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Zhao",
"middle": [],
"last": "Yan",
"suffix": ""
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1706.02027"
]
},
"num": null,
"urls": [],
"raw_text": "Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou. 2017. Question answering and ques- tion generation as dual tasks. arXiv preprint arXiv:1706.02027.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Learning to collaborate for question answering and asking",
"authors": [
{
"first": "Duyu",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Nan",
"middle": [],
"last": "Duan",
"suffix": ""
},
{
"first": "Zhao",
"middle": [],
"last": "Yan",
"suffix": ""
},
{
"first": "Zhirui",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yibo",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Shujie",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Yuanhua",
"middle": [],
"last": "Lv",
"suffix": ""
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "1564--1574",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, and Ming Zhou. 2018. Learning to collaborate for question answering and asking. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1564- 1574.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Combining active and semisupervised learning for spoken language understanding",
"authors": [
{
"first": "Gokhan",
"middle": [],
"last": "Tur",
"suffix": ""
},
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-T\u00fcr",
"suffix": ""
},
{
"first": "Robert",
"middle": [
"E"
],
"last": "Schapire",
"suffix": ""
}
],
"year": 2005,
"venue": "Speech Communication",
"volume": "45",
"issue": "2",
"pages": "171--186",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gokhan Tur, Dilek Hakkani-T\u00fcr, and Robert E. Schapire. 2005. Combining active and semi- supervised learning for spoken language under- standing. Speech Communication, 45(2):171-186.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Grammar as a foreign language",
"authors": [
{
"first": "Oriol",
"middle": [],
"last": "Vinyals",
"suffix": ""
},
{
"first": "\u0141ukasz",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "Terry",
"middle": [],
"last": "Koo",
"suffix": ""
},
{
"first": "Slav",
"middle": [],
"last": "Petrov",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Geoffrey",
"middle": [],
"last": "Hinton",
"suffix": ""
}
],
"year": 2015,
"venue": "Advances in neural information processing systems",
"volume": "",
"issue": "",
"pages": "2773--2781",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oriol Vinyals, \u0141ukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton. 2015. Gram- mar as a foreign language. In Advances in neural information processing systems, pages 2773-2781.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "Morpho-syntactic lexical generalization for CCG semantic parsing",
"authors": [
{
"first": "Adrienne",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Tom",
"middle": [],
"last": "Kwiatkowski",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "1284--1295",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Adrienne Wang, Tom Kwiatkowski, and Luke Zettle- moyer. 2014. Morpho-syntactic lexical generaliza- tion for CCG semantic parsing. In Proceedings of the 2014 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP), pages 1284- 1295, Doha, Qatar. Association for Computational Linguistics.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "Building a semantic parser overnight",
"authors": [
{
"first": "Yushi",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Berant",
"suffix": ""
},
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "1332--1342",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yushi Wang, Jonathan Berant, and Percy Liang. 2015. Building a semantic parser overnight. In Proceed- ings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th Interna- tional Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers), pages 1332-1342.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "Learning synchronous grammars for semantic parsing with lambda calculus",
"authors": [
{
"first": "Yuk",
"middle": [
"Wah"
],
"last": "Wong",
"suffix": ""
},
{
"first": "Raymond",
"middle": [],
"last": "Mooney",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 45th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yuk Wah Wong and Raymond Mooney. 2007. Learn- ing synchronous grammars for semantic parsing with lambda calculus. In Proceedings of the 45th",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "Annual Meeting of the Association of Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "960--967",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association of Computational Linguistics, pages 960-967.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "Google's neural machine translation system: Bridging the gap between human and machine translation",
"authors": [
{
"first": "Yonghui",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "Zhifeng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Quoc",
"suffix": ""
},
{
"first": "Mohammad",
"middle": [],
"last": "Le",
"suffix": ""
},
{
"first": "Wolfgang",
"middle": [],
"last": "Norouzi",
"suffix": ""
},
{
"first": "Maxim",
"middle": [],
"last": "Macherey",
"suffix": ""
},
{
"first": "Yuan",
"middle": [],
"last": "Krikun",
"suffix": ""
},
{
"first": "Qin",
"middle": [],
"last": "Cao",
"suffix": ""
},
{
"first": "Klaus",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Macherey",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1609.08144"
]
},
"num": null,
"urls": [],
"raw_text": "Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google's neural ma- chine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.",
"links": null
},
"BIBREF47": {
"ref_id": "b47",
"title": "Dual supervised learning",
"authors": [
{
"first": "Yingce",
"middle": [],
"last": "Xia",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jiang",
"middle": [],
"last": "Bian",
"suffix": ""
},
{
"first": "Nenghai",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Tie-Yan",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 34th International Conference on Machine Learning",
"volume": "",
"issue": "",
"pages": "3789--3798",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, and Tie-Yan Liu. 2017. Dual supervised learn- ing. In Proceedings of the 34th International Con- ference on Machine Learning, pages 3789-3798.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "Sequence-based structured prediction for semantic parsing",
"authors": [
{
"first": "Chunyang",
"middle": [],
"last": "Xiao",
"suffix": ""
},
{
"first": "Marc",
"middle": [],
"last": "Dymetman",
"suffix": ""
},
{
"first": "Claire",
"middle": [],
"last": "Gardent",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1341--1350",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chunyang Xiao, Marc Dymetman, and Claire Gardent. 2016. Sequence-based structured prediction for se- mantic parsing. In Proceedings of the 54th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1341- 1350.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Dualgan: Unsupervised dual learning for image-to-image translation",
"authors": [
{
"first": "Zili",
"middle": [],
"last": "Yi",
"suffix": ""
},
{
"first": "Hao",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the IEEE International Conference on Computer Vision",
"volume": "",
"issue": "",
"pages": "2849--2857",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zili Yi, Hao Zhang, Ping Tan, and Minglun Gong. 2017. Dualgan: Unsupervised dual learning for image-to-image translation. In Proceedings of the IEEE International Conference on Computer Vision, pages 2849-2857.",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "The value of semantic parse labeling for knowledge base question answering",
"authors": [
{
"first": "Matthew",
"middle": [],
"last": "Wen-Tau Yih",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Richardson",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Meek",
"suffix": ""
},
{
"first": "Jina",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Suh",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "201--206",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wen-tau Yih, Matthew Richardson, Chris Meek, Ming- Wei Chang, and Jina Suh. 2016. The value of se- mantic parse labeling for knowledge base question answering. In Proceedings of the 54th Annual Meet- ing of the Association for Computational Linguistics (Volume 2: Short Papers), pages 201-206.",
"links": null
},
"BIBREF51": {
"ref_id": "b51",
"title": "A syntactic neural model for general-purpose code generation",
"authors": [
{
"first": "Pengcheng",
"middle": [],
"last": "Yin",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "440--450",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pengcheng Yin and Graham Neubig. 2017. A syntactic neural model for general-purpose code generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 440-450.",
"links": null
},
"BIBREF52": {
"ref_id": "b52",
"title": "Tranx: A transition-based neural abstract syntax parser for semantic parsing and code generation",
"authors": [
{
"first": "Pengcheng",
"middle": [],
"last": "Yin",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
"volume": "",
"issue": "",
"pages": "7--12",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pengcheng Yin and Graham Neubig. 2018. Tranx: A transition-based neural abstract syntax parser for se- mantic parsing and code generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstra- tions, pages 7-12.",
"links": null
},
"BIBREF53": {
"ref_id": "b53",
"title": "StructVAE: Tree-structured latent variable models for semi-supervised semantic parsing",
"authors": [
{
"first": "Pengcheng",
"middle": [],
"last": "Yin",
"suffix": ""
},
{
"first": "Chunting",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Junxian",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "754--765",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pengcheng Yin, Chunting Zhou, Junxian He, and Gra- ham Neubig. 2018. StructVAE: Tree-structured la- tent variable models for semi-supervised semantic parsing. In Proceedings of the 56th Annual Meet- ing of the Association for Computational Linguis- tics (Volume 1: Long Papers), pages 754-765, Mel- bourne, Australia. Association for Computational Linguistics.",
"links": null
},
"BIBREF54": {
"ref_id": "b54",
"title": "Learning to parse database queries using inductive logic programming",
"authors": [
{
"first": "M",
"middle": [],
"last": "John",
"suffix": ""
},
{
"first": "Raymond J",
"middle": [],
"last": "Zelle",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mooney",
"suffix": ""
}
],
"year": 1996,
"venue": "Proceedings of the national conference on artificial intelligence",
"volume": "",
"issue": "",
"pages": "1050--1055",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John M Zelle and Raymond J Mooney. 1996. Learn- ing to parse database queries using inductive logic programming. In Proceedings of the national con- ference on artificial intelligence, pages 1050-1055.",
"links": null
},
"BIBREF55": {
"ref_id": "b55",
"title": "Online learning of relaxed ccg grammars for parsing to logical form",
"authors": [
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Luke Zettlemoyer and Michael Collins. 2007. Online learning of relaxed ccg grammars for parsing to log- ical form. In Proceedings of the 2007 Joint Con- ference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL).",
"links": null
},
"BIBREF56": {
"ref_id": "b56",
"title": "Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars",
"authors": [
{
"first": "Luke",
"middle": [
"S"
],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2005,
"venue": "UAI",
"volume": "",
"issue": "",
"pages": "658--666",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Luke S. Zettlemoyer and Michael Collins. 2005. Learning to map sentences to logical form: Struc- tured classification with probabilistic categorial grammars. In UAI, pages 658-666.",
"links": null
},
"BIBREF57": {
"ref_id": "b57",
"title": "Type-driven incremental semantic parsing with polymorphism",
"authors": [
{
"first": "Kai",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "1416--1421",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kai Zhao and Liang Huang. 2015. Type-driven in- cremental semantic parsing with polymorphism. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 1416-1421.",
"links": null
},
"BIBREF58": {
"ref_id": "b58",
"title": "A hierarchical decoding model for spoken language understanding from unaligned data",
"authors": [
{
"first": "Zijian",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Su",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "7305--7309",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zijian Zhao, Su Zhu, and Kai Yu. 2019. A hierar- chical decoding model for spoken language under- standing from unaligned data. In ICASSP 2019- 2019 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pages 7305-7309. IEEE.",
"links": null
},
"BIBREF59": {
"ref_id": "b59",
"title": "Semantic parser enhancement for dialogue domain extension with little data",
"authors": [
{
"first": "Su",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Lu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Da",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2014,
"venue": "Spoken Language Technology Workshop (SLT)",
"volume": "",
"issue": "",
"pages": "336--341",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Su Zhu, Lu Chen, Kai Sun, Da Zheng, and Kai Yu. 2014. Semantic parser enhancement for dia- logue domain extension with little data. In Spoken Language Technology Workshop (SLT), 2014 IEEE, pages 336-341. IEEE.",
"links": null
},
"BIBREF60": {
"ref_id": "b60",
"title": "Robust spoken language understanding with unsupervised asr-error adaptation",
"authors": [
{
"first": "Su",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Ouyu",
"middle": [],
"last": "Lan",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "IEEE International Conference on Acoustics, Speech and Signal Processing",
"volume": "",
"issue": "",
"pages": "6179--6183",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Su Zhu, Ouyu Lan, and Kai Yu. 2018. Robust spoken language understanding with unsupervised asr-error adaptation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018, pages 6179-6183. IEEE.",
"links": null
},
"BIBREF61": {
"ref_id": "b61",
"title": "Concept transfer learning for adaptive language understanding",
"authors": [
{
"first": "Su",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue",
"volume": "",
"issue": "",
"pages": "391--399",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Su Zhu and Kai Yu. 2018. Concept transfer learning for adaptive language understanding. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 391-399. Association for Compu- tational Linguistics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"num": null,
"text": "Synthesis of logical forms by replacement.",
"type_str": "figure",
"uris": null
},
"FIGREF1": {
"num": null,
"text": "",
"type_str": "figure",
"uris": null
},
"FIGREF2": {
"num": null,
"text": "Test accuracies on ATIS. It varies the ratio of labeled data, and keeps the rest as unlabeled data.",
"type_str": "figure",
"uris": null
},
"TABREF1": {
"type_str": "table",
"text": "",
"content": "<table/>",
"html": null,
"num": null
},
"TABREF3": {
"type_str": "table",
"text": "Test accuracies on OVERNIGHT compared with previous systems.",
"content": "<table/>",
"html": null,
"num": null
},
"TABREF5": {
"type_str": "table",
"text": "Test accuracies on ATIS compared with previous systems.",
"content": "<table/>",
"html": null,
"num": null
},
"TABREF8": {
"type_str": "table",
"text": "Dual Learning Framework for Semantic ParsingInput:1: Supervised dataset T = { x, y }; Unsupervised dataset for queries Q and logical forms LF; 2: Pre-trained models on T : Q2LF model P (y|x; \u0398 Q2LF ), LF 2Q model P (x|y; \u0398 LF 2Q ); 3: Pre-trained model on Q and queries of T : Language Model for queries LM q ; 4: Indicator performs surface and semantic check for a logical form: grammar_error_indicator(\u2022); 5: Beam search size k, hyper parameters \u03b1 and \u03b2, learning rate \u03b7 1 for Q2LF and \u03b7 2 for LF 2Q; Output: Parameters \u0398 Q2LF of Q2LF model",
"content": "<table><tr><td colspan=\"2\">A Detailed algorithm</td></tr><tr><td colspan=\"2\">Algorithm 2 6: repeat</td></tr><tr><td>7:</td><td>Reinforcement learning process uses unlabeled data, also reuses labeled data</td></tr><tr><td>8:</td><td>Sample a query x from Q \u222a T ;</td></tr><tr><td>9:</td><td>Q2LF model generates k logical forms</td></tr><tr><td/><td>y 1 , y 2 , \u2022 \u2022 \u2022 , y k via beam search;</td></tr><tr><td>10:</td><td/></tr><tr><td/><td>to model structured logical</td></tr><tr><td/><td>forms.</td></tr></table>",
"html": null,
"num": null
},
"TABREF9": {
"type_str": "table",
"text": "Examples of synthesized logical forms on ATIS. SW.listValue ( call SW.getProperty ( ( lambda s ( call SW.filter ( var s ) ( string position ) ( string ! = ) en.position.point_guard ) ) ( call SW.domain ( string player ) ) ) ( string player ) ) ) new ( call SW.listValue ( call SW.getProperty ( ( lambda s ( call SW.filter ( var s ) ( string position ) ( string ! = ) en.position.forward ) ) ( call SW.domain ( string player ) ) ) ( string player ) ) ) Blo. pre ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.block ) ( string ! type ) ) ( string shape ) ( string = ) en.shape.pyramid ) ) new ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.block ) ( string ! type ) ) ( string shape ) ( string = ) en.shape.cube ) ) Cal. pre ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.location ) ( string ! type ) ) ( call SW.reverse ( string location ) ) ( string = ) en.meeting.weekly_standup ) ) new ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.location ) ( string ! type ) ) ( call SW.reverse ( string location ) ) ( string = ) en.meeting.annual_review ) ) Hou. pre ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.housing_unit ) ( string ! type ) ) ( string housing_type ) ( string = ) ( call SW.concat en.housing.apartment en.housing.condo ) ) ) new ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.housing_unit ) ( string ! type ) ) ( string housing_type ) ( string = ) ( call SW.concat en.housing.condo en.housing.apartment ) ) ) Pub. pre ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.article ) ( string ! type ) ) ( string author ) ( string = ) en.person.efron ) ) new ( call SW.listValue ( call SW.filter ( call SW.getProperty ( call SW.singleton en.article ) ( string ! type ) ) ( string author ) ( string = ) en.person.lakoff ) ) Rec. pre ( call SW.listValue ( call SW.getProperty en.recipe.rice_pudding ( string cuisine ) ) ) new ( call SW.listValue ( call SW.getProperty en.recipe.quiche ( string cuisine ) ) ) Res. pre ( call SW.listValue ( call SW.getProperty en.restaurant.thai_cafe ( string neighborhood ) ) ) new ( call SW.listValue ( call SW.getProperty en.restaurant.pizzeria_juno ( string neighborhood ) ) ) Soc. pre ( call SW.listValue ( call SW.getProperty ( ( lambda s ( call SW.filter ( var s ) ( string field_of_study ) ( string ! = ) en.field.computer_science ) ) ( call SW.domain ( string student ) ) ) ( string student ) ) ) new ( call SW.listValue ( call SW.getProperty ( ( lambda s ( call SW.filter ( var s ) ( string field_of_study ) ( string !",
"content": "<table><tr><td>Domain</td><td/><td>Logical Forms</td></tr><tr><td>Bas.</td><td>pre</td><td>( call</td></tr></table>",
"html": null,
"num": null
}
}
}
} |