File size: 112,104 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 |
{
"paper_id": "2019",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:30:22.911608Z"
},
"title": "Introducing Aspects of Creativity in Automatic Poetry Generation",
"authors": [
{
"first": "Brendan",
"middle": [],
"last": "Bena",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Drury University",
"location": {
"addrLine": "900 N. Benton Ave. Springfield",
"postCode": "65109",
"region": "Missouri"
}
},
"email": ""
},
{
"first": "Jugal",
"middle": [],
"last": "Kalita",
"suffix": "",
"affiliation": {},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams-called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.",
"pdf_parse": {
"paper_id": "2019",
"_pdf_hash": "",
"abstract": [
{
"text": "Poetry Generation involves teaching systems to automatically generate text that resembles poetic work. A deep learning system can learn to generate poetry on its own by training on a corpus of poems and modeling the particular style of language. In this paper, we propose taking an approach that fine-tunes GPT-2, a pre-trained language model, to our downstream task of poetry generation. We extend prior work on poetry generation by introducing creative elements. Specifically, we generate poems that express emotion and elicit the same in readers, and poems that use the language of dreams-called dream poetry. We are able to produce poems that correctly elicit the emotions of sadness and joy 87.5 and 85 percent, respectively, of the time. We produce dreamlike poetry by training on a corpus of texts that describe dreams. Poems from this model are shown to capture elements of dream poetry with scores of no less than 3.2 on the Likert scale. We perform crowdsourced human-evaluation for all our poems. We also make use of the Coh-Metrix tool, outlining metrics we use to gauge the quality of text generated.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Many natural language processing tasks require the generation of human-like language. Some tasks, such as image and video captioning and automatic weather and sports reporting, convert nontextual data to text. Some others, such as summarization and machine translation, convert one text to another. There are additional tasks that aim to produce text, given a topic or a few keywords such as story generation, joke generation, and poetry generation, among others.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Poetry generation produces creative content, and delivers the content in an aesthetically pleasing manner, usually following a specific structure.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Thus, in addition to generating text as if in a story, the lines produced usually have a certain length, quite frequently there is a rhyming scheme as well as rhythm, and organization into structures such as couplets, quatrains, quintets, and stanzas. Among other tools, creativity comes from unusual usage of words through effects such as alliteration, assonance, and elision; use of metaphors, symbolism, and other linguistic devices; licensing of underlying imagery with expressed feelings, sentiments, and emotions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Work in natural language generation can be traced to pioneering rule-based simulations of chatbots such as the \"psychotherapist\" Eliza (Weizenbaum et al., 1966) and paranoid schizophrenia-suffering PARRY (Colby, 1981) . Surveys such as (Hovy, 1990; Reiter and Dale, 2000; Gatt and Krahmer, 2018; Santhanam and Shaikh, 2019) have described the progress in natural language generation over 50 years. Of late, the use of deep learning has produced enviable progress in natural language generation, especially in topics such as machine translation (Bahdanau et al., 2014; Wu et al., 2016) , image captioning (Mao et al., 2014) and dialogue generation (Li et al., 2016) .",
"cite_spans": [
{
"start": 135,
"end": 160,
"text": "(Weizenbaum et al., 1966)",
"ref_id": "BIBREF35"
},
{
"start": 204,
"end": 217,
"text": "(Colby, 1981)",
"ref_id": "BIBREF3"
},
{
"start": 236,
"end": 248,
"text": "(Hovy, 1990;",
"ref_id": "BIBREF10"
},
{
"start": 249,
"end": 271,
"text": "Reiter and Dale, 2000;",
"ref_id": "BIBREF26"
},
{
"start": 272,
"end": 295,
"text": "Gatt and Krahmer, 2018;",
"ref_id": "BIBREF5"
},
{
"start": 296,
"end": 323,
"text": "Santhanam and Shaikh, 2019)",
"ref_id": "BIBREF28"
},
{
"start": 544,
"end": 567,
"text": "(Bahdanau et al., 2014;",
"ref_id": "BIBREF0"
},
{
"start": 568,
"end": 584,
"text": "Wu et al., 2016)",
"ref_id": "BIBREF37"
},
{
"start": 604,
"end": 622,
"text": "(Mao et al., 2014)",
"ref_id": "BIBREF16"
},
{
"start": 647,
"end": 664,
"text": "(Li et al., 2016)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "This paper discusses the automatic generation of natural-sounding poems that are creative. Creativity comes in many hues, and we experiment with a few established ways of creative expression in poetry generation. First, we generate poetry that can potentially evoke a response from the readers or hearers in terms of emotions and feelings they generate. Additionally, we choose the idea of mimicking the language of dreams as another form of creative expression due to its longstanding history in poetry. Dream poetry dates back to medieval times where famous fourteenth century authors, like Chaucer, experimented using dreams as the structure for an image or picture they wished to paint with a poem (Spearing, 1976a) . A dream poem is said to be characterized by the 'I' of the poem and its substance of a dream or a vision included (Lynch, 1998) . To the best of our knowledge, prior work on poetry generation, whether using deep learning or not, has not explored the incorporation of emotion-eliciting phraseology or elements of creativity such as dream poetry.",
"cite_spans": [
{
"start": 702,
"end": 719,
"text": "(Spearing, 1976a)",
"ref_id": "BIBREF30"
},
{
"start": 836,
"end": 849,
"text": "(Lynch, 1998)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our research provides the following contributions:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 generating grammatical, coherent, and flowing poetry using the powerful and versatile GPT-2 architecture, \u2022 successfully generating poetry that elicits certain emotions in readers, and \u2022 generating poems that follow time-honored tradition of dream-like language usage and imagery.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "This paper is organized as follows. Section 2 presents related work. Section 3 discusses our approach to creative text generation including pre-processing steps, architecture used, and approaches to training. Section 4 discusses our experiments and results. Finally, we present evaluation of our research in Section 5, followed by conclusions and future work in Section 6.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Early methods for poetry generation made use of template-oriented and rule-based techniques. These approaches often required a large amount of feature picking and knowledge of syntactic and semantic rules in a language (Oliveira, 2009 (Oliveira, , 2012 . Other methods treated poetry generation as special cases of machine translation or summarization tasks (Yan et al., 2013; He et al., 2012) . We believe that forcing a model to adhere to specific rules or templates, or summarizing or translating a given text to generate new poetry is unlikely to lead to the artistically expressive quality we seek to generate.",
"cite_spans": [
{
"start": 219,
"end": 234,
"text": "(Oliveira, 2009",
"ref_id": "BIBREF20"
},
{
"start": 235,
"end": 252,
"text": "(Oliveira, , 2012",
"ref_id": "BIBREF21"
},
{
"start": 358,
"end": 376,
"text": "(Yan et al., 2013;",
"ref_id": null
},
{
"start": 377,
"end": 393,
"text": "He et al., 2012)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "More recently, deep learning methods have become prevalent in natural language generation, including poetry generation. Zhang and Lapata (2014) for instance, used Convolutional (CNN) and Recurrent Neural Networks (RNN) to generate Chinese Poetry. RNNs allow for shortterm memory of the language to be maintained by inputting the generated output of a network cell back into itself, essentially building context. Ghazvininejad et al. (2017) used Long Short-Term Memory (LSTM) units, which are advanced gated versions of RNNs, to the task of poetry generation. Wei et al. (2018) attempted to address the style issue by training the networks using particular poets and controlling for style in Chinese poetry. They found that with enough training data, adequate results could be achieved. Problems related to poetic structure were addressed by Hopkins and Kiela (2017) . They generated rhythmic poetry by training the network on only a single type of poetry to ensure produced poems adhered to a single rhythmic structure. It was found in human evaluations that while the poems produced were rated to be of lower quality than human produced poems, they were indistinguishable from human produced poems. Lau et al. (2018) took the LSTM approach one step further with the Deepspeare model by employing an attention mechanism to model interactions among generated words. They also use three neural networks, one for rhythm, one for rhyming and another for word choice in their quest to generate Shakespeare-like sonnets. Vaswani et al. (2017) developed a deep neural architecture called the Transformer that did away with any sort of need for recurrence. The Transformer also employed an elaborate attention mechanism that has been shown to be useful in natural language tasks. Radford et al. (2019) used this architecture in their Generative Pretrained Transformer 2 (GPT-2) model. GPT-2 is capable of many downstream tasks like text generation but to our knowledge, research has not been published using the GPT-2 model specifically for poetry generation.",
"cite_spans": [
{
"start": 120,
"end": 143,
"text": "Zhang and Lapata (2014)",
"ref_id": "BIBREF40"
},
{
"start": 412,
"end": 439,
"text": "Ghazvininejad et al. (2017)",
"ref_id": "BIBREF6"
},
{
"start": 559,
"end": 576,
"text": "Wei et al. (2018)",
"ref_id": "BIBREF34"
},
{
"start": 841,
"end": 865,
"text": "Hopkins and Kiela (2017)",
"ref_id": "BIBREF9"
},
{
"start": 1200,
"end": 1217,
"text": "Lau et al. (2018)",
"ref_id": "BIBREF12"
},
{
"start": 1515,
"end": 1536,
"text": "Vaswani et al. (2017)",
"ref_id": "BIBREF32"
},
{
"start": 1772,
"end": 1793,
"text": "Radford et al. (2019)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "On a slightly different but related note, natural language generation influenced by multi-modal input was attempted by Vechtomova et al. (2018) to generate song lyrics in the style of specific artists by fusing outputs coming from lyrical inputs processed by an RNN and audio clips processed by a CNN. Text generation has also been influenced, in a cross domain manner, through images. The works of Liu et al. (2018) have shown that coupled visual-poetic embeddings can be used to pick out poetic clues in images, which in turn can be used to inspire the generated text. Though influenced natural language generation in and of itself is not a novel idea, we feel our attempt to style text with the intent of eliciting particular emotions provides a creative way to explore this subtask.",
"cite_spans": [
{
"start": 119,
"end": 143,
"text": "Vechtomova et al. (2018)",
"ref_id": "BIBREF33"
},
{
"start": 399,
"end": 416,
"text": "Liu et al. (2018)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our goal is to successfully demonstrate the introduction of creative flair in automatic poetry generation in two exemplar ways: explicit show of emotion and the use of language that is predominantly first person with dream-like imagery. To enable the expression of emotion in generated poems, our work involves a preliminary step of scoring a corpus of downloaded poems for emotion to produce subsets of poems that express one of eight different identified emotions. This step is followed by the actual generation of poems by fine-tuning the pre-trained GPT-2 natural language model. We train eight separate models for eight different emotions, each on a sub-corpus predominantly demonstrating a particular emotion. To generate poems that use dream-like language, we create a text corpus composed of a large number of dream transcriptions created in first person by actual viewers of dreams. In this case, we apply transfer learning by fine-tuning the pre-trained GPT-2 on the dream corpus, followed by training again on poetry. We evaluate the generated poems using automated techniques as well as humans. A high-level overview of the emotion elicitation portion of our project is shown in Figure 1 . To create a corpus of poems based on the emotions they elicit, we make use of the EmoLex dic-tionary (Mohammad and Turney, 2013). EmoLex is a word-level emotion lexicon that associates English words with the eight different emotion categories we wish to explore. Each poem (or book of poems) in our dataset is given a score that is the total of the associated emotion scores in EmoLex for each word. The maximum emotion word score is taken and the poem is labeled under that emotion category. We create eight such datasets, one corresponding to each emotion category supported by EmoLex. This approach allows us to to train multiple models on our split dataset.",
"cite_spans": [],
"ref_spans": [
{
"start": 1191,
"end": 1199,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Approach",
"sec_num": "3"
},
{
"text": "Currently, the emotions of joy, anticipation, trust, anger, and sadness represent a large portion of our data while the emotions of surprise, disgust, and fear are severely underrepresented. Table 1 shows key differences in models including the number of tokens in the text and the final average loss during training.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Poem Emotion Scoring",
"sec_num": "3.1"
},
{
"text": "To create a model for poetic language, we propose finetuning OpenAI's GPT-2 architecture. GPT-2 is a Transformer-based model that was trained simply to predict the next word in a 40GB text corpus (Radford et al., 2019) . This 40GB dataset, WebText, was scraped from the internet with certain heuristics that aimed to gather only quality text (i.e. only outbound Reddit links from posts with a karma rating of 3 stars or better). By training on such a large, all-encompassing corpus of text, the architecture has proven to model the English language well and has obtained state-of-theart results on downstream text-based tasks such as machine translation, question answering, and summarization. We leverage GPT-2's pre-trained knowledge of language for our downstream task of peotry generation.",
"cite_spans": [
{
"start": 196,
"end": 218,
"text": "(Radford et al., 2019)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "GPT Architecture",
"sec_num": "3.2"
},
{
"text": "GPT-2 is the successor of OpenAI's first Transformer-based architecture, GPT (Radford et al., 2018) , with a few changes to the structure. The medium version of GPT-2 we use contains 345M parameters and is a 24 layer, decoder-only Transformer architecture. GPT-2 moves layer normalization to the input of each sub-block, adds another layer normalization after the final selfattention block and increases context size from 512 to 1024 tokens. This architecture allows for long term dependencies to be captured better in language modeling. GPT-2's attention mechanism is referred to as a masked multi self-attention head. This technique allows for a relationship to be modeled for all words in an input sequence. Words that have multiple meanings can then be represented based on the context they appear in. Higher attention scores from surrounding words relate to a larger contribution to the representation of a word. GPT-2 makes use of byte-pair encoding (BPE) like its predecessor GPT but on UTF-8 byte sequences (Sennrich et al., 2015) . GPT-2's encoding is somewhere in between character level and word level. The model also prevents different versions of common words from being duplicated (i.e. fate!, fate?, and fate would not be joined). This technique improves the quality of the final byte segmentation. GPT-2's encoding rids the need for pre-processing or tokenization of data and is able to assign a probability to any Unicode string.",
"cite_spans": [
{
"start": 77,
"end": 99,
"text": "(Radford et al., 2018)",
"ref_id": "BIBREF24"
},
{
"start": 1015,
"end": 1038,
"text": "(Sennrich et al., 2015)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "GPT Architecture",
"sec_num": "3.2"
},
{
"text": "The task-agnostic nature of GPT-2 allows us to take a fine-tuning approach to our downstream task of poetry generation. Our approach to generating poems that exhibit emotion as well as dream-like imagery involves training the pretrained GPT-2 model. Our training protocol for the two cases are stated briefly below.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training for Creative Poem Generation",
"sec_num": "3.3"
},
{
"text": "Poetry is a personal form of writing that expresses human feelings, and Mill (1860) famously said \"What is poetry, but thoughts and words in which emotion spontaneously embodies itself?\" Mill (1833) also said \"The object of poetry is confessedly to act upon the emotions\". Expressing emotions, with possible motive of eliciting the same emotions in readers, is a basic characteristic of poems. Our goal in this paper is to use artificial neural networks to generate poems that explicitly evoke certain specific emotions. To generate poems with emotional content, we have split our poetry data into sub-corpora, one sub-corpus for each emotion. We train the already pre-trained GPT-2 on a sub-corpus of poems that demonstrate a certain emotion. Pre-trained GPT-2 has a very strong foundational knowledge of English. We find that training it again on emotionbearing poetry seems to enable it to generate high quality poetry, which is even able to use emotionladen words for the correct form of elicitation. We also find that the poems we generate seem to exhibit proper punctuation as well as lines that have poem-appropriate length and sentences that are grammatically correct. In addition, the poems we generate seem to be quite readable and demonstrate high coherence. Detailed analyses are reported in the next section.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Generating Emotion Poems",
"sec_num": "3.3.1"
},
{
"text": "Dream poems represent a style of poetry that was \"astonishingly\" popular in the 14th through the 16th centuries (Spearing, 1976b; Windeatt, 2003) and are still popular (Russo, 2003) . Such poems tell a story based on a dream or a number of dreams, dreamt by the narrator or by a character that the poet introduces. Spearing (1976b) claimed that dream poems are based on objective experience, but at the same time they are free of constraints of everyday possibilities. Such poems represent the outcome of a poetic process with many different influences, models, and analogues (Windeatt, 2003) , but without going into such details, our goal is to see if an ANN can produce poems which share characteristics with dream po-ems.",
"cite_spans": [
{
"start": 112,
"end": 129,
"text": "(Spearing, 1976b;",
"ref_id": "BIBREF31"
},
{
"start": 130,
"end": 145,
"text": "Windeatt, 2003)",
"ref_id": "BIBREF36"
},
{
"start": 168,
"end": 181,
"text": "(Russo, 2003)",
"ref_id": "BIBREF27"
},
{
"start": 315,
"end": 331,
"text": "Spearing (1976b)",
"ref_id": "BIBREF31"
},
{
"start": 576,
"end": 592,
"text": "(Windeatt, 2003)",
"ref_id": "BIBREF36"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Generating Dream Poems",
"sec_num": "3.3.2"
},
{
"text": "To generate poems that demonstrate first-person language with dream-like imagery, we take a similar approach. However, in this case, GPT-2 undergoes three separate training cycles. The first cycle is the pre-training that GPT-2 goes through before release to the public by OpenAI. Second, we train the pre-trained model on a corpus of firstperson dream descriptions. Third, we train again on poems. Our hypothesis is that pre-training by OpenAI results in good basic knowledge of English; that training on the dream corpus endows the network with the knowledge of first-person imagery-based language; and that the last training cycle teaches the network language of poems. We demonstrate in the next section that we are not far off from our being successful in our hypothesis.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Generating Dream Poems",
"sec_num": "3.3.2"
},
{
"text": "As stated by Radford et al. (2019) , the core approach of GPT-2 is language modeling. A language model can be thought of as a probability distribution over a sequence of words in the form:",
"cite_spans": [
{
"start": 13,
"end": 34,
"text": "Radford et al. (2019)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Text Generation and Sampling",
"sec_num": "3.4"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(w 1 , ..., w n ).",
"eq_num": "(1)"
}
],
"section": "Text Generation and Sampling",
"sec_num": "3.4"
},
{
"text": "Likewise, natural language tends to have a sequential order so it can be modeled in terms of the conditional probability of a word given the words preceding it (Bengio et al., 2003) :",
"cite_spans": [
{
"start": 160,
"end": 181,
"text": "(Bengio et al., 2003)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Text Generation and Sampling",
"sec_num": "3.4"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(w n |w 1 , ..., w n\u22121 ).",
"eq_num": "(2)"
}
],
"section": "Text Generation and Sampling",
"sec_num": "3.4"
},
{
"text": "We make use of this probabilistic style of language modeling by sampling from the distribution in a semi-random fashion. Just as the GPT-2 paper does for its text generation, we make use of Top K sampling, limiting the possible guesses of words to 40. In addition to Top K, we make use of a temperature constant of 0.75 which controls randomness in the distribution. A temperature closer to 0 correlates to less randomness while a temperature closer to 1 relates to more randomness. Finally, at the end of the generation process, we employ a simple text cleaning algorithm that allows poems to end more naturally and rather than trail off as they do sometimes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Text Generation and Sampling",
"sec_num": "3.4"
},
{
"text": "In order to classify emotion-eliciting poems or books, we use the NRC Word-Emotion Asso-ciation Lexicon (EmoLex) resource. EmoLex 1 was created by the National Research Council of Canada and includes 14,182 English words that are associated with different emotions and positive or negative sentiment (Mohammad and Turney, 2013). Words in EmoLex have been manually annotated via crowd-sourcing and emotions fall into one or more categories of eight basic emotions: joy, trust, fear, surprise, sadness, anticipation, anger, and disgust (Plutchik, 2014) . We elect to use this simplified version of the Wheel of Emotions due to its parallels with the available EmoLex dataset. This resource provides us with a way to fabricate a ground truth in the types of emotion-infused texts we wish to use for training data. To handle the training and generation portions of the project, we draw data from the Project Gutenberg website 2 . Project Gutenberg is a massive online database containing over 59,000 eBooks. We limit this corpus to a smaller subcorpus using an adaptation of the GutenTag tool (Brooke et al., 2015) . This tool allows us to place constraints on the amount of literature we choose to use in our work. Our final dataset includes approximately three million lines of poetic text from the Gutenberg database and is further divided by poem/book into our eight emotion categories.",
"cite_spans": [
{
"start": 534,
"end": 550,
"text": "(Plutchik, 2014)",
"ref_id": null
},
{
"start": 1089,
"end": 1110,
"text": "(Brooke et al., 2015)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets and Resources",
"sec_num": "4.1"
},
{
"text": "We attempt to create dream poetry by making use of the DreamBank dataset. The Dream-Bank was created by Schneider & Domhoff at UC-Santa Cruz 3 . The dataset contains a collection of over 20,000 dreams from users age 7 to 74. We scraped this dataset from the website assuring that dreams collected were recorded only in English. The DreamBank allows us to attempt trans-Heard I a song of joy, A song of happy sound, Fills all the air I breathe, To him I sing, to him I sing the happy song. All night long on the steep green grass I ride and sing Figure 4 : A hand-picked, automatically generated poem from the joy model",
"cite_spans": [],
"ref_spans": [
{
"start": 545,
"end": 553,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets and Resources",
"sec_num": "4.1"
},
{
"text": "The other, who with one accord Wrote my essay, in that he was dear And good, and knew well, how we ought to treat A man of such renown, and such love? He's a good honest man, no doubt Figure 5 : A hand-picked, automatically generated poem from the trust model fer learning by finetuning on the dream dataset first, then further finetuning on our poetry dataset. Initially, we retrained 6 GPT-2 based models. Default training parameters were used each of the 5 different emotion datasets and our dream dataset. All were trained for 12,000 steps (except for our dream model which was trained for 12k steps on dreams and on poetry) with a learning rate of 0.0001. When generating text, we do not input context: we allow the model to write the poem entirely through the sampling of conditional probability from the language it has modeled.",
"cite_spans": [],
"ref_spans": [
{
"start": 184,
"end": 192,
"text": "Figure 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets and Resources",
"sec_num": "4.1"
},
{
"text": "Figures 4 through 8 give examples of 5 poems that we have hand-picked to illustrate the quality of poems generated. A cursory glance at the poems reveals the high quality of the text in terms of lexical choice, grammatical integrity, and semantic cohesion. We discuss how we quantitatively assess the poems below.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets and Resources",
"sec_num": "4.1"
},
{
"text": "In the first crowd-sourced analysis of our emotioneliciting poetry we presented four poems from each category (of the five data-represented emotion categories) to ten human reviewers with undergraduate level educational backgrounds. All reviewers are native speakers of English. Poems presented were randomly selected from the top 20 EmoLex scored poems out of a pool of 1,000 generated poems. These reviewers were asked to rate each poem based on the emotions elicited within",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "We have reached the peak of the highest mountain in the world The mountain of dreams. This is the view Across the valley, One hour's journey back, We crossed it on the way between A band of beautiful young women.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "There was Figure 6 : A hand-picked, automatically generated poem from the anticipation model A long trail of falling mist Had made its way here, and now Aerily it seemed, as if to drown The discordant thunder clang. It seemed to drown the music of the rain;",
"cite_spans": [],
"ref_spans": [
{
"start": 10,
"end": 18,
"text": "Figure 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "In this lost place of sorrow Far off Figure 7 : A hand-picked, automatically generated poem from the sadness model Amidst the chaos throng'd, with angry voices each His rival's mockery; loud their scorn was fill'd; So fierce their rage, and in their eager power Met on the walls of Troy, were fill'd with dismay. Figure 8 : A hand-picked, automatically generated poem from the anger model A thousand stars at once, An hundred thousand stars!",
"cite_spans": [],
"ref_spans": [
{
"start": 37,
"end": 45,
"text": "Figure 7",
"ref_id": null
},
{
"start": 313,
"end": 321,
"text": "Figure 8",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "The sun was low, And the stars were bright, My heart would do the same.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "A thousand stars at once, A hundred thousand stars! The night had begun, And the stars were all the same. When I came back from the dead, I saw the stars Figure 9 : A hand-picked, automatically generated poem from the dream model For she was mine. I was the only one She had, And a thousand other friends, And a hundred more She held me dear. Her eyes were clear, her cheeks were bright, Her heart was like a rose, Her mouth was full of music, Her lips were white As snow, And the music she sang Figure 10 : A hand-picked poem, automatically generated from the dream model Emotion Anger Antic. Joy Sad. Trust % 65 40 85 87.5 32.5 Table 2 : Average percentage of correctly elicited emotion across four poems in each category them after reading. An emotion was deemed correctly elicited if the associated Likert score was 4 or greater from the reviewer. Table 2 illustrates the results from our evaluation. When taking the average percentage of correct emotion-eliciting poems, the models of joy, sadness, and anger produced the most promising results while the trust and anticipation models were less than satisfactory. We believe this is because joy, sadness and anger are basic or fundamental emotions compared to trust and anticipation, which are more complex and difficult to explain. Although there are many opinions among psychologists about what constitute basic emotions, joy, sadness and anger, (especially the last two) seem to occur the most often in proposals that demarcate a set of basic emotions (Ortony and Turner, 1990) . To preserve consistency in our experiments, we evaluate our dream model poetry in a manner similar to our evaluation of the emotion poems. Four poems from the model were presented to the same ten judges and they were asked to assess the poems based on qualities of dream poetry. These poems were cherry picked from a pool of 1,000 generated poems. A dream poem is said to have the following qualities (Windeatt, 2003; Spearing, 1976b; Russo, 2003) among many other qualities. We believe these three are the least ambiguous and easiest to decipher for human evaluation.",
"cite_spans": [
{
"start": 1510,
"end": 1535,
"text": "(Ortony and Turner, 1990)",
"ref_id": "BIBREF22"
},
{
"start": 1939,
"end": 1955,
"text": "(Windeatt, 2003;",
"ref_id": "BIBREF36"
},
{
"start": 1956,
"end": 1972,
"text": "Spearing, 1976b;",
"ref_id": "BIBREF31"
},
{
"start": 1973,
"end": 1985,
"text": "Russo, 2003)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [
{
"start": 154,
"end": 162,
"text": "Figure 9",
"ref_id": null
},
{
"start": 496,
"end": 505,
"text": "Figure 10",
"ref_id": "FIGREF0"
},
{
"start": 630,
"end": 637,
"text": "Table 2",
"ref_id": null
},
{
"start": 852,
"end": 859,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "Poem 1 2 3 4 Qual 1 5 4.9 4.8 4.5 Qual 2 3.5 4.1 3.2 3.3 Qual 3 3.9 4.2 3.7 3.7 Analysis of results show that machine generated poems are able to capture the first person perspective well, achieving between 4.5 and 5 average Likert scores. The poems often appear to retell a story or an event, scoring between 3.7 and 4.2 average Likert scores. The nature of poetry and dream recounts that make up our data is often narrative, so this result stands to reason. However, Quality 2 scores of the poem substance containing a dream or vision are questionable. We suspect the Quality 2 score is lower due to the ambiguity in ascertaining dream text from regular text. Table 3 highlights our results for the dream model.",
"cite_spans": [],
"ref_spans": [
{
"start": 662,
"end": 669,
"text": "Table 3",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "Currently, there exists no widely available standard for evaluating poetry generation. Scores like BLEU, ROUGE, METEOR, etc. are more suited for Machine Translation (MT) tasks (Zhang et al., 2019) . For example, they compare how similar sentence P is to translated-sentenceP. Instead, we outline some metrics from the Coh-Metrix web tool that helps us further quantitatively evaluate the quality of text generated. With the goal of eliciting emotions, we claim that subjective analysis of generated poetry is superior to any available objective metrics.",
"cite_spans": [
{
"start": 176,
"end": 196,
"text": "(Zhang et al., 2019)",
"ref_id": "BIBREF39"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "5"
},
{
"text": "To provide a quantitative calculation of the caliber of text our models produce, we outline in this section relevant metrics from the University of Memphis Coh-Metrix tool (Graesser et al., 2004) . Coh-Metrix is a text evaluation software kit and from it, we have chosen 8 forms of assessment. The first two, Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE), are two standard measures that deal with text readability and ease (Klare, 1974) . The FKGL scores a text from grade level 0 to 18, while the FRE score is a 0-100 index with 100 being an easily readable text. We aim to produce text that is readable by all, so a low FKGL score and high FRE score would be ideal. The next metrics we employ evaluate at the word level. The word imageability (IMGc) and word concreteness (CNCc) scores measure content words on their ability to create an image in the reader's mind and their ability to appeal to a reader's senses, respectively (Coltheart, 1981) . We aim for our art to create a connection between the reader and poem, so we believe imageability and concreteness of content words are two good measures with this in mind. We also make use of three text easibility principal component scores: narrativity (PCNARp), referential cohesion (PCREFp), and syntactic simplicity (PC-SYNp) (Graesser et al., 2004) . The text easibility PC scores are percentile scales, and thus we aim for higher numbers for these scores. Finally, we make use of the Lexical Diversity Type:Token Ratio score (LDTTRa) for all words. LDTTRa measures the ratio of type (unique) words to all tokens in the text. Because our text is relatively short, we aim for a middle ground in the LDTTRa ratio, meaning there is uniqueness in the word choice of the text, but cohesion is still upheld.",
"cite_spans": [
{
"start": 172,
"end": 195,
"text": "(Graesser et al., 2004)",
"ref_id": "BIBREF7"
},
{
"start": 441,
"end": 454,
"text": "(Klare, 1974)",
"ref_id": "BIBREF11"
},
{
"start": 948,
"end": 965,
"text": "(Coltheart, 1981)",
"ref_id": null
},
{
"start": 1299,
"end": 1322,
"text": "(Graesser et al., 2004)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Coh-Metrix",
"sec_num": "5.1"
},
{
"text": "Inspection of our Coh-Metrix results show that randomly selected poems from all models fall at or below the 2nd-grade reading level (in FKGL scores) and are greater than 93 on the FRE scale. This suggests generated poems are easily readable by the majority of viewers. Looking at the IMGc and CNCc scores, we see that our poems, except for the dream model concreteness, fall in the 400s. Words with higher imageability and concreteness fall around the low 600s while words that are lower fall around the upper 200s on this scale. These scores reveal that our models are generating text that is concrete in word choice and that paint a picture. Our dream model scoring lower in the concreteness is reasonable as the word choice of dreams tends to be more abstract. Lastly, percentile scores of PCSYNp and PCNARp show that the majority of models are producing poems that are both syntactically simplistic and narrative. Most PCREFp scores are on the lower end of the scale. We suspect the reason these scores are lower is because the poems are not necessarily related and were all input at once. Table 4 highlights these scores for each poetry model.",
"cite_spans": [],
"ref_spans": [
{
"start": 1094,
"end": 1101,
"text": "Table 4",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Coh-Metrix",
"sec_num": "5.1"
},
{
"text": "In this paper we influenced automatic natural language generation to create poetry through the use of classified emotion poems and dream text. To do so, we first leveraged a word-level emotion lexicon to construct a meaning for emotion-eliciting text and used that text to train separate language models. Next, we gathered data of dream records and employed transfer learning in attempts to generate dream-like poetry. The work reported in this paper seeks to create art in the form of auto-generated poetry while opening the door to more projects involving emotion-eliciting text-based tasks and influenced creative neural generation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "6"
},
{
"text": "We would like to thank the reviewers for their feedback on this project. Comments and suggestions from reviewers -both those that the were incorporated into this article and those on which we will report in future work -provide invaluable insight as to improving our results. Importantly, our continuing research involves gathering a more comprehensive human-evaluation with a larger number of reviewers and poems to be judged. We also wish to gather data for the underrepresented emotion categories, leading, ideally, to a more robust language model for each emotion. Our work thus far provides a baseline for introducing emotions into generated text via a word-level lexicon, but we wish to employ other tools -segment-level lexicons, for example -in an attempt to better capture the contextual dependencies of emotion. Additionally, the word-level baseline we have produced focuses on generating single-emotion text. We are interested in examining poems of multiple emotions and different levels of intensity to expand on this study. Finally, we wish to seek out additional forms of replicating creativity that artists incorporate in their work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "6"
},
{
"text": "https://saifmohammad.com/WebPages/ NRC-Emotion-Lexicon.htm 2 https://www.gutenberg.org/ 3 https://www.dreambank.net/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"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": "A neural probabilistic language model",
"authors": [
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "R\u00e9jean",
"middle": [],
"last": "Ducharme",
"suffix": ""
},
{
"first": "Pascal",
"middle": [],
"last": "Vincent",
"suffix": ""
},
{
"first": "Christian",
"middle": [],
"last": "Jauvin",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of machine learning research",
"volume": "3",
"issue": "",
"pages": "1137--1155",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoshua Bengio, R\u00e9jean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic lan- guage model. Journal of machine learning research, 3(Feb):1137-1155.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Gutentag: an nlp-driven tool for digital humanities research in the project gutenberg corpus",
"authors": [
{
"first": "Julian",
"middle": [],
"last": "Brooke",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Hammond",
"suffix": ""
},
{
"first": "Graeme",
"middle": [],
"last": "Hirst",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the Fourth Workshop on Computational Linguistics for Literature",
"volume": "",
"issue": "",
"pages": "42--47",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julian Brooke, Adam Hammond, and Graeme Hirst. 2015. Gutentag: an nlp-driven tool for digital hu- manities research in the project gutenberg corpus. In Proceedings of the Fourth Workshop on Compu- tational Linguistics for Literature, pages 42-47.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Modeling a paranoid mind",
"authors": [
{
"first": "Kenneth",
"middle": [],
"last": "Mark",
"suffix": ""
},
{
"first": "Colby",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 1981,
"venue": "Behavioral and Brain Sciences",
"volume": "4",
"issue": "4",
"pages": "515--534",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenneth Mark Colby. 1981. Modeling a paranoid mind. Behavioral and Brain Sciences, 4(4):515- 534.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "The mrc psycholinguistic database",
"authors": [],
"year": 1981,
"venue": "The Quarterly Journal of Experimental Psychology Section A",
"volume": "33",
"issue": "4",
"pages": "497--505",
"other_ids": {
"DOI": [
"10.1080/14640748108400805"
]
},
"num": null,
"urls": [],
"raw_text": "Max Coltheart. 1981. The mrc psycholinguistic database. The Quarterly Journal of Experimental Psychology Section A, 33(4):497-505.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Survey of the state of the art in natural language generation: Core tasks, applications and evaluation",
"authors": [
{
"first": "Albert",
"middle": [],
"last": "Gatt",
"suffix": ""
},
{
"first": "Emiel",
"middle": [],
"last": "Krahmer",
"suffix": ""
}
],
"year": 2018,
"venue": "Journal of Artificial Intelligence Research",
"volume": "61",
"issue": "",
"pages": "65--170",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Albert Gatt and Emiel Krahmer. 2018. Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artifi- cial Intelligence Research, 61:65-170.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Hafez: an interactive poetry generation system",
"authors": [
{
"first": "Marjan",
"middle": [],
"last": "Ghazvininejad",
"suffix": ""
},
{
"first": "Xing",
"middle": [],
"last": "Shi",
"suffix": ""
},
{
"first": "Jay",
"middle": [],
"last": "Priyadarshi",
"suffix": ""
},
{
"first": "Kevin",
"middle": [],
"last": "Knight",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of ACL 2017, System Demonstrations",
"volume": "",
"issue": "",
"pages": "43--48",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Marjan Ghazvininejad, Xing Shi, Jay Priyadarshi, and Kevin Knight. 2017. Hafez: an interactive poetry generation system. In Proceedings of ACL 2017, System Demonstrations, pages 43-48, Vancouver, Canada. Association for Computational Linguistics.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Coh-metrix: Analysis of text on cohesion and language",
"authors": [
{
"first": "Arthur",
"middle": [
"C"
],
"last": "Graesser",
"suffix": ""
},
{
"first": "Danielle",
"middle": [
"S"
],
"last": "Mcnamara",
"suffix": ""
},
{
"first": "Max",
"middle": [
"M"
],
"last": "Louwerse",
"suffix": ""
},
{
"first": "Zhiqiang",
"middle": [],
"last": "Cai",
"suffix": ""
}
],
"year": 2004,
"venue": "Behavior Research Methods, Instruments, & Computers",
"volume": "36",
"issue": "",
"pages": "193--202",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Arthur C. Graesser, Danielle S. McNamara, Max M. Louwerse, and Zhiqiang Cai. 2004. Coh-metrix: Analysis of text on cohesion and language. Behav- ior Research Methods, Instruments, & Computers, 36:193-202.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Generating chinese classical poems with statistical machine translation models",
"authors": [
{
"first": "Jing",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Ming",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Long",
"middle": [],
"last": "Jiang",
"suffix": ""
}
],
"year": 2012,
"venue": "Twenty-Sixth AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jing He, Ming Zhou, and Long Jiang. 2012. Generat- ing chinese classical poems with statistical machine translation models. In Twenty-Sixth AAAI Confer- ence on Artificial Intelligence.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Automatically generating rhythmic verse with neural networks",
"authors": [
{
"first": "Jack",
"middle": [],
"last": "Hopkins",
"suffix": ""
},
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 55th Annual Meeting of the ACL",
"volume": "1",
"issue": "",
"pages": "168--178",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jack Hopkins and Douwe Kiela. 2017. Automatically generating rhythmic verse with neural networks. In Proceedings of the 55th Annual Meeting of the ACL (Volume 1: Long Papers), pages 168-178.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Pragmatics and natural language generation",
"authors": [
{
"first": "H",
"middle": [],
"last": "Eduard",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 1990,
"venue": "Artificial Intelligence",
"volume": "43",
"issue": "2",
"pages": "153--197",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eduard H Hovy. 1990. Pragmatics and natural lan- guage generation. Artificial Intelligence, 43(2):153- 197.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Assessing readability. Reading research quarterly",
"authors": [
{
"first": "",
"middle": [],
"last": "George R Klare",
"suffix": ""
}
],
"year": 1974,
"venue": "",
"volume": "",
"issue": "",
"pages": "62--102",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "George R Klare. 1974. Assessing readability. Reading research quarterly, pages 62-102.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Deep-speare: A joint neural model of poetic language, meter and rhyme",
"authors": [
{
"first": "Trevor",
"middle": [],
"last": "Jey Han Lau",
"suffix": ""
},
{
"first": "Timothy",
"middle": [],
"last": "Cohn",
"suffix": ""
},
{
"first": "Julian",
"middle": [],
"last": "Baldwin",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Brooke",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hammond",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jey Han Lau, Trevor Cohn, Timothy Baldwin, Julian Brooke, and Adam Hammond. 2018. Deep-speare: A joint neural model of poetic language, meter and rhyme. CoRR, abs/1807.03491.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Deep reinforcement learning for dialogue generation",
"authors": [
{
"first": "Jiwei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Will",
"middle": [],
"last": "Monroe",
"suffix": ""
},
{
"first": "Alan",
"middle": [],
"last": "Ritter",
"suffix": ""
},
{
"first": "Michel",
"middle": [],
"last": "Galley",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1606.01541"
]
},
"num": null,
"urls": [],
"raw_text": "Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep rein- forcement learning for dialogue generation. arXiv preprint arXiv:1606.01541.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Beyond narrative description: Generating poetry from images by multi-adversarial training",
"authors": [
{
"first": "Bei",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Jianlong",
"middle": [],
"last": "Fu",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Makoto",
"suffix": ""
},
{
"first": "Masatoshi",
"middle": [],
"last": "Kato",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yoshikawa",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 26th ACM International Conference on Multimedia, MM '18",
"volume": "",
"issue": "",
"pages": "783--791",
"other_ids": {
"DOI": [
"10.1145/3240508.3240587"
]
},
"num": null,
"urls": [],
"raw_text": "Bei Liu, Jianlong Fu, Makoto P. Kato, and Masatoshi Yoshikawa. 2018. Beyond narrative description: Generating poetry from images by multi-adversarial training. In Proceedings of the 26th ACM Interna- tional Conference on Multimedia, MM '18, pages 783-791, New York, NY, USA. ACM.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Medieval Dream-Poetry",
"authors": [
{
"first": "L",
"middle": [],
"last": "Kathryn",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lynch",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kathryn L. Lynch. 1998. Medieval Dream-Poetry. Cambridge University Press.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Deep captioning with multimodal recurrent neural networks (m-rnn)",
"authors": [
{
"first": "Junhua",
"middle": [],
"last": "Mao",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Jiang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Zhiheng",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Alan",
"middle": [],
"last": "Yuille",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1412.6632"
]
},
"num": null,
"urls": [],
"raw_text": "Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, and Alan Yuille. 2014. Deep captioning with multimodal recurrent neural networks (m-rnn). arXiv preprint arXiv:1412.6632.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "What is poetry? Monthly Repository",
"authors": [
{
"first": "John",
"middle": [],
"last": "Stuart Mill",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "7",
"issue": "",
"pages": "60--70",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John Stuart Mill. 1833. What is poetry? Monthly Repository, 7(73):60-70.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "1860. Thoughts on poetry and its varieties. The Crayon",
"authors": [
{
"first": "John",
"middle": [],
"last": "Stuart Mill",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "7",
"issue": "",
"pages": "123--128",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John Stuart Mill. 1860. Thoughts on poetry and its va- rieties. The Crayon, 7(5):123-128.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Crowdsourcing a word-emotion association lexicon",
"authors": [
{
"first": "M",
"middle": [],
"last": "Saif",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mohammad",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Peter",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Turney",
"suffix": ""
}
],
"year": 2013,
"venue": "",
"volume": "29",
"issue": "",
"pages": "436--465",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Saif M. Mohammad and Peter D. Turney. 2013. Crowdsourcing a word-emotion association lexicon. 29(3):436-465.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Automatic generation of poetry: an overview",
"authors": [
{
"first": "Hugo",
"middle": [],
"last": "Oliveira",
"suffix": ""
}
],
"year": 2009,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hugo Oliveira. 2009. Automatic generation of poetry: an overview. Universidade de Coimbra.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Poetryme: a versatile platform for poetry generation",
"authors": [
{
"first": "",
"middle": [],
"last": "Hugo Gon\u00e7alo Oliveira",
"suffix": ""
}
],
"year": 2012,
"venue": "Concept Invention, and General Intelligence",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hugo Gon\u00e7alo Oliveira. 2012. Poetryme: a versatile platform for poetry generation. Computational Cre- ativity, Concept Invention, and General Intelligence, 1:21.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "What's basic about basic emotions? Psychological review",
"authors": [
{
"first": "Andrew",
"middle": [],
"last": "Ortony",
"suffix": ""
},
{
"first": "Terence",
"middle": [
"J"
],
"last": "Turner",
"suffix": ""
}
],
"year": 1990,
"venue": "",
"volume": "97",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andrew Ortony and Terence J Turner. 1990. What's basic about basic emotions? Psychological review, 97(3):315.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Improving language understanding by generative pre-training",
"authors": [
{
"first": "Alec",
"middle": [],
"last": "Radford",
"suffix": ""
},
{
"first": "Karthik",
"middle": [],
"last": "Narasimhan",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Salimans",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openai- assets/researchcovers/languageunsupervised/language understanding paper. pdf.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Language models are unsupervised multitask learners",
"authors": [
{
"first": "Alec",
"middle": [],
"last": "Radford",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Rewon",
"middle": [],
"last": "Child",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Luan",
"suffix": ""
},
{
"first": "Dario",
"middle": [],
"last": "Amodei",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
}
],
"year": 2019,
"venue": "OpenAI Blog",
"volume": "",
"issue": "8",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8).",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Building natural language generation systems",
"authors": [
{
"first": "Ehud",
"middle": [],
"last": "Reiter",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Dale",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ehud Reiter and Robert Dale. 2000. Building natural language generation systems. Cambridge university press.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Dream poetry as dream work",
"authors": [
{
"first": "A",
"middle": [],
"last": "Richard",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Russo",
"suffix": ""
}
],
"year": 2003,
"venue": "Dreaming",
"volume": "13",
"issue": "1",
"pages": "13--27",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard A Russo. 2003. Dream poetry as dream work. Dreaming, 13(1):13-27.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "A survey of natural language generation techniques with a focus on dialogue systems-past, present and future directions",
"authors": [
{
"first": "Sashank",
"middle": [],
"last": "Santhanam",
"suffix": ""
},
{
"first": "Samira",
"middle": [],
"last": "Shaikh",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1906.00500"
]
},
"num": null,
"urls": [],
"raw_text": "Sashank Santhanam and Samira Shaikh. 2019. A sur- vey of natural language generation techniques with a focus on dialogue systems-past, present and future directions. arXiv preprint arXiv:1906.00500.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Neural machine translation of rare words with subword units",
"authors": [
{
"first": "Rico",
"middle": [],
"last": "Sennrich",
"suffix": ""
},
{
"first": "Barry",
"middle": [],
"last": "Haddow",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Birch",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1508.07909"
]
},
"num": null,
"urls": [],
"raw_text": "Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "The High Medieval Dream",
"authors": [
{
"first": "A",
"middle": [
"C"
],
"last": "Spearing",
"suffix": ""
}
],
"year": 1976,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. C. Spearing. 1976a. The High Medieval Dream. Stanford University Press.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Medieval dreampoetry",
"authors": [
{
"first": "Anthony Colin",
"middle": [],
"last": "Spearing",
"suffix": ""
}
],
"year": 1976,
"venue": "CUP Archive",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anthony Colin Spearing. 1976b. Medieval dream- poetry. CUP Archive.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Attention is all you need",
"authors": [
{
"first": "Ashish",
"middle": [],
"last": "Vaswani",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Aidan",
"middle": [
"N"
],
"last": "Gomez",
"suffix": ""
},
{
"first": "Illia",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Polosukhin",
"suffix": ""
}
],
"year": 2017,
"venue": "Advances in Neural Information Processing Systems",
"volume": "30",
"issue": "",
"pages": "5998--6008",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Gar- nett, editors, Advances in Neural Information Pro- cessing Systems 30, pages 5998-6008. Curran As- sociates, Inc.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Generating lyrics with variational autoencoder and multi-modal artist embeddings",
"authors": [
{
"first": "Olga",
"middle": [],
"last": "Vechtomova",
"suffix": ""
},
{
"first": "Hareesh",
"middle": [],
"last": "Bahuleyan",
"suffix": ""
},
{
"first": "Amirpasha",
"middle": [],
"last": "Ghabussi",
"suffix": ""
},
{
"first": "Vineet",
"middle": [],
"last": "John",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Olga Vechtomova, Hareesh Bahuleyan, Amirpasha Ghabussi, and Vineet John. 2018. Generating lyrics with variational autoencoder and multi-modal artist embeddings. CoRR, abs/1812.08318.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Poet-based poetry generation: Controlling personal style with recurrent neural networks",
"authors": [
{
"first": "Jia",
"middle": [],
"last": "Wei",
"suffix": ""
},
{
"first": "Qiang",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Yici",
"middle": [],
"last": "Cai",
"suffix": ""
}
],
"year": 2018,
"venue": "2018 International Conference on Computing, Networking and Communications (ICNC)",
"volume": "",
"issue": "",
"pages": "156--160",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jia Wei, Qiang Zhou, and Yici Cai. 2018. Poet-based poetry generation: Controlling personal style with recurrent neural networks. In 2018 International Conference on Computing, Networking and Com- munications (ICNC), pages 156-160. IEEE.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Eliza-a computer program for the study of natural language communication between man and machine",
"authors": [
{
"first": "Joseph",
"middle": [],
"last": "Weizenbaum",
"suffix": ""
}
],
"year": 1966,
"venue": "Communications of the ACM",
"volume": "9",
"issue": "1",
"pages": "36--45",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joseph Weizenbaum et al. 1966. Eliza-a computer program for the study of natural language communi- cation between man and machine. Communications of the ACM, 9(1):36-45.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Literary structures in chaucer. The Cambridge Companion to Chaucer",
"authors": [
{
"first": "Barry",
"middle": [],
"last": "Windeatt",
"suffix": ""
}
],
"year": 2003,
"venue": "",
"volume": "",
"issue": "",
"pages": "214--232",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Barry Windeatt. 2003. Literary structures in chaucer. The Cambridge Companion to Chaucer, pages 214- 232.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Google's neural machine translation system",
"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": "Bridging the gap between human and machine translation",
"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 machine translation system: Bridging the gap between hu- man and machine translation. arXiv preprint arXiv:1609.08144.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "2013. I, poet: automatic chinese poetry composition through a generative summarization framework under constrained optimization",
"authors": [
{
"first": "Rui",
"middle": [],
"last": "Yan",
"suffix": ""
},
{
"first": "Han",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
},
{
"first": "Shou-De",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Xueqiang",
"middle": [],
"last": "Lv",
"suffix": ""
},
{
"first": "Xiaoming",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": null,
"venue": "Twenty-Third International Joint Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rui Yan, Han Jiang, Mirella Lapata, Shou-De Lin, Xueqiang Lv, and Xiaoming Li. 2013. I, poet: au- tomatic chinese poetry composition through a gen- erative summarization framework under constrained optimization. In Twenty-Third International Joint Conference on Artificial Intelligence.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Bertscore: Evaluating text generation with BERT. CoRR",
"authors": [
{
"first": "Tianyi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Varsha",
"middle": [],
"last": "Kishore",
"suffix": ""
},
{
"first": "Felix",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Kilian",
"middle": [
"Q"
],
"last": "Weinberger",
"suffix": ""
},
{
"first": "Yoav",
"middle": [],
"last": "Artzi",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with BERT. CoRR, abs/1904.09675.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Chinese poetry generation with recurrent neural networks",
"authors": [
{
"first": "Xingxing",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "670--680",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xingxing Zhang and Mirella Lapata. 2014. Chinese poetry generation with recurrent neural networks. In Proceedings of the 2014 Conference on Em- pirical Methods in Natural Language Processing (EMNLP), pages 670-680.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "A high-level overview of our project implementation for emotion eliciting poetry",
"uris": null,
"num": null
},
"FIGREF1": {
"type_str": "figure",
"text": "GPT Architecture. Adapted from(Radford et al., 2018(Radford et al., , 2019",
"uris": null,
"num": null
},
"FIGREF2": {
"type_str": "figure",
"text": "American pyschologist Robert Plutchik's Wheel of Emotions",
"uris": null,
"num": null
},
"TABREF1": {
"type_str": "table",
"content": "<table/>",
"html": null,
"text": "Comparison of 5 emotion models trained.",
"num": null
},
"TABREF2": {
"type_str": "table",
"content": "<table><tr><td>\u2022 Quality 1: The poem is generally a first-</td></tr><tr><td>person expression</td></tr><tr><td>\u2022 Quality 2: The poem's main substance is</td></tr><tr><td>dream or vision like</td></tr><tr><td>\u2022 Quality 3: The poem recounts or foretells an</td></tr><tr><td>experience or event</td></tr></table>",
"html": null,
"text": "Average Likert score of users for each poem",
"num": null
},
"TABREF4": {
"type_str": "table",
"content": "<table/>",
"html": null,
"text": "Average Coh-Metrix evaluations across 25 randomly selected poems from each model.",
"num": null
}
}
}
} |