| model,24-2-N01-1003,ak | training <term> SPoT </term> on the basis of <term> | feedback | </term> provided by <term> human judges </term> | #1359 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. | |
| other,13-7-N01-1003,ak | a <term> sentence plan </term> whose <term> | rating | </term> on average is only 5 % worse than | #1446 We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. | |
| tech,1-6-N01-1003,ak | <term> top-ranked plan </term> . The <term> | SPR | </term> uses <term> ranking rules </term> automatically | #1423 The SPR uses ranking rules automatically learned from training data. | |
| tech,5-7-N01-1003,ak | </term> . We show that the trained <term> | SPR | </term> learns to select a <term> sentence | #1438 We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. | |
| tool,19-2-N01-1003,ak | methodology for automatically training <term> | SPoT | </term> on the basis of <term> feedback </term> | #1354 In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. |
