| measure(ment),15-2-H01-1058,bq | interpolation </term> , improve the <term> | performance | </term> but fall short of the <term> performance | #1059 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
| measure(ment),21-2-H01-1058,bq | performance </term> but fall short of the <term> | performance | </term> of an <term> oracle </term> . The <term> | #1065 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. |
| other,24-2-H01-1058,bq | the <term> performance </term> of an <term> | oracle | </term> . The <term> oracle </term> knows the | #1068 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle . |
| other,1-3-H01-1058,bq | </term> of an <term> oracle </term> . The <term> | oracle | </term> knows the <term> reference word string | #1071 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
| measure(ment),15-3-H01-1058,bq | <term> word string </term> with the best <term> | performance | </term> ( typically , <term> word or semantic | #1085 The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
| other,3-4-H01-1058,bq | different <term> LM </term> . Actually , the <term> | oracle | </term> acts like a <term> dynamic combiner | #1118 Actually, the oracle acts like a dynamic combiner with hard decisions using the reference. |
| other,14-4-H01-1058,bq | <term> hard decisions </term> using the <term> | reference | </term> . We provide experimental results | #1129 Actually, the oracle acts like a dynamic combiner with hard decisions using the reference . |
| measure(ment),18-5-H01-1058,bq | combination </term> to improve the <term> | performance | </term> further . We suggest a method that | #1149 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. |
| other,10-6-H01-1058,bq | method that mimics the behavior of the <term> | oracle | </term> using a <term> neural network </term> | #1162 We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. |
| other,13-7-H01-1058,bq | measures </term> and picking the best <term> | hypothesis | </term> corresponding to the <term> LM </term> | #1185 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence. |
| model,17-7-H01-1058,bq | hypothesis </term> corresponding to the <term> | LM | </term> with the best <term> confidence </term> | #1189 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence. |
| measure(ment),21-7-H01-1058,bq | to the <term> LM </term> with the best <term> | confidence | </term> . We describe a three-tiered approach | #1193 The method amounts to tagging LMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence . |
