| measure(ment),15-2-H01-1058,ak | 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 theperformance but fall short of the performance of an oracle. | |
| measure(ment),15-3-H01-1058,ak | <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 bestperformance (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),18-5-H01-1058,ak | model 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 theperformance further. | |
| measure(ment),19-3-H01-1058,ak | <term> performance </term> ( typically , <term> | word or semantic error rate | </term> ) from a list of <term> word strings | #1089 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),21-2-H01-1058,ak | 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 theperformance of an oracle. | |
| measure(ment),21-7-H01-1058,ak | 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 bestconfidence. | |
| measure(ment),7-7-H01-1058,ak | amounts to tagging <term> LMs </term> with <term> | confidence measures | </term> and picking the best <term> hypothesis | #1179 The method amounts to tagging LMs withconfidence measures and picking the best hypothesis corresponding to the LM with the best confidence. | |
| model,11-1-H01-1058,ak | address the problem of combining several <term> | language models ( LMs ) | </term> . We find that simple <term> interpolation | #1038 In this paper, we address the problem of combining severallanguage models ( LMs ). | |
| model,14-4-H01-1058,ak | </term> with hard decisions using the <term> | reference | </term> . We provide experimental results | #1129 Actually, the oracle acts like a dynamic combiner with hard decisions using thereference. | |
| model,17-7-H01-1058,ak | 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 theLM with the best confidence. | |
| model,43-3-H01-1058,ak | been obtained by using a different <term> | LM | </term> . Actually , the <term> oracle </term> | #1113 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 differentLM. | |
| model,5-7-H01-1058,ak | </term> . The method amounts to tagging <term> | LMs | </term> with <term> confidence measures </term> | #1177 The method amounts to taggingLMs with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence. | |
| other,10-3-H01-1058,ak | word string </term> and selects the <term> | word string | </term> with the best <term> performance </term> | #1080 The oracle knows the reference word string and selects theword 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,13-7-H01-1058,ak | 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 besthypothesis corresponding to the LM with the best confidence. | |
| other,29-3-H01-1058,ak | error rate </term> ) from a list of <term> | word strings | </term> , where each <term> word string </term> | #1099 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 ofword strings, where each word string has been obtained by using a different LM. | |
| other,34-3-H01-1058,ak | <term> word strings </term> , where each <term> | word string | </term> has been obtained by using a different | #1104 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 eachword string has been obtained by using a different LM. | |
| other,4-3-H01-1058,ak | </term> . The <term> oracle </term> knows the <term> | reference word string | </term> and selects the <term> word string </term> | #1074 The oracle knows thereference 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. | |
| tech,1-3-H01-1058,ak | </term> of an <term> oracle </term> . The <term> | oracle | </term> knows the <term> reference word string | #1071 Theoracle 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. | |
| tech,10-6-H01-1058,ak | 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 theoracle using a neural network or a decision tree. | |
| tech,11-5-H01-1058,ak | results that clearly show the need for a <term> | dynamic language model combination | </term> to improve the <term> performance </term> | #1142 We provide experimental results that clearly show the need for adynamic language model combination to improve the performance further. |
