| We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> . | #583 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems. | |
| A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> . | #633 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words. | |
| We integrate a <term> spoken language understanding system </term> with <term> intelligent mobile agents </term> that mediate between <term> users </term> and <term> information sources </term> . | #806 We integrate a spoken language understanding system with intelligent mobile agentsthat mediate between users and information sources. | |
| We find that simple <term> interpolation methods </term> , like <term> log-linear and linear interpolation </term> , improve the <term> performance </term> but fall short of the <term> performance </term> of an <term> oracle </term> . | #1046 We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle. | |
| We provide experimental results that clearly show the need for a <term> dynamic language model combination </term> to improve the <term> performance </term> further . | #1135 We provide experimental results that clearly show the need for a dynamic language model combination to improve the performance further. | |
| We suggest a method that mimics the behavior of the <term> oracle </term> using a <term> neural network </term> or a <term> decision tree </term> . | #1156 We suggest a method that mimics the behavior of the oracle using a neural network or a decision tree. | |
| We show that the trained <term> SPR </term> learns to select a <term> sentence plan </term> whose <term> rating </term> on average is only 5 % worse than the <term> top human-ranked sentence plan </term> . | #1435 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. | |
| Over two distinct <term> datasets </term> , we find that <term> indexing </term> according to simple <term> character bigrams </term> produces a <term> retrieval accuracy </term> superior to any of the tested <term> word N-gram models </term> . | #1538 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. | |
| We also provide evidence that our findings are scalable . | #1591 We also provide evidence that our findings are scalable. | |
| I show that the <term> performance </term> of a <term> search engine </term> can be improved dramatically by incorporating an approximation of the <term> formal analysis </term> that is compatible with the <term> search engine </term> 's <term> operational semantics </term> . | #1873 I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysis that is compatible with the search engine's operational semantics. | |
| I show that the <term> performance </term> of a <term> search engine </term> can be improved dramatically by incorporating an approximation of the <term> formal analysis </term> that is compatible with the <term> search engine </term> 's <term> operational semantics </term> . | #1892 I show that the performance of a search engine can be improved dramatically by incorporating an approximation of the formal analysisthat is compatible with the search engine's operational semantics. | |
| The value of this approach is that as the <term> operational semantics </term> of <term> natural language applications </term> improve , even larger improvements are possible . | #1909 The value of this approach is that as the operational semantics of natural language applications improve, even larger improvements are possible. | |
| We provide a <term> logical definition </term> of <term> Minimalist grammars </term> , that are <term> Stabler 's formalization </term> of <term> Chomsky 's minimalist program </term> . | #1935 We provide a logical definition of Minimalist grammars, that are Stabler's formalization of Chomsky's minimalist program. | |
| We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> . | #2101 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system. | |
| This paper describes a method for <term> utterance classification </term> that does not require <term> manual transcription </term> of <term> training data </term> . | #2213 This paper describes a method for utterance classificationthat does not require manual transcription of training data. | |
| The method combines <term> domain independent acoustic models </term> with off-the-shelf <term> classifiers </term> to give <term> utterance classification performance </term> that is surprisingly close to what can be achieved using conventional <term> word-trigram recognition </term> requiring <term> manual transcription </term> . | #2238 The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performancethat is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. | |
| We present our <term> multi-level answer resolution algorithm </term> that combines results from the <term> answering agents </term> at the <term> question , passage , and/or answer levels </term> . | #2379 We present our multi-level answer resolution algorithmthat combines results from the answering agents at the question, passage, and/or answer levels. | |
| We conducted an <term> annotation experiment </term> and showed that <term> human annotators </term> can reliably differentiate between semantically coherent and incoherent <term> speech recognition hypotheses </term> . | #2486 We conducted an annotation experiment and showed that human annotators can reliably differentiate between semantically coherent and incoherent speech recognition hypotheses. | |
| An evaluation of our <term> system </term> against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in a <term> German corpus </term> of 2.284 <term> SRHs </term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . | #2511 An evaluation of our system against the annotated data shows that, it successfully classifies 73.2% in a German corpus of 2.284 SRHs as either coherent or incoherent (given a baseline of 54.55%). | |
| We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare several , previously proposed <term> phrase-based translation models </term> . | #2549 We propose a new phrase-based translation model and decoding algorithmthat enables us to evaluate and compare several, previously proposed phrase-based translation models. |
