| other,16-3-H01-1042,bq | the <term> intelligibility </term> of <term> MT | output | </term> . A <term> language learning experiment | #626 This, the first experiment in a series of experiments, looks at the intelligibility of MT output. |
| other,11-3-I05-6011,bq | </term> and improving <term> machine translation | outputs | </term> . Annotating <term> honorifics </term> | #8614 This referential information is vital for resolving zero pronouns and improving machine translation outputs. |
| other,30-2-H05-2007,bq | patterns </term> in <term> machine translation | output | </term> . Automatic <term> evaluation metrics | #7676 We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output. |
| </term> of a <term> parser </term> 's multiple | output | . Some examples of <term> paraphrasing </term> | #15726 This paper presents a new interactive disambiguation scheme based on the paraphrasing of a parser's multiple output. | |
| other,16-6-H01-1042,bq | experiment using <term> machine translation | output | </term> . Subjects were given a set of up | #680 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output. |
| other,11-8-H01-1042,bq | </term> , others were <term> machine translation | outputs | </term> . The subjects were given three minutes | #710 Some of the extracts were expert human translations, others were machine translation outputs. |
| other,38-4-I05-2014,bq | </term> which usually segment their <term> | outputs | </term> . We present the first known <term> | #7784 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment theiroutputs. |
| other,9-4-E06-1035,bq | <term> performance </term> of using <term> ASR | output | </term> as opposed to <term> human transcription | #10519 We then explore the impact on performance of using ASR output as opposed to human transcription. |
| the <term> shared derivation forest </term> | output | by a prior <term> RCL parser </term> for a | #1723 The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the shared derivation forestoutput by a prior RCL parser for a suitable superset of L. | |
| other,15-5-N03-1026,bq | <term> grammaticality </term> of the <term> system | output | </term> due to the use of a <term> constraint-based | #2899 Overall summarization quality of the proposed system is state-of-the-art, with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator. |
| . In this paper we show how two standard | outputs | from <term> information extraction ( IE ) | #282 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser. | |
| other,9-6-E06-1035,bq | transcription errors </term> inevitable in <term> ASR | output | </term> have a negative impact on models | #10618 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks. |
| measure(ment),6-3-H05-1117,bq | <term> methods </term> for <term> scoring system | output | </term> is an impediment to progress in the | #7578 The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. |
| article considers approaches which rerank the | output | of an existing <term> probabilistic parser | #8656 This article considers approaches which rerank the output of an existing probabilistic parser. | |
| other,38-1-N03-1018,bq | through its transformation into the <term> noisy | output | </term> of an <term> OCR system </term> . The | #2706 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. |
| other,16-2-N03-1018,bq | on <term> post-processing </term> the <term> | output | </term> of black-box <term> OCR systems </term> | #2728 The model is designed for use in error correction, with a focus on post-processing theoutput of black-box OCR systems in order to make it more useful for NLP tasks. |
| other,28-1-H01-1042,bq | human language learners </term> , to the <term> | output | </term> of <term> machine translation ( MT | #572 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to theoutput of machine translation (MT) systems. |
| by gathering <term> statistics </term> on the | output | of other <term> linguistic tools </term> . | #16664 The scheme was implemented by gathering statistics on the output of other linguistic tools. | |
| tech,3-7-P05-1067,bq | <term> model </term> . We evaluate the <term> | outputs | </term> of our <term> MT system </term> using | #9512 We evaluate theoutputs of our MT system using the NIST and Bleu automatic MT evaluation software. |
| other,21-3-N03-1001,bq | particular <term> domain </term> ; the <term> | output | </term> of <term> recognition </term> with this | #2276 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; theoutput of recognition with this model is then passed to a phone-string classifier. |
