| other,14-1-P01-1070,bq | experiments centering on the construction of <term> | statistical | models </term> of <term> WH-questions </term> | #2138 We describe a set of supervised machine learning experiments centering on the construction ofstatistical models of WH-questions. |
| tech,17-2-N03-1004,bq | mechanisms </term> and the other adopting <term> | statistical | techniques </term> . We present our <term> | #2369 The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adoptingstatistical techniques. |
| tech,11-1-N03-2036,bq | phrase-based unigram model </term> for <term> | statistical | machine translation </term> that uses a much | #3400 In this paper, we describe a phrase-based unigram model forstatistical machine translation that uses a much simpler set of model parameters than similar phrase-based models. |
| other,11-5-P03-1031,bq | this <term> ambiguity </term> based on <term> | statistical | information </term> obtained from <term> dialogue | #4227 This paper proposes a method for resolving this ambiguity based onstatistical information obtained from dialogue corpora. |
| tech,6-2-P03-1050,bq | <term> stemming model </term> is based on <term> | statistical | machine translation </term> and it uses an | #4452 The stemming model is based onstatistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
| tech,20-1-C04-1112,bq | for <term> Dutch </term> which combines <term> | statistical | classification ( maximum entropy ) </term> | #5999 In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combinesstatistical classification (maximum entropy) with linguistic information. |
| tech,9-1-N04-1022,bq | Bayes-Risk ( MBR ) decoding </term> for <term> | statistical | machine translation </term> . This statistical | #6552 We present Minimum Bayes-Risk (MBR) decoding forstatistical machine translation. |
| statistical machine translation </term> . This | statistical | approach aims to minimize <term> expected | #6557 This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. | |
| tech,11-5-N04-1022,bq | decoding </term> can be used to tune <term> | statistical | MT </term> performance for specific <term> | #6637 Our results show that MBR decoding can be used to tunestatistical MT performance for specific loss functions. |
| tech,4-1-H05-1095,bq | This paper presents a <term> phrase-based | statistical | machine translation method </term> , based | #7342 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps. |
| tech,1-3-H05-1095,bq | word-aligned corpora </term> is proposed . A <term> | statistical | translation model </term> is also presented | #7371 Astatistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. |
| tech,31-4-I05-2014,bq | texts </term> with , for instance , <term> | statistical | MT systems </term> which usually segment | #7777 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 their outputs. |
| tech,13-3-I05-2021,bq | improvements in the <term> BLEU scores </term> of <term> | statistical | machine translation ( SMT ) </term> suggests | #7869 At the same time, the recent improvements in the BLEU scores ofstatistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences. |
| other,9-4-I05-2048,bq | intended to give an introduction to <term> | statistical | machine translation </term> with a focus | #8066 This workshop is intended to give an introduction tostatistical machine translation with a focus on practical considerations. |
| tech,3-8-I05-2048,bq | into practice . <term> STTK </term> , a <term> | statistical | machine translation tool kit </term> , will | #8123 STTK, astatistical machine translation tool kit, will be introduced and used to build a working translation system. |
| tech,18-4-J05-4003,bq | performance of a state-of-the-art <term> | statistical | machine translation system </term> . We also | #9063 We evaluate the quality of the extracted data by showing that it improves the performance of a state-of-the-artstatistical machine translation system. |
| tech,10-1-P05-1032,bq | data structure </term> for <term> phrase-based | statistical | machine translation </term> which allows | #9132 In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. |
| tech,6-1-P05-1034,bq | describe a novel <term> approach </term> to <term> | statistical | machine translation </term> that combines | #9207 We describe a novel approach tostatistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. |
| tech,16-1-P05-1048,bq | sense disambigation models </term> help <term> | statistical | machine translation </term><term> quality | #9327 We directly investigate a subject of much recent debate: do word sense disambigation models helpstatistical machine translation quality? |
| tool,15-3-P05-1048,bq | candidates </term> for a typical <term> IBM | statistical | MT system </term> , we find that <term> word | #9363 Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system, we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone. |
