The Experts below are selected from a list of 41802 Experts worldwide ranked by ideXlab platform
Kathleen R Mckeown - One of the best experts on this subject based on the ideXlab platform.
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using a supertagged dependency language model to select a Good Translation in system combination
North American Chapter of the Association for Computational Linguistics, 2013Co-Authors: Kathleen R MckeownAbstract:We present a novel, structured language model - Supertagged Dependency Language Model to model the syntactic dependencies between words. The goal is to identify ungrammatical hypotheses from a set of candidate Translations in a MT system combination framework and help select the best Translation candidates using a variety of sentence-level features. We use a two-step mechanism based on constituent parsing and elementary tree extraction to obtain supertags and their dependency relations. Our experiments show that the structured language model provides significant improvement in the framework of sentence-level system combination.
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HLT-NAACL - Using a Supertagged Dependency Language Model to Select a Good Translation in System Combination
2013Co-Authors: Kathleen R MckeownAbstract:We present a novel, structured language model - Supertagged Dependency Language Model to model the syntactic dependencies between words. The goal is to identify ungrammatical hypotheses from a set of candidate Translations in a MT system combination framework and help select the best Translation candidates using a variety of sentence-level features. We use a two-step mechanism based on constituent parsing and elementary tree extraction to obtain supertags and their dependency relations. Our experiments show that the structured language model provides significant improvement in the framework of sentence-level system combination.
Martha Shumway - One of the best experts on this subject based on the ideXlab platform.
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how Good is very Good Translation effect in the racial ethnic variation in self rated health status
Quality of Life Research, 2014Co-Authors: Sukyong Seo, Sukyung Chung, Martha ShumwayAbstract:To examine the influence of Translation when measuring and comparing self-rated health (SRH) measured with five response categories (excellent, very Good, Good, fair, and poor), across racial/ethnic groups. Using data from the California Health Interview Survey, which were administered in five languages, we analyzed variations in the five-category SRH across five racial/ethnic groups: non-Hispanic white, Latino, Chinese, Vietnamese, and Korean. Logistic regression was used to estimate independent effects of race/ethnicity, culture, and Translation on SRH, after controlling for risk factors and other measures of health status. Latinos, Chinese, Vietnamese, and Koreans were less likely than non-Hispanic whites to rate their health as excellent or very Good and more likely to rate it as Good, fair, or poor. This racial/ethnic difference diminished when adjusting for acculturation. Independently of race/ethnicity, respondents using non-English surveys were less likely to answer excellent (OR = 0.24–0.55) and very Good (OR = 0.30–0.34) and were more likely to answer fair (OR = 2.48–4.10) or poor (OR = 2.87–3.51), even after controlling for other measures of SRH. Responses to the five-category SRH question depend on interview language. When responding in Spanish, Chinese, Korean, or Vietnamese, respondents are more likely to choose a lower level SRH category, “fair” in particular. If each SRH category measured in different languages is treated as equivalent, racial/ethnic disparities in SRH among Latinos and Asian subgroups, as compared to non-Hispanic whites, may be exaggerated.
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How Good is “very Good”? Translation effect in the racial/ethnic variation in self-rated health status
Quality of life research : an international journal of quality of life aspects of treatment care and rehabilitation, 2013Co-Authors: Sukyong Seo, Sukyung Chung, Martha ShumwayAbstract:To examine the influence of Translation when measuring and comparing self-rated health (SRH) measured with five response categories (excellent, very Good, Good, fair, and poor), across racial/ethnic groups. Using data from the California Health Interview Survey, which were administered in five languages, we analyzed variations in the five-category SRH across five racial/ethnic groups: non-Hispanic white, Latino, Chinese, Vietnamese, and Korean. Logistic regression was used to estimate independent effects of race/ethnicity, culture, and Translation on SRH, after controlling for risk factors and other measures of health status. Latinos, Chinese, Vietnamese, and Koreans were less likely than non-Hispanic whites to rate their health as excellent or very Good and more likely to rate it as Good, fair, or poor. This racial/ethnic difference diminished when adjusting for acculturation. Independently of race/ethnicity, respondents using non-English surveys were less likely to answer excellent (OR = 0.24–0.55) and very Good (OR = 0.30–0.34) and were more likely to answer fair (OR = 2.48–4.10) or poor (OR = 2.87–3.51), even after controlling for other measures of SRH. Responses to the five-category SRH question depend on interview language. When responding in Spanish, Chinese, Korean, or Vietnamese, respondents are more likely to choose a lower level SRH category, “fair” in particular. If each SRH category measured in different languages is treated as equivalent, racial/ethnic disparities in SRH among Latinos and Asian subgroups, as compared to non-Hispanic whites, may be exaggerated.
Sukyong Seo - One of the best experts on this subject based on the ideXlab platform.
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how Good is very Good Translation effect in the racial ethnic variation in self rated health status
Quality of Life Research, 2014Co-Authors: Sukyong Seo, Sukyung Chung, Martha ShumwayAbstract:To examine the influence of Translation when measuring and comparing self-rated health (SRH) measured with five response categories (excellent, very Good, Good, fair, and poor), across racial/ethnic groups. Using data from the California Health Interview Survey, which were administered in five languages, we analyzed variations in the five-category SRH across five racial/ethnic groups: non-Hispanic white, Latino, Chinese, Vietnamese, and Korean. Logistic regression was used to estimate independent effects of race/ethnicity, culture, and Translation on SRH, after controlling for risk factors and other measures of health status. Latinos, Chinese, Vietnamese, and Koreans were less likely than non-Hispanic whites to rate their health as excellent or very Good and more likely to rate it as Good, fair, or poor. This racial/ethnic difference diminished when adjusting for acculturation. Independently of race/ethnicity, respondents using non-English surveys were less likely to answer excellent (OR = 0.24–0.55) and very Good (OR = 0.30–0.34) and were more likely to answer fair (OR = 2.48–4.10) or poor (OR = 2.87–3.51), even after controlling for other measures of SRH. Responses to the five-category SRH question depend on interview language. When responding in Spanish, Chinese, Korean, or Vietnamese, respondents are more likely to choose a lower level SRH category, “fair” in particular. If each SRH category measured in different languages is treated as equivalent, racial/ethnic disparities in SRH among Latinos and Asian subgroups, as compared to non-Hispanic whites, may be exaggerated.
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How Good is “very Good”? Translation effect in the racial/ethnic variation in self-rated health status
Quality of life research : an international journal of quality of life aspects of treatment care and rehabilitation, 2013Co-Authors: Sukyong Seo, Sukyung Chung, Martha ShumwayAbstract:To examine the influence of Translation when measuring and comparing self-rated health (SRH) measured with five response categories (excellent, very Good, Good, fair, and poor), across racial/ethnic groups. Using data from the California Health Interview Survey, which were administered in five languages, we analyzed variations in the five-category SRH across five racial/ethnic groups: non-Hispanic white, Latino, Chinese, Vietnamese, and Korean. Logistic regression was used to estimate independent effects of race/ethnicity, culture, and Translation on SRH, after controlling for risk factors and other measures of health status. Latinos, Chinese, Vietnamese, and Koreans were less likely than non-Hispanic whites to rate their health as excellent or very Good and more likely to rate it as Good, fair, or poor. This racial/ethnic difference diminished when adjusting for acculturation. Independently of race/ethnicity, respondents using non-English surveys were less likely to answer excellent (OR = 0.24–0.55) and very Good (OR = 0.30–0.34) and were more likely to answer fair (OR = 2.48–4.10) or poor (OR = 2.87–3.51), even after controlling for other measures of SRH. Responses to the five-category SRH question depend on interview language. When responding in Spanish, Chinese, Korean, or Vietnamese, respondents are more likely to choose a lower level SRH category, “fair” in particular. If each SRH category measured in different languages is treated as equivalent, racial/ethnic disparities in SRH among Latinos and Asian subgroups, as compared to non-Hispanic whites, may be exaggerated.
Uwe Kussner - One of the best experts on this subject based on the ideXlab platform.
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learning to select a Good Translation
International Conference on Computational Linguistics, 2000Co-Authors: Dan Tidhar, Uwe KussnerAbstract:Within the machine Translation system Verbmobil, Translation is performed simultaneously by four independent Translation modules. The four competing Translations are combined by a selection module so as to form a single optimal output for each input utterance. The selection module relies on confidence values that are delivered together with each of the alternative Translations. Since the confidence values are computed by four independent modules that are fundamentally different from one another, they are not directly comparable and need to be rescaled in order to gain comparative significance. In this paper we describe a machine learning method tailored to overcome this difficulty by using off-line human feedback to determine an appropriate confidence rescaling scheme. Additionally, we describe some other sources of information that are used for selecting between the competing Translations, and describe the way in which the selection process relates to quality of service specifications.
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COLING - Learning to select a Good Translation
Proceedings of the 18th conference on Computational linguistics -, 2000Co-Authors: Dan Tidhar, Uwe KussnerAbstract:Within the machine Translation system Verbmobil, Translation is performed simultaneously by four independent Translation modules. The four competing Translations are combined by a selection module so as to form a single optimal output for each input utterance. The selection module relies on confidence values that are delivered together with each of the alternative Translations. Since the confidence values are computed by four independent modules that are fundamentally different from one another, they are not directly comparable and need to be rescaled in order to gain comparative significance. In this paper we describe a machine learning method tailored to overcome this difficulty by using off-line human feedback to determine an appropriate confidence rescaling scheme. Additionally, we describe some other sources of information that are used for selecting between the competing Translations, and describe the way in which the selection process relates to quality of service specifications.
Joanna Neill - One of the best experts on this subject based on the ideXlab platform.
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A systematic review comparing sex differences in cognitive function in schizophrenia and in rodent models for schizophrenia, implications for improved therapeutic strategies
Neuroscience and Biobehavioral Reviews, 2016Co-Authors: Marianne Leger, Joanna NeillAbstract:Sex is often overlooked in animal and human research. Cognitive impairment associated with schizophrenia (CIAS) remains an unmet clinical need, as current antipsychotic medication does not provide clinically meaningful improvements. One explanation could be lack of appreciation of gender differences in CIAS. Animal models play a critical role in drug development and improved Translation to the clinic is an on-going process. Our systematic review aims to evaluate how well the animal studies translate into clinical findings. Supporting clinical results, our review highlights a male working memory advantage and a female advantage for visual memory and social cognition in rodent models for schizophrenia. Not investigated in animals, a female advantage for attention and speed of processing has been found in schizophrenia patients. Sex differences in reasoning and problem solving are poorly investigated in both human and animal studies. Overall, our review provides evidence of Good Translation from the animal models into the clinic when sexual dimorphism is assessed. Enhanced understanding of these sex differences will improve the management of CIAS.
