92 research outputs found
Searching to Translate and Translating to Search: When Information Retrieval Meets Machine Translation
With the adoption of web services in daily life, people have access to tremendous amounts of information, beyond any human's reading and comprehension capabilities. As a result, search technologies have become a fundamental tool for accessing information. Furthermore, the web contains information in multiple languages, introducing another barrier between people and information.
Therefore, search technologies need to handle content written in
multiple languages, which requires techniques to account for the linguistic differences. Information Retrieval (IR) is the study of search techniques, in which the task is to find material relevant to a given information need. Cross-Language Information Retrieval (CLIR) is a special case of IR when the search takes place in a multi-lingual collection.
Of course, it is not helpful to retrieve content in languages the user cannot understand. Machine Translation (MT) studies the translation of text from one language into another efficiently (within a reasonable amount of time) and effectively (fluent and retaining the original meaning), which helps people understand what is being written, regardless of the source language.
Putting these together, we observe that search and translation technologies are part of an important user application, calling for a better integration of search (IR) and translation (MT), since these two technologies need to work together to produce high-quality output.
In this dissertation, the main goal is to build better connections between IR and MT, for which we present solutions to two problems: Searching to translate explores approximate search techniques for extracting bilingual data from multilingual Wikipedia collections to train better translation models. Translating to search explores the integration of a modern statistical MT system into the cross-language search processes. In both cases, our best-performing approach yielded improvements over strong baselines for a variety of language pairs.
Finally, we propose a general architecture, in which various components of IR and MT systems can be connected together into a feedback loop, with potential improvements to both search and translation tasks. We hope that the ideas presented in this dissertation will spur more interest in the integration of search and
translation technologies
Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe
In order to build better human-friendly human-computer interfaces,
such interfaces need to be enabled with capabilities to perceive
the user, his location, identity, activities and in particular his interaction
with others and the machine. Only with these perception capabilities
can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the
development of novel techniques for the visual perception of humans and
their activities, in order to facilitate perceptive multimodal interfaces,
humanoid robots and smart environments. My work includes research
on person tracking, person identication, recognition of pointing gestures,
estimation of head orientation and focus of attention, as well as
audio-visual scene and activity analysis. Application areas are humanfriendly
humanoid robots, smart environments, content-based image and
video analysis, as well as safety- and security-related applications. This
article gives a brief overview of my ongoing research activities in these
areas
Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article intends to systematically identify and comparatively analyze
state-of-the-art supply chain (SC) forecasting strategies and technologies. A
novel framework has been proposed incorporating Big Data Analytics in SC
Management (problem identification, data sources, exploratory data analysis,
machine-learning model training, hyperparameter tuning, performance evaluation,
and optimization), forecasting effects on human-workforce, inventory, and
overall SC. Initially, the need to collect data according to SC strategy and
how to collect them has been discussed. The article discusses the need for
different types of forecasting according to the period or SC objective. The SC
KPIs and the error-measurement systems have been recommended to optimize the
top-performing model. The adverse effects of phantom inventory on forecasting
and the dependence of managerial decisions on the SC KPIs for determining model
performance parameters and improving operations management, transparency, and
planning efficiency have been illustrated. The cyclic connection within the
framework introduces preprocessing optimization based on the post-process KPIs,
optimizing the overall control process (inventory management, workforce
determination, cost, production and capacity planning). The contribution of
this research lies in the standard SC process framework proposal, recommended
forecasting data analysis, forecasting effects on SC performance, machine
learning algorithms optimization followed, and in shedding light on future
research
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