Multitask learning for large-scale semantic change detection
نویسندگان
چکیده
منابع مشابه
Feature Hashing for Large Scale Multitask Learning
Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case — multit...
متن کاملLarge-scale Multitask Learning for Machine Translation Quality Estimation
Multitask learning has been proven a useful technique in a number of Natural Language Processing applications where data is scarce and naturally diverse. Examples include learning from data of different domains and learning from labels provided by multiple annotators. Tasks in these scenarios would be the domains or the annotators. When faced with limited data for each task, a framework for the...
متن کاملDeep Multitask Learning for Semantic Dependency Parsing
We present a deep neural architecture that parses sentences into three semantic dependency graph formalisms. By using efficient, nearly arc-factored inference and a bidirectional-LSTM composed with a multi-layer perceptron, our base system is able to significantly improve the state of the art for semantic dependency parsing, without using hand-engineered features or syntax. We then explore two ...
متن کاملSemantic Constraint and QoS-Aware Large-Scale Web Service Composition
Service-oriented architecture facilitates the running time of interactions by using business integration on the networks. Currently, web services are considered as the best option to provide Internet services. Due to an increasing number of Web users and the complexity of users’ queries, simple and atomic services are not able to meet the needs of users; and to provide complex services, it requ...
متن کاملMultitask learning for semantic sequence prediction under varying data conditions
Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and cor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Vision and Image Understanding
سال: 2019
ISSN: 1077-3142
DOI: 10.1016/j.cviu.2019.07.003