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Found 8 papers, 2 papers with code
deepQuest-py: Large and Distilled Models for Quality Estimation
We introduce deepQuest-py, a framework for training and evaluation of large and light-weight models for Quality Estimation (QE).
Bayesian Model-Agnostic Meta-Learning with Matrix-Valued Kernels for Quality Estimation
To address these challenges, we propose a Bayesian meta-learning approach for adapting QE models to the needs and preferences of each user with limited supervision.
Continual Quality Estimation with Online Bayesian Meta-Learning
Most current quality estimation (QE) models for machine translation are trained and evaluated in a static setting where training and test data are assumed to be from a fixed distribution.
Knowledge Distillation for Quality Estimation
Quality Estimation (QE) is the task of automatically predicting Machine Translation quality in the absence of reference translations, making it applicable in real-time settings, such as translating online social media conversations.
Meta-Learning Improves Lifelong Relation Extraction
Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations.
Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision
In this paper we frame the task of supervised relation classification as an instance of meta-learning.
Zero-shot Relation Classification as Textual Entailment
We consider the task of relation classification, and pose this task as one of textual entailment.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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