Search Results for author:
Found 9 papers, 4 papers with code
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data
Providing technologies to communities or domains where training data is scarce or protected e. g., for privacy reasons, is becoming increasingly important.
PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging
This paper describes PTST, a source-free unsupervised domain adaptation technique for sequence tagging, and its application to the SemEval-2021 Task 10 on time expression recognition.
Causal Bias Quantification for Continuous Treatments
We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models.
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation
Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision.
Grounding learning of modifier dynamics: An application to color naming
In addition, we present a model that operates in the HSV color space.
Variational Inference and Deep Generative Models
Using DGMs one can easily design latent variable models that account for missing observations and thereby enable unsupervised and semi-supervised learning with neural networks.
A Stochastic Decoder for Neural Machine Translation
The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence.
Fast Collocation-Based Bayesian HMM Word Alignment
In order to make our model useful in practice, we devise an auxiliary variable Gibbs sampler that allows us to resample alignment links in constant time independently of the target sentence length.
