3 research outputs found
Model Comparison for Semantic Grouping
We introduce a probabilistic framework for quantifying the semantic
similarity between two groups of embeddings. We formulate the task of semantic
similarity as a model comparison task in which we contrast a generative model
which jointly models two sentences versus one that does not. We illustrate how
this framework can be used for the Semantic Textual Similarity tasks using
clear assumptions about how the embeddings of words are generated. We apply
model comparison that utilises information criteria to address some of the
shortcomings of Bayesian model comparison, whilst still penalising model
complexity. We achieve competitive results by applying the proposed framework
with an appropriate choice of likelihood on the STS datasets.Comment: Proceedings of the 36th International Conference on Machine Learnin
Multilingual Factor Analysis
In this work we approach the task of learning multilingual word
representations in an offline manner by fitting a generative latent variable
model to a multilingual dictionary. We model equivalent words in different
languages as different views of the same word generated by a common latent
variable representing their latent lexical meaning. We explore the task of
alignment by querying the fitted model for multilingual embeddings achieving
competitive results across a variety of tasks. The proposed model is robust to
noise in the embedding space making it a suitable method for distributed
representations learned from noisy corpora.Comment: Proceedings of the 57th Annual Meeting of the Association for
Computational Linguistic
FPR -- Fast Path Risk Algorithm to Evaluate Collision Probability
As mobile robots and autonomous vehicles become increasingly prevalent in
human-centred environments, there is a need to control the risk of collision.
Perceptual modules, for example machine vision, provide uncertain estimates of
object location. In that context, the frequently made assumption of an exactly
known free-space is invalid. Clearly, no paths can be guaranteed to be
collision free. Instead, it is necessary to compute the probabilistic risk of
collision on any proposed path. The FPR algorithm, proposed here, efficiently
calculates an upper bound on the risk of collision for a robot moving on the
plane. That computation orders candidate trajectories according to (the bound
on) their degree of risk. Then paths within a user-defined threshold of primary
risk could be selected according to secondary criteria such as comfort and
efficiency. The key contribution of this paper is the FPR algorithm and its
`convolution trick' to factor the integrals used to bound the risk of
collision. As a consequence of the convolution trick, given obstacles and
candidate paths, the computational load is reduced from the naive ,
to the qualitatively faster .Comment: To appear in IEEE Robotics and Automation Letters (RA-L