31,648 research outputs found
Translation inference by concept propagation
This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin
Feedback-prop: Convolutional Neural Network Inference under Partial Evidence
We propose an inference procedure for deep convolutional neural networks
(CNNs) when partial evidence is available. Our method consists of a general
feedback-based propagation approach (feedback-prop) that boosts the prediction
accuracy for an arbitrary set of unknown target labels when the values for a
non-overlapping arbitrary set of target labels are known. We show that existing
models trained in a multi-label or multi-task setting can readily take
advantage of feedback-prop without any retraining or fine-tuning. Our
feedback-prop inference procedure is general, simple, reliable, and works on
different challenging visual recognition tasks. We present two variants of
feedback-prop based on layer-wise and residual iterative updates. We experiment
using several multi-task models and show that feedback-prop is effective in all
of them. Our results unveil a previously unreported but interesting dynamic
property of deep CNNs. We also present an associated technical approach that
takes advantage of this property for inference under partial evidence in
general visual recognition tasks.Comment: Accepted to CVPR 201
- …