1,914 research outputs found
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently
exploit these data sources for modeling a target domain. Variants of this
problem have been studied in many contexts, such as cross-domain translation
and domain adaptation. We propose AlignFlow, a generative modeling framework
that models each domain via a normalizing flow. The use of normalizing flows
allows for a) flexibility in specifying learning objectives via adversarial
training, maximum likelihood estimation, or a hybrid of the two methods; and b)
learning and exact inference of a shared representation in the latent space of
the generative model. We derive a uniform set of conditions under which
AlignFlow is marginally-consistent for the different learning objectives.
Furthermore, we show that AlignFlow guarantees exact cycle consistency in
mapping datapoints from a source domain to target and back to the source
domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image
translation and unsupervised domain adaptation and can be used to
simultaneously interpolate across the various domains using the learned
representation.Comment: AAAI 202
Cross-lingual Distillation for Text Classification
Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.Comment: Accepted at ACL 2017; Code available at
https://github.com/xrc10/cross-distil
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing
Cross-lingual semantic parsing transfers parsing capability from a
high-resource language (e.g., English) to low-resource languages with scarce
training data. Previous work has primarily considered silver-standard data
augmentation or zero-shot methods, however, exploiting few-shot gold data is
comparatively unexplored. We propose a new approach to cross-lingual semantic
parsing by explicitly minimizing cross-lingual divergence between probabilistic
latent variables using Optimal Transport. We demonstrate how this direct
guidance improves parsing from natural languages using fewer examples and less
training. We evaluate our method on two datasets, MTOP and MultiATIS++SQL,
establishing state-of-the-art results under a few-shot cross-lingual regime.
Ablation studies further reveal that our method improves performance even
without parallel input translations. In addition, we show that our model better
captures cross-lingual structure in the latent space to improve semantic
representation similarity.Comment: Accepted to TACL 2023. Pre-MIT Press publication. 17 pages, 3
figures, 6 table
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