1,460 research outputs found
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
This paper demonstrates that word sense disambiguation (WSD) can improve
neural machine translation (NMT) by widening the source context considered when
modeling the senses of potentially ambiguous words. We first introduce three
adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant
processes, and random walks, which are then applied to large word contexts
represented in a low-rank space and evaluated on SemEval shared-task data. We
then learn word vectors jointly with sense vectors defined by our best WSD
method, within a state-of-the-art NMT system. We show that the concatenation of
these vectors, and the use of a sense selection mechanism based on the weighted
average of sense vectors, outperforms several baselines including sense-aware
ones. This is demonstrated by translation on five language pairs. The
improvements are above one BLEU point over strong NMT baselines, +4% accuracy
over all ambiguous nouns and verbs, or +20% when scored manually over several
challenging words.Comment: To appear in TAC
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
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