1,228 research outputs found
Trans-gram, Fast Cross-lingual Word-embeddings
We introduce Trans-gram, a simple and computationally-efficient method to
simultaneously learn and align wordembeddings for a variety of languages, using
only monolingual data and a smaller set of sentence-aligned data. We use our
new method to compute aligned wordembeddings for twenty-one languages using
English as a pivot language. We show that some linguistic features are aligned
across languages for which we do not have aligned data, even though those
properties do not exist in the pivot language. We also achieve state of the art
results on standard cross-lingual text classification and word translation
tasks.Comment: EMNLP 201
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
We construct a multilingual common semantic space based on distributional
semantics, where words from multiple languages are projected into a shared
space to enable knowledge and resource transfer across languages. Beyond word
alignment, we introduce multiple cluster-level alignments and enforce the word
clusters to be consistently distributed across multiple languages. We exploit
three signals for clustering: (1) neighbor words in the monolingual word
embedding space; (2) character-level information; and (3) linguistic properties
(e.g., apposition, locative suffix) derived from linguistic structure knowledge
bases available for thousands of languages. We introduce a new
cluster-consistent correlational neural network to construct the common
semantic space by aligning words as well as clusters. Intrinsic evaluation on
monolingual and multilingual QVEC tasks shows our approach achieves
significantly higher correlation with linguistic features than state-of-the-art
multi-lingual embedding learning methods do. Using low-resource language name
tagging as a case study for extrinsic evaluation, our approach achieves up to
24.5\% absolute F-score gain over the state of the art.Comment: 10 page
Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and
distributed representations for bilingual data. By marginalizing over word
alignments the model captures a larger semantic context than prior work relying
on hard alignments. The advantage of this approach is demonstrated in a
cross-lingual classification task, where we outperform the prior published
state of the art.Comment: Proceedings of ACL 2014 (Short Papers
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