Cross-Lingual Transfer
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Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different language families and written in 28 different scripts. Finally, we introduce a new test set of aligned sentences in 122 languages based on the Tatoeba corpus, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages.
CROSS-LINGUAL BITEXT MINING CROSS-LINGUAL DOCUMENT CLASSIFICATION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE CROSS-LINGUAL TRANSFER DOCUMENT CLASSIFICATION JOINT MULTILINGUAL SENTENCE REPRESENTATIONS PARALLEL CORPUS MINING
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
EMNLP 2018 • cambridgeltl/adversarial-postspec
While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space.
Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!
COLING 2018 • UKPLab/coling2018-xling_argument_mining
Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity.
Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
In this paper we investigate the predictability of these two concepts via supervised learning, using word embeddings as explanatory variables. We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20% in correlation when predicting across languages.