A Survey of Cross-lingual Word Embedding Models
نویسندگان
چکیده
منابع مشابه
A survey of cross-lingual embedding models
Cross-lingual embedding models allow us to project words from different languages into a shared embedding space. This allows us to apply models trained on languages with a lot of data, e.g. English to low-resource languages. In the following, we will survey models that seek to learn cross-lingual embeddings. We will discuss them based on the type of approach and the nature of parallel data that...
متن کاملCross-lingual Models of Word Embeddings: An Empirical Comparison
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We perform an extensive evaluation of four popular approaches of inducing cross-lingual embeddings, each requiring a different form of supervision, on four typologically different language pairs. Our evaluation setup spans...
متن کاملCross-Lingual Word Sense Disambiguation
Word Sense Disambiguation using Cross-Lingual approach has been used successfully for languages like Farsi and Hindi. However, a comparable corpus in the form of Wikipedia articles available in English and Hindi has been used for such a task. This motivated us to further the approach and test the results when a parallel corpus is used. In this project, we specifically wanted to observe if the a...
متن کاملWord Alignment and Cross-Lingual Resource Acquisition
Annotated corpora are valuable resources for developing Natural Language Processing applications. This work focuses on acquiring annotated data for multilingual processing applications. We present an annotation environment that supports a web-based user-interface for acquiring word alignments between English and Chinese as well as a visualization tool for researchers to explore the annotated data.
متن کاملTrans-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 lang...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2019
ISSN: 1076-9757
DOI: 10.1613/jair.1.11640