Bilingual Word Embeddings

We introduce bilingual word embeddings: se- mantic embeddings associated across two lan- guages in the context of neural language mod- els. We propose a method to learn bilingual embeddings from …...

We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word em- beddings. We use a small dictionary of easily-obtainable task-specific word equiva- lence …...

AbstractWe introduce a distribution based model to learn bilingual word embeddings from monolingual data. It is simple, effective and does not require any parallel data or any seed lexicon. We …...

AbstractCross-lingual sentiment classification (CLSC) seeks to use resources from a source language in order to detect sentiment and classify text in a target language. Almost all research into CLSC has …...

AbstractIn this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant …...

AbstractMost methods to learn bilingual word embeddings rely on large parallel corpora, which is difficult to obtain for most language pairs. This has motivated an active research line to relax …...

AbstractWe propose a new method to detect when users express the intent to leave a service, also known as churn. While previous work focuses solely on social media, we show …...

AbstractArgumentation 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 …...

AbstractEfficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great …...

AbstractMining parallel sentences from comparable corpora is of great interest for many downstream tasks. In the BUCC 2017 shared task, systems performed well by training on gold standard parallel sentences. …...

AbstractWord embeddings learned in two languages can be mapped to a common space to produce Bilingual Word Embeddings (BWE). Unsupervised BWE methods learn such a mapping without any parallel data. …...

AbstractBilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite …...

AbstractState-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space. Despite its remarkable results, unsupervised mapping is also …...

AbstractMining parallel sentences from comparable corpora is important. Most previous work relies on supervised systems, which are trained on parallel data, thus their applicability is problematic in low-resource scenarios. Recent …...

AbstractBilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not …...

This study conducted an experiment to com- pare the word embeddings of a compound word and a word in Japanese on the same vec- tor space using bilingual word embeddings. …...

AbstractThis paper investigates the use of bilingual word embeddings for mining Hiligaynon translations of English words. There is very little research on Hiligaynon, an extremely low-resource language of Malayo-Polynesian origin …...

Bilingual word embeddings (BWEs) facilitate the translation of single source language words to single target language words. However, often a single source word must be translated using two target words. …...