Learning Crosslingual Word Embeddings without Bilingual Corpora
Crosslingual word embeddings represent lexical items from different languages in the same vector space, enabling transfer of NLP tools. However, previous attempts had expensive resource requirements, difficulty incorporating monolingual data or were unable to handle polysemy. We address these drawbacks in our method which takes advantage of a high coverage dictionary in an EM style training algorithm over monolingual corpora in two languages. Our model achieves state-of-the-art performance on bilingual lexicon induction task exceeding models using large bilingual corpora, and competitive results on the monolingual word similarity and cross-lingual document classification task.
Code (1)
Tasks
Bilingual Lexicon InductionCross-Lingual Document ClassificationDocument ClassificationGeneral ClassificationWord EmbeddingsWord SimilaritySimilar Papers 제목 키워드 기반
Multilingual Training of Crosslingual Word Embeddings
Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer. Most prior work constructs embeddings for a pair of languages, with English on on…
Bilingual Lexicon InductionDependency ParsingDocument ClassificationGeneral Classification+6Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense o…
Representation LearningWord EmbeddingsBilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel senten…
DecoderSentenceWord EmbeddingsImproving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation
Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires th…
Bilingual Lexicon InductionLanguage ModelingLanguage ModellingMachine Translation+2Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation
In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future con…
RetrievalSentenceSentence RetrievalWord Similarity