Papers with bilingual word embeddings
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance (D18-1)
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| Challenge: | a framework for cross-domain and cross-language transfer has hardly been explored . cross-linguistic and cross language transfer methods are used for multilingual applications . |
| Approach: | They propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks and bilingual word embeddings to train a model on labeled data from one language pair. |
| Outcome: | The proposed model outperforms existing models even when trained in the lazy setup . the proposed model can be applied to nine English-German and nine English - french domain pairs without retraining . |
Evaluating bilingual word embeddings on the long tail (N18-2)
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| Challenge: | Bilingual word embeddings are useful for bilingual lexicon induction, but they focus on frequent words in general domains. |
| Approach: | They propose to evaluate bilingual word embeddings on rare words in different domains . they propose to use a multilingual dataset to build and combine BWEs based on a single word . |
| Outcome: | The proposed evaluations show that state-of-the-art methods fail on rare words . the proposed evaluation is based on a gold standard dataset and code . |
Anchor-based Bilingual Word Embeddings for Low-Resource Languages (2021.acl-short)
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| Challenge: | Existing approaches to build monolingual word embeddings rely on a cheap bilingual signal and monolingual data. |
| Approach: | They propose a method where the vector space of the high resource source language is used as a starting point for training an embedding space for the low resource target language. |
| Outcome: | The proposed approach improves bilingual lexicon induction performance and target language MWE quality. |
Neural Cross-Lingual Named Entity Recognition with Minimal Resources (D18-1)
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| Challenge: | Named-entity recognition (NER) models are highly dependent on large amounts of labeled data. |
| Approach: | They propose a method that finds translations based on bilingual word embeddings . they also propose 'self-attention' which allows for a degree of flexibility with respect to word order . |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on common languages with lower resource requirements than previous approaches. |
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality (N18-1)
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| Challenge: | Efficient word representations play an important role in solving various problems related to NLP, data mining, text mining etc. |
| Approach: | They propose to leverage bilingual word embeddings learned through a parallel corpus to minimize the effect of data sparsity. |
| Outcome: | The proposed model is tested against state-of-the-art methods in two experimental setups. |
Exploring Bilingual Word Embeddings for Hiligaynon, a Low-Resource Language (2020.lrec-1)
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| Challenge: | Existing studies on Hiligaynon, a low-resource language of Malayo-Polynesian origin, have not explored the use of bilingual word embeddings in NLP. |
| Approach: | They use a publicly available Hiligaynon corpus with only 300K words to match it with a comparable English corpus. |
| Outcome: | The proposed model outperforms results from a low-resource language of Malayo-Polynesian origin with over 9 million speakers in the Philippines. |
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction (2020.coling-main)
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| Challenge: | Bilingual dictionary induction (BDI) is a task of finding target language translations of source language words. |
| Approach: | They propose to use bilingual orthography Embeddings to enrich BWE-based BDI with transliteration information to make a decision on which information source is more reliable for a particular word pair. |
| Outcome: | The proposed system improves on English-Russian BDI and shows that it can be built with only weak bilingual signals and even without any bilingual signal. |