Papers with Cross-lingual word embeddings
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages (2021.starsem-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings provide a way for information to be transferred between languages. |
| Approach: | They propose a joint training approach that incorporates sub-word information during training to learn cross-lingual embeddings. |
| Outcome: | The proposed method improves bilingual lexicon induction, especially for out-of-vocabulary words (OOVs) it is able to represent out- of-vocal words (OVs) and is more isomorphic than previous methods. |
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
| Approach: | They evaluate CLE models for a large number of language pairs on bilingual lexicon induction and three downstream tasks. |
| Outcome: | The proposed model performance is based on supervised and unsupervised models on bilingual lexicon induction and three downstream tasks. |
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank (D19-1)
Copied to clipboard
| Challenge: | Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. |
| Approach: | They propose to map dependency arcs from source treebank to target translation according to word alignments. |
| Outcome: | Experiments on university dependency treebanks show that translated treebank translations are more effective than translated treebans. |
Why Overfitting Isn’t Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies only evaluate cross-lingual word embeddings on bilingual lexicon induction (BLI) however, underfitting can hinder generalization to other downstream tasks. |
| Approach: | They retrofit cross-lingual word embeddings to the training dictionary and a synthetic dictionary to improve their results. |
| Outcome: | The proposed method improves accuracy on two downstream tasks, despite underfitting the training dictionary. |
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization (P19-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings (CLWE) are used to perform multilingual natural language processing tasks. |
| Approach: | They propose a method that transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language’s average vector is zero. |
| Outcome: | The proposed method improves translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (2% to 44% test accuracy). |
Evaluating Sub-word Embeddings in Cross-lingual Models (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing approaches to learning sub-word embeddings for out-of-vocabulary words have not considered sub- word embedds in cross-lingual models. |
| Approach: | They propose to use sub-word embeddings to form cross-lingual embeddables for out-of-vocabulary (OOV) words for which no embeddibles are available. |
| Outcome: | The proposed bilingual lexicon induction task shows that sub-word embeddings can be leveraged to form cross-lingual embeddables for OOV words. |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches to learn cross-lingual word embeddings are sense agnostic . a novel framework to align contextual embeddables at the sense level is proposed . |
| Approach: | They propose a framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. |
| Outcome: | The proposed framework improves word sense disambiguation tasks by leveraging bilingual dictionaries . compared with baseline results, the proposed models achieve 0.52%, 2.09% and 1.29% performance improvements . |
A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity (P19-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. |
| Approach: | They show that modularity can be used to improve unsupervised word embeddings . they show that it is useful for low-resource languages where one often has few bilingual pairs . |
| Outcome: | The proposed model improves unsupervised cross-lingual word embeddings on distant language pairs in low-resource settings. |
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning (2020.lrec-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. |
| Approach: | They propose to evaluate multiple cross-lingual word embedding models and compare their strengths and limitations to evaluate their effectiveness. |
| Outcome: | The proposed models perform well with noisy text and language pairs with major differences. |
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
Copied to clipboard
| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
| Approach: | They propose a multilingual word embedding corpus which is acquired by neural machine translation and is based on monolingual data. |
| Outcome: | The proposed method is competitive with existing methods but on the cross-lingual document classification task, it obtains the best figures. |