Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces (2020.acl-main)
Copied to clipboard
| Challenge: | InstaMap is a non-parametric model that learns a global linear projection by iterating between finding an optimal rotation of the source embedding space and moving each point along an instance-specific translation vector. |
| Approach: | They propose a non-parametric model that learns a global linear projection by iteratively finding an optimal rotation of the source embedding space and moving each point along an instance-specific translation vector. |
| Outcome: | The proposed model performs better than previous models on bilingual lexicon induction across a set of 28 diverse language pairs. |
Similar Papers
LNMap: Departures from Isomorphic Assumption in Bilingual Lexicon Induction Through Non-Linear Mapping in Latent Space (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for bilingual lexicon induction are mapping-based, but they do not hold for closely related languages. |
| Approach: | They propose a semi-supervised method to learn cross-lingual word embeddings for BLI using a linear mapping function and a latent space of two independently trained autoencoders. |
| Outcome: | The proposed method outperforms existing models on 15 different language pairs on both directions. |
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). |
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring (2021.acl-long)
Copied to clipboard
| Challenge: | Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embedders. |
| Approach: | They propose an unsupervised mapping approach that fixes fixed embeddings and learns new ones for the source language that are aligned with them. |
| Outcome: | The proposed method outperforms conventional mapping methods on bilingual lexicon induction and obtains competitive results in the downstream XNLI task. |
Analyzing the Limitations of Cross-lingual Word Embedding Mappings (P19-1)
Copied to clipboard
| Challenge: | Existing methods for cross-lingual word embeddings have limited results . existing methods require little or no cross-linguistic signal to work . |
| Approach: | They compare offline mapping methods to an extension of skip-gram that jointly learns both embedding spaces. |
| Outcome: | The proposed method yields more isomorphic embeddings, is less sensitive to hubness, and achieves stronger results in bilingual lexicon induction. |
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing work on cross-lingual word embeddings rely on linear mappings, but this assumption is not true for all language pairs. |
| Approach: | They propose a non-linear mapping approach which can find non-linesar relationships between languages by kernel Canonical Correlation Analysis. |
| Outcome: | The proposed approach improves on five language pairs on supervised and self-learning scenarios. |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent work on unsupervised cross-lingual embeddings in the bilingual setting has given the impetus to learning a shared embeddable space for several languages. |
| Approach: | They propose to solve two sub-problems together to learn a shared embedding space for several languages. |
| Outcome: | The proposed approach outperforms existing methods in bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing tasks. |
Hierarchical Mapping for Crosslingual Word Embedding Alignment (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing strategies that map word embeddings into a crosslingual space are biased towards the choice of the pivot language. |
| Approach: | They propose to map any two languages into a different middle space by learning mappings across languages in a hierarchical way. |
| Outcome: | The proposed strategy significantly improves vocabulary induction scores in all existing benchmarks and in a new non-English–centered benchmark. |
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for word embeddings are limited to a single, unannotated corpus, which means that word representations with similar meaning in distinct languages can be very different. |
| Approach: | They propose an unsupervised method for cross-lingual word embedding mapping that uses stochastic initialization and isometric initialization to verify the method's robustness. |
| Outcome: | The proposed method is robust on different embedding representations and new language pairs, particularly those involving Slavic languages like Polish or Czech. |
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
| Approach: | They propose an unsupervised initialization method that exploits structural similarity of embeddings and a robust self-learning algorithm that iteratively improves it. |
| Outcome: | The proposed method achieves the best published results in standard datasets even surpassing previous supervised systems. |
Unsupervised Cross-Lingual Representation Learning (P19-4)
Copied to clipboard
| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |