| Challenge: | Existing methods for learning vector space representations of words are based on word-context information. |
| Approach: | They propose a method for estimating vector space representations of words by concept induction. |
| Outcome: | The proposed method performs better on crosslingual word similarity and sentiment analysis on a parallel corpus. |
Similar Papers
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. |
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. |
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. |
Relation Induction in Word Embeddings Revisited (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
| Approach: | They propose to use Gaussian to explicitly model the variability of translations and Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words. |
| Outcome: | The proposed models are based on translations but use Gaussian to model the variability of translations and encode soft constraints on the source and target words that may be chosen. |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
Copied to clipboard
| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |
NORMA: Neighborhood Sensitive Maps for Multilingual Word Embeddings (D18-1)
Copied to clipboard
| Challenge: | Existing methods for learning multilingual word embeddings assume that embeddable spaces of different languages exhibit similar structures. |
| Approach: | They propose a method for learning neighborhood sensitive maps to capture such differences . aim is to learn word vectors where similar words have similar vector representations . |
| Outcome: | The proposed method outperforms state-of-the-art methods for translation between distant languages. |
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs (N19-1)
Copied to clipboard
| Challenge: | Recent research has found that a shared bilingual word embedding space can be induced by projecting monolingual word embeds from two languages without any bilingual supervision. |
| Approach: | They propose a framework for learning unsupervised multilingual word embeddings that mitigates instability issues for distant language pairs. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two downstream tasks outperforming even supervised baselines. |
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. |
Learning Word Meta-Embeddings by Autoencoding (C18-1)
Copied to clipboard
| Challenge: | Existing word embeddings have shown superior performance in numerous Natural Language Processing (NLP) tasks, however, their performances vary significantly across different tasks. |
| Approach: | They propose to combine distributed word embeddings to produce more accurate and complete meta-embeddings of words. |
| Outcome: | The proposed meta-embeddings outperform the state-of-the-art in multiple tasks. |
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. |