Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (N19-1)
Copied to clipboard
| Challenge: | Unsupervised BWE methods are evaluated on word translation or word similarity tasks. |
| Approach: | They propose a method that learns sentiment-specific word representations for two languages in a common space without cross-lingual supervision. |
| Outcome: | The proposed method outperforms previous unsupervised BWE methods and even supervised Bwe methods on three language pairs for cross-lingual sentiment analysis. |
Similar Papers
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to sentiment analysis in low-resource languages lack annotated corpora or do not capture sentiment information. |
| Approach: | They propose a model that represents sentiment in a source and target language without annotated corpus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four out of six setups and captures complementary information to machine translation. |
Unsupervised Joint Training of Bilingual Word Embeddings (P19-1)
Copied to clipboard
| Challenge: | Existing methods for unsupervised bilingual word embeddings are limited by the dissimilarity between the word embedded spaces. |
| Approach: | They propose a method that trains unsupervised bilingual word embeddings jointly on parallel data generated through unsupervised machine translation. |
| Outcome: | The proposed method outperforms unsupervised mapped bilingual word embeddings in cross-lingual NLP tasks. |
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. |
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. |
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. |
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
| Approach: | They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words. |
| Outcome: | The proposed method can produce domain-common embeddings and domain-specific embedds. |
Unsupervised Cross-lingual Transfer of Word Embedding Spaces (D18-1)
Copied to clipboard
| Challenge: | Existing methods for cross-lingual word mapping require cross-linguistic supervision, but this is not available for many low resource languages. |
| Approach: | They propose an unsupervised method that learns transformation functions over corresponding word embedding spaces using a distributed distributional matching algorithm. |
| Outcome: | The proposed method performs better on bilingual lexicon induction and cross-lingual word similarity prediction datasets than other supervised and unsupervised methods. |
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. |
Unsupervised Multilingual Word Embeddings (D18-1)
Copied to clipboard
| Challenge: | Prior art for learning UMWEs relies on a number of independently trained UBWEs to obtain multilingual embeddings. |
| Approach: | They propose a fully unsupervised framework that exploits the relations between all language pairs to learn multilingual embeddings without cross-lingual supervision. |
| Outcome: | The proposed framework outperforms supervised approaches on multilingual word translation and cross-lingual word similarity and beats a number of other approaches trained with cross-linguistic resources. |
CLUSE: Cross-Lingual Unsupervised Sense Embeddings (D18-1)
Copied to clipboard
| Challenge: | Existing models for learning bilingual sense embeddings that encode semantics are weak for recognizing multi-sense word representations . elucidation of word embeddables is difficult because they do not allow a word to have different meanings in different contexts. |
| Approach: | They propose a sense induction and representation learning model that learns bilingual sense embeddings that align well in the vector space. |
| Outcome: | The proposed model shows that the learned embeddings are aligned well in the vector space. |