Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings (N19-4)
Copied to clipboard
| Challenge: | Many queries are submitted to search engines on the Web every day to retrieve linguistic information for learning a second language (L2) due to limited L2 vocabulary knowledge, users often submit mix-coded queries without converting them into target language queries. |
| Approach: | They propose a method for learning bilingual word embeddings to support second language learners . mixed-code queries are transformed into target language queries . preliminary evaluation shows method performs reasonablly well . |
| Outcome: | The proposed method performs reasonablly well on a list of common word-translation queries. |
Similar Papers
Word Embeddings for Code-Mixed Language Processing (D18-1)
Copied to clipboard
| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging 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. |
Evaluating bilingual word embeddings on the long tail (N18-2)
Copied to clipboard
| 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 . |
Exploring Multilingual Syntactic Sentence Representations (D19-55)
Copied to clipboard
| Challenge: | Recent studies on language models that learn syntactic information focus on learning the semantic structures of language. |
| Approach: | They propose to use a multilingual parallel corpus augmented by universal part-of-speech tags to learn syntactic sentence embeddings. |
| Outcome: | The proposed method performs better than state-of-the-art language models in low-resource languages. |
A Bilingual Generative Transformer for Semantic Sentence Embedding (2020.emnlp-main)
Copied to clipboard
| Challenge: | Semantic sentence embedding models encode natural language sentences into vectors, such that closeness in embeddable space indicates closeness of semantics between the sentences. |
| Approach: | They propose a deep latent variable model that attempts to perform source separation on parallel sentences, isolating what they have in common in a latent semantic vector, and explaining what is left over with language-specific latent vectors. |
| Outcome: | The proposed model outperforms the state-of-the-art on a standard suite of unsupervised semantic similarity evaluations. |
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
Copied to clipboard
| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
| Approach: | They propose to use character-based embedding models to combine different embeddable models . they emphasize how character-driven embeddance models outperform other models on quality . |
| Outcome: | The proposed model outperforms other models on quality of extracted bilingual lexicons . comparable corpora are an interesting and practical alternative to parallel corporation . |
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs (2020.lrec-1)
Copied to clipboard
| Challenge: | Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018. |
| Approach: | They propose a dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. |
| Outcome: | The proposed bilingual lexicons have high coverage and achieve competitive translation quality for several language pairs. |
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)
Copied to clipboard
| Challenge: | Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored. |
| Approach: | They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser. |
| Outcome: | The proposed method improves on the standard multilingual GeoQuery dataset. |
Bilingual Lexicon Induction through Unsupervised Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Existing methods for bilingual lexicon induction use nearest neighbor or related retrieval methods to induce word translation pairs. |
| Approach: | They propose a method that aligns word embeddings in two languages and uses them to build a phrase-table and a language model to extract the bilingual lexicon. |
| Outcome: | The proposed method improves accuracy 6 points over nearest neighbor and 4 points over CSLS retrieval on the same cross-lingual embeddings. |
A Multi-task Approach to Learning Multilingual Representations (P18-2)
Copied to clipboard
| Challenge: | Using a multi-task model, we learn word and sentence embeddings in a single task. |
| Approach: | They propose a multi-task modeling approach that trains a skip-gram model and a cross-lingual sentence similarity model to learn word and sentence embeddings together. |
| Outcome: | The proposed model can learn word and sentence embeddings in a multilingual distributed representations of text using a cross-lingual sentence similarity model. |