| Challenge: | Existing methods for word embedding evaluation are computationally expensive and task-specific. |
| Approach: | They propose a minimally supervised method for generating word embedding evaluation datasets for a large number of languages using existing dependency treebanks and parsers. |
| Outcome: | The proposed method evaluates three popular word embedding algorithms against these datasets and shows that their performance varies between syntactic categories. |
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Multilingual Culture-Independent Word Analogy Datasets (2020.lrec-1)
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| Challenge: | In text processing, deep neural networks use word embeddings as an input. |
| Approach: | They propose to use benchmark datasets to compare the quality of word embeddings in text processing . they use a word analogy task in Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish . |
| Outcome: | The proposed datasets are culturally independent and cross-lingual for the languages used. |
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| 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. |
IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set (2022.lrec-1)
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| Challenge: | a new test set that measures word embeddings' ability to recognize linguistic regularities is presented in a paper in elijsson, iran . the test sets are a good quality estimator for extrinsic evaluation of word embedded models . |
| Approach: | They propose a test set that measures language models' ability to recognize linguistic regularities in a balanced way. |
| Outcome: | The proposed set is apt at measuring the capabilities of word embedding models. |
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| 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 . |
Language Embeddings for Typology and Cross-lingual Transfer Learning (2021.acl-long)
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
| Outcome: | The proposed model can be leveraged in cross-lingual tasks without parallel data . the proposed model is based on the World Atlas of Language Structures (WALS) and two extrinsic tasks . |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
A Simple Approach to Learning Unsupervised Multilingual Embeddings (2020.emnlp-main)
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| 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. |
Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
| Outcome: | The proposed method improves on training with multiple treebanks for a single language. |
Language Agnostic Code Embeddings (2024.naacl-long)
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| Challenge: | Recent studies show that code language models have strong cross-lingual traits, but their multilingual representations can be dissected into a language-specific syntax component and a semantic component. |
| Approach: | They propose to isolate and eliminate language-specific components from multilingual code embeddings to improve downstream code retrieval tasks. |
| Outcome: | The proposed model improves retrieval tasks by removing language-specific components . the proposed model can be used to perform a variety of code generation tasks . |
Word Embeddings for Code-Mixed Language Processing (D18-1)
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| 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. |