| 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. |
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Cross-lingual Sentence Embedding using Multi-Task Learning (2021.emnlp-main)
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| Challenge: | Existing multilingual sentence embedding models require large parallel corpora to learn efficiently, limiting their scope. |
| Approach: | They propose a sentence embedding framework based on an unsupervised loss function . they capture semantic similarity and relatedness between sentences using a multi-task loss function. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on STS, BUCC and Tatoeba benchmarks and on a monolingual benchmark. |
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. |
Exploring Multilingual Syntactic Sentence Representations (D19-55)
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| 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. |
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 . |
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. |
Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
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| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
| Approach: | They propose to use multilingual masked language modeling to train a model on concatenated text from multiple languages to find universal latent symmetries in embedding spaces. |
| Outcome: | The proposed models can be trained on concatenated text from multiple languages without shared vocabulary or domain similarity. |
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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| Challenge: | obtaining document embeddings at document level is challenging due to computational requirements and lack of appropriate data. |
| Approach: | They compare methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. |
| Outcome: | The proposed methods produce document-level representations from sentences in 8 languages . the results show that a clever combination of sentence embeddings is usually better than encoding the full document as a single unit. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-66)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
| Outcome: | The proposed model achieves state-of-the-art in retrieval and caption-caption tasks while adapting existing language alignments. |
Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)
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Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, Ray Kurzweil
| Challenge: | Using a multi-task trained dual-encoder, our models embed text from 16 languages into a shared semantic space. |
| Approach: | They propose retrieval focused multilingual sentence embedding models on TensorFlow Hub. |
| Outcome: | The models achieve state-of-the-art on monolingual and cross-lingual retrieval (SR) and retrieval question answering (ReQA) competitive performance is obtained on related tasks of translation pair bitext retrieval and retrieving question answering. |