Challenge: Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages.
Approach: They propose to use Unicoder to train models in one language and apply it to other languages.
Outcome: Experiments show that Unicoder learns the mappings among different languages from more perspectives.

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UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)

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Challenge: Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin.
Approach: They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages.
Outcome: The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions .
Multilingual Universal Sentence Encoder for Semantic Retrieval (2020.acl-demos)

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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.
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)

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Challenge: Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English.
Approach: They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach .
Outcome: The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)

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Challenge: XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios .
Approach: They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora.
Outcome: The proposed dataset is labeled in English and includes only natural language understanding tasks.
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation (2021.acl-long)

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Challenge: Existing work in multilingual pretraining relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages.
Approach: They propose to plug a cross-attention module into a Transformer encoder to explicitly build the interdependence between languages.
Outcome: The proposed model outperforms existing models on XTREME and English-to-French translation datasets.
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)

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Challenge: State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages.
Approach: They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules.
Outcome: The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules.
Unsupervised Cross-lingual Representation Learning at Scale (2020.acl-main)

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Challenge: Pretraining multilingual language models at scale leads to performance gains for cross-lingual transfer tasks.
Approach: They present a transformer-based multilingual masked language model pre-trained on 100 languages . they show that pretraining multilingual models at scale leads to significant performance gains .
Outcome: The proposed model outperforms multilingual BERT (mBERT) on cross-lingual benchmarks.
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)

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Challenge: Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources .
Approach: They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks.
Outcome: The proposed method reduces the size of the extracted representations by a factor of 16 to store them for later use.
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)

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Challenge: ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive .
Approach: They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora .
Outcome: The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost.
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.

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