Papers with pretrained multilingual models
Zero-shot Cross-lingual Transfer With Learned Projections Using Unlabeled Target-Language Data (2023.acl-short)
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| Challenge: | Zero-shot cross-lingual transfer is enabled by pairing the language adapter in the target language with an appropriate task adapter within a source language. |
| Approach: | They propose to use unlabeled text to enhance zero-shot transfer by pairing language adapters with task adapters in a target language. |
| Outcome: | The proposed framework improves on three cross-lingual tasks with up to 11% relative improvement in Named Entity Recognition (NER), Question Answering (QA) and Natural Language Inference (NLI). |
Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing methods to make multilingual systems expensive and tedious introduce pipeline of errors. |
| Approach: | They propose to use pre-trained multilingual models to enhance the transfer learning process by intermediate fine-tuning of pretrained multi-lingual models. |
| Outcome: | The proposed approach improves on the cross-lingual dialogue state tracking task with only 10% of the target language task data and zero-shot setup respectively. |
mOthello: When Do Cross-Lingual Representation Alignment and Cross-Lingual Transfer Emerge in Multilingual Models? (2024.findings-naacl)
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| Challenge: | Pretrained multilingual models exhibit cross-lingual transfer ability, which is often attributed to a learned language-neutral representation during pretraining. |
| Approach: | They propose a synthetic task, Multilingual Othello, as a testbed to investigate the factors that contribute to the learning of a language-neutral representation. |
| Outcome: | The proposed approach induces the learning of language-neutral representation and facilitates cross-lingual transfer. |
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario (2022.findings-acl)
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| Challenge: | Recent work on phonetic transcription is reported to be the bottleneck in endangered languages . however, when a single speaker is involved, small amounts of training are needed . |
| Approach: | They compare automatic speech recognition (ASR) approaches to speaker-dependent phonetic transcription using a common dataset of 11 languages. |
| Outcome: | The proposed system handles morphologically complex languages and writing systems for which no pronunciation dictionary exists. |
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing training data is limited for languages other than English, so is the performance of the developed parsers. |
| Approach: | They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available. |
| Outcome: | The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features. |
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts (2021.emnlp-main)
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| Challenge: | Massively multilingual language models offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks, but there is a profound performance gap between resource-rich and resource-poor target languages. |
| Approach: | They propose a series of data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to low-resource languages and unseen scripts. |
| Outcome: | The proposed methods improve learning of the new dedicated embedding matrix in the target language and for low-resource languages written in unseen scripts. |
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer (2025.emnlp-main)
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| Challenge: | NN-Rank is an algorithm for ranking source languages for cross-lingual transfer . it leverages hidden representations from multilingual models and unlabeled target-language data . |
| Approach: | They propose an algorithm for ranking source languages for cross-lingual transfer which leverages hidden representations from multilingual models and unlabeled target-language data. |
| Outcome: | The proposed algorithm outperforms state-of-the-art models on in-domain data and shows that it can achieve 92.8% of the NDCG achieved using all available target data. |