| Challenge: | a large dataset is required to achieve competitive performance in most natural language tasks. large datasets are expensive, time consuming, and error-prone. |
| Approach: | They propose a transfer-learning framework that leverages bilingual corpora for natural language text classification using no task-specific data. |
| Outcome: | The proposed framework can achieve good performance on formality classification and sarcasm detection tasks without any task-specific labeled data. |
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Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
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| Challenge: | despite the large amount of labeled datasets, there is an imbalance in data availability across languages. |
| Approach: | They explore cross-lingual knowledge transfer methods avoiding manual data curation . they use large multilingual encoders and translation systems, LLMs, and language adapters . |
| Outcome: | The proposed approaches are tested on three text classification tasks in Ukrainian . the authors show that the proposed approaches avoid manual data curation . |
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese (P19-1)
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| Challenge: | Text-based transfer learning techniques can be used to perform downstream tasks. |
| Approach: | They propose to use text-based transfer learning techniques to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. |
| Outcome: | The proposed model performs better than task-specific models trained on 3 times as much data and is as effective for language modeling pre-trained on 1/30 of the data. |
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks (2021.eacl-main)
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| Challenge: | a large amount of work on cross-lingual transfer learning focused on typological and genealogical similarities between languages. |
| Approach: | They propose three features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics. |
| Outcome: | The proposed features capture cross-cultural similarities manifest in linguistic patterns and quantify aspects of language pragmatics. |
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification (2023.tacl-1)
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| Challenge: | Existing approaches to cross-lingual text classification leverage text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Approach: | They propose to combine a neural machine translator and a text classifier trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Outcome: | The proposed approach significantly improves over a baseline approach. |
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features (2023.findings-emnlp)
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| Challenge: | Current knowledge is limited on whether cultural features can predict cross-cultural transfer learning success for subjective tasks. |
| Approach: | They advocate integration of cultural information into datasets and cultural adaptability . findings suggest cultural features can predict cross-cultural transfer learning success . |
| Outcome: | The findings suggest that cultural features can predict cross-cultural transfer learning success in OLD tasks. |
A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
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| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
| Outcome: | The proposed subset of the Reuters corpus has balanced class priors for eight languages. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |
Cross-lingual intent classification in a low resource industrial setting (D19-1)
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| Challenge: | Recent advances in multilingual text representations promise cross-lingual transfer for classifiers . but practical application calls for a combination of techniques for useful results . |
| Approach: | They investigate different approaches to multilingual intent classification in a low resource setting. |
| Outcome: | The proposed methods show promise, but practical application calls for a combination of techniques. |
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis (2023.emnlp-main)
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| Challenge: | Existing evidence of demographic bias in SA systems is limited to a handful of languages, and it is costly to create supervised training data in a new language. |
| Approach: | They use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer . r&r is much more prevalent than gender biase . |
| Outcome: | The proposed model is compared with monolingual systems in five languages and shows that it is biased more than monolingual ones. |
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph (2021.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual text classification require task-specific training data in high-resource sources . labeling cost, task characteristics, and privacy concerns can hinder the use of cross-linguistic training . |
| Approach: | They propose a dictionary-based heterogeneous graph (DHGNet) that uses bilingual dictionaries for task-independent word embeddings. |
| Outcome: | The proposed method outperforms pretrained models even though it does not access to large corpora. |