Papers with cross-lingual settings
Multilingual Relation Classification via Efficient and Effective Prompting (2022.emnlp-main)
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| Challenge: | Existing work on prompt-based multilingual relation classification (RC) uses pre-trained language models with limited resources. |
| Approach: | They propose a prompt-based multilingual relation classification method that constructs relation triples from relation triple labels and requires minimal translation for the class labels. |
| Outcome: | The proposed method outperforms baselines in English-task training in cross-lingual settings and in fully supervised and few-shot scenarios. |
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations (2024.eacl-long)
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| Challenge: | Acoustic word embeddings are vector representations of spoken words . self-supervised learning (SSL)-based speech models are popular for speech recognition . |
| Approach: | They explore the effectiveness of the Correspondence Auto-Encoder to obtain improved AWEs . they use self-supervised learning on large amounts of unlabelled speech data . |
| Outcome: | The Correspondence Auto-Encoder (CAE) model outperforms MFCC models on language discrimination . the model achieves best results in Polish, Portuguese, Spanish, French, and English . |
Do LLM hallucination detectors suffer from low-resource effect? (2026.eacl-long)
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| Challenge: | a long line of work suggests that LLMs face issues along both dimensions . |
| Approach: | They investigate hallucination detectors' failure modes and their effects on the task accuracy of four LLMs and three halluciner detectors. |
| Outcome: | The models show impressive performance in high-resource languages like English but the performance degrades significantly in low-resourced languages like Bengali. |
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition (D19-1)
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| Challenge: | Existing work on name-switching focuses on word-level aspects but neglects subword-level characteristics shared across languages. |
| Approach: | They propose hierarchical meta-Embeddings that combine word-level and subword-level embeddings to create language-agnostic lexical representations. |
| Outcome: | The proposed model achieves state-of-the-art in English-Spanish code-switching scenarios. |
Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations (2025.findings-emnlp)
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| Challenge: | a study focuses on evaluating watermarking methods for the English language . the literature for evaluating cross-lingual watermarks is scarce . |
| Approach: | They evaluate representative watermarking methods in four different languages . they examine the quality of text under different watermark procedures . |
| Outcome: | The proposed method is compared with other evaluation methods in four different languages. |
Detecting and Mitigating Hallucinations in Multilingual Summarisation (2023.emnlp-main)
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| Challenge: | Existing faithfulness metrics for abstractive summarisation models focus on English . metric mFACT is best suited to detect hallucinations in cross-lingual transfer . |
| Approach: | They propose a method to evaluate the faithfulness of non-English summaries by translation-based transfer from multiple English faithfulness metrics. |
| Outcome: | The proposed method reduces hallucinations in cross-lingual transfer by weighing the loss of each training example by its faithfulness score. |
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model (2024.findings-acl)
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| Challenge: | supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language models (LLMs) to specific preferences. |
| Approach: | They propose a training-free alignment method that uses minimal prior tokens to bridge the foundation LLM and the SFT LLM. |
| Outcome: | The proposed method achieves comparable performance without training on machine translation and part-of-speech tagging across seven languages. |
Evaluating Factuality in Cross-lingual Summarization (2023.findings-acl)
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| Challenge: | Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summmarization. |
| Approach: | They propose to analyze cross-lingual factuality by collecting annotations and generated summaries from models at summary level and sentence level. |
| Outcome: | The proposed dataset shows that over 50% of generated summaries contain factual errors with different characteristics from monolingual summarization. |
SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents (2026.acl-long)
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| Challenge: | Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment . but as a standalone task, it has received little attention, a new study shows . evaluating semantic differences between documents is an underexplored challenge in natural language understanding . |
| Approach: | They introduce SwissGov-RSD, the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition. |
| Outcome: | The proposed dataset shows that current approaches perform poorly on monolingual, sentence-level and synthetic benchmarks. |