| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
| Approach: | They propose a novel approach which leverages labeled data in one language to identify relative perspective of a document with respect to a claim in a different target language. |
| Outcome: | The proposed approach can deal with the challenge of limited labeled data in the target language. |
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Cross-Domain Label-Adaptive Stance Detection (2021.emnlp-main)
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
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A Reinforcement Learning Framework for Cross-Lingual Stance Detection Using Chain-of-Thought Alignment (2025.findings-acl)
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| Challenge: | Existing approaches to cross-lingual stance detection can't effectively perform cross-linguistic transfer of complex reasoning processes. |
| Approach: | They propose a framework to facilitate cross-lingual transfer of complex reasoning processes in stance detection by using cross-linguistic Chain-of-Thought alignment to obtain high-quality CoTs generated from target language inputs. |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus (2020.lrec-1)
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| Challenge: | stance detection is a method to determine the attitude of a text with respect to a specific topic or claim. |
| Approach: | They propose a multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages. |
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Cross-Lingual Event Detection via Optimized Adversarial Training (2022.naacl-main)
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| Challenge: | Recent work in this area has harnessed the language-invariant qualities of pre-trained Multi-lingual Language Models. |
| Approach: | They propose to use adversarial language adaptation to train a model to detect events in a target language. |
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Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context. |
| Approach: | They propose a method where representations are augmented with additional context to bridge the gap between languages while enriching contextual information to facilitate ED. |
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Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (2023.emnlp-main)
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| Challenge: | Existing studies on stance detection were conducted mainly in English due to the low-resource problem in most non-English languages. |
| Approach: | They propose to use a cross-lingual teacher and a teacher to transfer knowledge from source to target language to bridge the discrepancy between languages. |
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Cross-lingual Spoken Language Understanding with Regularized Representation Alignment (2020.emnlp-main)
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| Challenge: | despite promising results, current cross-lingual models suffer from imperfect cross-linguistic representation alignments between the source and target languages, which makes the performance sub-optimal. |
| Approach: | They propose a regularization approach to align word-level and sentence-level representations across languages without external resources. |
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Stance Detection with Hierarchical Attention Network (C18-1)
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| Challenge: | Recent studies have focused on document-level opinion mining, but linguistic information is correlated with the stance of the document. |
| Approach: | They propose a hierarchical attention neural model to employ various linguistic information to construct the document representation. |
| Outcome: | The proposed model can detect stance of documents on two datasets. |