Papers with cross-lingual
Transferability of Syntax-Aware Graph Neural Networks in Zero-Shot Cross-Lingual Semantic Role Labeling (2024.findings-emnlp)
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| Challenge: | Existing studies in cross-lingual semantic role labeling (SRL) lack a comprehensive analysis of their network selection. |
| Approach: | They compare the transferability of graph neural network-based models with universal dependency trees to English and 23 target languages. |
| Outcome: | The proposed models perform better in resource-poor languages than in resource rich ones. |
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)
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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt
| Challenge: | Large language models excel at generating English counterfactuals but their effectiveness in generating multilingual counterfacts remains unclear. |
| Approach: | They conduct automatic evaluations on both directly generated and derived counterfactuals in six languages and find that cross-lingual perturbations follow common strategic principles. |
| Outcome: | The proposed models show that translation-based counterfactuals offer higher validity than their directly generated counterparts, but still fall short of matching the quality of the original English counterf actuals. |
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models for named entity recognition only consider the potential transferability between two identical tasks across both domains. |
| Approach: | They propose to use a similarity metric model to improve cross-lingual named entity recognition task on target domain. |
| Outcome: | Empirical studies on 7 different languages confirm the effectiveness of the proposed model. |
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)
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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei
| Challenge: | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. |
| Approach: | They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model. |
| Outcome: | The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance. |
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
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| Challenge: | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities. |
| Approach: | They propose a bilingual word embedding mapping approach for cross-lingual RE model transfer . they use a small bilingual dictionary with only 1K word pairs to embed word pairs . |
| Outcome: | The proposed approach achieves very good performance on target and target languages . it uses bilingual word embedding mapping to transfer a source-language model . |
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing (2021.findings-acl)
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| Challenge: | Existing work on meaning representations for English and other languages finds that concepts in their predicted AMR graphs are less specific. |
| Approach: | They propose a cross-lingual AMR parser that can predict more precise concepts by translating translated texts and non-English texts. |
| Outcome: | The proposed model surpasses state-of-the-art parser by 10.6 points on Smatch F1 score. |
XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
Cross-Lingual Summarization with Pseudo-Label Regularization (2024.findings-naacl)
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| Challenge: | Existing approaches to cross-lingual summarization use only a single reference, resulting in an underrepresented hypothesis space. |
| Approach: | They propose to use pseudo-labels to regularize cross-lingual summarization training by combining a single reference and a network to perform the model training. |
| Outcome: | The proposed approach significantly improves over gold reference training in XLS with 8 languages from different families. |
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship (2022.lrec-1)
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Temuulen Khishigsuren, Gábor Bella, Khuyagbaatar Batsuren, Abed Alhakim Freihat, Nandu Chandran Nair, Amarsanaa Ganbold, Hadi Khalilia, Yamini Chandrashekar, Fausto Giunchiglia
| Challenge: | a method to enrich lexical resources with content relating to linguistic diversity is proposed . Typology-based approaches are being used to improve cross-lingual NLP tasks . |
| Approach: | They propose a method to enrich lexical resources with content relating to linguistic diversity based on lexica. |
| Outcome: | The proposed method can be used to improve cross-lingual NLP tasks by removing the need for parallel textual corpora or cross-linguistic transfer from high-to-low-resourced languages. |
Alignment-free Cross-lingual Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Existing approaches to semantic role labeling rely on word alignments, translation engines or preprocessing tools. |
| Approach: | They propose a cross-lingual semantic role labeling model which only requires annotations in a source language and access to raw text in . |
| Outcome: | The proposed model minimizes the effort required to construct annotations or models for a new target language. |
Genre as Weak Supervision for Cross-lingual Dependency Parsing (2021.emnlp-main)
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| Challenge: | Recent work shows that monolingual masked language models learn to represent data-driven notions of language variation. |
| Approach: | They harness genre metadata as a weak supervision signal for targeted data selection in zero-shot dependency parsing. |
| Outcome: | The proposed method outperforms baseline and embedding-based methods for 12 low-resource language treebanks and three of these target languages. |
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. |
| Outcome: | The proposed framework outperforms competing models on four multilingual datasets. |