| Challenge: | Existing approaches to cross-lingual hypernymy detection are sparse and can be trained on related languages with negligible loss of performance. |
| Approach: | They propose a family of unsupervised approaches for cross-lingual hypernymy detection which learns sparse, bilingual word embeddings based on dependency contexts. |
| Outcome: | The proposed approach significantly improves performance on this task, compared to approaches based only on lexical context. |
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| Challenge: | Recent efforts to leverage multilingual datasets highlight potential of multilingual models that can perform well across various languages. |
| Approach: | They propose to generate language representations that capture relationships among languages and evaluate them using WALS and two extrinsic tasks. |
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Hypernymy Detection for Low-Resource Languages via Meta Learning (2020.acl-main)
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| Challenge: | Existing studies focus on monolingual hypernymy detection on high-resource languages, but few investigate low-resourced scenarios. |
| Approach: | They propose to combine high-resource languages to solve low-resourced hypernymy detection problem . they extensively compare three joint training paradigms and propose meta learning . |
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A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
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| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
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Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring (2021.acl-long)
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| Challenge: | Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embedders. |
| Approach: | They propose an unsupervised mapping approach that fixes fixed embeddings and learns new ones for the source language that are aligned with them. |
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A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020.lrec-1)
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| Challenge: | Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping . |
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Unsupervised Cross-Lingual Representation Learning (P19-4)
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
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Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples (2025.findings-emnlp)
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| Challenge: | Cross-Lingual Semantic Discrimination (CLSD) is a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
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Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes (2021.emnlp-main)
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| Challenge: | Existing studies on whether multilingual embeddings can be aligned in a shared space across languages are lacking. |
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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. |
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Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning (2024.lrec-main)
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| Challenge: | Low-resource languages and computational expenses pose significant challenges in the domain of large language models. |
| Approach: | They propose a novel approach that uses adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models. |
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