Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation (D19-1)
Copied to clipboard
| Challenge: | Existing work characterizes differences in meaning between words across languages using semantic relations . however, because of translation ambiguity, semantic relations are not always preserved by translation. |
| Approach: | They propose a cross-lingual relation classifier trained only with English examples and a bilingual dictionary to account for translation ambiguity when transferring knowledge from English to cross-linguistic settings. |
| Outcome: | The proposed model outperforms baselines that rely on bilingual embeddings or dictionaries for cross-lingual transfer and approaches fully supervised systems on English tasks. |
Similar Papers
Distilling Linguistic Context for Language Model Compression (2021.emnlp-main)
Copied to clipboard
| Challenge: | Knowledge distillation is a major technique for deploying vast language models in resource-strapped environments. |
| Approach: | They propose a method that transfers contextual knowledge via Word Relation and Layer Transforming Relation. |
| Outcome: | The proposed method is able to transfer contextual knowledge without restrictions on architectural changes between teacher and student on language understanding tasks. |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent? (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain factuality of model knowledge across languages, and preserve parity of language model performance. |
| Approach: | They examine pretrained and tuned models with code-mixed coreferential statements that convey identical knowledge across languages. |
| Outcome: | The proposed model shows different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and bottlenecks on a particular layer. |
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Current approaches for relation classification are focused on the English language and require lots of training data with human annotations. |
| Approach: | They propose a baseline model based on Multilingual BERT and a new multilingual pretraining setup . they propose 'relationship classification' models that use distant supervision . |
| Outcome: | The proposed model significantly improves the baseline model with distant supervision. |
Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
Copied to clipboard
| 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 . |
Cross-lingual Text Classification Transfer: The Case of Ukrainian (2025.coling-main)
Copied to clipboard
| 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 . |
Unsupervised Cross-Lingual Representation Learning (P19-4)
Copied to clipboard
| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
| Approach: | This tutorial provides a comprehensive survey of recent work on weakly-supervised and unsupervised cross-lingual word representations. |
| Outcome: | This tutorial provides a comprehensive survey of cutting-edge weakly-supervised and unsupervised word representations. |
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to crosslingual Relation and Event Extraction (REE) suffer from monolingual bias due to training of models on source language data. |
| Approach: | They propose to use unlabeled data in target language to aid alignment of crosslingual representations by fooling a language discriminator. |
| Outcome: | The proposed method significantly advances the state-of-the-art in crosslingual REE tasks. |
When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent studies have found that Large Language Models process multilingual inputs in shared concept spaces, thought to support generalization and cross-lingual transfer. |
| Approach: | They investigate the development of language-agnostic concept spaces during pretraining of EuroLLM using the causal interpretability method of activation patching. |
| Outcome: | The proposed model is language-agnostic and enables cross-lingual transfer . the model is able to process multilingual inputs, but lacks cross-linguistic alignment . |
Unsupervised Cross-lingual Transfer of Word Embedding Spaces (D18-1)
Copied to clipboard
| Challenge: | Existing methods for cross-lingual word mapping require cross-linguistic supervision, but this is not available for many low resource languages. |
| Approach: | They propose an unsupervised method that learns transformation functions over corresponding word embedding spaces using a distributed distributional matching algorithm. |
| Outcome: | The proposed method performs better on bilingual lexicon induction and cross-lingual word similarity prediction datasets than other supervised and unsupervised methods. |