Papers with language embeddings
Contextual Parameter Generation for Universal Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to multilingual neural machine translation lack language-specific parameterization. |
| Approach: | They propose a modification to existing neural machine translation models that allows for language specific parameterization and domain adaptation. |
| Outcome: | The proposed model surpasses state-of-the-art for both the IWSLT-15 and IWSTL-17 datasets and can perform zero-shot translation. |
From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings (N18-1)
Copied to clipboard
| Challenge: | linguistic typology is the classification of languages according to their linguistic properties. |
| Approach: | They learn distributed language representations which can be used to predict typological properties on a massively multilingual scale. |
| Outcome: | The proposed model can predict typological properties on a massively multilingual scale. |
LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)
Copied to clipboard
| Challenge: | Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information. |
| Approach: | They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction. |
| Outcome: | The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks. |
UDapter: Language Adaptation for Truly Universal Dependency Parsing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Cross-language interference and restrained model capacity remain major obstacles in multilingual dependency parsing. |
| Approach: | They propose a multilingual task adaptation approach based on contextual parameter generation and adapter modules that learn adapters via language embeddings while sharing model parameters across languages. |
| Outcome: | The proposed approach outperforms strong monolingual and multilingual baselines on most languages on high-resource and low-resourced (zero-shot) languages. |
Unlocking the Planning Capabilities of Large Language Models with Maximum Diversity Fine-tuning (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
| Approach: | They propose a method which uses a random sampling method to select diverse and representative data to enhance sample efficiency and the model’s generalization capability. |
| Outcome: | The proposed method outperforms baseline methods across scales and multiple benchmark domains. |
UNQOVERing Stereotyping Biases via Underspecified Questions (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Language embeddings have been shown to have stereotyping biases, but how these biase affecting downstream question answering models remains unexplored. |
| Approach: | They propose a general framework to probe biases through underspecified questions by building minimal context and building minimal questions. |
| Outcome: | The proposed framework isolates two types of reasoning errors and identifies stereotyping biases in gender, nationality, ethnicity, and religion classes. |
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods of fine-tuning vision-language navigation models require extra human-labeled data and lack self-exploration capabilities in environments. |
| Approach: | They propose a method that can self-explore environments without human labeling . they use a large-scale cross-modal pretrained model to build an in-domain dataset . |
| Outcome: | The proposed model can self-explore environments without human labeling without human supervision and generates structured instructions without human intervention. |
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing multilingual models cannot fully leverage training data when it is available in different task-language combinations. |
| Approach: | They propose a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation. |
| Outcome: | The proposed model achieves the best or competitive gain when a mixture of multiple resources is available while being significantly more efficient than existing models. |