Challenge: Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach: They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome: The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

Similar Papers

Prix-LM: Pretraining for Multilingual Knowledge Base Construction (2022.acl-long)

Copied to clipboard

Challenge: Existing methods to build and enrich multilingual knowledge bases have not been successful . knowledge expressed in different languages may be complementary and unequally distributed .
Approach: They propose a model that integrates useful multilingual and KB-based factual knowledge into a single model.
Outcome: The proposed model can provide richer combined knowledge than monolingual KBs.
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (2021.acl-short)

Copied to clipboard

Challenge: Existing work on transferring domain-specific knowledge from a pretraining model to a resource-poor language is limited to English . a novel cross-lingual biomedical entity linking task is proposed to improve this capability.
Approach: They propose a cross-lingual biomedical entity linking task and establish a new benchmark spanning 10 typologically diverse languages.
Outcome: The proposed methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language and without any parallel data.
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

Copied to clipboard

Challenge: Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach: They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome: The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.
Can Language Models be Biomedical Knowledge Bases? (2021.emnlp-main)

Copied to clipboard

Challenge: Existing studies have focused on probing LMs in the general domain but little attention has been given to whether they can be used as domain knowledge bases.
Approach: They propose to use 49K biomedical factual knowledge triples to probe LMs for biomedically . they find that biomedic LM can achieve up to 18.51% Acc@5 on retrieving biomedcial knowledge.
Outcome: The proposed biomedical factual knowledge probing benchmark achieves 18.51% Acc@5 on biomedically-relevant knowledge retrieval.
Multilingual BERT Post-Pretraining Alignment (2021.naacl-main)

Copied to clipboard

Challenge: Recent work improves on the success of monolingual pretrained language models by adding cross-lingual tasks that always involve English.
Approach: They propose a method to align multilingual contextual embeddings as a post-pretraining step for improved cross-lingual transferability of pretrained language models.
Outcome: The proposed model outperforms XLM-R_Base on translation-train tasks while using less parallel data and fewer parameters.
NeighXLM: Enhancing Cross-Lingual Transfer in Low-Resource Languages via Neighbor-Augmented Contrastive Pretraining (2025.findings-emnlp)

Copied to clipboard

Challenge: NeighXLM is a neighbor-augmented contrastive pretraining framework . it exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs.
Approach: They propose a neighbor-augmented contrastive pretraining framework that mines semantic neighbors from unlabeled corpora to enrich target-language supervision.
Outcome: The proposed framework enriches target-language supervision by mining semantic neighbors from unlabeled corpora.
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain (2023.acl-long)

Copied to clipboard

Challenge: Increasing number of NLP benchmarks highlight need for multilingual models for job-related tasks.
Approach: They introduce a language model called ESCOXLM-R that uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations taxonomy.
Outcome: The proposed model outperforms XLM-R-large on short spans and entity-level and surface-level span-F1 tasks on entity- and surface level.
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)

Copied to clipboard

Challenge: Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors.
Approach: They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems.
Outcome: The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks.
Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of Biomedical Adaptation (2026.eacl-long)

Copied to clipboard

Challenge: Multilingual domain adaptation (ML-DA) enables large language models to acquire domain knowledge across languages.
Approach: They propose an adaptive evaluation method that constructs multiple-choice QA datasets from the same bilingual domain corpus used for training.
Outcome: The proposed method constructs multiple-choice QA datasets from the same bilingual domain corpus used for training, thereby enabling direct analysis of multilingual knowledge acquisition.
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)

Copied to clipboard

Challenge: Pre-trained language models have been shown to improve performance in many natural language tasks.
Approach: They propose to combine cross-lingual and visual pre-training to learn visually-grounded cross-linguistic representations using masked region classification and three-way parallel vision & language corpora.
Outcome: The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations