Papers by Gorka Azkune
Do Multilingual Language Models Think Better in English? (2024.naacl-short)
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| Challenge: | Existing studies show that translation-test improves performance of multilingual models by translating the input into English using an external machine translation system. |
| Approach: | They propose a new approach that leverages the few-shot translation capabilities of multilingual language models. |
| Outcome: | The proposed approach outperforms direct inference on 5 tasks. |
Improving the Efficiency of Visually Augmented Language Models (2025.coling-main)
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| Challenge: | Autoregressive Language Models lack visual knowledge due to reporting bias in textual corpora. |
| Approach: | They propose to use visual representations obtained from CLIP multimodal system to augment autoregressive language models with visual knowledge. |
| Outcome: | The proposed model outperforms VALM for visual language understanding, natural language understanding and language modeling tasks despite being significantly more efficient and simpler. |
EnerGIZAr: Leveraging GIZA++ for Effective Tokenizer Initialization (2025.findings-acl)
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| Challenge: | Continual pre-training has long been considered the default strategy for adapting models to non-English languages, but struggles with initializing new embeddings, especially for non-Latin scripts. |
| Approach: | They propose a method that leverages statistical word alignment techniques to improve continual pre-training by leveraging word alignment matrix between source and target tokens. |
| Outcome: | The proposed method outperforms existing methods on key NLP tasks including POS tagging, Sentiment Analysis, NLI, and NER in Hindi, Basque, Arabic and Korean. |
Vision-Language Models Struggle to Align Entities across Modalities (2025.findings-acl)
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| Challenge: | Several real-world applications require the ability to perform cross-modal entity linking . cross-functional entity linking is a skill needed for multimodal code generation and scene understanding . |
| Approach: | They propose a task and benchmark to evaluate cross-modal entity linking performance . they use visual scenes aligned with their textual representations to evaluate performance a question-answering task . |
| Outcome: | The proposed task and benchmark aims to improve cross-modal entity linking performance . it evaluates state-of-the-art vision-language models and humans on the task . |
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning (2020.coling-main)
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| Challenge: | In most applications, users are not able to provide the correct answer to the system, but they are able provide binary (correct, incorrect) feedback. |
| Approach: | They propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. |
| Outcome: | The proposed method improves on an initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC) and matching in out-of-domain experiment (DoQA). |