Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage (2020.acl-main)
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| Challenge: | Existing datasets have extensive labeled data for En-glish, but labeles are extremely scarce in other languages. |
| Approach: | They propose a method that leverages existing annotations with machine translation capabilities to create cross-modal language generation systems at web-scale. |
| Outcome: | The proposed model outperforms other candidates in evaluations performed over 5 target languages. |
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| Challenge: | Instruction tuning has advanced large language models (LLMs) but its application in lower-resource languages faces challenges due to the imbalanced foundational abilities of LLMs across different languages. |
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Using Visual Feature Space as a Pivot Across Languages (2020.findings-emnlp)
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| Challenge: | We show that models trained to generate textual captions in more than one language can leverage their jointly trained feature space during inference to pivot across languages. |
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
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Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation (2021.emnlp-main)
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Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
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Plug-in Language Model: Controlling Text Generation with a Simple Regression Model (2024.findings-naacl)
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| Challenge: | Large-scale pre-trained language models have demonstrated unrivaled capacity in generating text that closely resembles human-written content. |
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PROM: Pivoted and Regulated Optimization for Multilingual Instruction Learning (2025.naacl-short)
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| Challenge: | Existing solutions to large language models (LLMs) are English-centric, hindering their application to 6500+ existing languages. |
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Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment (2023.acl-long)
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| Challenge: | Current captioning models are limited to the English language due to the largescale paired image-caption datasets. |
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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-66)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
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Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task (D19-64)
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| Challenge: | Existing methods to learn multimodal multilingual embeddings for text and image retrieval tasks are limited to English. |
| Approach: | They propose a new approach to learn multimodal multilingual embeddings for matching images and captions in two languages by combing two existing objective functions and adapting alignment between existing languages. |
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