Probing Multi-modal Machine Translation with Pre-trained Language Model (2021.findings-acl)
Copied to clipboard
| Challenge: | Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation but recent studies showed that visual features are either negligible or incremental. |
| Approach: | They propose to incorporate a visual language model on the source side to improve multi-modal translation quality significantly. |
| Outcome: | The proposed model improves the translation quality significantly on the multi-modal dataset. |
Similar Papers
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal machine translation aims at improving translation performance by incorporating visual information. |
| Approach: | They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task. |
| Outcome: | The proposed approach achieves comparable or even better performance than state-of-the-art models. |
On Vision Features in Multimodal Machine Translation (2022.acl-long)
Copied to clipboard
| Challenge: | Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models. |
| Approach: | They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation. |
| Outcome: | The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models. |
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
Copied to clipboard
Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| 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. |
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
| Approach: | They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus. |
| Outcome: | The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions. |
Latent Variable Model for Multi-modal Translation (P19-1)
Copied to clipboard
| Challenge: | Libovick and Helcl (2017) show improvements due to imposing a constraint on the KL term to promote models with non-negligible mutual information between inputs and latent variable and training on additional target-language image descriptions. |
| Approach: | They propose to model interaction between visual and textual features for multi-modal neural machine translation (MMT) using a latent variable model. |
| Outcome: | The proposed model improves over baselines including a multi-task learning approach and a conditional variational auto-encoder approach. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing V&L pre-training methods rely on strictly-aligned multilingual image-text pairs generated from English-centric datasets. |
| Approach: | They propose a regularized cross-lingual visual contrastive learning objective that constrains representation proximity of weakly-aligned multilingual image-text pairs. |
| Outcome: | The proposed model outperforms competing models with weak zero-shot capability on 5 multi-modal tasks across 6 languages. |
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Multi-modal machine translation methods are underperforming compared to pre-trained models due to lack of triplet training data. |
| Approach: | They propose a multi-modal machine translation method that integrates images and visual modality to enhance language understanding. |
| Outcome: | The proposed method can enrich the original samples and expand the dataset without requiring external images and text. |
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
Copied to clipboard
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)
Copied to clipboard
Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling. |
| Approach: | They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks. |