| 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. |
Similar Papers
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. |
Multimodal Neural Machine Translation Using Synthetic Images Transformed by Latent Diffusion Model (2023.acl-srw)
Copied to clipboard
| Challenge: | Existing methods to translate source language sentences using images are not optimal for machine translation. |
| Approach: | They propose a new multimodal neural machine translation model using synthetic images transformed by a latent diffusion model. |
| Outcome: | The proposed model improves translation performance on English-German translation tasks using the Multi30k dataset. |
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. |
A Stochastic Decoder for Neural Machine Translation (P18-1)
Copied to clipboard
| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
| Outcome: | The proposed model consistently improves over strong baselines on several different language pairs. |
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. |
Probing the Need for Visual Context in Multimodal Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Current work on multimodal machine translation (MMT) suggests that the visual modality is either unnecessary or only marginally beneficial. |
| Approach: | They propose to use the visual modality to combine visual and textual information to generate better translations by partially depriving models from source-side textual context. |
| Outcome: | The proposed model can combine visual and textual information to generate better translations under limited textual context. |
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. |
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies report improvements when equipping models with multimodal information, but it remains unclear whether such improvements actually come from the multimodal part. |
| Approach: | They propose to extend conventional text-only translation models with multimodal information by extending them with visual input. |
| Outcome: | The proposed models replicate similar gains as recently developed multimodal-integrated systems achieved, but learn to ignore multimodal information. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
Latent-Variable Generative Models for Data-Efficient Text Classification (D19-1)
Copied to clipboard
| Challenge: | Generative classifiers offer potential advantages over discriminative classifications, including data efficiency and zero-shot learning. |
| Approach: | They introduce discrete latent variables into generative story to improve classifiers' performance . they empirically characterize performance of their models on six text classification datasets . |
| Outcome: | The proposed model outperforms discriminative and generative classifiers on six text classification datasets. |