ViTA: Visual-Linguistic Translation by Aligning Object Tags

• URL: http://arxiv.org/abs/2106.00250v1
• Date: Tue, 1 Jun 2021 06:19:29 GMT
• Title: ViTA: Visual-Linguistic Translation by Aligning Object Tags
• Authors: Kshitij Gupta, Devansh Gautam, Radhika Mamidi
• Abstract summary: Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. We propose our system for the Multimodal Translation Task of WAT 2021 from English to Hindi.
• Score: 7.817598216459955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. It has gained popularity in recent years, and several pipelines have been proposed in the same direction. Yet, the task lacks quality datasets to illustrate the contribution of visual modality in the translation systems. In this paper, we propose our system for the Multimodal Translation Task of WAT 2021 from English to Hindi. We propose to use mBART, a pretrained multilingual sequence-to-sequence model, for the textual-only translations. Further, we bring the visual information to a textual domain by extracting object tags from the image and enhance the input for the multimodal task. We also explore the robustness of our system by systematically degrading the source text. Finally, we achieve a BLEU score of 44.6 and 51.6 on the test set and challenge set of the task.

Related papers

• AnyTrans: Translate AnyText in the Image with Large Scale Models [88.5887934499388]
  This paper introduces AnyTrans, an all-encompassing framework for the task-Translate AnyText in the Image (TATI) Our framework incorporates contextual cues from both textual and visual elements during translation. We have meticulously compiled a test dataset called MTIT6, which consists of multilingual text image translation data from six language pairs.
  arXiv  Detail & Related papers  (2024-06-17T11:37:48Z)
• Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets [3.54128607634285]
  We study the impact of the visual modality on translation efficacy by leveraging real-world translation datasets. We find that the visual modality proves advantageous for the majority of authentic translation datasets. Our results suggest that visual information serves a supplementary role in multimodal translation and can be substituted.
  arXiv  Detail & Related papers  (2024-04-09T08:19:10Z)
• Translation-Enhanced Multilingual Text-to-Image Generation [61.41730893884428]
  Research on text-to-image generation (TTI) still predominantly focuses on the English language. In this work, we thus investigate multilingual TTI and the current potential of neural machine translation (NMT) to bootstrap mTTI systems. We propose Ensemble Adapter (EnsAd), a novel parameter-efficient approach that learns to weigh and consolidate the multilingual text knowledge within the mTTI framework.
  arXiv  Detail & Related papers  (2023-05-30T17:03:52Z)
• Scene Graph as Pivoting: Inference-time Image-free Unsupervised Multimodal Machine Translation with Visual Scene Hallucination [88.74459704391214]
  In this work, we investigate a more realistic unsupervised multimodal machine translation (UMMT) setup. We represent the input images and texts with the visual and language scene graphs (SG), where such fine-grained vision-language features ensure a holistic understanding of the semantics. Several SG-pivoting based learning objectives are introduced for unsupervised translation training. Our method outperforms the best-performing baseline by significant BLEU scores on the task and setup.
  arXiv  Detail & Related papers  (2023-05-20T18:17:20Z)
• Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task [26.046624228278528]
  We propose a novel text translation enhanced text image translation, which trains the end-to-end model with text translation as an auxiliary task. By sharing model parameters and multi-task training, our model is able to take full advantage of easily-available large-scale text parallel corpus.
  arXiv  Detail & Related papers  (2022-10-08T02:35:45Z)
• Exploiting BERT For Multimodal Target SentimentClassification Through Input Space Translation [75.82110684355979]
  We introduce a two-stream model that translates images in input space using an object-aware transformer. We then leverage the translation to construct an auxiliary sentence that provides multimodal information to a language model. We achieve state-of-the-art performance on two multimodal Twitter datasets.
  arXiv  Detail & Related papers  (2021-08-03T18:02:38Z)
• FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task [36.51221186190272]
  We describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign. Our system is built by leveraging transfer learning across modalities, tasks and languages.
  arXiv  Detail & Related papers  (2021-07-14T19:43:44Z)
• UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training [52.852163987208826]
  UC2 is the first machine translation-augmented framework for cross-lingual cross-modal representation learning. We propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM) Our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
  arXiv  Detail & Related papers  (2021-04-01T08:30:53Z)
• Simultaneous Machine Translation with Visual Context [42.88121241096681]
  Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. We analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks.
  arXiv  Detail & Related papers  (2020-09-15T18:19:11Z)
• Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting [105.5303416210736]
  Unsupervised machine translation (MT) has recently achieved impressive results with monolingual corpora only. It is still challenging to associate source-target sentences in the latent space. As people speak different languages biologically share similar visual systems, the potential of achieving better alignment through visual content is promising.
  arXiv  Detail & Related papers  (2020-05-06T20:11:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.