English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning (D19-52)
Copied to clipboard
| Challenge: | Multi-modal translation is an emerging task of the MT community, where visual features of image combine with textual features of parallel source-target text to translate sentences. |
| Approach: | They propose to use convolutional neural net-works and visual geometry to extract image features and attention-based Neural MachineTranslation (NMT) system for translation. |
| Outcome: | The proposed multi-modal translation system improves translation quality and improves the quality of the captions of the images. |
Similar Papers
Translation-Enhanced Multilingual Text-to-Image Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing models for text-to-image generation are mostly based on the English language due to the lack of annotated image-caption data in other languages. |
| Approach: | They propose to use a multilingual multi-modal encoder to bootstrap mTTI systems that can be translated into other languages. |
| Outcome: | The proposed approach mitigates the language gap and improves on standard mTTI datasets. |
LTRC-MT Simple & Effective Hindi-English Neural Machine Translation Systems at WAT 2019 (D19-52)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) is a promising approach for low resource languages. |
| Approach: | They propose to use both Recurrent Neural Networks & Transformer architectures to train NMT models. |
| Outcome: | The proposed model outperforms Statistical Machine Translation (SMT) techniques on a low resource Hindi-English language pair. |
Linguistically Informed Hindi-English Neural Machine Translation (2020.lrec-1)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) is a promising approach to machine translation . lack of parallel training data for Hindi-English is limiting . |
| Approach: | They propose to incorporate linguistic knowledge encoded by Hindi phenomena into a Transformer model to improve the translation performance. |
| Outcome: | The proposed model incorporates linguistic features to improve the translation performance. |
CVIT’s submissions to WAT-2019 (D19-52)
Copied to clipboard
| Challenge: | In this paper, we explore multiway-models for Indian languages. |
| Approach: | They propose to use a Transformer architecture to experiment with multilingual models and methods for low-resource languages. |
| Outcome: | The proposed system is feasible in low-resource languages. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
Copied to clipboard
| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
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. |
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
| Approach: | They propose a technique called Unified Transliteration and Subword Segmentation to leverage language similarity while exploiting parallel data from related languages. |
| Outcome: | The proposed approach improves translation accuracy by 5 BLEU points over the standard Transformer-based NMT models. |
WAT2019: English-Hindi Translation on Hindi Visual Genome Dataset (D19-52)
Copied to clipboard
| Challenge: | A multimodal translation is a task of translating a source language to a target language . a parallel text corpus and images are used to represent the contextual details of the text . |
| Approach: | They compare a multimodal approach to a parallel text corpus and image caption generation approach to translate text in English to Hindi as a part of WAT2019 shared task. |
| Outcome: | The proposed approach improves the translation of English to Hindi in three tasks . the proposed system is based on a neural network and can be used in healthcare, government, disaster management, etc. |
Idiap NMT System for WAT 2019 Multimodal Translation Task (D19-52)
Copied to clipboard
| Challenge: | In the past few decades, multi-modality has received critical attention in translation studies, although the benefit of visual modality in machine translation is still in debate. |
| Approach: | They propose to use the Transformer model and IITB English-Hindi parallel corpus as additional data sources for the evaluation and challenge test sets. |
| Outcome: | The proposed system outperforms systems that consider visual information in the English-Hindi Multi-Modal Translation task. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |