Cultural and Geographical Influences on Image Translatability of Words across Languages (2021.naacl-main)
Copied to clipboard
| Challenge: | Neural machine translation models produce poor translations when there are few/no parallel sentences to train the models. |
| Approach: | They define image translatability as the translability of words as images associated with words in different languages that have a high degree of visual similarity. |
| Outcome: | The proposed model improves upon text-only models only marginally. |
Similar Papers
Learning Translations via Images with a Massively Multilingual Image Dataset (P18-1)
Copied to clipboard
| Challenge: | Existing datasets for learning translations of words are limited to a few high-resource languages and unrealistically easy settings. |
| Approach: | They propose a large-scale multilingual corpus of images labeled with the word they represent to facilitate translation research. |
| Outcome: | The proposed method improves on an unsupervised technique that has been limited to a few languages and unrealistic settings. |
An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance (2024.emnlp-main)
Copied to clipboard
| Challenge: | a new task is to translate images to make them culturally relevant . currently, translation systems focus on translating words and images . |
| Approach: | They propose a task of translating images to make them culturally relevant . they build pipelines comprising state-of-the-art generative models to do the task . |
| Outcome: | The proposed pipelines can translate only 5% of translated images for some countries and no translation is successful for others. |
Cross-Lingual and Cross-Cultural Variation in Image Descriptions (2025.naacl-long)
Copied to clipboard
| Challenge: | Behavioural and cognitive studies report cultural effects on perception, but these are limited in scope and hard to replicate. |
| Approach: | They develop a method to accurately identify entities mentioned in captions and present in images, then measure how they vary across languages. |
| Outcome: | The proposed method corroborates previous studies showing that languages that are geographically or genetically closer mention entities more frequently than others. |
A Systematic Analysis of Subwords and Cross-Lingual Transfer in Multilingual Translation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Multilingual modelling can improve machine translation for low-resource languages, partly through shared subword representations. |
| Approach: | They propose to use subword regularisation to promote synergy and BPE to facilitate cross-lingual transfer. |
| Outcome: | The proposed methods promote synergy and prevent interference across different linguistic typologies. |
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Using a shared vocabulary is common practice in multilingual machine translation . however, when words overlap is small, e.g., using different writing systems, knowledge transfer is inhibited . |
| Approach: | They propose a re-parameterized method for building word embeddings using word equivalence classes and graph networks to fuse word embeds across languages. |
| Outcome: | The proposed method achieves evident BLEU improvements on high- and low-resource MNMT scenarios. |
Multilingual Neural Machine Translation with Language Clustering (D19-1)
Copied to clipboard
| Challenge: | Existing work on multilingual neural machine translation has been neglected due to its burdensome training process. |
| Approach: | They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster. |
| Outcome: | The proposed model reduces the cost of training and improves translation accuracy. |
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown that contextualized word representations are a viable alternative to simple word prediction tasks. |
| Approach: | They propose to use subword units and characters to model morphology, syntax, and semantics instead of word embeddings. |
| Outcome: | The proposed representations are better for modeling syntax and more robust to noisy input. |
Translation Artifacts in Cross-lingual Transfer Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing cross-lingual transfer learning techniques involve human and machine translations. |
| Approach: | They propose to use machine translation to translate test set or training set to introduce subtle artifacts that have a notable impact in existing cross-lingual models. |
| Outcome: | The proposed translation process reduces the lexical overlap between the premise and hypothesis by 4.3 and 2.8 points . the proposed translation-test and zero-shot approaches improve on previous work . |
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent research has demonstrated that large language models (LLMs) can translate cultural elements in languages such as idioms and proverbs. |
| Approach: | They propose to use large language models to translate culturally rooted proverbs in conversation and between languages with similar cultural backgrounds to compare their results. |
| Outcome: | The proposed models can achieve good translation between languages with similar cultural backgrounds and outperform NMT models in proverb translation. |
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. |