A Multi-Modal Multilingual Benchmark for Document Image Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing document image classification datasets have several limitations and we present two new datasets that overcome these limitations. |
| Approach: | They propose to use two newly curated multilingual datasets that overcome these limitations and propose to develop multilingual Document AI models. |
| Outcome: | The proposed datasets overcome limitations in document image classification and open the door for future research into improving Document AI models. |
Similar Papers
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |
A Corpus for Multilingual Document Classification in Eight Languages (L18-1)
Copied to clipboard
| Challenge: | a subset of the Reuters corpus volume 2 is used to evaluate cross-lingual document classification . current best practice is to evaluate document classification on resources in one language and transfer it to another without additional resources. |
| Approach: | They propose to use a subset of the Reuters corpus to evaluate cross-lingual document classification . they propose to add Italian, Russian, Japanese and Chinese to the subset . |
| Outcome: | The proposed subset of the Reuters corpus has balanced class priors for eight languages. |
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer (2021.emnlp-main)
Copied to clipboard
| Challenge: | MULTI-EURLEX is a dataset for topic classification of EU legal documents . fine-tuning a multilingually pretrained model in a single source language leads to catastrophic forgetting of multilingual knowledge and poor zero-shot transfer to other languages. |
| Approach: | They propose to use the dataset as a testbed for zero-shot cross-lingual transfer to exploit annotated training documents in one language to classify documents in another language. |
| Outcome: | The proposed model can be used to classify EU legal documents in other languages without a single source language and retain multilingual knowledge. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
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. |
Rethinking Complex Neural Network Architectures for Document Classification (N19-1)
Copied to clipboard
| Challenge: | Neural network models for many NLP tasks have grown increasingly complex in recent years . authors of recent papers question the necessity of such architectures and find them quite effective . |
| Approach: | They propose to use regularization techniques borrowed from language modeling to improve model accuracy . they find that a simple biLSTM architecture with appropriate regularization yields competitive results . |
| Outcome: | a simple biLSTM model outperforms the state-of-the-art on four benchmark datasets . authors say that improvements are not real, but are attributed to mundane reasons . |
Multilingual Image Corpus – Towards a Multimodal and Multilingual Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | The goal of the project Multilingual Image Corpus is to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Approach: | They propose to provide a large image dataset with annotated objects and object descriptions in 24 languages. |
| Outcome: | The project provides a large image dataset with annotated objects and object descriptions in 24 languages. |
MultiSubs: A Large-scale Multimodal and Multilingual Dataset (2022.lrec-1)
Copied to clipboard
| Challenge: | a large-scale multimodal and multilingual dataset is used to facilitate research on visual grounding of words to images in their contextual usage in language. |
| Approach: | They propose a large-scale multimodal and multilingual dataset that aims to facilitate research on grounding words to images in their contextual usage in language. |
| Outcome: | The proposed dataset will facilitate research on visual grounding of words in their contextual usage in language. |
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual text classification leverage text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Approach: | They propose to combine a neural machine translator and a text classifier trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Outcome: | The proposed approach significantly improves over a baseline approach. |
DocHieNet: A Large and Diverse Dataset for Document Hierarchy Parsing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for document hierarchy parsing are limited due to the small scale and inconsistency of datasets. |
| Approach: | They propose a document hierarchy parsing dataset to compensate for the data scarcity problem and propose 'dHP' framework to grasp fine-grained text content and coarse-grounded pattern at layout element level. |
| Outcome: | The proposed framework grasps both fine-grained text content and coarse-grounded pattern at layout element level, enhancing the capacity of pre-trained text-layout models in handling multi-page and multi-level challenges. |