Constructing a Japanese Document-Level Relation Extraction Dataset through Cross-Lingual Transfer and Human Annotation

Conceitos essenciais

This work presents the construction of JacRED, the first benchmark for general-purpose Japanese document-level relation extraction, by leveraging existing English resources and human annotation.

Resumo

This paper explores how to utilize existing English document-level relation extraction (DocRE) resources to construct resources for other languages, using Japanese as the representative case. The authors first attempt to automatically construct a Japanese DocRE dataset, called Re-DocREDja, by translating the English Re-DocRED dataset into Japanese. However, they observe that models trained on Re-DocREDja suffer from low recall when extracting relation triples from raw Japanese text, due to the discrepancies between the translated documents and those composed by native speakers.
To address this issue, the authors adopt a semi-automatic, edit-based annotation scheme, where annotators edit machine recommendations provided by a model trained on Re-DocREDja. This approach reduces the human effort by approximately 50% compared to previous methods that rely on knowledge base queries. The resulting dataset, named JacRED, is the first benchmark for general-purpose Japanese DocRE.
The authors analyze JacRED in detail and compare it to existing DocRE datasets. They find that JacRED introduces extra challenges beyond those in Re-DocRED, possibly due to the characteristics of the Japanese language. Experiments show that existing DocRE models, including large language models, perform worse on JacRED than on Re-DocRED, highlighting the difficulty of Japanese DocRE. The authors also evaluate the cross-lingual transferability of DocRE models between English and Japanese, observing challenges due to the complexity of document semantics.

Estatísticas

Documents in JacRED contain an average of 8.39 sentences and 17.87 entities.
JacRED includes an average of 21.12 relation instances and 1.67 evidence sentences per document.
Compared to models trained on the automatically constructed Re-DocREDja dataset, models trained on the human-annotated JacRED dataset achieve a 15 F1-point higher performance on the test set.

Citações

"Compared with model predictions, Wikidata provides only half as many recommendations: To reach the human annotations, 50% (1,572 out of 3,200) of the recommendations need to be revised, with another 200% instances to be added."
"All models exhibited a decreased accuracy in the target language. Different from sentence-level tasks, DocRE requires not only an understanding of individual sentences but also inter-sentence semantics within the whole document, which improves the difficulty of building cross-lingual models."

Principais Insights Extraídos De

Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer

by Youmi Ma,An ... às arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16506.pdf

Perguntas Mais Profundas

How can the insights gained from constructing JacRED be applied to build DocRE resources for other non-English languages

The insights gained from constructing JacRED can be valuable in building DocRE resources for other non-English languages. One key application is the utilization of existing English resources to promote DocRE studies in other languages. By transferring knowledge and methodologies from English datasets, researchers can adapt similar approaches to construct datasets in different languages. This includes using machine translation for automatic dataset construction, leveraging models trained on transferred datasets for human annotation assistance, and refining the dataset through human edits. Additionally, the process of reducing relation label sets to improve annotation quality can be applied to other languages to enhance dataset construction efficiency. By following a similar pipeline as in the construction of JacRED, researchers can establish DocRE datasets for various languages, contributing to the advancement of cross-lingual relation extraction research.

What are the potential challenges in developing cross-lingual DocRE models that can effectively capture the complex semantics across documents

Developing cross-lingual DocRE models that effectively capture complex semantics across documents poses several potential challenges. One major challenge is the variation in language structures and semantics across different languages. Models trained on one language may struggle to generalize well to another language due to differences in syntax, word order, and cultural nuances. Additionally, the need to understand inter-sentence relationships within a document adds complexity to cross-lingual DocRE tasks. Ensuring that models can comprehend and extract relations that span multiple sentences accurately is crucial but challenging. Furthermore, the domain-specific knowledge and context present in documents from different languages can further complicate the task of building robust cross-lingual DocRE models. Addressing these challenges requires careful consideration of language-specific characteristics, effective cross-lingual transfer techniques, and robust model architectures capable of capturing diverse linguistic patterns.

How can the JacRED dataset be leveraged to improve downstream tasks such as question answering and reading comprehension in the Japanese language

The JacRED dataset can be leveraged to improve downstream tasks such as question answering and reading comprehension in the Japanese language by serving as a high-quality training and evaluation resource. Models trained on JacRED can be fine-tuned for specific downstream tasks, utilizing the rich annotations and diverse document-level relations present in the dataset. By incorporating the knowledge and insights gained from JacRED, models can better understand the complex relationships between entities in documents, leading to improved performance in tasks like question answering and reading comprehension. Additionally, the evidence sentences annotated in JacRED can enhance the contextual understanding of relations, aiding in more accurate information extraction and reasoning in downstream applications. Overall, leveraging the JacRED dataset can enhance the performance of models in various natural language processing tasks in the Japanese language.