Predicting Plurality and Definiteness in Chinese Noun Phrases

Conceitos essenciais

The author explores the predictability of plurality and definiteness in Chinese noun phrases through computational models, highlighting the frequent omission of markers. The study aims to understand how context influences the interpretation of these linguistic features.

Resumo

The study delves into the predictability of plurality and definiteness in Chinese noun phrases by analyzing a corpus with labels for singularity/plurality and definiteness/indefiniteness. Various models, including classic machine learning and pre-trained language models, were trained to predict these linguistic features based on context. Results show that BERT-wwm performed best, emphasizing the importance of context in understanding these linguistic aspects.
The research also assessed the impact of context size on model performance, revealing that wider contexts did not necessarily improve predictions. Additionally, joint prediction of plurality and definiteness yielded better results than separate binary predictions. The study highlighted potential biases in datasets and pre-trained language models while acknowledging limitations in generalizing findings across different genres.

Estatísticas

train: 79158 singular, 24528 plural, 48471 definite, 55215 indefinite
dev: 7894 singular, 2474 plural, 4777 definite, 5591 indefinite
test: 7925 singular, 2444 plural, 4844 definite, 5525 indefinite

Citações

"Understanding sentences in 'cooler' languages requires more work from readers."
"Chinese speakers frequently drop plural and definiteness markers."

Principais Insights Extraídos De

Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases

by Yuqi Liu,Gua... às arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04376.pdf

Perguntas Mais Profundas

How does the predictability of plurality and definiteness impact natural language understanding?

The predictability of plurality and definiteness in Chinese noun phrases plays a crucial role in enhancing natural language understanding. By investigating the omission of markers for these linguistic features, computational models can learn to infer the intended meaning based on context. This ability mirrors how speakers of "cool" languages like Chinese rely on context to interpret phrases with omitted markers. The findings suggest that listeners can understand sentences even when explicit markers are missing, showcasing the importance of contextual cues in language comprehension.

What are the implications of biases in datasets and pre-trained language models on computational linguistics research?

Biases present in datasets used for training computational models can significantly impact research outcomes in computational linguistics. If a dataset contains biased or toxic content, it may lead to skewed results and inaccurate predictions by the model. Similarly, pre-trained language models that have not been properly vetted for biases could perpetuate stereotypes or misinformation when applied to various tasks. These biases pose ethical concerns and highlight the need for thorough data curation and bias mitigation strategies in computational linguistics research.

How can computational models be improved to better capture linguistic nuances beyond explicit expressions?

To enhance their ability to capture linguistic nuances beyond explicit expressions, computational models can benefit from incorporating wider contexts during training. By feeding models with more extensive contextual information, they can better grasp subtle meanings conveyed through implicit cues within text. Additionally, leveraging multi-task learning approaches where multiple linguistic features are predicted simultaneously could improve overall performance by allowing models to learn interdependencies between different aspects of language structure. Furthermore, continual evaluation and refinement based on human assessments will help ensure that these models accurately reflect real-world linguistic complexities.