Supervised Fine-Tuning of Large Language Models for Homonym Sense Disambiguation in the Georgian Language

Conceitos Básicos

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Georgian language, based on supervised fine-tuning of pre-trained Large Language Models (LLMs) and recurrent neural networks.

Resumo

This research addresses the challenge of accurately disambiguating homonyms in the Georgian language, an agglutinative language belonging to the Kartvelian language family. The authors propose two approaches:

Transformer models:

Fine-tuning a pre-trained Georgian Language Model based on DistilBERT-base-uncased architecture using Masked Language Modeling.
Utilizing the pre-trained Georgian Language Model for text classification, where sentences are labeled according to the definitions of the homonyms.

Recurrent Neural Networks:

Employing Long Short-Term Memory (LSTM) networks with custom word embeddings trained on the CC100 dataset for the Georgian language.

The authors created a dataset of over 7,500 hand-classified sentences containing the homonym "ბარი" (transliteration: "bari") with three common definitions: "Shovel," "Lowland," and "Cafe." This dataset was used to train and evaluate the proposed models.
The results show that both the transformer-based and LSTM-based models achieved an accuracy of around 95% in predicting the lexical meanings of the homonym. The authors also experimented with modern chatbots, such as ChatGPT and Bard, but found that they currently lack the capability to understand the Georgian language well enough for homonym disambiguation.
The authors emphasize the potential for generalizing this approach to other homonyms in the Georgian language by obtaining and classifying additional sentences. They also discuss strategies for scaling up the number of homonym classes and leveraging larger Georgian language models as more data becomes available.
The dataset, model implementations, and testing codes will be made publicly available, serving as a benchmark for evaluating progress in the WSD task for the Georgian language.

Estatísticas

The dataset comprises over 7,500 hand-classified sentences containing the homonym "ბარი" (transliteration: "bari") with three common definitions: "Shovel," "Lowland," and "Cafe".

Citações

"Accurately disambiguating homonyms is crucial in natural language processing, especially for tasks like semantic analysis."
"To address this issue, we propose a novel approach to the WSD task based on fine-tuning a pre-trained Large Language Model (LLM) to obtain a classifier for words with multiple senses, as well as a much lighter recurrent neural network model in terms of memory requirements."
"The techniques discussed in the article achieve 95% accuracy for predicting lexical meanings of homonyms using a hand-classified dataset of over 7500 sentences."

Principais Insights Extraídos De

Homonym Sense Disambiguation in the Georgian Language

by Davit Meliki... às arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00710.pdf

Perguntas Mais Profundas

How can the proposed approaches be extended to handle a larger number of homonyms in the Georgian language?

To extend the proposed approaches to handle a larger number of homonyms in the Georgian language, several strategies can be implemented. Firstly, by expanding the dataset to include more sentences containing a variety of homonyms, the models can be trained on a more diverse set of examples. This would involve sourcing additional text data, filtering it for relevant homonyms, and manually classifying the sentences to create a comprehensive dataset.
Moreover, the models can be modified to accommodate a broader range of homonyms by adjusting the classification mechanism. Instead of focusing on a single homonym like "ბარი," the models can be generalized to work with multiple homonyms simultaneously. This would involve creating separate classifiers for each homonym and fine-tuning the models to distinguish between the various meanings of each homonym.
Additionally, leveraging more advanced transformer models or exploring ensemble learning techniques could enhance the models' ability to handle a larger number of homonyms. By combining the strengths of different models or incorporating more sophisticated architectures, the disambiguation task can be scaled up to encompass a wider range of homonyms in the Georgian language.

What are the potential challenges in scaling up the dataset and models to cover a broader range of homonyms?

Scaling up the dataset and models to cover a broader range of homonyms in the Georgian language may pose several challenges. One significant challenge is the availability of annotated data for training the models. As the number of homonyms increases, the need for manually labeled examples also grows, requiring substantial human effort and time to create a comprehensive dataset.
Furthermore, the imbalance in the distribution of homonyms and their meanings within the dataset can lead to biased models. As more homonyms are included, ensuring a proportional representation of each homonym and its senses becomes increasingly complex. Addressing this imbalance while maintaining the quality and diversity of the dataset is crucial for the models' performance.
Moreover, the computational resources required to train and fine-tune models on a larger dataset can be substantial. As the dataset size increases, so does the memory and processing power needed to train sophisticated transformer models effectively. Balancing the computational demands with the scalability of the models presents a practical challenge in scaling up the dataset and models for a broader range of homonyms.

How can the insights from this research be applied to other under-resourced languages with similar linguistic complexities?

The insights from this research on homonym sense disambiguation in the Georgian language can be valuable for addressing similar challenges in other under-resourced languages with comparable linguistic complexities. By adapting the proposed approaches and methodologies, researchers working on languages with limited linguistic resources can benefit in the following ways:

Dataset Creation: The methodology for creating a dataset by filtering and classifying sentences containing homonyms can be applied to other languages. By leveraging available text corpora and manually annotating examples, researchers can generate datasets for homonym disambiguation tasks in under-resourced languages.

Model Architecture: The use of transformer models and recurrent neural networks, as demonstrated in this research, can be applied to other languages with similar linguistic structures. By fine-tuning pre-trained language models and experimenting with different architectures, researchers can develop effective models for homonym sense disambiguation.

Transfer Learning: The concept of leveraging pre-trained language models and fine-tuning them on specific tasks can be extended to other languages. By utilizing contextual embeddings and transfer learning techniques, researchers can adapt existing models to handle homonym disambiguation in diverse linguistic contexts.

Benchmarking: The hand-classified dataset created in this research can serve as a benchmark for evaluating the performance of homonym disambiguation models in other languages. By sharing datasets and methodologies, researchers working on under-resourced languages can compare their results and progress in addressing similar linguistic challenges.