Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages

Core Concepts

Unsupervised method for building bilingual lexicons for low-resource languages against high-resource languages.

Abstract

Introduction Existing methods rely on embeddings and bilingual supervision. Low-resource languages lack quality embeddings. Related Work Interest in unsupervised BLI. Contextual embeddings show promise. Method Iterative process using HRL MLM to extract translation equivalents. Experimental Settings Monolingual data sources and models used. Results and Discussion Baselines compared, with our methods outperforming. Details of released lexicons Publicly available lexicons for several low-resource languages. Conclusion Novel method shows superior performance for low-resource languages.

Stats

Most existing approaches depend on good quality static or contextual embeddings requiring large monolingual corpora for both languages. State-of-the-art BLI methods exhibit near-zero performance for severely data-imbalanced language pairs, indicating the need for more robust techniques.

Quotes

"Most existing approaches depend on good quality static or contextual embeddings requiring large monolingual corpora." "State-of-the-art BLI methods exhibit near-zero performance for severely data-imbalanced language pairs."

Key Insights Distilled From

When your Cousin has the Right Connections

by Niya... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2305.14012.pdf

Deeper Inquiries

How can this method be adapted to handle multi-token words and expressions?

The method described in the context for unsupervised bilingual lexicon induction can be adapted to handle multi-token words and expressions by incorporating span-filling language models. These models are designed to predict missing spans of text within a sentence, making them suitable for handling cases where a single word in one language corresponds to multiple tokens in another language. By utilizing span-filling models, the system can process and generate accurate translations for multi-token words or expressions more effectively.

What are the ethical considerations when relying on predictions from large language models?

When relying on predictions from large language models, several ethical considerations must be taken into account. One key consideration is bias in the training data used to develop these models, which may lead to biased or discriminatory outcomes in translation tasks. It is essential to address biases related to gender, race, culture, and other sensitive attributes present in the training data. Another important ethical concern is transparency and accountability regarding how these language models make decisions. Users should have visibility into how predictions are generated and understand the reasoning behind each translation suggestion. Additionally, ensuring user privacy and data security when using these models is crucial. Moreover, there may be concerns about intellectual property rights when using large language models developed by commercial entities. It is essential to respect copyright laws and licensing agreements when utilizing these tools for bilingual lexicon induction tasks.

How can this method be extended to incorporate supervision from bilingual lexicons obtained from parallel data?

To incorporate supervision from bilingual lexicons obtained from parallel data into the unsupervised BLI method described in the context, a semi-supervised approach can be adopted. The existing model can use seed pairs of known translations as initial points for alignment between languages during training. Additionally, techniques such as self-training or co-training could be employed where initially identified translation pairs are used as pseudo-labeled examples that guide further learning iterations of the model with additional unlabeled data. Furthermore, active learning strategies could be implemented where the model selectively queries human annotators for translations of ambiguous or challenging word pairs based on uncertainty estimates derived during inference. By integrating supervision from bilingual lexicons obtained from parallel data through semi-supervised learning paradigms like self-training or active learning strategies, it would enhance performance while leveraging both labeled (supervised) information along with unannotated (unsupervised) resources efficiently.