Multilingual Semantic Textual Relatedness: Augmentation and Adaptation for Improved Performance

Core Concepts

Our system AAdaM achieves competitive results in the SemEval-2024 Task 1 on Semantic Textual Relatedness for African and Asian languages, by leveraging data augmentation, task-adaptive pre-training, and adapter-based tuning.

Abstract

This paper presents the AAdaM system developed for the SemEval-2024 Task 1 on Semantic Textual Relatedness (STR) for African and Asian languages. The task aims to measure the semantic relatedness between pairs of sentences in a range of under-represented languages. The key highlights and insights are: Data Augmentation: To address the challenge of limited training data for non-English languages, the authors perform data augmentation via machine translation from English resources. Task-Adaptive Pre-training: The authors apply task-adaptive pre-training on unlabeled task data to better adapt the pre-trained language model to the STR task. Model Tuning: The authors explore both full fine-tuning and adapter-based tuning, and find that adapter-based tuning can achieve comparable performance to full fine-tuning, while being more parameter-efficient. Cross-lingual Transfer: For cross-lingual transfer in subtask C, the authors utilize the MAD-X framework, which enables efficient zero-shot transfer by replacing only the language-specific adapters. Evaluation: In subtask A (supervised learning), the authors' system ranks first out of 40 teams on average, and performs the best in Spanish. In subtask C (cross-lingual transfer), the system ranks first among 18 teams on average, and achieves the best performance in Indonesian and Punjabi. Analysis: The authors provide a fine-grained analysis, revealing that capturing nuanced semantic relationships remains a challenge, especially for languages with lower relatedness scores.

Stats

The SemRel dataset covers 14 languages from 5 distinct language families, with English data comprising 32% of the entire dataset. The data sizes vary widely across languages, constrained by the availability of resources.

Quotes

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Key Insights Distilled From

AAdaM at SemEval-2024 Task 1

by Miaoran Zhan... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01490.pdf

Deeper Inquiries

How can the model's performance be further improved for languages with lower semantic relatedness scores?

To improve the model's performance for languages with lower semantic relatedness scores, several strategies can be considered: Fine-tuning on specific language data: Fine-tuning the model on specific language data can help it better capture the nuances and intricacies of the language, leading to improved performance in capturing semantic relatedness. Domain adaptation: If the low semantic relatedness scores are due to domain mismatches between the pre-training data and the task data, domain adaptation techniques can be employed to align the model's representations with the specific domain of the task data. Ensemble learning: Combining predictions from multiple models or ensembles of models can help mitigate errors and improve overall performance, especially in challenging cases with low semantic relatedness scores. Transfer learning from related languages: Leveraging transfer learning from languages that are linguistically or typologically closer to the target language can help improve performance by transferring knowledge and representations that are more relevant. Fine-grained analysis and error diagnosis: Conducting in-depth analysis of model predictions, especially for low semantic relatedness scores, can help identify specific patterns or challenges that the model faces in understanding the semantic relationships in those languages. This analysis can guide targeted improvements.

How do the linguistic and typological characteristics of the target languages impact the cross-lingual transfer performance, and how can this be better understood and leveraged?

The linguistic and typological characteristics of target languages play a crucial role in cross-lingual transfer performance. Here are some ways these characteristics impact performance and how they can be better understood and leveraged: Language similarity: Languages that are more similar in terms of syntax, phonology, or vocabulary are likely to have better transfer performance. Understanding the linguistic distances between languages can help in selecting optimal source languages for transfer learning. Data distribution: Variations in data distribution across languages can affect transfer performance. Analyzing the data distributions of target languages and source languages can provide insights into potential challenges and opportunities for transfer learning. Typological features: Considering typological features such as word order, morphology, and syntactic structures can help in designing language-specific adaptations or fine-tuning strategies that align better with the linguistic characteristics of the target languages. Error analysis: Conducting detailed error analysis for different target languages can reveal specific linguistic or typological challenges that impact transfer performance. This analysis can guide the development of language-specific strategies to address these challenges. Multimodal approaches: Incorporating multimodal information, such as images or audio, along with text data can help in capturing language-specific nuances and improving cross-lingual transfer performance, especially for languages with unique linguistic characteristics. Understanding and leveraging the linguistic and typological characteristics of target languages can lead to more effective cross-lingual transfer strategies and improved performance in multilingual NLP tasks.

What other techniques, beyond data augmentation and task-adaptive pre-training, could be explored to address the challenge of limited training data in low-resource languages?

In addition to data augmentation and task-adaptive pre-training, several other techniques can be explored to address the challenge of limited training data in low-resource languages: Zero-shot learning: Leveraging zero-shot learning techniques that enable models to make predictions for languages without explicit training data can be beneficial in low-resource settings. Techniques like few-shot learning or meta-learning can also be explored. Semi-supervised learning: Incorporating semi-supervised learning approaches that utilize a small amount of labeled data along with a large amount of unlabeled data can help in improving model performance in low-resource languages. Active learning: Implementing active learning strategies to intelligently select the most informative data points for annotation can optimize the use of limited labeled data and improve model performance. Domain adaptation: Adapting pre-trained models or features from high-resource languages to the specific domain of the low-resource language data can help in transferring knowledge and improving performance. Multilingual pre-training: Utilizing multilingual pre-trained models that have been trained on a diverse set of languages can provide a good starting point for low-resource languages by leveraging shared representations and transfer learning. Cross-lingual knowledge transfer: Leveraging knowledge graphs, cross-lingual embeddings, or cross-lingual lexical resources to transfer knowledge from high-resource languages to low-resource languages can help in improving model performance. By exploring these additional techniques in combination with data augmentation and task-adaptive pre-training, it is possible to enhance model performance and address the challenges posed by limited training data in low-resource languages.