MaiNLP's Submission to SemEval-2024 Task 1: Exploring Source Language Selection Strategies for Cross-Lingual Semantic Textual Relatedness

Core Concepts

This paper presents the MaiNLP team's system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), focusing on the cross-lingual track. The team explores different source language selection strategies, including single-source transfer, multi-source transfer, and transfer from nearest language neighbors, to improve zero-shot cross-lingual performance on the STR task.

Abstract

The paper presents the MaiNLP team's approach to the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), specifically focusing on the cross-lingual track (Track C). The task aims to detect semantic relatedness of two sentences in a given target language without access to direct supervision (i.e., zero-shot cross-lingual transfer).
The team experiments with the following approaches:

Single-source transfer: Fine-tuning pre-trained language models (XLM-R and FURINA) on English data.

Multi-source transfer: Fine-tuning the models on the concatenation of all available source language datasets (except the target language).

Transfer from nearest language neighbors: Fine-tuning the models on the two languages most similar to the target language, based on language similarity measures (e.g., cosine similarity of language vectors).

Transliteration and machine translation-based data augmentation: Exploring the impact of script differences and using machine translation to expand the training data.

The results show that:

Multi-source transfer outperforms single-source transfer, indicating the potential of leveraging knowledge from multiple source languages.
Training on languages from the same family can improve performance on some target languages, but can also lead to negative interference for others.
Careful selection of source languages based on language similarity measures can be beneficial, but the optimal selection is not always straightforward.
Transliteration and machine translation-based data augmentation have mixed results, highlighting the challenges in addressing script differences and preserving label semantics.
The team's submission using the FURINA model fine-tuned on the two nearest languages to Kinyarwanda (kin) achieved the first place on the kin test set.

Stats

The STR dataset contains 14 languages, with 9 languages in Track A (source) and 12 languages in Track C (target).
The total number of training instances across all source languages is 15,123.
The English training dataset has 5,500 instances, while the other source language datasets range from 778 to 1,736 instances.

Quotes

"Previous work on multilingual NLP has illustrated the curse of multilinguality (Conneau et al., 2020), that is, diminishing returns for training a single system on many languages due to language interference."
"Motivated by these two aspects, we set out to study the use of fewer but more relevant source languages for a given target language."

Key Insights Distilled From

MaiNLP at SemEval-2024 Task 1

by Shijia Zhou,... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02570.pdf

Deeper Inquiries

How can the team's findings on source language selection be extended to other cross-lingual NLP tasks beyond semantic textual relatedness

The team's findings on source language selection can be extended to other cross-lingual NLP tasks by considering the linguistic proximity between source and target languages. By identifying the most similar languages based on typological features, language vectors, or language families, researchers can optimize the transfer learning process for various tasks such as machine translation, sentiment analysis, named entity recognition, and more. Understanding the impact of source language selection on model performance can help researchers fine-tune their approaches for different cross-lingual tasks, ensuring better transfer learning outcomes.

What other language similarity measures or meta-information could be leveraged to further improve the selection of source languages for cross-lingual transfer

To further improve the selection of source languages for cross-lingual transfer, researchers can leverage additional language similarity measures or meta-information. Some potential approaches include:

Phonetic Similarity: Considering phonetic features of languages to identify phonetically similar languages for transfer learning.
Morphological Similarity: Analyzing morphological structures of languages to determine morphologically related languages for better transfer performance.
Geographical Proximity: Exploring the geographical proximity of languages as a factor in selecting source languages for cross-lingual tasks.
Cultural and Historical Connections: Taking into account cultural and historical relationships between languages to guide source language selection for transfer learning.

By incorporating these additional measures and meta-information, researchers can enhance the accuracy and efficiency of source language selection in cross-lingual NLP tasks.

Given the challenges in preserving label semantics during data augmentation, what alternative approaches could be explored to enhance cross-lingual transfer performance without compromising the quality of the training data

To address the challenges in preserving label semantics during data augmentation for cross-lingual transfer, researchers can explore alternative approaches such as:

Adversarial Training: Introducing adversarial training techniques to generate augmented data that maintains the original label semantics while increasing the diversity of the training set.
Semantic Similarity Constraints: Incorporating semantic similarity constraints during data augmentation to ensure that the augmented samples retain the same semantic meaning as the original data.
Knowledge Distillation: Leveraging knowledge distillation methods to transfer knowledge from a large pre-trained model to a smaller model, reducing the risk of label semantics distortion during data augmentation.
Unsupervised Data Augmentation: Exploring unsupervised data augmentation techniques that do not rely on labeled data, ensuring that the augmented samples align with the original data distribution without compromising label semantics.

By exploring these alternative approaches, researchers can enhance cross-lingual transfer performance without compromising the quality of the training data and maintaining the integrity of label semantics.