A New Cross-Lingual Natural Language Inference Dataset for the Low-Resource Basque Language

Core Concepts

The core message of this paper is to introduce a new cross-lingual Natural Language Inference (NLI) dataset for Basque, a low-resource language, and to analyze the impact of different cross-lingual strategies and data sources on the performance of NLI models for Basque.

Abstract

The paper presents the development and release of a new cross-lingual NLI dataset for Basque, called XNLIeu. The dataset was created by machine-translating the English XNLI corpus into Basque, followed by a manual post-editing step. The authors also release a machine-translated only version (XNLIeuMT) and a small native Basque dataset to analyze the impact of translation-based datasets. The authors conduct a series of experiments using mono- and multilingual language models, both discriminative and generative, to assess: The effect of professional post-edition on the machine-translated dataset. The best cross-lingual strategy for NLI in Basque (zero-shot transfer vs. translate-train). Whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation. The results show that post-edition is necessary to obtain a reliable NLI dataset, and that the translate-train cross-lingual strategy obtains better results overall, although the gain is lower when tested on the native Basque dataset. The authors also analyze the biases and artifacts present in the translated datasets compared to the native one.

Stats

The average length of hypotheses for each semantic relation type (entailment, contradiction, neutral) is shorter in the Basque datasets compared to the original English XNLI. The Basque datasets exhibit a bias towards negation words in the contradiction instances, which is not present in the native dataset. The lexical overlap between premises and hypotheses is higher for entailment instances in the translated datasets, but not in the native dataset.

Quotes

"The results show that post-edition is necessary and that the translate-train cross-lingual strategy obtains better results overall, although the gain is lower when tested in a dataset that has been built natively from scratch." "The native dataset does not seem to be biased towards negation words, since the guidelines specifically asked the annotators to avoid using artifacts as much as possible."

Key Insights Distilled From

XNLIeu

by Maite Heredi... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06996.pdf

Deeper Inquiries

How can the findings from this study be applied to improve cross-lingual NLI in other low-resource languages beyond Basque?

The findings from this study can be applied to improve cross-lingual NLI in other low-resource languages by emphasizing the importance of post-editing in creating reliable evaluation benchmarks. By conducting experiments with machine-translated and post-edited datasets, the study highlights the significant impact of post-editing on dataset quality. This insight can be applied to other low-resource languages to ensure that machine-translated datasets are carefully post-edited by professionals to correct errors and maintain accuracy. Additionally, the study underscores the effectiveness of the translate-train approach for cross-lingual NLI, especially when there is no mismatch between the origin of the training and test data. This strategy can be adopted in other languages to improve the performance of NLI models in cross-lingual settings.

What other techniques, beyond post-editing, could be used to mitigate the biases and artifacts introduced by translation-based datasets?

In addition to post-editing, several techniques can be employed to mitigate biases and artifacts introduced by translation-based datasets in cross-lingual NLI: Back-translation: Utilizing back-translation, where the translated text is translated back to the original language and compared to the source text, can help identify and correct errors introduced during translation. Adversarial Training: Implementing adversarial training techniques can help models learn to be robust against artifacts and biases present in the data by introducing adversarial examples during training. Data Augmentation: Augmenting the dataset with diverse examples and variations can help reduce biases and improve the generalization of models by providing a more comprehensive training set. Bias Detection Algorithms: Implementing bias detection algorithms to identify and mitigate biases in the dataset can help ensure that models do not rely on superficial patterns or artifacts during inference. Human-in-the-Loop Approaches: Involving human annotators in the validation and correction of translated datasets can help ensure the quality and accuracy of the data by incorporating human judgment and expertise.

Given the importance of native datasets for robust evaluation, how can the creation of such datasets be scaled up and made more efficient for low-resource languages?

Creating native datasets for low-resource languages can be scaled up and made more efficient through the following strategies: Collaboration with Native Speakers: Collaborating with native speakers and language experts can expedite the process of dataset creation by leveraging their linguistic knowledge and cultural insights to generate high-quality native text. Crowdsourcing Platforms: Utilizing crowdsourcing platforms to collect and annotate data from native speakers can help scale up the creation of native datasets by tapping into a larger pool of contributors. Transfer Learning: Leveraging transfer learning techniques and pre-trained models in high-resource languages to bootstrap the creation of native datasets can accelerate the process and reduce the manual effort required. Automated Data Generation: Implementing automated data generation techniques, such as data synthesis and augmentation, can help generate diverse and representative samples for native datasets, making the process more efficient. Iterative Refinement: Adopting an iterative approach to dataset creation, where initial versions are continuously refined based on feedback and evaluation, can ensure the ongoing improvement and efficiency of native dataset development for low-resource languages.