Multilingual Natural Language Processing

insight - Multilingual Natural Language Processing

Multilingual Text Editing Models Trained via Instruction Tuning

We introduce MEDIT, a set of multilingual models capable of performing various text editing tasks like grammatical error correction, text simplification, and paraphrasing across multiple languages by fine-tuning large pre-trained language models via instruction tuning.

Meta4XNLI: A Parallel Corpus for Multilingual Metaphor Detection and Interpretation

Meta4XNLI is a parallel corpus in Spanish and English that provides metaphor annotations for both detection at the token level and interpretation through Natural Language Inference.

Comprehensive Survey on Techniques for Improving Cross-Lingual Alignment in Multilingual Language Models

Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, is crucial for zero-shot cross-lingual transfer. This survey provides a comprehensive overview of techniques to improve cross-lingual alignment, including objectives using parallel data, contrastive learning, modified pre-training schemes, adapter tuning, and data augmentation.

Multilingual Knowledge Editing Benchmark for Large Language Models

MLaKE is a novel benchmark for evaluating the multilingual knowledge editing capabilities of large language models, comprising 5,360 single-hop and 4,072 multi-hop questions across five languages (English, Chinese, Japanese, French, German).

Multilingual Large Language Models: A Comprehensive Survey of Resources, Taxonomy, and Emerging Frontiers

This paper presents a comprehensive survey of the recent progress and emerging trends in multilingual large language models (MLLMs), offering a unified perspective through a novel taxonomy based on alignment strategies.

Uncovering Pervasive Bias in Text Data Across Multiple Languages

Bias exists in text data across multiple languages, including benchmark datasets on the English GLUE/SuperGLUE leaderboards, as well as datasets in Italian, Dutch, German, and Swedish.

Leveraging In-Context Tuning for Efficient One-Shot Cross-Lingual Text Classification

The proposed In-Context Cross-Lingual Transfer (IC-XLT) approach effectively leverages target-language demonstrations during inference to improve cross-lingual text classification performance, especially in scenarios with limited source-language data.

Multilingual Semantic Textual Relatedness: Augmentation and Adaptation for Improved Performance

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.

Scaling Up Multilingual News Framing Analysis through Crowdsourcing and Automatic Translation

Leveraging crowdsourcing and automatic translation to expand the coverage and scale of news framing analysis beyond English, while demonstrating the effectiveness of combining expert-annotated and crowd-sourced data.

Multilingual Summarization with Parameter-Efficient Fine-Tuning: An Empirical Study of Low-Rank Adaptation

Low-Rank Adaptation (LoRA) is a competitive alternative to full fine-tuning for multilingual summarization, particularly in low-data and cross-lingual transfer scenarios.

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