Multilingual Prompt Translator for Cross-Lingual Natural Language Inference

แนวคิดหลัก

Efficient cross-lingual transfer with a Multilingual Prompt Translator enhances natural language inference by translating soft prompts to target languages.

บทคัดย่อ

Authors and Affiliations:

Xiaoyu Qiu, University of Science and Technology of China, Hefei, China
Yuechen Wang, University of Science and Technology of China, Hefei, China
Jiaxin Shi, Cloud BU, Huawei Technologies, Beijing, China
Wengang Zhou, University of Science and Technology of China, Hefei, China
Houqiang Li, University of Science and Technology of China, Hefei, China

Abstract:

Multilingual Pre-trained Models (PLMs) enable effective cross-lingual transfer with prompt learning.

Introduction:

PLMs require vast annotated data for fine-tuning in low-resource languages.

Methodology:

Multilingual Prompt Translator (MPT) optimizes soft prompts for efficient cross-lingual transfer.

Experiments:

MPT outperforms baselines in few-shot settings on XNLI by enhancing performance significantly.

Related Work:

Prior works focus on cross-lingual transfer leveraging multilingual PLMs and prompt learning techniques.

สถิติ

To efficiently transfer soft prompt, we propose a novel framework called Multilingual Prompt Translator (MPT).
MPT demonstrates superiority over baselines in few-shot settings on XNLI.
MPT showcases an impressive enhancement in relative performance when transferring to languages quite distinct from the source language.

คำพูด

"Based on multilingual pre-trained models, cross-lingual transfer with prompt learning has shown promising effectiveness."
"MPT is more prominent compared with vanilla prompting when transferring to languages quite distinct from the source language."

ข้อมูลเชิงลึกที่สำคัญจาก

Cross-Lingual Transfer for Natural Language Inference via Multilingual Prompt Translator

by Xiaoyu Qiu,Y... ที่ arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12407.pdf

สอบถามเพิ่มเติม

How can the concept of a Multilingual Prompt Translator be applied to other NLP tasks beyond natural language inference

The concept of a Multilingual Prompt Translator can be extended to various other NLP tasks beyond natural language inference. For instance, in machine translation, the translator could be utilized to convert prompts from one language to another, aiding in cross-lingual transfer for improved translation accuracy. Similarly, in sentiment analysis across multiple languages, the translator could assist in adapting prompts for different languages, enabling more effective sentiment classification. Additionally, in text generation tasks such as summarization or dialogue systems, the multilingual prompt translator could help generate diverse and contextually appropriate responses across different languages.

What are potential drawbacks or limitations of relying heavily on prompt translation for cross-lingual transfer

While prompt translation offers significant benefits for cross-lingual transfer in NLP tasks, there are potential drawbacks and limitations to consider. One limitation is the reliance on accurate translations; inaccuracies or nuances lost during translation may impact the performance of the model negatively. Moreover, translating prompts adds an additional computational overhead and complexity to the training process which might increase training time and resource requirements significantly. Another drawback is that prompt translation may not fully capture language-specific characteristics or cultural nuances present in different languages leading to suboptimal performance on certain tasks.

How might the development of multilingual knowledge through prompt translation impact the future evolution of NLP technologies

The development of multilingual knowledge through prompt translation has profound implications for the future evolution of NLP technologies. By equipping models with multilingual capabilities through translated prompts, we pave the way for more robust and versatile AI systems capable of understanding and generating content across various languages seamlessly. This advancement can lead to enhanced communication tools that break down language barriers effectively by facilitating accurate translations and interpretations across diverse linguistic contexts. Furthermore, it opens up possibilities for creating truly global AI applications that cater to a wide range of users speaking different languages without compromising on performance or accuracy levels.