Enhancing Unsupervised Cross-Lingual Transfer by Integrating Lexical and Syntactic Knowledge

Alapfogalmak

Integrating lexical and syntactic knowledge can consistently improve the performance of unsupervised cross-lingual transfer across various NLP tasks.

Kivonat

The paper presents a novel framework called "Lexicon-Syntax Enhanced Multilingual BERT" (LS-mBERT) that combines both lexical and syntactic knowledge to enhance unsupervised cross-lingual transfer.
The key highlights are:

Lexical Knowledge: The framework employs a code-switching technique to implicitly teach the model lexical alignment information by replacing some words in the source language with their translations from other languages.

Syntactic Knowledge: A graph attention network (GAT) is designed to encode the syntactic structure of the input text, and its output is integrated into the attention heads of the multilingual BERT model to guide it in focusing on language-specific structures.

Integration: The code-switched text is input into both the syntactic module and the multilingual BERT base model simultaneously, allowing the model to learn from both lexical and syntactic knowledge during training.

The extensive experimental results demonstrate that the proposed LS-mBERT framework consistently outperforms all baselines in zero-shot cross-lingual transfer across various tasks, including text classification, named entity recognition, and semantic parsing. The gains range from 1.0 to 3.7 points compared to the best-performing baseline.
The authors also analyze the impact of important parameters, such as the replacement ratio and choice of replacement languages in the code-switching technique, and find that carefully selecting these parameters can further improve the performance.

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Idézetek

"Unsupervised cross-lingual transfer involves transferring knowledge between languages without explicit supervision."
"Since each type of information offers unique advantages and no previous attempts have combined both, we attempt to explore the potential of this approach."
"Our extensive experimental results demonstrate this framework can consistently outperform all baselines of zero-shot cross-lingual transfer, with the gains of 1.0∼3.7 points on text classification, named entity recognition (ner), and semantic parsing tasks."

Főbb Kivonatok

Incorporating Lexical and Syntactic Knowledge for Unsupervised Cross-Lingual Transfer

by Jianyu Zheng... : arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16627.pdf

Mélyebb kérdések

How can the code-switching technique be further improved to better align lexical knowledge across languages?

To enhance the code-switching technique for better alignment of lexical knowledge across languages, several improvements can be considered:

Contextual Word Substitution: Instead of randomly selecting words for replacement, the code-switching process can be improved by considering the context of the sentence. This can involve analyzing the surrounding words to ensure that the substituted word fits well within the sentence context.
Language Model Integration: Incorporating pre-trained language models that are specifically designed for code-switching can help in selecting more appropriate word substitutions. These models can learn the patterns of code-switching and make more informed decisions.
Bilingual Embeddings: Utilizing bilingual word embeddings can aid in finding more accurate translations for code-switching. These embeddings capture the semantic relationships between words in different languages, leading to better alignment.
Fine-tuning Strategies: Implementing fine-tuning techniques on the code-switching process based on the specific language pairs or domains can improve the quality of word substitutions. This can involve training the model on a smaller dataset of code-switched sentences to adapt to the nuances of each language pair.

How can the proposed framework be extended to handle low-resource languages or language pairs with significant structural differences?

To extend the proposed framework to handle low-resource languages or language pairs with significant structural differences, the following strategies can be implemented:

Data Augmentation Techniques: Incorporating data augmentation methods specific to low-resource languages can help in generating more training data. Techniques like back-translation, synthetic data generation, or unsupervised pre-training can be utilized.
Transfer Learning: Leveraging transfer learning approaches where the model is pre-trained on a resource-rich language and fine-tuned on the low-resource language can be beneficial. This helps in transferring knowledge from high-resource to low-resource languages.
Multilingual Embeddings: Using multilingual embeddings that capture semantic similarities across languages can aid in handling structural differences. These embeddings provide a shared representation space for different languages, enabling better alignment.
Domain Adaptation: Implementing domain adaptation techniques to adapt the model to the specific characteristics of the low-resource language or domain can improve performance. Fine-tuning on in-domain data can help in capturing language-specific nuances.

What other types of linguistic knowledge, beyond lexical and syntactic, could be incorporated to enhance cross-lingual transfer?

In addition to lexical and syntactic knowledge, the following types of linguistic knowledge can be incorporated to enhance cross-lingual transfer:

Semantic Knowledge: Including semantic knowledge such as word meanings, relationships, and semantic roles can improve understanding and transfer of meaning across languages.
Pragmatic Knowledge: Incorporating pragmatic knowledge related to speech acts, implicatures, and conversational implicatures can enhance the model's ability to interpret language in context.
Discourse Knowledge: Integrating discourse knowledge, including discourse markers, coherence relations, and discourse structure, can aid in capturing the flow of information in texts and conversations.
Morphological Knowledge: Considering morphological knowledge such as word formation, inflection, and derivational morphology can help in handling morphologically rich languages and improving word representations.
Phonological Knowledge: Utilizing phonological knowledge related to pronunciation, phonetic features, and phonotactics can be beneficial for tasks involving speech recognition or phonetic transcriptions in cross-lingual settings.