içgörü - Cross-lingual Natural Language Processing - # Cross-lingual Sentence Embedding

Enhancing Cross-lingual Sentence Embedding for Low-resource Languages through Explicit Word Alignment

Q: How can the proposed framework be extended to capture phrase-level alignment between high-resource and low-resource languages?

To extend the proposed framework to capture phrase-level alignment between high-resource and low-resource languages, a few key steps can be taken: Phrase Embeddings: Instead of focusing solely on word-level alignment, the framework can incorporate mechanisms to capture phrase-level semantics. This can be achieved by considering embeddings of phrases or chunks of text in addition to individual words. Contextual Information: By leveraging contextual information from pre-trained language models, the framework can learn to align phrases based on their surrounding context. This can help in capturing the nuanced relationships between phrases in different languages. Alignment Strategies: Implementing alignment strategies specifically designed for capturing phrase-level similarities can enhance the framework's ability to align phrases across languages. Techniques like attention mechanisms or alignment models tailored for phrases can be explored. Training Objectives: Introducing specific training objectives that focus on aligning phrases rather than just words can further improve the framework's capability to capture phrase-level alignment. These objectives can be designed to optimize the alignment of semantically similar phrases in different languages. By incorporating these strategies, the framework can be extended to effectively capture phrase-level alignment between high-resource and low-resource languages, enhancing the overall cross-lingual sentence embeddings.

Q: How can the performance of the word alignment model (WSPAlign) be improved, especially for low-resource languages, to further enhance the cross-lingual sentence embeddings?

Improving the performance of the word alignment model, WSPAlign, especially for low-resource languages, is crucial for enhancing cross-lingual sentence embeddings. Here are some strategies to enhance the performance of WSPAlign: Data Augmentation: Augmenting the training data for WSPAlign with techniques like back-translation, synthetic data generation, or data sampling can help in improving the model's performance, especially for low-resource languages where training data is limited. Fine-tuning: Fine-tuning the word alignment model on domain-specific or language-specific data can enhance its ability to capture word-level alignments accurately. Fine-tuning on parallel corpora specific to low-resource languages can improve alignment quality. Multi-task Learning: Incorporating multi-task learning where the word alignment model is trained on multiple related tasks simultaneously can lead to better alignment performance. Tasks like part-of-speech tagging, named entity recognition, or syntactic parsing can provide additional signals for word alignment. Model Architecture: Experimenting with different architectures for the word alignment model, such as transformer-based models or bi-directional LSTM models, can help in capturing complex relationships between words in parallel sentences. Evaluation Metrics: Using appropriate evaluation metrics that consider the nuances of low-resource languages can provide insights into the model's performance and areas for improvement. By implementing these strategies, the performance of WSPAlign can be enhanced, leading to improved cross-lingual sentence embeddings, especially for low-resource languages.

Q: What other token-level or sub-word level objectives could be explored to improve cross-lingual sentence embeddings for low-resource languages without relying solely on parallel data?

To improve cross-lingual sentence embeddings for low-resource languages without relying solely on parallel data, exploring additional token-level or sub-word level objectives can be beneficial. Some objectives to consider include: Morphological Similarity: Designing objectives that focus on capturing morphological similarities between words in different languages can help in learning cross-lingual representations. Tasks like morphological inflection prediction or morphological analogy detection can be explored. Phonetic Alignment: Introducing objectives that align words based on their phonetic properties can aid in capturing cross-lingual similarities. Tasks like phonetic similarity prediction or phonetic analogy detection can be valuable for low-resource languages. Semantic Role Labeling: Incorporating objectives related to semantic role labeling can help in understanding the relationships between words in sentences across languages. Tasks like semantic role prediction or argument identification can provide valuable signals for cross-lingual embeddings. Cross-lingual Named Entity Recognition: Training the model to recognize named entities in different languages and align them across languages can improve the model's ability to capture cross-lingual semantic information. Cross-lingual Sentiment Analysis: Including sentiment analysis tasks that require understanding the sentiment of sentences in different languages can enhance the model's cross-lingual capabilities. By exploring these token-level or sub-word level objectives, the cross-lingual sentence embeddings for low-resource languages can be improved without relying solely on parallel data, leading to more robust and accurate representations.

Temel Kavramlar

Leveraging word alignment models to explicitly align semantically equivalent words between high-resource and low-resource languages can enhance cross-lingual sentence embeddings, particularly for low-resource languages.

Özet

The paper addresses the problem of cross-lingual sentence embedding, particularly for low-resource languages. It observes that current cross-lingual models trained solely with sentence-level alignment objectives exhibit under-alignment of word representations between high-resource and low-resource languages.

To address this, the paper proposes a novel framework called WACSE that incorporates three training objectives:

Translation Ranking (TR): Aligns the sentence-level semantics between parallel sentences.
Aligned Word Prediction (AWP): Utilizes the contextual representations of masked words to predict their aligned counterparts in another language, aiming to align word-level semantics.
Word Translation Ranking (WTR): Aligns word-level semantically equivalent units within parallel sentences.

The experiments on the Tatoeba dataset demonstrate that the proposed word-aligned training objectives can substantially improve cross-lingual sentence embedding, especially for low-resource languages. The model also retains competitive performance on a broader range of tasks, including STS22, BUCC, and XNLI, where most languages are high-resource.

The analysis further reveals that incorporating language identification information can be beneficial for low-resource languages, while it may be detrimental for the overall 36-language setting. Additionally, the AWP and WTR objectives prove to be effective in enhancing cross-lingual sentence embeddings, with the combination of the three objectives (TR, AWP, and WTR) yielding the optimal results.

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İstatistikler

The training dataset consists of 36 language pairs with a total of 36 million parallel sentences.
The low-resource languages considered in the experiments are: tl, jv, sw, ml, te, mr, kk, and ka.
The number of Wikipedia articles available per language is used as one of the criteria to identify low-resource languages.

Alıntılar

"The field of cross-lingual sentence embedding has recently seen great advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora."
"To address this under-alignment, we introduce a new framework featuring two word-level alignment objectives: aligned word prediction and word translation ranking."
"The experiment results demonstrate that the proposed word-aligned training objectives can enhance cross-lingual sentence embedding, particularly for low-resource languages, as evidenced on the Tatoeba dataset."

Önemli Bilgiler Şuradan Elde Edildi

Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment

by Zhongtao Mia... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02490.pdf

Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment

Daha Derin Sorular

How can the proposed framework be extended to capture phrase-level alignment between high-resource and low-resource languages?

To extend the proposed framework to capture phrase-level alignment between high-resource and low-resource languages, a few key steps can be taken:

Phrase Embeddings: Instead of focusing solely on word-level alignment, the framework can incorporate mechanisms to capture phrase-level semantics. This can be achieved by considering embeddings of phrases or chunks of text in addition to individual words.

Contextual Information: By leveraging contextual information from pre-trained language models, the framework can learn to align phrases based on their surrounding context. This can help in capturing the nuanced relationships between phrases in different languages.

Alignment Strategies: Implementing alignment strategies specifically designed for capturing phrase-level similarities can enhance the framework's ability to align phrases across languages. Techniques like attention mechanisms or alignment models tailored for phrases can be explored.

Training Objectives: Introducing specific training objectives that focus on aligning phrases rather than just words can further improve the framework's capability to capture phrase-level alignment. These objectives can be designed to optimize the alignment of semantically similar phrases in different languages.

By incorporating these strategies, the framework can be extended to effectively capture phrase-level alignment between high-resource and low-resource languages, enhancing the overall cross-lingual sentence embeddings.

How can the performance of the word alignment model (WSPAlign) be improved, especially for low-resource languages, to further enhance the cross-lingual sentence embeddings?

Improving the performance of the word alignment model, WSPAlign, especially for low-resource languages, is crucial for enhancing cross-lingual sentence embeddings. Here are some strategies to enhance the performance of WSPAlign:

Data Augmentation: Augmenting the training data for WSPAlign with techniques like back-translation, synthetic data generation, or data sampling can help in improving the model's performance, especially for low-resource languages where training data is limited.

Fine-tuning: Fine-tuning the word alignment model on domain-specific or language-specific data can enhance its ability to capture word-level alignments accurately. Fine-tuning on parallel corpora specific to low-resource languages can improve alignment quality.

Multi-task Learning: Incorporating multi-task learning where the word alignment model is trained on multiple related tasks simultaneously can lead to better alignment performance. Tasks like part-of-speech tagging, named entity recognition, or syntactic parsing can provide additional signals for word alignment.

Model Architecture: Experimenting with different architectures for the word alignment model, such as transformer-based models or bi-directional LSTM models, can help in capturing complex relationships between words in parallel sentences.

Evaluation Metrics: Using appropriate evaluation metrics that consider the nuances of low-resource languages can provide insights into the model's performance and areas for improvement.

By implementing these strategies, the performance of WSPAlign can be enhanced, leading to improved cross-lingual sentence embeddings, especially for low-resource languages.

What other token-level or sub-word level objectives could be explored to improve cross-lingual sentence embeddings for low-resource languages without relying solely on parallel data?

To improve cross-lingual sentence embeddings for low-resource languages without relying solely on parallel data, exploring additional token-level or sub-word level objectives can be beneficial. Some objectives to consider include:

Morphological Similarity: Designing objectives that focus on capturing morphological similarities between words in different languages can help in learning cross-lingual representations. Tasks like morphological inflection prediction or morphological analogy detection can be explored.

Phonetic Alignment: Introducing objectives that align words based on their phonetic properties can aid in capturing cross-lingual similarities. Tasks like phonetic similarity prediction or phonetic analogy detection can be valuable for low-resource languages.

Semantic Role Labeling: Incorporating objectives related to semantic role labeling can help in understanding the relationships between words in sentences across languages. Tasks like semantic role prediction or argument identification can provide valuable signals for cross-lingual embeddings.

Cross-lingual Named Entity Recognition: Training the model to recognize named entities in different languages and align them across languages can improve the model's ability to capture cross-lingual semantic information.

Cross-lingual Sentiment Analysis: Including sentiment analysis tasks that require understanding the sentiment of sentences in different languages can enhance the model's cross-lingual capabilities.

By exploring these token-level or sub-word level objectives, the cross-lingual sentence embeddings for low-resource languages can be improved without relying solely on parallel data, leading to more robust and accurate representations.