Incorporating Lexical Features Improves Bilingual Lexicon Induction

核心概念

Incorporating lexical features such as part-of-speech information and term frequency into a learning-to-rank approach can significantly improve performance on bilingual lexicon induction tasks, especially for low-resource language pairs.

要約

The paper proposes a method called Lexical-Feature Boosted BLI (LFBB) that incorporates lexical features such as part-of-speech (POS) information and term frequency into a learning-to-rank approach for bilingual lexicon induction (BLI).
The key insights are:

Lexical features like POS and term frequency tend to be correlated across language pairs, indicating their potential usefulness for BLI.
The authors leverage these lexical features in an XGBoost-based learning-to-rank model, taking as input the features for the source word and its candidate translations.
This LFBB model is used to rescore the top candidates retrieved by a base BLI model, improving performance over state-of-the-art approaches like BLICEr, especially on low-resource language pairs.
The authors show that the LFBB model makes predictions that are more aligned with the ground truth in terms of term frequency differences between source and target words.
While the approach uses a relatively simple learning-to-rank method, it demonstrates the value of incorporating lexical features to tackle the challenges of BLI, particularly the hubness problem in the embedding space.

統計

The mean absolute difference in term frequency between the source word and the predicted target word is lower for the LFBB model compared to the baseline XLM-R model.
The LFBB model outperforms the state-of-the-art BLICEr model by an average of 2% across all language pairs in the 1k semi-supervised setting, and 6 out of 7 pairs in the 5k supervised setting.

引用

"We argue that the incorporation of additional lexical information into the recent retrieve-and-rank approach should improve lexicon induction."
"Our approach yields improved results even in the absence of this additional step."
"Owing to the hubness issue we often retrieve many close candidates highlighting the need for better reranking and additional tools to deduce the correct correspondence."

抽出されたキーインサイト

How Lexical is Bilingual Lexicon Induction?

by Harsh Kohli,... 場所 arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04221.pdf

深掘り質問

How could the proposed LFBB approach be extended to leverage more sophisticated neural network architectures for the learning-to-rank component, potentially capturing more complex interactions between the lexical features?

The LFBB approach could be extended by incorporating more sophisticated neural network architectures, such as deep learning models like neural networks with multiple layers or transformer-based models. These architectures can handle complex interactions between lexical features more effectively by capturing non-linear relationships and dependencies among the features. For instance, a neural network with multiple layers can learn intricate patterns in the data, enabling the model to make more nuanced decisions based on the lexical features. Additionally, transformer-based models, known for their ability to capture long-range dependencies, could be employed to better understand the contextual information provided by the lexical features. By leveraging these advanced architectures, the LFBB model can potentially improve its performance by capturing more intricate relationships between the features and enhancing the learning-to-rank component's ability to make accurate predictions.

How might the performance of the LFBB model be affected by the quality and coverage of the POS tagging and term frequency data, especially for low-resource languages?

The performance of the LFBB model is significantly influenced by the quality and coverage of the POS tagging and term frequency data, particularly in the case of low-resource languages.

Quality of POS Tagging:

High-Quality POS Tags: Accurate and reliable POS tagging is crucial for the model to correctly identify the parts of speech of words in different languages. High-quality POS tagging ensures that the model receives accurate information about the linguistic properties of the words, leading to more precise alignment and better performance.
Errors in POS Tags: Inaccurate POS tagging can introduce noise and ambiguity into the model, affecting its ability to make correct predictions. Errors in POS tags can lead to misalignments between source and target words, reducing the model's accuracy.

Coverage of Term Frequency Data:

Sufficient Data: Adequate coverage of term frequency data is essential for the model to learn meaningful patterns and relationships between words. A lack of data can result in sparse representations and hinder the model's ability to generalize effectively.
Low-Resource Languages: In the case of low-resource languages, limited availability of term frequency data can pose a challenge. Sparse data can lead to difficulties in capturing the nuances of the language, potentially impacting the model's performance on these language pairs.

Improving the quality and coverage of POS tagging and term frequency data, especially for low-resource languages, through data augmentation, domain adaptation, or leveraging transfer learning techniques can enhance the LFBB model's performance in cross-lingual tasks.

Could the insights from this work on leveraging lexical features be applied to other cross-lingual tasks beyond bilingual lexicon induction, such as cross-lingual document classification or dependency parsing?

The insights gained from leveraging lexical features in bilingual lexicon induction can indeed be applied to other cross-lingual tasks beyond just this specific domain. Here's how these insights can be extended to tasks like cross-lingual document classification and dependency parsing:

Cross-Lingual Document Classification:

Feature Engineering: Similar to how lexical features were used in the LFBB model, incorporating features like part-of-speech information, term frequency, and semantic similarities can enhance cross-lingual document classification. These features can provide valuable linguistic cues for understanding the content and context of documents in different languages.
Learning-to-Rank: Employing a learning-to-rank approach, similar to LFBB, can help prioritize relevant documents in cross-lingual settings. By considering lexical features and their interactions, the model can better discriminate between documents in various languages.

Cross-Lingual Dependency Parsing:

Lexical Information: Leveraging lexical features such as part-of-speech tags, word frequencies, and semantic similarities can aid in cross-lingual dependency parsing. These features can assist in identifying syntactic relationships between words in different languages.
Learning Complex Interactions: By extending the model to capture complex interactions between lexical features using neural network architectures, the performance of cross-lingual dependency parsing can be enhanced. Advanced models can learn intricate dependencies and patterns crucial for accurate parsing across languages.

By adapting the principles of incorporating lexical features and learning-to-rank strategies from the LFBB model, these insights can be effectively applied to a broader range of cross-lingual tasks, contributing to improved performance and generalization in diverse linguistic contexts.