Improving Low-Resource Named Entity Recognition through Cross-Lingual Character-Level Neural Conditional Random Fields

Core Concepts

Cross-lingual transfer learning using character-level neural conditional random fields can significantly improve named entity recognition performance in low-resource settings.

Abstract

The paper presents a novel cross-lingual architecture for named entity recognition (NER) that leverages character-level neural conditional random fields (CRFs) to enable transfer learning across related languages.
The key insights are:

In low-resource settings, traditional log-linear CRFs outperform neural CRFs, as neural models require large amounts of training data.

By incorporating cross-lingual information through shared character-level representations, the neural CRF model can learn general entity representations that transfer effectively to low-resource target languages.

Experiments on 15 diverse languages show that the cross-lingual neural CRF approach can improve F1 scores by up to 9.8 points over the log-linear CRF baseline in low-resource settings.

The character-level neural architecture is able to abstract the notion of named entities across related languages, enabling effective cross-lingual transfer learning.

While there is still a gap between the high-resource and transferred low-resource performance, the results demonstrate the viability of the cross-lingual neural CRF approach for reducing the data requirements of state-of-the-art NER models.

Stats

The paper uses a dataset of 15 languages, including Galician, West Frisian, Ukrainian, Marathi, and Tagalog as low-resource target languages, and related source languages such as Spanish, Catalan, Italian, French, Romanian, Dutch, Russian, Cebuano, Hindi, and Urdu.
For each target language, the authors create a low-resource setting with 100 training sentences and a high-resource setting with 10,000 training sentences. The source languages also have 10,000 training sentences each.

Quotes

"Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a log-linear CRF baseline."
"We show empirically that this improves the quality of the resulting model."
"With experiments on 15 languages, we confirm that feature-based CRFs outperform the neural methods consistently in the low-resource training scenario. However, with the addition of cross-lingual information, the tables turn and the neural methods are again on top, demonstrating that cross-lingual supervision is a viable method to reduce the training data state-of-the-art neural approaches require."

Key Insights Distilled From

Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

by Ryan Cottere... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09383.pdf

Deeper Inquiries

How can the cross-lingual transfer learning approach be extended to other sequence labeling tasks beyond named entity recognition

The cross-lingual transfer learning approach can be extended to other sequence labeling tasks beyond named entity recognition by leveraging the underlying principles of shared representations and transfer learning. One way to extend this approach is by applying it to tasks such as part-of-speech tagging, chunking, or semantic role labeling. By training models on high-resource languages and transferring knowledge to low-resource languages, similar improvements in performance can be achieved for these tasks as well. Additionally, adapting the neural CRF architecture to suit the specific requirements of each sequence labeling task can further enhance the effectiveness of cross-lingual transfer learning.

What other techniques, beyond shared character-level representations, could be used to further improve cross-lingual transfer learning for low-resource NER

Beyond shared character-level representations, several techniques can be employed to enhance cross-lingual transfer learning for low-resource NER. One approach is to incorporate language-specific features or embeddings that capture unique linguistic characteristics of each language. By combining these language-specific features with shared representations, the model can better adapt to the nuances of different languages. Additionally, utilizing adversarial training or domain adaptation techniques can help the model generalize more effectively across languages by reducing the domain gap between source and target languages. Furthermore, exploring multi-task learning frameworks that jointly train on multiple related tasks can lead to improved performance in low-resource settings by leveraging the shared knowledge across tasks.

How do the learned character-level representations in the neural CRF model differ across related languages, and what insights can be gained about the linguistic properties being captured

The learned character-level representations in the neural CRF model exhibit interesting differences across related languages, providing insights into the linguistic properties captured by the model. These representations encode information about the morphology, syntax, and semantics of words in each language, reflecting the structural similarities and differences between languages. By analyzing the learned representations, we can gain insights into the phonological and orthographic patterns specific to each language, as well as the commonalities shared among related languages. The variations in character-level representations highlight the model's ability to capture language-specific features while also abstracting general concepts of named entities across different linguistic contexts.