Enhancing Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training

Core Concepts

Incorporating supervised high-quality boundary information into BABERT's pre-training process to improve the boundary awareness of the language model and enhance its performance on Chinese sequence labeling tasks.

Abstract

The paper presents Semi-BABERT, a novel approach that builds upon BABERT by incorporating supervised lexicon-based boundary information during pre-training. The key highlights are:

Data Source and Preprocessing:

The data is derived from a knowledge graph (for supervised boundary information) and a crowdsourced corpus (for high-quality text data).
Rule-based lexicon filtering and LLM-based corpus filtering are employed to ensure the quality of the training data.
Positive-Unlabeled (PU) learning is used to handle the incompleteness of the lexicon.

Boundary Recognition Pre-training:

A span-based boundary recognition (SBR) task is introduced to identify word boundaries in the text based on the lexicon.
The SBR task is combined with the Masked Language Modeling (MLM) and Unsupervised Boundary-Aware (UBA) tasks from BABERT to form the overall pre-training objective of Semi-BABERT.

Boundary Information Metric (BIM):

A novel metric is proposed to quantify the boundary awareness of Chinese PLMs without task-specific fine-tuning.
BIM measures the similarity between characters within words (SIMpos) and across word boundaries (SIMneg), and calculates the difference between them.

Experiments and Analysis:

Extensive evaluations on 13 Chinese sequence labeling datasets demonstrate that Semi-BABERT outperforms various baselines, including the state-of-the-art BABERT.
Semi-BABERT also exhibits strong performance on a broader range of Chinese NLP tasks, including text classification and machine reading comprehension.
The analysis includes few-shot experiments, probing, BIM evaluation, and a case study, providing further insights into the effectiveness of Semi-BABERT.

Stats

"The knowledge graph provides supervised lexical boundary information via rule-based filtering, whereas the crowded corpus supplies high-quality text data filtered by a large language model (LLM)."
"We compile a mixed corpus from Chinese Wikipedia and Baidu Baike for our pre-training, consisting of 3 billion tokens and 62 million sentences."
"After applying the filtering rules, we obtain a lexicon of 30 million words."
"To ensure the quality of the corpus, we leverage the generation capabilities of LLMs and remove the 10% of the corpus with the lowest quality score."

Quotes

"Chinese sequence labeling tasks are heavily reliant on accurate word boundary demarcation. Although current pre-trained language models (PLMs) have achieved substantial gains on these tasks, they rarely explicitly incorporate boundary information into the modeling process."
"BABERT (Jiang et al., 2022) is one of the few exceptions that inject unsupervised statistical boundary information into vanilla BERT, resulting in considerable performance gains on Chinese sequence labeling tasks. Nevertheless, BABERT has a notable limitation: due to the long tail problem in calculating these unsupervised statistical signals, the statistical boundary information extracted from raw mining corpus could be unstable and low-quality."
"To enhance the boundary encoding capability of PLM, we introduce Semi-BABERT, a novel approach that incorporates supervised lexicon boundary information into BABERT through a pre-training task called supervised boundary recognition (SBR task)."

Key Insights Distilled From

Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training

by Longhui Zhan... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05560.pdf

Deeper Inquiries

How can the proposed Semi-BABERT model be further extended to incorporate additional types of boundary information, such as contextual or linguistic cues, to further improve its performance on Chinese sequence labeling tasks?

To further enhance the performance of the Semi-BABERT model, additional types of boundary information can be incorporated to provide more context and linguistic cues. One approach could involve integrating syntactic information, such as part-of-speech tags or dependency parsing, to help the model better understand the relationships between words in a sentence. By incorporating syntactic cues, the model can gain a deeper understanding of the structure of the text, which can aid in more accurate boundary identification. Additionally, semantic information from knowledge graphs or ontologies can be leveraged to provide semantic context for the words in a sentence, further improving the model's ability to identify boundaries accurately. By combining multiple types of information, the model can create a more comprehensive representation of the text, leading to enhanced performance on sequence labeling tasks.

What are the potential limitations of the Boundary Information Metric (BIM) in accurately capturing the boundary awareness of Chinese PLMs, and how could it be improved or complemented by other evaluation approaches?

While the Boundary Information Metric (BIM) provides a valuable measure of boundary awareness for Chinese PLMs, it may have some limitations in accurately capturing the full extent of the model's boundary encoding capabilities. One potential limitation is that BIM relies on character-level similarity metrics, which may not fully capture the complex relationships between words in a sentence. Additionally, BIM's focus on character pairs within and across words may not account for higher-level linguistic features that influence boundary awareness.
To address these limitations, BIM could be complemented by other evaluation approaches that consider higher-level linguistic structures, such as word embeddings or syntactic dependencies. By incorporating these additional features, the evaluation metric can provide a more comprehensive assessment of the model's boundary awareness. Furthermore, conducting qualitative analyses, such as error analysis and case studies, can offer deeper insights into the model's performance on specific tasks and help identify areas for improvement.

Given the success of Semi-BABERT on Chinese NLP tasks, how could the insights and techniques from this work be applied to enhance the boundary awareness and performance of language models for other languages that also lack explicit word boundary markers, such as Japanese or Korean?

The insights and techniques from the success of Semi-BABERT in enhancing boundary awareness for Chinese NLP tasks can be applied to improve language models for other languages that lack explicit word boundary markers, such as Japanese or Korean. One approach is to adapt the span-based boundary recognition task used in Semi-BABERT to these languages, considering their specific linguistic characteristics and boundary conventions. By incorporating language-specific lexicons and linguistic rules, the model can learn to identify boundaries accurately in Japanese or Korean text.
Additionally, the concept of combining unsupervised statistical information with supervised high-quality information, as done in Semi-BABERT, can be applied to other languages. By leveraging a combination of data-driven statistical signals and curated linguistic resources, language models for Japanese or Korean can benefit from enhanced boundary awareness and improved performance on sequence labeling tasks. Furthermore, the development of language-specific evaluation metrics, similar to BIM, can help assess the boundary encoding capabilities of models in these languages and guide further improvements.