Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation

Core Concepts

Proposing a unified pre-trained language model incorporating heterogeneous knowledge for all forms of text.

Abstract

The article introduces a heterogeneous knowledge language model (HKLM) that captures multi-format text relationships. It discusses pre-training methods, downstream tasks, and experimental results in the tourism domain. Directory: Introduction to PLMs Expansion Methods Importance of Multi-Format Text in Pre-Training Modeling Multi-Format Text Challenges and Solutions Training Mechanism for HKLM Fine-Tuning TravelBERT for Tourism NLP Tasks Experiments on Pre-training and Downstream Datasets

Stats

The results show that the approach outperforms plain text pre-training using only 1/4 of the data. The HKLM achieves performance gains on the XNLI dataset.

Quotes

Key Insights Distilled From

Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation

by Hongyin Zhu,... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2109.01048.pdf

Deeper Inquiries

How can the proposed HKLM be adapted for other domains beyond tourism?

The proposed Heterogeneous Knowledge Language Model (HKLM) can be adapted for other domains by following a similar approach of incorporating multi-format text data specific to that domain. For instance, in a medical domain, one could utilize medical literature as unstructured text, medical ontologies as structured knowledge triples, and clinical guidelines as semi-structured text. By constructing a corpus relevant to the new domain and pre-training the model with diverse textual formats, it can learn to capture relationships among different types of knowledge sources effectively.

What potential limitations or biases could arise from relying heavily on structured knowledge triples?

Relying heavily on structured knowledge triples may introduce certain limitations and biases: Limited Coverage: The information encoded in structured triples may not cover all nuances present in free-form text. Semantic Ambiguity: Structured triples might oversimplify complex concepts leading to loss of context or ambiguity. Data Quality Issues: If the quality of the underlying data used to generate these triples is poor or biased, it can propagate those issues into the model. Overfitting: Depending too much on structured data without balancing it with unstructured text may lead to overfitting on specific patterns within the triple dataset.

How might incorporating document structure impact the model's ability to generalize across different types of text formats?

Incorporating document structure into a language model like HKLM can have several impacts on its generalization abilities: Hierarchical Understanding: Document structure helps in understanding hierarchical relationships between sections, paragraphs, and sentences which aids in capturing context more effectively. Improved Coherence: By considering how information is organized within documents, models can better maintain coherence and flow within longer texts. Task Adaptability: Models trained with an understanding of document structures are likely better equipped to handle various NLP tasks that require comprehension beyond sentence-level analysis. Domain Transferability: Understanding document structures allows for easier adaptation to new domains by leveraging similarities in how information is presented across different types of texts. By incorporating document structure alongside heterogeneous knowledge sources like unstructured text and well-structured triples, models like HKLM can enhance their ability to generalize across diverse text formats efficiently while maintaining contextual relevance throughout different levels of granularity within documents.