Hierarchical Transformer Text Segmentation with Adapter Fine-Tuning
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Solution Overview
Problem
Existing text segmentation techniques struggle to effectively organize long texts into coherent segments, especially across different domains, leading to inefficiencies in human comprehension and downstream computer processes like text summarization and sentiment analysis.
Innovation Solution
A hierarchical transformer network model is employed for text segmentation, comprising a token transformer, a sentence transformer, and a segmentation classifier. This model is trained using a dataset from the educational domain and utilizes adapter-based fine-tuning to preserve distributional knowledge and prevent overfitting, enabling effective domain transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing text segmentation techniques are used, then the segmentation process is simple, but the segmentation precision is poor and coherence is lost
Solution Approach 1:
The model processes text in a hierarchical manner, first segmenting at the token level within sentences, then at the sentence level within paragraphs, and finally at the paragraph level within documents. This multi-level segmentation approach improves segmentation precision by capturing coherence at different granularities without requiring a single complex model to handle all levels simultaneously.
Solution Approach 2:
The hierarchical transformer network embeds multiple levels of processing within each other: token transformations are nested within sentence transformations, which are nested within paragraph transformations. Each level operates on representations from the previous level, creating a nested structure that systematically builds coherence from fine-grained to coarse-grained segments.
2Adaptability or versatility
If a model is trained on one domain, then it performs well on that domain, but it fails to generalize to other domains
Solution Approach 1:
The hierarchical transformer network is designed with universal components that can process various text types and domains. The token transformer, sentence transformer, and paragraph transformer modules are domain-agnostic and can be applied to educational texts, news articles, scientific papers, and other text types, enabling the model to generalize across domains while maintaining reliable performance.
3Difficulty of detecting and measuring
If traditional segmentation methods are used, then computational resources are saved, but the ability to capture long-range dependencies is insufficient
Solution Approach 1:
By segmenting text hierarchically into tokens, sentences, and paragraphs, the model breaks down long-range dependency detection into manageable steps. Each transformer layer captures dependencies within its scope, and the hierarchical structure allows information to propagate across multiple levels, effectively handling long-range dependencies without requiring a single massive computational operation.
Solution Approach 2:
The model adds hierarchical dimensions to the processing architecture, moving from flat token-level processing to multi-level hierarchical processing. This dimensional expansion allows the model to capture long-range dependencies by leveraging contextual information from multiple levels (token, sentence, paragraph) simultaneously, rather than relying solely on extended sequence lengths in a single dimension.
Data Source
AI summary
Data such as unstructured text is received that includes a sequence of sentences. This received data is then tokenized into a plurality of tokens. The received data is segmented using a hierarchical transformer network model including a token transformer, a sentence transformer, and a segmentation classifier. The token transformer contextualizes tokens within sentences and yields sentence embeddings. The sentences transformer contextualizes sentence representations based on the sentence embedddings. The segmentation classifier predicts segments of the received data based on the contextualized sentence representations. Data can be provided which characterizes the segmentation of the received data. Related apparatus, systems, techniques and articles are also described.