Mixed-Granularity Language Representation Model for Semantic Parsing
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Solution Overview
Problem
Existing language representation models face issues with incorrect attention weights assigned to words and incorrect semantic parsing due to improper segmentation, leading to inaccurate comprehension of sentences.
Innovation Solution
A mixed-granularity language representation model that includes both word and phrase granularity sub-models, allowing for semantic vector representation at both word and phrase levels, which are trained separately and then combined to enhance comprehension accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single granularity language representation model is used, then the model structure is simple, but the processing precision of downstream natural language processing tasks deteriorates due to incorrect semantic parsing
Solution Approach 1:
The language representation model is segmented into multiple sub-models operating at different granularities: word-level sub-models and phrase-level sub-models. Each sub-model processes linguistic units at its specific granularity, with word-level sub-models handling individual words and phrase-level sub-models handling multi-word expressions. This segmentation allows the system to capture semantic information at appropriate levels, improving processing precision without requiring a completely complex monolithic structure.
Solution Approach 2:
The model introduces a granularity dimension by operating at multiple levels (word-level and phrase-level) simultaneously. This dimensional approach allows the system to represent and process language at different scales, enabling more accurate semantic parsing by considering both fine-grained word meanings and coarser-grained phrase meanings, thereby improving processing precision.
2Measurement precision
If phrase granularity segmentation is used, then semantic parsing accuracy improves, but incorrect attention weights are assigned to individual words
Solution Approach 1:
The model segments the attention mechanism into multiple granularity levels. Word-level sub-models compute attention weights for individual words, while phrase-level sub-models compute attention weights for phrases. This segmentation allows each sub-model to assign attention weights appropriate to its granularity, preventing the propagation of incorrect attention weights from one level to another.
Solution Approach 2:
Different sub-models have specialized attention mechanisms tailored to their specific granularity. Word-level sub-models use attention mechanisms optimized for individual word relationships, while phrase-level sub-models use attention mechanisms optimized for multi-word expression relationships. This local specialization ensures that attention weight assignment is reliable at each granularity level.
3Measurement precision
If word granularity segmentation is used, then individual word representation accuracy improves, but overall sentence comprehension deteriorates due to loss of phrase-level semantic information
Solution Approach 1:
The model segments the representation task into word-level and phrase-level components. Word-level sub-models generate accurate representations for individual words, while phrase-level sub-models generate representations that capture multi-word expression semantics. Both representations are preserved and integrated, preventing information loss at either granularity level.
Solution Approach 2:
The model employs a nested structure where word-level representations are embedded within phrase-level representations. The phrase-level sub-models can access and integrate the detailed word-level semantic information while adding their own phrase-level semantic context. This nested arrangement allows fine-grained word information to be preserved within the coarser-grained phrase representations.
Data Source
AI summary
Disclosed are a language representation model system, a language representation model pre-training method, a natural language processing method, an electronic device, and a storage medium. The language representation model system includes: a word granularity language representation sub-model based on segmentation in units of words, and a phrase granularity language representation sub-model based on segmentation in units of words. The word granularity language representation sub-model is configured to output, based on a sentence segmented in units of words, a first semantic vector corresponding to a semantic expressed by each segmented word in the sentence. The phrase granularity language representation sub-model is configured to output, based on the sentence segmented in units of phrases, a second semantic vector corresponding to a semantic expressed by each segmented phrase in the sentence.