Semantic Pre-training via AMR Graphs for Dialogue Models
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Solution Overview
Problem
Existing dialogue models are weak in understanding the main semantic meaning of a dialogue context and require human annotations or external parsers, leading to increased costs and error propagation, as they focus on surface dialogue text and domain-specific data, failing to exploit the general potentiality of semantic structures.
Innovation Solution
A semantic-based pre-training framework that generates dialogue-level abstract meaning representation (AMR) graphs to learn core semantic units, semantic relations, and overall agreement, using a pre-trained AMR parser to transform utterances into AMR graphs and employing a contrastive learning framework with cosine similarity to enhance dialogue model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing dialogue models are trained on surface dialogue text, then the training process is simple, but the model's ability to understand semantic meaning is weak
Solution Approach 1:
The patent applies preliminary action by pre-training the dialogue model using dialogue-level AMR graphs before fine-tuning on specific tasks. The AMR graphs are constructed in advance to capture semantic structures, allowing the model to learn semantic representations beforehand. This resolves the contradiction by establishing strong semantic understanding capabilities through preliminary semantic pre-training, which then improves downstream task performance without requiring complex training procedures at the task level.
Solution Approach 2:
The patent introduces dialogue-level AMR graphs as an intermediary between surface dialogue text and the model's semantic understanding. These graphs serve as a mediator that bridges the gap between raw text and semantic meaning, allowing the model to learn from structured semantic representations. This intermediary structure enables the model to capture core semantic information and relationships without directly processing complex surface text patterns.
2Measurement precision
If human annotations or external parsers are used to obtain semantic structures, then semantic information is accurate, but costs increase and error propagation occurs
Solution Approach 1:
The patent applies self-service by enabling the dialogue model to generate its own semantic representations through self-training on dialogue-level AMR graphs. Instead of relying on external parsers or human annotations, the model learns to construct and utilize semantic structures autonomously during pre-training. This self-service approach eliminates the need for expensive external semantic processing resources while still achieving accurate semantic understanding.
Solution Approach 2:
The patent performs preliminary construction of dialogue-level AMR graphs during the pre-training phase, creating accurate semantic structures in advance before the model needs to use them. By pre-computing and storing these semantic representations, the system avoids the need for expensive real-time parsing or annotation during deployment, resolving the contradiction between accuracy and cost.
3Adaptability or versatility
If domain-specific benchmark data is used for training, then performance on specific tasks improves, but the general potentiality of semantic structures is unexploited
Solution Approach 1:
The patent applies universality by designing a dialogue-level AMR graph construction method that is domain-agnostic and can be applied across different dialogue domains and tasks. The semantic representation framework is built to be universally applicable, allowing the same pre-trained model to serve multiple functions across different applications. This universal semantic foundation enables the model to adapt to various domains while maintaining reliable task-specific performance through fine-tuning.
Data Source
AI summary
Systems and methods for pre-training a dialogue model with semantic information include: generating a dialogue-level abstract meaning representation (AMR) graph for an input dialogue associated with a speaker, learning core semantic units of the input dialogue based on nodes of the dialogue-level AMR graph, learning semantic relations between words of a sentence of the input dialogue based on edges of the dialogue-level AMR graph, learning an overall agreement of the input dialogue and the dialogue-level AMR graph, and training the dialogue model based on the learned core semantic units, semantic relations between words, and overall agreement.