Joint Training Dialogue Understanding Model Intent Classification
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Solution Overview
Problem
Existing natural language processing technologies face challenges in adapting to dialogue understanding tasks, leading to reduced effectiveness in intent classification and slot labeling due to the use of general pre-training models that are not specifically tailored for dialogue understanding, resulting in suboptimal performance.
Innovation Solution
A method for training a dialogue understanding model by performing joint training using dialogue understanding training data, which includes intent and slot pre-training tasks, leveraging search engine data and knowledge graphs to enhance the model's adaptability and understanding capabilities, and optimizing the training process with additional layers such as part-of-speech and named entity vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general pre-training model is used for semantic understanding, then the model can be trained with general data and have broad applicability, but the performance in dialogue understanding tasks (intent classification and slot labeling) deteriorates
Solution Approach 1:
The training process is segmented into two distinct phases: pre-training phase using general corpus data to build foundational language understanding, and fine-tuning phase using dialogue-specific training data to specialize the model for intent classification and slot labeling tasks. This segmentation allows the model to acquire both broad applicability and task-specific precision.
Solution Approach 2:
The pre-training phase serves as a preliminary action that equips the model with general language understanding capabilities before it is specialized for dialogue understanding. By performing this preliminary training with general data first, the model develops a robust foundation that can then be adapted to specific dialogue tasks with improved efficiency and performance.
2Quantity of substance
If a general pre-training model is used, then training data requirements are reduced, but the dialogue understanding effectiveness deteriorates
Solution Approach 1:
The model undergoes preliminary pre-training on general corpus data to acquire fundamental language understanding skills. This preliminary action reduces the amount of dialogue-specific training data needed later, as the model already possesses basic NLP capabilities that can be fine-tuned for specific dialogue tasks.
Solution Approach 2:
The training data is segmented into two types: general corpus data for pre-training and dialogue-specific data for fine-tuning. This segmentation allows efficient use of data resources by leveraging large amounts of general data for foundational learning while using smaller, targeted dialogue datasets for task-specific optimization.
3Adaptability or versatility
If joint training for dialogue understanding pre-training task and general pre-training task is performed, then the model becomes specifically adapted for dialogue tasks, but the training complexity increases
Solution Approach 1:
The training process is divided into distinct phases (pre-training and fine-tuning) with different objectives and data types. This segmentation manages complexity by breaking down the overall training process into manageable stages, each with clear goals and optimized configurations, rather than attempting to optimize for all tasks simultaneously.
Solution Approach 2:
The pre-training phase serves as a preliminary action that establishes a solid foundation before the more complex fine-tuning process. This preliminary step simplifies the overall training complexity by preparing the model in advance, making the subsequent task-specific training more efficient and less computationally intensive.
Data Source
AI summary
The present disclosure provides a method, apparatus, device and storage medium for training a dialogue understanding model, and relates to technical field of computers, and specifically to the technical field of artificial intelligence such as natural language processing and deep learning. The method for training a dialogue understanding model includes: obtaining dialogue understanding training data; performing joint training for a dialogue understanding pre-training task and a general pre-training task by using the dialogue understanding training data, to obtain a dialogue understanding model. According to the present disclosure, a model specially adapted for a dialogue understanding task may be obtained by training.