Dialogue Summary Model Training with Speaker Embedding Transfer
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Solution Overview
Problem
Current natural language generation models, such as encoder-decoder-based transformers, are not well-suited for summarizing dialogues due to their limitations in handling heterogeneous dialogue datasets with non-sequential information flow, pronoun changes, colloquial language, and the scarcity of high-quality dialogue data, which hinders their learning efficiency with limited training data.
Innovation Solution
A method involving a parameter transferer to transfer learning parameter values from a pre-trained natural language processing model to a sequence-to-sequence-based dialogue summary model, utilizing self-supervised learning tasks like masking speaker, switching speaker, switching utterance, and inserting utterance tasks to pre-train the model, and then updating the dialogue summary model's parameters based on the loss between inferred and correct summary texts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If encoder-decoder-based models like transformers are used for dialogue summarization, then the model structure can handle sequential information, but the model fails to capture non-sequential information flow and speaker-specific characteristics in dialogues
Solution Approach 1:
The patent segments the dialogue processing by introducing separate speaker embedding modules for each speaker in the dialogue. Each speaker has their own embedding layer that processes their utterances independently, allowing the model to capture speaker-specific characteristics while maintaining the overall sequence processing capability. This segmentation resolves the contradiction by enabling both sequential processing and speaker-specific adaptation simultaneously.
Solution Approach 2:
The patent applies local quality by making different parts of the model (speaker embedding layers) have different parameters and processing capabilities tailored to their specific function. Each speaker's embedding layer learns speaker-specific representations independently, allowing the model to adapt to local dialogue characteristics while maintaining global coherence through the encoder-decoder structure.
2Adaptability or versatility
If pre-trained models are fine-tuned with limited dialogue data, then the model can adapt to dialogue-specific tasks, but the learning efficiency remains low due to data scarcity
Solution Approach 1:
The patent applies preliminary action by pre-training speaker embedding layers using large-scale speech data before fine-tuning on dialogue summarization tasks. This pre-training phase allows the model to learn speaker-specific characteristics from abundant data, and then these pre-learned representations are transferred to the dialogue task, improving learning efficiency when only limited dialogue data is available for fine-tuning.
3Ease of manufacture
If standard natural language processing models are applied to dialogue data, then the model structure is simple and easy to implement, but the model cannot effectively handle heterogeneous dialogue characteristics such as pronoun changes and colloquial language
Solution Approach 1:
The patent introduces speaker embedding layers as intermediary components between the input dialogue text and the encoder-decoder model. These intermediary layers process speaker-specific information and transform the raw dialogue text into speaker-aware representations, enabling the standard encoder-decoder model to effectively handle heterogeneous dialogue characteristics without requiring complex architectural changes.
Data Source
AI summary
An apparatus for training a dialogue summary model according to an embodiment includes a parameter transferer configured to transfer one or more learning parameter values of a pre-trained natural language processing model to a sequence-to-sequence-based dialogue summary model, and a model trainer configured to train the dialogue summary model by using the transferred learning parameter values as initial values for learning parameters of each of an encoder and a decoder in the dialogue summary model.