End-to-End Memory Networks for Multi-Turn Language Understanding
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Solution Overview
Problem
Traditional spoken language understanding (SLU) components in dialogue systems treat each input utterance in isolation, leading to errors being propagated across turns and a lack of knowledge carryover from long histories, which degrades performance and limits the accuracy and speed of conversation understanding.
Innovation Solution
The implementation of end-to-end memory networks that encode inputs with intents and slots as embeddings, allowing for the exploitation of latent contextual information to model knowledge carryover in multi-turn conversations, using attention models for semantic parsing and joint intent prediction and slot tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLU components treat each utterance in isolation, then processing speed is maintained, but accuracy degrades due to error propagation and lack of knowledge carryover
Solution Approach 1:
The patent merges multiple SLU tasks (domain classification, intent prediction, slot filling) into a unified neural network architecture that processes multi-turn conversations end-to-end. This integration allows the system to maintain accuracy across turns by preserving contextual information while avoiding the error propagation inherent in traditional multi-stage pipelines.
Solution Approach 2:
The patent introduces memory networks as an intermediary component between the input utterances and the SLU tasks. These memory networks store and retrieve contextual information from previous turns, acting as a mediator that enables knowledge carryover without requiring complex architectural changes to the core SLU components.
2Loss of information
If knowledge from long history is aggregated across multiple turns, then contextual understanding improves, but processing time increases
Solution Approach 1:
The patent performs preliminary encoding of utterances into embeddings and stores them in memory networks before the actual SLU processing. This preliminary action prepares contextual information in advance, allowing the system to quickly retrieve and utilize knowledge from long history during inference without incurring significant processing delays.
Solution Approach 2:
The patent creates vector representations (embeddings) as copies of the original utterances and stores these copies in memory networks. This copying mechanism enables efficient retrieval of contextual information without requiring access to the original lengthy conversation history, thus reducing processing time while maintaining knowledge carryover.
3Reliability
If end-to-end memory networks are implemented for multi-turn SLU, then accuracy and knowledge carryover improve, but computational complexity increases
Solution Approach 1:
The patent segments the end-to-end memory network into distinct functional components: embedding layers for input representation, memory networks for contextual storage and retrieval, and separate heads for domain classification, intent prediction, and slot filling. This segmentation makes the complex model more manageable and enables targeted optimization of each component.
Solution Approach 2:
The patent implements a universal memory network structure that serves multiple SLU tasks simultaneously. The same memory network components are used for domain classification, intent prediction, and slot filling, allowing the system to achieve reliable multi-turn understanding without requiring separate complex models for each task.
Data Source
AI summary
A processing unit can extract salient semantics to model knowledge carryover, from one turn to the next, in multi-turn conversations. Architecture described herein can use the end-to-end memory networks to encode inputs, e.g., utterances, with intents and slots, which can be stored as embeddings in memory, and in decoding the architecture can exploit latent contextual information from memory, e.g., demographic context, visual context, semantic context, etc. e.g., via an attention model, to leverage previously stored semantics for semantic parsing, e.g., for joint intent prediction and slot tagging. In examples, architecture is configured to build an end-to-end memory network model for contextual, e.g., multi-turn, language understanding, to apply the end-to-end memory network model to multiple turns of conversational input; and to fill slots for output of contextual, e.g., multi-turn, language understanding of the conversational input. The neural network can be learned using backpropagation from output to input using gradient descent optimization.