Global-to-local Memory Pointer Networks for Task-Oriented Dialogue (1901.04713v2)

Abstract: End-to-end task-oriented dialogue is challenging since knowledge bases are usually large, dynamic and hard to incorporate into a learning framework. We propose the global-to-local memory pointer (GLMP) networks to address this issue. In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge. The encoder encodes dialogue history, modifies global contextual representation, and generates a global memory pointer. The decoder first generates a sketch response with unfilled slots. Next, it passes the global memory pointer to filter the external knowledge for relevant information, then instantiates the slots via the local memory pointers. We empirically show that our model can improve copy accuracy and mitigate the common out-of-vocabulary problem. As a result, GLMP is able to improve over the previous state-of-the-art models in both simulated bAbI Dialogue dataset and human-human Stanford Multi-domain Dialogue dataset on automatic and human evaluation.

Global-to-local Memory Pointer Networks for Task-Oriented Dialogue

The paper "Global-to-local Memory Pointer Networks for Task-Oriented Dialogue" addresses the significant challenges in designing effective end-to-end dialogue systems that can directly incorporate large, dynamic, and complex knowledge bases (KB) into their learning framework. The authors propose a novel model architecture, the global-to-local memory pointer (GLMP) networks, which dynamically integrates dialogue context with external KB information to generate task-oriented dialogues.

Model Overview

The GLMP model is built upon a dual-layer memory pointer structure comprising a global memory encoder and a local memory decoder. The global encoder processes the dialogue history and generates a memory pointer that influences the retrieval of relevant KB elements. The context-driven memory encoder is specifically designed to leverage the sequential and contextual information embedded in dialogue history, thus mitigating the deficiencies in existing memory network models that struggle with dependencies between memories. The local memory decoder then interfaces with the encoder's output to generate sketch responses and perform precise slot-filling using local memory pointers, ensuring better entity and slot capture even in out-of-vocabulary (OOV) scenarios.

Empirical Evaluation

The GLMP model demonstrates improved performance in both simulated and human-human dialogue datasets, notably achieving a 92.0% accuracy on the comprehensive Task 5 of the bAbI Dialogue dataset, which surpasses prior models by a significant 7.5% margin. The model also shows minimal performance degradation across OOV settings, maintaining high accuracy and completion rates. Moreover, in the Stanford Multi-domain Dialogue (SMD) dataset, GLMP attains superior BLEU scores and entity F1 scores, substantiating its capability in handling diverse conversational domains and complex KBs effectively.

Significance and Implications

The incorporation of a multi-hop reasoning mechanism within an end-to-end framework enables GLMP to strengthen the copy mechanism—a key requirement for generating coherent task-oriented dialogues. By writing contextual representations into external memory, the model adeptly manages entity recognition and slot-filling, reducing the reliance on hand-crafted labels and predefined dialogue states.

The implications of this research extend substantially into developing more sophisticated dialogue systems capable of operating across multiple domains with potentially zero-shot domain transferability. Furthermore, with robust entity management and improved scalability, such approaches could pave the way for advancements in other AI applications, including question answering and complex task automation systems, where dynamic interaction with large knowledge bases is crucial.

Future Directions

The paper suggests potential areas for advancement, such as optimizing entity recognition in low-resource environments and further refining memory attention mechanisms to enhance model generalization across novel domains. By leveraging external knowledge reading and writing capacities, future models can integrate even more intricate KB structures and achieve higher performance benchmarks in task-oriented dialogues or related language-based AI tasks.

In conclusion, the GLMP proposes a notable advancement in dialogue systems by effectively merging global contextual understanding with local detail resolution, thereby presenting a more nuanced and capable approach to handling intricate task-oriented dialogues in varied environments.