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

Chien-Sheng Wu, Richard Socher, Caiming Xiong

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.

Keywords: pointer networks, memory networks, task-oriented dialogue systems, natural language processing

TL;DR: GLMP: Global memory encoder (context RNN, global pointer) and local memory decoder (sketch RNN, local pointer) that share external knowledge (MemNN) are proposed to strengthen response generation in task-oriented dialogue.

Code: [ jasonwu0731/GLMP](https://github.com/jasonwu0731/GLMP) + [ 3 community implementations](https://paperswithcode.com/paper/?openreview=ryxnHhRqFm)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/global-to-local-memory-pointer-networks-for/code)