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Relational Memory Augmented Language Models

2022-01-24 13:25:41
Qi Liu, Dani Yogatama, Phil Blunsom
arXiv_AI
           
Abstract

We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model with a knowledge graph for a more coherent and logical generation.

Abstract (translated)

URL

https://arxiv.org/abs/2201.09680

PDF

https://arxiv.org/pdf/2201.09680.pdf

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