Relational Memory Augmented Language Models

• URL: http://arxiv.org/abs/2201.09680v1
• Date: Mon, 24 Jan 2022 13:25:41 GMT
• Title: Relational Memory Augmented Language Models
• Authors: Qi Liu, Dani Yogatama, Phil Blunsom
• Abstract summary: We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. Our approach produces a better language model in terms of perplexity and bits per character.
• Score: 40.626389607433936
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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