Relational Memory-Augmented Language Models

doi:10.1162/tacl_a_00476

Relational Memory-Augmented Language Models

2022-05-04

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DOI：10.1162/tacl_a_00476

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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 and a knowledge graph for more coherent and logical generation.

Relational Memory-Augmented Language Models

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