Mention Memory: incorporating textual knowledge into Transformers through entity mention attention
Abstract
Natural language understanding tasks such as open-domain question answering often require retrieving and assimilating factual information from multiple sources. We propose to address this problem by integrating a semi-parametric representation of a large text corpus into a Transformer model as a source of factual knowledge.
Specifically, our method represents knowledge as a ``mention memory" containing a dense vector representation of every entity mention in a corpus. The Transformer model accesses the information through internal memory layers in which each entity mention in the passage being read attends to the mention memory. This approach enables synthesis of and reasoning over many disparate sources of information \textit{within} a single Transformer model.
In experiments using a memory of ~150 million Wikipedia mentions, our model provides to strong improvements in performance on several open-domain knowledge-intensive tasks, including the claim verification benchmarks FEVER and HoVeR and several entity-based QA benchmarks. We also show that the model learns to attend to informative mentions without any direct supervision. Finally we show that the model can be adapted to generalize to new unseen entities by updating the memory, without retraining.
Specifically, our method represents knowledge as a ``mention memory" containing a dense vector representation of every entity mention in a corpus. The Transformer model accesses the information through internal memory layers in which each entity mention in the passage being read attends to the mention memory. This approach enables synthesis of and reasoning over many disparate sources of information \textit{within} a single Transformer model.
In experiments using a memory of ~150 million Wikipedia mentions, our model provides to strong improvements in performance on several open-domain knowledge-intensive tasks, including the claim verification benchmarks FEVER and HoVeR and several entity-based QA benchmarks. We also show that the model learns to attend to informative mentions without any direct supervision. Finally we show that the model can be adapted to generalize to new unseen entities by updating the memory, without retraining.
Research Areas
