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    <name type="personal">
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    <abstract>Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context. The existing works represent the entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation. To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history. To achieve this, we propose a COntext-aware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities. Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.353</identifier>
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%0 Conference Proceedings
%T Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems
%A Gou, Yanjie
%A Lei, Yinjie
%A Liu, Lingqiao
%A Dai, Yong
%A Shen, Chunxu
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F gou-etal-2021-contextualize
%X Incorporating knowledge bases (KB) into end-to-end task-oriented dialogue systems is challenging, since it requires to properly represent the entity of KB, which is associated with its KB context and dialogue context. The existing works represent the entity with only perceiving a part of its KB context, which can lead to the less effective representation due to the information loss, and adversely favor KB reasoning and response generation. To tackle this issue, we explore to fully contextualize the entity representation by dynamically perceiving all the relevant entities and dialogue history. To achieve this, we propose a COntext-aware Memory Enhanced Transformer framework (COMET), which treats the KB as a sequence and leverages a novel Memory Mask to enforce the entity to only focus on its relevant entities and dialogue history, while avoiding the distraction from the irrelevant entities. Through extensive experiments, we show that our COMET framework can achieve superior performance over the state of the arts.
%R 10.18653/v1/2021.emnlp-main.353
%U https://aclanthology.org/2021.emnlp-main.353
%U https://doi.org/10.18653/v1/2021.emnlp-main.353
%P 4300-4310

Markdown (Informal)

[Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems](https://aclanthology.org/2021.emnlp-main.353) (Gou et al., EMNLP 2021)

• Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (Gou et al., EMNLP 2021)

ACL

• Yanjie Gou, Yinjie Lei, Lingqiao Liu, Yong Dai, and Chunxu Shen. 2021. Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4300–4310, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.