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        <title>Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference</title>
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    <abstract>Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the previous models learn the context memory with only one objective to maximizing the SLU performance, leaving the context memory under-exploited. In this paper, we propose a new dialogue logistic inference (DLI) task to consolidate the context memory jointly with SLU in the multi-task framework. DLI is defined as sorting a shuffled dialogue session into its original logical order and shares the same memory encoder and retrieval mechanism as the SLU model. Our experimental results show that various popular contextual SLU models can benefit from our approach, and improvements are quite impressive, especially in slot filling.</abstract>
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%0 Conference Proceedings
%T Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
%A Bai, He
%A Zhou, Yu
%A Zhang, Jiajun
%A Zong, Chengqing
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bai-etal-2019-memory
%X Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations. However, most of the previous models learn the context memory with only one objective to maximizing the SLU performance, leaving the context memory under-exploited. In this paper, we propose a new dialogue logistic inference (DLI) task to consolidate the context memory jointly with SLU in the multi-task framework. DLI is defined as sorting a shuffled dialogue session into its original logical order and shares the same memory encoder and retrieval mechanism as the SLU model. Our experimental results show that various popular contextual SLU models can benefit from our approach, and improvements are quite impressive, especially in slot filling.
%R 10.18653/v1/P19-1541
%U https://aclanthology.org/P19-1541
%U https://doi.org/10.18653/v1/P19-1541
%P 5448-5453

Markdown (Informal)

[Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference](https://aclanthology.org/P19-1541) (Bai et al., ACL 2019)

• Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (Bai et al., ACL 2019)

ACL

• He Bai, Yu Zhou, Jiajun Zhang, and Chengqing Zong. 2019. Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5448–5453, Florence, Italy. Association for Computational Linguistics.