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    <abstract>Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically follows the intent-slot framework, which is easy for annotation yet limited in scalability for complex linguistic expressions. A line of works alleviates the representation issue by introducing hierarchical structures but challenging to express complex compositional semantics, such as negation and coreference. We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges to represent rich compositional semantics. Moreover, we propose an inheritance hierarchy mechanism focusing on domain extensibility. Additionally, we annotated DMR-FastFood, a multi-turn dialogue dataset with more than 70k utterances, with DMR. We propose two evaluation tasks to evaluate different dialogue models and a novel coreference resolution model GNNCoref for the graph-based coreference resolution task. Experiments show that DMR can be parsed well with pre-trained Seq2Seq models, and GNNCoref outperforms the baseline models by a large margin. The dataset and code are available at https://github.com/amazon-research/dialogue-meaning-representation</abstract>
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%0 Conference Proceedings
%T Dialogue Meaning Representation for Task-Oriented Dialogue Systems
%A Hu, Xiangkun
%A Dai, Junqi
%A Yan, Hang
%A Zhang, Yi
%A Guo, Qipeng
%A Qiu, Xipeng
%A Zhang, Zheng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F hu-etal-2022-dialogue
%X Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically follows the intent-slot framework, which is easy for annotation yet limited in scalability for complex linguistic expressions. A line of works alleviates the representation issue by introducing hierarchical structures but challenging to express complex compositional semantics, such as negation and coreference. We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue. Our representation contains a set of nodes and edges to represent rich compositional semantics. Moreover, we propose an inheritance hierarchy mechanism focusing on domain extensibility. Additionally, we annotated DMR-FastFood, a multi-turn dialogue dataset with more than 70k utterances, with DMR. We propose two evaluation tasks to evaluate different dialogue models and a novel coreference resolution model GNNCoref for the graph-based coreference resolution task. Experiments show that DMR can be parsed well with pre-trained Seq2Seq models, and GNNCoref outperforms the baseline models by a large margin. The dataset and code are available at https://github.com/amazon-research/dialogue-meaning-representation
%R 10.18653/v1/2022.findings-emnlp.17
%U https://aclanthology.org/2022.findings-emnlp.17
%U https://doi.org/10.18653/v1/2022.findings-emnlp.17
%P 223-237

Markdown (Informal)

[Dialogue Meaning Representation for Task-Oriented Dialogue Systems](https://aclanthology.org/2022.findings-emnlp.17) (Hu et al., Findings 2022)

• Dialogue Meaning Representation for Task-Oriented Dialogue Systems (Hu et al., Findings 2022)

ACL

• Xiangkun Hu, Junqi Dai, Hang Yan, Yi Zhang, Qipeng Guo, Xipeng Qiu, and Zheng Zhang. 2022. Dialogue Meaning Representation for Task-Oriented Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 223–237, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.