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Hierarchical history based information selection for document grounded dialogue generation

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Abstract

Selecting appropriate information from the dialogue history and the document is a prerequisite for a high-quality response in document grounded dialogue generation task. Most of the existing works take dialogue history information as a sequence to interact with documents. In fact, dialogue history has an internal hierarchical structure, which can provide constraints on the selection of information and the interaction with document information. Therefore, this paper proposes a model that uses the hierarchical structure of dialogue history for key information selection. The main idea is to locate important information in history and document by merging both word-level and utterance-level attention of history, and then to generate a better response. The experimental results on two public data sets show that our method significantly outperforms the baseline models.

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Notes

  1. We use NLG evaluation toolkit [18] from https://github.com/Maluuba/nlg-eval.

  2. We use the code published at https://github.com/facebookresearch/ParlAI/blob/master/parlai/core/metrics.py to calculate uni-gram F1.

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Acknowledgements

We thank the anonymous reviewers for their insightful comments. The research is supported in part by the National Natural Science Foundation of China (Grant No. NSFC62076032) and the BUPT Excellent Ph.D. Students Foundation (Grant No. CX2019002).

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  1. Center for Intelligence of Science and Technology (CIST), Beijing University of Posts and Telecommunications, Beijing, China

    Meiqi Wang, Shiyu Tian, Ziwei Bai, Caixia Yuan & Xiaojie Wang

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Appendix : A

Appendix : A

1.1 A.1: Real data examples for ‘Attention update’ in HDIS

The equations of HDIS in detail is complex, so we give the corresponding real data examples for HDIS in Fig. 8.

In hierarchical history based document information selection (HDIS) module, there are three variables: the word-level history representation Hw, the utterance-level history representation Hu and the word-level document representation Dw.

For ease of understanding, it is assumed here that the document has 5 words, the history has two utterances and each utterance has 3 words, and the dimension represented by each word is 2, we assign relatively simple values for each word representation. Note that the red values indicates that they are related to the first utterance in history, and the green values indicate that they are related to the second utterance of history.

1.2 A.2: Specific values of the key parameters

We give a description of the specific values of the key parameters of our algorithm when the best results are achieved in Fig. 7.

Fig. 7
figure 7

The specific values of the key parameters of our model

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Fig. 8
figure 8

A real data example to illustrate the equations of HDIS in detail

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1.3 A.3: Real data examples for static and dynamic ways to obtain utterance-level history

The real data examples for static and dynamic ways to obtain utterance-level history in HHIS mentioned in section ‘Experiments-Implement Details.’ is shown in Fig. 9.

Fig. 9
figure 9

A real data example of static and dynamic ways to obtain utterance-level history in HHIS

Full size image

In hierarchical history information selection (HHIS) module, there are two variables: the word-level history representation Hw and the last time step decoding vector st− 1.

For ease of understanding, it is assumed here that the history has two utterances and each utterance has 3 words, and the dimension represented by each word is 2, we assign relatively simple values for each word representation. Note that the red values indicates that they are related to the first utterance in history, and the green values indicate that they are related to the second utterance of history.

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Wang, M., Tian, S., Bai, Z. et al. Hierarchical history based information selection for document grounded dialogue generation. Appl Intell 53, 17139–17153 (2023). https://doi.org/10.1007/s10489-022-04373-8

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