@inproceedings{zhou-etal-2022-augmentation,
title = "Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach",
author = "Zhou, Bo and
Wang, Chenhao and
Chen, Yubo and
Liu, Kang and
Zhao, Jun and
Xu, Jiexin and
Jiang, Xiaojian and
Li, Qiuxia",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.161",
pages = "1865--1874",
abstract = "Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is \textit{event sequence prediction}, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a \textit{statistical induction} problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2022-augmentation">
<titleInfo>
<title>Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach</title>
</titleInfo>
<name type="personal">
<namePart type="given">Bo</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chenhao</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yubo</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiexin</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaojian</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qiuxia</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 29th International Conference on Computational Linguistics</title>
</titleInfo>
<originInfo>
<publisher>International Committee on Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is event sequence prediction, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.</abstract>
<identifier type="citekey">zhou-etal-2022-augmentation</identifier>
<location>
<url>https://aclanthology.org/2022.coling-1.161</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>1865</start>
<end>1874</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach
%A Zhou, Bo
%A Wang, Chenhao
%A Chen, Yubo
%A Liu, Kang
%A Zhao, Jun
%A Xu, Jiexin
%A Jiang, Xiaojian
%A Li, Qiuxia
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F zhou-etal-2022-augmentation
%X Being able to infer possible events related to a specific target is critical to natural language processing. One challenging task in this line is event sequence prediction, which aims at predicting a sequence of events given a goal. Currently existing approach models this task as a statistical induction problem, to predict a sequence of events by exploring the similarity between the given goal and the known sequences of events. However, this statistical based approach is complex and predicts a limited variety of events. At the same time this approach ignores the rich knowledge of external events that is important for predicting event sequences. In this paper, in order to predict more diverse events, we first reformulate the event sequence prediction problem as a sequence generation problem. Then to leverage external event knowledge, we propose a three-stage model including augmentation, retrieval and generation. Experimental results on the event sequence prediction dataset show that our model outperforms existing methods, demonstrating the effectiveness of the proposed model.
%U https://aclanthology.org/2022.coling-1.161
%P 1865-1874
Markdown (Informal)
[Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach](https://aclanthology.org/2022.coling-1.161) (Zhou et al., COLING 2022)
ACL
- Bo Zhou, Chenhao Wang, Yubo Chen, Kang Liu, Jun Zhao, Jiexin Xu, Xiaojian Jiang, and Qiuxia Li. 2022. Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1865–1874, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.