@inproceedings{hou-etal-2024-choice-75,
title = "Choice-75: A Dataset on Decision Branching in Script Learning",
author = "Hou, Zhaoyi and
Zhang, Li and
Callison-Burch, Chris",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.285",
pages = "3215--3223",
abstract = "Script learning studies how daily events unfold. It enables machines to reason about narratives with implicit information. Previous works mainly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people{'}s circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performances, there is still notable headroom in hard scenarios.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hou-etal-2024-choice-75">
<titleInfo>
<title>Choice-75: A Dataset on Decision Branching in Script Learning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhaoyi</namePart>
<namePart type="family">Hou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Li</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chris</namePart>
<namePart type="family">Callison-Burch</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Script learning studies how daily events unfold. It enables machines to reason about narratives with implicit information. Previous works mainly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people’s circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performances, there is still notable headroom in hard scenarios.</abstract>
<identifier type="citekey">hou-etal-2024-choice-75</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.285</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>3215</start>
<end>3223</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Choice-75: A Dataset on Decision Branching in Script Learning
%A Hou, Zhaoyi
%A Zhang, Li
%A Callison-Burch, Chris
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F hou-etal-2024-choice-75
%X Script learning studies how daily events unfold. It enables machines to reason about narratives with implicit information. Previous works mainly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people’s circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performances, there is still notable headroom in hard scenarios.
%U https://aclanthology.org/2024.lrec-main.285
%P 3215-3223
Markdown (Informal)
[Choice-75: A Dataset on Decision Branching in Script Learning](https://aclanthology.org/2024.lrec-main.285) (Hou et al., LREC-COLING 2024)
ACL
- Zhaoyi Hou, Li Zhang, and Chris Callison-Burch. 2024. Choice-75: A Dataset on Decision Branching in Script Learning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 3215–3223, Torino, Italia. ELRA and ICCL.