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@inproceedings{du-etal-2024-qa, title = "{QA}-Driven Zero-shot Slot Filling with Weak

Supervision Pretraining", author = "Du, Xinya and He, Luheng and Li, 5️⃣ Qi and Yu, Dian

and Pasupat, Panupong and Zhang, Yuan", editor = "Zong, Chengqing and Xia, Fei and Li,

Wenjie 5️⃣ and Navigli, Roberto", booktitle = "Proceedings of the 59th Annual Meeting of

the Association for Computational Linguistics and the 11th 5️⃣ International Joint

Conference on Natural Language Processing (Volume 2: Short Papers)", month = aug, year

= "2024", address = "Online", 5️⃣ publisher = "Association for Computational Linguistics",

url = "//aclanthology/2024.acl-short.83", doi = "10.18653/v1/2024.acl-short.83",

pages = "654--664", abstract = "Slot-filling is an 5️⃣ essential component for building

task-oriented dialog systems. In this work, we focus on the zero-shot slot-filling

problem, where the model 5️⃣ needs to predict slots and their values, given utterances from

new domains without training on the target domain. Prior methods 5️⃣ directly encode slot

descriptions to generalize to unseen slot types. However, raw slot descriptions are

often ambiguous and do not 5️⃣ encode enough semantic information, limiting the models{'}

zero-shot capability. To address this problem, we introduce QA-driven slot filling

(QASF), which 5️⃣ extracts slot-filler spans from utterances with a span-based QA model. We

use a linguistically motivated questioning strategy to turn descriptions 5️⃣ into

questions, allowing the model to generalize to unseen slot types. Moreover, our QASF

model can benefit from weak supervision 5️⃣ signals from QA pairs synthetically generated

from unlabeled conversations. Our full system substantially outperforms baselines by

over 5{\%} on the 5️⃣ SNIPS benchmark.", }



Xinya

type="family">Du

type="text">author

type="given">Luheng He

authority="marcrelator" 5️⃣ type="text">author

type="personal"> Qi

type="family">Li

type="text">author

type="given">Dian Yu

authority="marcrelator" type="text">author

type="personal"> Panupong

type="family">Pasupat

type="text">author

type="given">Yuan Zhang

authority="marcrelator" type="text">author

2024-08 text

Chengqing

type="family">Zong

type="text">editor

5️⃣ type="given">Fei Xia

authority="marcrelator" type="text">editor

type="personal"> Wenjie

type="family">Li

type="text">editor 5️⃣

type="given">Roberto Navigli

editor

Association for Computational Linguistics

Online

authority="marcgt">conference publication Slot-filling

is an essential component for building task-oriented dialog systems. In this work, 5️⃣ we

focus on the zero-shot slot-filling problem, where the model needs to predict slots and

their values, given utterances from 5️⃣ new domains without training on the target domain.

Prior methods directly encode slot descriptions to generalize to unseen slot types.

5️⃣ However, raw slot descriptions are often ambiguous and do not encode enough semantic

information, limiting the models’ zero-shot capability. To 5️⃣ address this problem, we

introduce QA-driven slot filling (QASF), which extracts slot-filler spans from

utterances with a span-based QA model. 5️⃣ We use a linguistically motivated questioning

strategy to turn descriptions into questions, allowing the model to generalize to

unseen slot 5️⃣ types. Moreover, our QASF model can benefit from weak supervision signals

from QA pairs synthetically generated from unlabeled conversations. Our 5️⃣ full system

substantially outperforms baselines by over 5% on the SNIPS benchmark.

du-etal-2024-qa

type="doi">10.18653/v1/2024.acl-short.83

//aclanthology/2024.acl-short.83

5️⃣ 2024-08 654 664

%0 Conference Proceedings %T QA-Driven Zero-shot

Slot Filling with Weak Supervision Pretraining 5️⃣ %A Du, Xinya %A He, Luheng %A Li, Qi %A

Yu, Dian %A Pasupat, Panupong %A Zhang, Yuan %Y Zong, 5️⃣ Chengqing %Y Xia, Fei %Y Li,

Wenjie %Y Navigli, Roberto %S Proceedings of the 59th Annual Meeting of the Association

5️⃣ for Computational Linguistics and the 11th International Joint Conference on Natural

Language Processing (Volume 2: Short Papers) %D 2024 %8 5️⃣ August %I Association for

Computational Linguistics %C Online %F du-etal-2024-qa %X Slot-filling is an essential

component for building task-oriented dialog 5️⃣ systems. In this work, we focus on the

zero-shot slot-filling problem, where the model needs to predict slots and their

5️⃣ values, given utterances from new domains without training on the target domain. Prior

methods directly encode slot descriptions to generalize 5️⃣ to unseen slot types. However,

raw slot descriptions are often ambiguous and do not encode enough semantic

information, limiting the 5️⃣ models’ zero-shot capability. To address this problem, we

introduce QA-driven slot filling (QASF), which extracts slot-filler spans from

utterances with 5️⃣ a span-based QA model. We use a linguistically motivated questioning

strategy to turn descriptions into questions, allowing the model to 5️⃣ generalize to

unseen slot types. Moreover, our QASF model can benefit from weak supervision signals

from QA pairs synthetically generated 5️⃣ from unlabeled conversations. Our full system

substantially outperforms baselines by over 5% on the SNIPS benchmark. %R

10.18653/v1/2024.acl-short.83 %U //aclanthology/2024.acl-short.83 5️⃣ %U

//doi/10.18653/v1/2024.acl-short.83 %P 654-664

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