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Desde 2 de fevereiro de 2024, a palavra

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Uma Fácil e Rápida Apresentação

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

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

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

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

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

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

= "2024", address = "Online", 📉 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 📉 essential component for building

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

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

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

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

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

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

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

use a linguistically motivated questioning strategy to turn descriptions 📉 into

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

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

from unlabeled conversations. Our full system substantially outperforms baselines by

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



Xinya

type="family">Du

type="text">author

type="given">Luheng He

authority="marcrelator" 📉 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

📉 type="given">Fei Xia

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

type="personal"> Wenjie

type="family">Li

type="text">editor 📉

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, 📉 we

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

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

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

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

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

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

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

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

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

from QA pairs synthetically generated from unlabeled conversations. Our 📉 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

📉 2024-08 654 664

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

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

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

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

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

Language Processing (Volume 2: Short Papers) %D 2024 %8 📉 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 📉 systems. In this work, we focus on the

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

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

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

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

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

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

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

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

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

from QA pairs synthetically generated 📉 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 📉 %U

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

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