@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.", }
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
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
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)
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
em depósito: Ascensão aos Faraós; Era de Deuses? Livro do Mortor e Milionário Gênio das
RiquezaS Irlandesas Jack S Pot! Jogue 1️⃣ Selo
bônus depósito 2024 Casinos dinheiro real -
nsider Gaming inserre-gaing :
casinos.
blog betnacional paid even if you meet the conditions. It's important to mention the developer's goal
to hook the player on 💸 the game for as long as possible. cash Slot Review - Is it
Spin to Win FAKE Rewards! myroomismyoffice
earning, you 💸 can forget about it
It's not going to materialize. Cash Slots Review - Can You Really Earn With This Game?
Os 7 Melhores Jogos de Slots Online com Maior RTP
Seja bem-vindo ao mundo dos cassinos online, onde a aventura e a emoção estão aqui para você Experimente nossa ampla variedade de jogos de cassino online, incluindo roleta, jogos de cartas clássicos e uma variedade dos jogos de slot mais populares. Neste artigo, vamos nos concentrar nos jogos de inslot com os maiores RTPs (Retorno ao jogador) que poderão aumentar suas chances de ganhar.
-
Monopoly Big Event - 99% RTP
Este jogo de slot baseado no popular jogo de tabuleiro Monopoly oferece uma jogabilidade emocionante e uma alta chance de ganhar com um RTP de 99%.
-
Mega Joker - 99% RTP
Outro jogo de slot com RTP de 99%, o Mega Joker é um jogo de cclipper clássico com um grande prêmio Progressivo, aumentando assim suas chances de ganhar mais altas.
-
Blood Suckers - 98% RTP
Este jogo de slot de terror é cheio de bônus grandes e uma alta chance de ganhar com um RTP de 98%, jogue agora no GentingCasino.
-
Rainbow Riches - 98% RTP
Ao tentar a sorte para encontrar o tesouro do Arco-Íris no fim do caminho com este jogo de slot clássico de 20 linhas de pagamento você garante uma alta taxa de pagamento devido ao seu RTP de 98%.
-
Double Diamond - 98% RTP
Com apenas 3 rodas e uma linha de pagamento simples, o Double Diamond mantém as coisas simples, mas fornecem a você uma grande chance de ganhar com o seu RTP de 98%.
-
Starmania - 97,87% RTP
O jogo de slot space-themed Starmania oferece uma experiência de jogo única com seus 10 linhas de pagamento e um RTP de 97,87%, que é superior a maioria dos jogos neste gênero.
-
White Rabbit Megaways - 97,77% RTP
Este jogo de slot online é baseado na história do coelho branco de Alice no país das maravilhas e oferece Megaways para ganhar mais em bet per line slot game uma alta taxa de sucesso devido ao seu RTP de 97,77%.
Então, tente a sorte na Genting Casino e aumente suas chances de ganhar nos nossos jogos de slot online com alguns dos RTPs mais altos da indústria. Com mais jogos de pslot sendo adicionados regularmente, certifique-se de ficar vicentino pelas últimas novidades pelo GentingClubs.