Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206516789> ?p ?o ?g. }
- W4206516789 endingPage "685" @default.
- W4206516789 startingPage "673" @default.
- W4206516789 abstract "With intelligent dialogue systems becoming more and more important in our daily lives, slot filling, one of the most important components of an intelligent dialogue system, has gotten a lot of attention from academia and industry. Despite many advancements in the single-domain learning paradigm for slot filling, leveraging resources from different domains to boost learning for a target domain remains a challenge. In contrast to prior methods that supplemented a sequence labeling model with slot meta-information, we address cross-domain slot filling as a machine reading comprehension (MRC) problem for the first time, where the extraction of slot values is viewed as a question answering process. In the framework above, we present both static and dynamic question generating mechanisms, which have complimentary effects in diverse cross-domain contexts. Furthermore, we devise a dynamic question generation approach that can generate numerous values for a slot at the same time. Finally, we construct a pre-training and fine-tuning training approach that enables us to improve learning by utilizing MRC’s resources. We conducted extensive experiments on four datasets to evaluate our approach, and the experimental results clearly justified the advantages of our approach in various cross-domain settings." @default.
- W4206516789 created "2022-01-26" @default.
- W4206516789 creator A5040304239 @default.
- W4206516789 creator A5043896172 @default.
- W4206516789 creator A5047459446 @default.
- W4206516789 creator A5050740365 @default.
- W4206516789 date "2022-01-01" @default.
- W4206516789 modified "2023-10-15" @default.
- W4206516789 title "Cross-Domain Slot Filling as Machine Reading Comprehension: A New Perspective" @default.
- W4206516789 cites W1649407914 @default.
- W4206516789 cites W2010152689 @default.
- W4206516789 cites W2064675550 @default.
- W4206516789 cites W2076440176 @default.
- W4206516789 cites W2077302143 @default.
- W4206516789 cites W2116053234 @default.
- W4206516789 cites W2120375264 @default.
- W4206516789 cites W2137871902 @default.
- W4206516789 cites W2206274971 @default.
- W4206516789 cites W2397579082 @default.
- W4206516789 cites W2399456070 @default.
- W4206516789 cites W2400801499 @default.
- W4206516789 cites W2473329891 @default.
- W4206516789 cites W2493916176 @default.
- W4206516789 cites W2557764419 @default.
- W4206516789 cites W2741239878 @default.
- W4206516789 cites W2798914047 @default.
- W4206516789 cites W2803237843 @default.
- W4206516789 cites W2803392141 @default.
- W4206516789 cites W2804945011 @default.
- W4206516789 cites W2805752625 @default.
- W4206516789 cites W2888851113 @default.
- W4206516789 cites W2891533927 @default.
- W4206516789 cites W2900987791 @default.
- W4206516789 cites W2941573484 @default.
- W4206516789 cites W2949922292 @default.
- W4206516789 cites W2951088751 @default.
- W4206516789 cites W2962739339 @default.
- W4206516789 cites W2962775474 @default.
- W4206516789 cites W2962809918 @default.
- W4206516789 cites W2963033987 @default.
- W4206516789 cites W2963050422 @default.
- W4206516789 cites W2963339397 @default.
- W4206516789 cites W2963578188 @default.
- W4206516789 cites W2963578915 @default.
- W4206516789 cites W2963963993 @default.
- W4206516789 cites W2963974889 @default.
- W4206516789 cites W2964236684 @default.
- W4206516789 cites W2970450228 @default.
- W4206516789 cites W2971167298 @default.
- W4206516789 cites W2997771882 @default.
- W4206516789 cites W3034238471 @default.
- W4206516789 cites W3035625205 @default.
- W4206516789 cites W3045689439 @default.
- W4206516789 cites W3045703328 @default.
- W4206516789 cites W3098800734 @default.
- W4206516789 cites W3099757670 @default.
- W4206516789 cites W3101469874 @default.
- W4206516789 cites W3102925419 @default.
- W4206516789 cites W3104597568 @default.
- W4206516789 cites W3117925982 @default.
- W4206516789 cites W3126145531 @default.
- W4206516789 cites W3161463965 @default.
- W4206516789 cites W4231466995 @default.
- W4206516789 doi "https://doi.org/10.1109/taslp.2022.3140559" @default.
- W4206516789 hasPublicationYear "2022" @default.
- W4206516789 type Work @default.
- W4206516789 citedByCount "2" @default.
- W4206516789 countsByYear W42065167892022 @default.
- W4206516789 countsByYear W42065167892023 @default.
- W4206516789 crossrefType "journal-article" @default.
- W4206516789 hasAuthorship W4206516789A5040304239 @default.
- W4206516789 hasAuthorship W4206516789A5043896172 @default.
- W4206516789 hasAuthorship W4206516789A5047459446 @default.
- W4206516789 hasAuthorship W4206516789A5050740365 @default.
- W4206516789 hasConcept C111919701 @default.
- W4206516789 hasConcept C119857082 @default.
- W4206516789 hasConcept C12713177 @default.
- W4206516789 hasConcept C134306372 @default.
- W4206516789 hasConcept C154945302 @default.
- W4206516789 hasConcept C17744445 @default.
- W4206516789 hasConcept C199360897 @default.
- W4206516789 hasConcept C199539241 @default.
- W4206516789 hasConcept C204321447 @default.
- W4206516789 hasConcept C2776502983 @default.
- W4206516789 hasConcept C2778112365 @default.
- W4206516789 hasConcept C2778780117 @default.
- W4206516789 hasConcept C2780801425 @default.
- W4206516789 hasConcept C33923547 @default.
- W4206516789 hasConcept C36503486 @default.
- W4206516789 hasConcept C41008148 @default.
- W4206516789 hasConcept C44291984 @default.
- W4206516789 hasConcept C511192102 @default.
- W4206516789 hasConcept C54355233 @default.
- W4206516789 hasConcept C554936623 @default.
- W4206516789 hasConcept C86803240 @default.
- W4206516789 hasConcept C98045186 @default.
- W4206516789 hasConceptScore W4206516789C111919701 @default.
- W4206516789 hasConceptScore W4206516789C119857082 @default.