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        <title>Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling</title>
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    <name type="personal">
        <namePart type="given">Yuanjun</namePart>
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        <namePart type="given">Linzhi</namePart>
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    <name type="personal">
        <namePart type="given">Minglai</namePart>
        <namePart type="family">Shao</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a novel domain whose samples are never seen during training. The recognition performance might be greatly degraded due to severe domain shifts. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings. To this end, we re-examine the typical metric-based methods, and propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling. Considering simplicity, efficiency and generalizability, we present a cascade-style joint learning framework coupled with context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems effectively. Extensive experiments on public benchmarks demonstrate the superiority of the proposed approach over a series of competitive baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.387</identifier>
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%0 Conference Proceedings
%T Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling
%A Shi, Yuanjun
%A Wu, Linzhi
%A Shao, Minglai
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F shi-etal-2023-adaptive
%X Recently slot filling has witnessed great development thanks to deep learning and the availability of large-scale annotated data. However, it poses a critical challenge to handle a novel domain whose samples are never seen during training. The recognition performance might be greatly degraded due to severe domain shifts. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. In practice, these dominant pipeline models may be limited in computational efficiency and generalization capacity because of non-parallel inference and context-free discrete label embeddings. To this end, we re-examine the typical metric-based methods, and propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling. Considering simplicity, efficiency and generalizability, we present a cascade-style joint learning framework coupled with context-aware soft label representations and slot-level contrastive representation learning to mitigate the data and label shift problems effectively. Extensive experiments on public benchmarks demonstrate the superiority of the proposed approach over a series of competitive baselines.
%R 10.18653/v1/2023.emnlp-main.387
%U https://aclanthology.org/2023.emnlp-main.387
%U https://doi.org/10.18653/v1/2023.emnlp-main.387
%P 6291-6301

Markdown (Informal)

[Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling](https://aclanthology.org/2023.emnlp-main.387) (Shi et al., EMNLP 2023)

• Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling (Shi et al., EMNLP 2023)

ACL

• Yuanjun Shi, Linzhi Wu, and Minglai Shao. 2023. Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6291–6301, Singapore. Association for Computational Linguistics.