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        <title>Evaluating the Utility of Hand-crafted Features in Sequence Labelling</title>
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        <namePart type="given">Minghao</namePart>
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    <abstract>Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60%, while retaining the same predictive accuracy.</abstract>
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%0 Conference Proceedings
%T Evaluating the Utility of Hand-crafted Features in Sequence Labelling
%A Wu, Minghao
%A Liu, Fei
%A Cohn, Trevor
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wu-etal-2018-evaluating
%X Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a F 1 of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60%, while retaining the same predictive accuracy.
%R 10.18653/v1/D18-1310
%U https://aclanthology.org/D18-1310
%U https://doi.org/10.18653/v1/D18-1310
%P 2850-2856

Markdown (Informal)

[Evaluating the Utility of Hand-crafted Features in Sequence Labelling](https://aclanthology.org/D18-1310) (Wu et al., EMNLP 2018)

• Evaluating the Utility of Hand-crafted Features in Sequence Labelling (Wu et al., EMNLP 2018)

ACL

• Minghao Wu, Fei Liu, and Trevor Cohn. 2018. Evaluating the Utility of Hand-crafted Features in Sequence Labelling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2850–2856, Brussels, Belgium. Association for Computational Linguistics.