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        <title>Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models</title>
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        <namePart type="given">Lingzhen</namePart>
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            <namePart type="given">Emily</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting – a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data.</abstract>
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        <url>https://aclanthology.org/C18-1185</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models
%A Chen, Lingzhen
%A Moschitti, Alessandro
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F chen-moschitti-2018-learning
%X In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting – a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data.
%U https://aclanthology.org/C18-1185
%P 2181-2191

Markdown (Informal)

[Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models](https://aclanthology.org/C18-1185) (Chen & Moschitti, COLING 2018)

• Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models (Chen & Moschitti, COLING 2018)

ACL

• Lingzhen Chen and Alessandro Moschitti. 2018. Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2181–2191, Santa Fe, New Mexico, USA. Association for Computational Linguistics.