Contextual String Embeddings for Sequence Labeling

Proceedings of the 27th International Conference on Computational Linguistics, pages 1638–1649 Santa Fe, New Mexico, USA, August 20-26, 2018. 1638 Contextual String Embeddings for Sequence Labeling Alan Akbik Zalando Research M¨uhlenstraße 25 10243 Berlin Duncan Blythe Zalando Research M¨uhlenstraße 25 10243 Berlin {firstname.lastname}@zalando.de Roland Vollgraf Zalando Research M¨uhlenstraße 25 10243 Berlin Abstract Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CONLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the re- search community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair 1 Introduction A large family of NLP tasks such as named entity recognition (NER) and part-of-speech (PoS) tagging may be formulated as sequence labeling problems; text is treated as a sequence of words to be labeled with linguistic tags. Current state-of-the-art approaches for sequence labeling typically use the LSTM variant of bidirectional recurrent neural networks (BiLSTMs), and a subsequent conditional random field (CRF) decoding layer (Huang et al., 2015; Ma and Hovy, 2016). A crucial component in such approaches are word embeddings, typically trained over very large collec- tions of unlabeled data to assist learning and generalization. Current state-of-the-art methods concatenate up to three distinct embedding types: 1. Classical word embeddings (Pennington et al., 2014; Mikolov et al., 2013), pre-trained over very large corpora and shown to capture latent syntactic and semantic similarities. 2. Character-level features (Ma and Hovy, 2016; Lample et al., 2016), which are not pre-trained, but trained on task data to capture task-specific subword features. 3. Contextualized word embeddings (Peters et al., 2017; Peters et al., 2018) that capture word seman- tics in context to address the polysemous and context-dependent nature of words. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ 1639 G e o r g e W a s h i n g t o n w a s b o r …

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