Annotating training data for sequence tagging tasks is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination in sequence tagging . We find that taggers based on deep pre-trained models can benefit from Bayesian query strategies with the help of the Monte Carlo (MC) dropout. Results of experiments with various uncertainty estimates and MC dropout variants show that the Bayesian active learning by disagreement query strategy coupled with the MC dropout applied only in the classification layer of a Transformer-based tagger is the best option in terms of quality. This option also has very little computational overhead. We also demonstrate that it is possible to reduce the computational overhead of AL by using a smaller distilled versionof a Transformer model for acquiring instances during AL .
Annotating training data for sequence tagging of texts is usually very time-consuming. Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We are the first to thoroughly investigate this powerful combination for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice .
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tasks
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in sequence tagging . We find that taggers based on deep
for the sequence tagging task. We conduct an extensive empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep
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models can benefit from Bayesian query strategies with the help of the Monte Carlo (MC) dropout. Results of experiments with various uncertainty estimates and MC dropout variants show that the Bayesian active learning by disagreement query strategy coupled with the MC dropout applied only in the classification layer of a Transformer-based tagger is the best option in terms of quality. This option also has very little computational overhead. We also demonstrate that it is possible to reduce the computational overhead of AL by using a smaller distilled versionof a Transformer model for acquiring instances during AL
models in the active learning framework and find the best combinations for different types of models. Besides, we also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance and reduces obstacles for applying deep active learning in practice