Empower Sequence Labeling with Task-Aware Neural Language Model

TL;DR

This paper introduces a task-aware neural framework that leverages pre-trained embeddings and character-level language models, enabling efficient sequence labeling without extra supervision and achieving high accuracy on benchmarks.

Contribution

It proposes a novel neural approach combining transfer learning and character-aware language models to improve sequence labeling efficiency and effectiveness without additional annotations.

Findings

Achieves 91.71 F1 score on CoNLL03 NER in 6 hours on a single GPU.

Effectively leverages character-level knowledge for sequence labeling.

Demonstrates efficient training without relying on extra supervision.

Abstract

Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible to build reliable models without handcrafted features. However, in many cases, it is hard to obtain sufficient annotations to train these models. In this study, we develop a novel neural framework to extract abundant knowledge hidden in raw texts to empower the sequence labeling task. Besides word-level knowledge contained in pre-trained word embeddings, character-aware neural language models are incorporated to extract character-level knowledge. Transfer learning techniques are further adopted to mediate different components and guide the language model towards the key knowledge. Comparing to previous methods, these task-specific knowledge allows us to adopt a more concise model and conduct more efficient training. Different from most transfer learning methods, the proposed framework does not rely on any additional supervision. It extracts knowledge from self-contained order information of training sequences. Extensive experiments on benchmark datasets demonstrate the effectiveness of leveraging character-level knowledge and the efficiency of co-training. For example, on the CoNLL03 NER task, model training completes in about 6 hours on a single GPU, reaching F1 score of 91.71$\pm$0.10 without using any extra annotation.