Empower Sequence Labeling with Task-Aware Neural Language Model (1709.04109v4)

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 LLMs are incorporated to extract character-level knowledge. Transfer learning techniques are further adopted to mediate different components and guide the LLM 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.

Empowering Sequence Labeling with Task-Aware Neural LLMs

The paper "Empower Sequence Labeling with Task-Aware Neural LLM" by Liu et al. introduces a novel framework, LM-LSTM-CRF, designed to enhance sequence labeling tasks such as Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and syntactic chunking. This framework leverages the strengths of neural LLMs to extract and utilize character-level information, thereby improving the performance and efficiency of sequence labeling models.

Summary

Sequence labeling is an essential component in NLP with applications across multiple sub-tasks. Traditional approaches, like Hidden Markov Models and Conditional Random Fields, depended heavily on handcrafted features, which made model adaptation challenging across different domains and languages. Neural networks, on the other hand, enable automatic feature extraction but face limitations concerning insufficient annotated data for effective training.

To address these challenges, the authors propose a comprehensive framework named LM-LSTM-CRF. This framework uses LLMs to harness task-specific knowledge from raw text. The integration of character-level information is achieved using a neural network that co-trains with the sequence labeling model. The LM-LSTM-CRF incorporates highway networks to mediate between the LLM outputs and sequence labeling inputs, alleviating the issues associated with traditional transfer learning which often suffers from irrelevant or redundant information.

The novelty of the framework lies in its ability to incorporate character-level knowledge through self-contained order information rather than relying on pre-trained LLMs requiring extensive computational resources and additional corpus. Their experiments show that LM-LSTM-CRF outperforms existing state-of-the-art models on multiple benchmarks, notably achieving a peak F1 score of 91.71 on the CoNLL03 NER dataset with significantly reduced training times.

Key Methodological Insights

• Neural Architecture: The proposed LM-LSTM-CRF utilizes a combination of bidirectional LSTMs for processing word sequences and character-level LLMs to capture fine-grained linguistic nuances. The architecture is specifically designed to work without additional annotations, relying on sequence order information embedded in the training data.
• Highway Networks: To address potential discrepancies between tasks, highway networks are employed, which facilitate the transformation of character-level knowledge into different semantic spaces required by language task and sequence labeling models. This mechanism ensures that character-level pre-processing becomes task-aware, ultimately contributing more relevant information to the labeling process.
• Objective Function: The model jointly optimizes the likelihood of the sequence labeling and LLMing tasks, capturing both contextual and syntactic nuances crucial for sequence prediction accuracy.

Results and Implications

The evaluation against standard datasets such as CoNLL03 NER, CoNLL00 chunking, and the WSJ portion of the Penn Treebank POS shows that the LM-LSTM-CRF model achieves superior F1 scores and tagging accuracy, setting new benchmarks in the field. Moreover, the experiments highlight the framework's efficiency, demonstrating its capability to train sophisticated models on single GPUs within shorter timeframes compared to traditional methods that require considerable pre-training efforts.

The implications of this work are manifold. Practically, it provides researchers and engineers with a more resource-efficient approach to build high-performance sequence labeling models, negating the need for extensive linguistic resources and runtime. Theoretically, it opens up avenues for further exploration into transfer learning methods that natively incorporate linguistic patterns and characteristics from unsupervised data.

Future Directions

The paper positions LM-LSTM-CRF as a promising baseline for further research into task-aware LLMs. Future developments may involve expanding this framework to multilingual and cross-domain contexts, thereby extending its applicability and robustness. Furthermore, exploring its integration with larger transformer architectures or attention mechanisms could potentially yield even more comprehensive models capable of tackling more complex NLP tasks.

Overall, this work signifies a compelling step forward in sequence labeling methodologies, providing a blueprint for more adaptable and efficient neural processing models in NLP.