Semi-supervised sequence tagging with bidirectional language models (1705.00108v1)

Abstract: Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pre- trained context embeddings from bidirectional LLMs to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.

• The paper introduces a method incorporating bidirectional LM embeddings into tagging models to enrich contextual token representations.
• It demonstrates state-of-the-art results on NER and chunking tasks, achieving over 1% F1 score improvement on CoNLL 2003.
• The approach reduces dependency on labeled data, enabling cross-domain adaptation through pre-trained language models.

Semi-supervised Sequence Tagging with Bidirectional LLMs

The paper entitled "Semi-supervised sequence tagging with bidirectional LLMs" by Peters et al., presents an approach leveraging pre-trained LLMs to enhance sequence labeling tasks such as Named Entity Recognition (NER) and chunking. The authors introduce a technique that integrates context embeddings from bidirectional LLMs (LMs) into sequence tagging systems, demonstrating state-of-the-art performance without the need for additional labeled data or task-specific resources.

Methodology

The core contribution of this work is the incorporation of LM embeddings into sequence tagging models. Traditional approaches utilize pre-trained word embeddings, capturing semantic and syntactic properties of tokens. However, for sequence tagging tasks, understanding a token's context is paramount. Peters et al. bypass the need for extensive labeled data by employing LMs pre-trained on large, unlabeled corpora. These LMs are then used to generate context-sensitive embeddings, which are fed into the supervised sequence tagging model.

The TagLM architecture extends a hierarchical neural tagging model by passing token representations through bidirectional RNN layers, augmented with LM embeddings. The LMs are trained bidirectionally and separately, allowing the forward and backward embeddings to provide comprehensive context for each token.

Experimental Results

The approach was evaluated on two benchmark datasets—CoNLL 2003 for NER and CoNLL 2000 for chunking—where it achieved significant enhancements in performance metrics. For the CoNLL 2003 NER task, the system gained an absolute increase of over 1% in F1 score compared to previous state-of-the-art systems that utilized additional labeled data and gazetteers. Similarly, for the CoNLL 2000 chunking task, the method achieved a new benchmark, exemplifying its efficacy.

The paper also identifies that using both forward and backward LM embeddings leads to superior performance, demonstrating the importance of bidirectional context understanding. Furthermore, they confirm the generalizability of their method by applying LMs trained in different domains, yielding positive results even with domain mismatches.

Implications and Future Directions

The implications of this research are notable. The reduction in dependency on labeled data is critical, especially for tasks or domains where obtaining annotations is labor-intensive or infeasible. The potential to deploy pre-trained LMs in varying domains also highlights a flexibility that broadens its applicability.

Future work could explore extensions such as more sophisticated integration mechanisms of LM embeddings within sequence models. Additionally, examining the impact of newer, larger-scale LLMs might yield further insights into scaling and adaptation across diverse NLP tasks.

This paper provides a compelling advancement in semi-supervised learning for NLP, underscoring the value of context-driven embeddings and expanding the frontier of sequence labeling methodologies.