Deep Semantic Role Labeling with Self-Attention (1712.01586v1)

Abstract: Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end SRL with recurrent neural networks (RNN) has gained increasing attention. However, it remains a major challenge for RNNs to handle structural information and long range dependencies. In this paper, we present a simple and effective architecture for SRL which aims to address these problems. Our model is based on self-attention which can directly capture the relationships between two tokens regardless of their distance. Our single model achieves F$_1=83.4$ on the CoNLL-2005 shared task dataset and F$_1=82.7$ on the CoNLL-2012 shared task dataset, which outperforms the previous state-of-the-art results by $1.8$ and $1.0$ F$_1$ score respectively. Besides, our model is computationally efficient, and the parsing speed is 50K tokens per second on a single Titan X GPU.

• The paper introduces DeepAtt, a self-attention model that computes token dependencies without relying on RNN structures.
• It achieves state-of-the-art F1 scores (83.4 on CoNLL-2005 and 82.7 on CoNLL-2012) while processing 50K tokens per second.
• The study demonstrates that replacing RNNs with self-attention improves efficiency and accuracy in parsing complex semantic roles.

Deep Semantic Role Labeling with Self-Attention: An Analysis

The paper "Deep Semantic Role Labeling with Self-Attention" by Zhixing Tan et al. introduces a novel architecture for Semantic Role Labeling (SRL) that leverages self-attention mechanisms to address challenges inherent in traditional recurrent neural network (RNN)-based approaches. SRL aims to parse sentences into elements describing actions and actors, providing significant utility for applications in NLP such as Information Extraction, Question Answering, and Machine Translation.

Core Contributions

The authors present a significant shift in approach by replacing RNN-based models, which struggle with long-range dependencies and structural complexities, with a self-attention-based deep neural network named DeepAtt. This paradigm allows the model to capture relationships between tokens in a sentence effectively, regardless of their positional distance. DeepAtt employs multiple variants that incorporate recurrent, convolutional, and feed-forward neural networks, enabling it to enhance representation learning in different ways.

Key Results

The model achieves new state-of-the-art results on two prominent datasets—CoNLL-2005 and CoNLL-2012—indicated by F$_1$ scores of 83.4 and 82.7, respectively. These results respectively surpass prior benchmarks by 1.8 and 1.0 F$_1$ points. Additionally, the proposed model is computationally efficient, parsing at a rate of 50K tokens per second on a single Titan X GPU, which signifies a substantial improvement over previous models in both speed and accuracy.

Methodological Insights

1. Self-Attention Mechanism: The central innovation of the paper lies in its use of self-attention, which allows direct token-to-token interaction, thereby facilitating the modeling of long-range dependencies without the constraints of RNNs. The multi-head attention mechanism, following the formulation of Vaswani et al., provides a robust mechanism through parallelization and efficient gradient flow.
2. Model Architecture: DeepAtt comprises N identical layers, each with nonlinear and attentional sub-layers, aiding in capturing deep hierarchical and dependency structures in sentences. Variants of the model that incorporate FFN sub-layers showed superior performance, highlighting the versatility of feed-forward networks in enhancing model expressiveness.
3. Position Encoding: The inclusion of timing signals as a position encoding method circumvents the need for additional positional embeddings, ensuring model simplicity while retaining positional context.

Implications and Future Directions

This work highlights the potential to move beyond traditional RNN-based sequence modeling in SRL tasks, asserting the efficacy of attention mechanisms in parsing complex dependencies within natural language. It suggests possible exploration into further optimizing attention-based frameworks for other NLP tasks requiring semantic understanding. Future research may expand upon this by investigating the impact of varying attention head configurations or combining self-attention with more advanced syntactic parsers to ameliorate constituent identification challenges.

The paper provides a compelling argument for the efficiency and efficacy of self-attention in SRL, paving the way for broader applications of such models in the growing field of NLP. The results indicate a promising trajectory for further enhancements in computational linguistics through refined attention-based architectures.