Constituency Parsing with a Self-Attentive Encoder (1805.01052v1)

Abstract: We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-of-the-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.

• The paper introduces a self-attentive encoder that improves parsing accuracy, reaching up to a 95.13 F1 score on the Penn Treebank with pre-trained embeddings.
• It separates positional and content information to better capture long-distance dependencies and enhance overall sentence structure parsing.
• The approach outperforms traditional LSTM models in both speed and precision, setting new state-of-the-art benchmarks across multiple languages.

Constituency Parsing with a Self-Attentive Encoder: An Overview

This paper presents a significant advancement in the field of natural language processing by exploring the replacement of an LSTM encoder with a self-attentive architecture for constituency parsing tasks. The paper utilizes a state-of-the-art discriminative parser and demonstrates that self-attention offers notable improvements both in parsing accuracy and computational effectiveness.

Model Architecture and Innovation

The proposed model harnesses an encoder-decoder architecture where self-attention distinctly dictates information flow between sentence locations. Unlike traditional encoder systems predominantly utilizing RNNs, this model eliminates recurrent connections, inspired by the Transformer architecture. The encoder is adapted for parsing, combining with a chart decoder to optimize constituency tree prediction.

A novel facet of the research is the separation of positional and content information within the encoder. This separation improves parsing accuracy, enabling the model to capture important sentence structure without conflating the distinct effects of token content and their positional attributes.

Key Findings and Results

The paper reports competitive results, achieving a test F1 score of 93.55 on the Penn Treebank without external data. Utilizing pre-trained word embeddings, the F1 score improves to 95.13, marking new state-of-the-art performance for this dataset. Additionally, the model is evaluated on the SPMRL dataset spanning nine languages, where it surpasses previous accuracy records on eight out of nine languages.

Analysis and Insights

One insightful aspect of the research is the analysis of attention mechanisms. The model's effectiveness originates largely from position-based attention, although content-based attention yields improvements, especially in the upper layers of the encoder. Long-distance dependencies are vital for achieving maximal accuracy, underscoring the model's capability to access sentence-wide information.

The paper also clarifies that alternative representation techniques, such as character-based embeddings, remain crucial for handling morphological variations, particularly for less common words. The combination of factored attention and robust lexical representation underpins the model's strong performance.

Implications and Future Directions

This research has practical implications for improving parsing models used in numerous NLP applications, from machine translation to information extraction. The theoretical implication underscores the versatility of self-attention mechanisms across various layers of linguistic abstraction.

Given these promising results, future work might explore finer-grained linguistic representation or extend these findings to different parsing frameworks. The success of ELMo further indicates the potential for integrating diverse pre-trained embeddings to bolster parsing systems.

In conclusion, this paper significantly contributes to parsing methodologies by leveraging self-attentive encoders, providing a foundation for continued innovation within the expansive field of AI-driven language processing.