Stack-Pointer Networks for Dependency Parsing (1805.01087v1)

Abstract: We introduce a novel architecture for dependency parsing: \emph{stack-pointer networks} (\textbf{\textsc{StackPtr}}). Combining pointer networks~\citep{vinyals2015pointer} with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The \textsc{StackPtr} parser benefits from the information of the whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet, the number of steps for building any (including non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with $O(n^2)$ time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performance on 21 of them.

• The paper introduces Stack-Pointer Networks (StackPtr), a novel architecture integrating pointer networks and a stack for efficient top-down, depth-first dependency parsing.
• StackPtr achieved state-of-the-art results on 21 out of 29 treebanks across 20 languages, demonstrating robustness and competitive performance with graph-based methods.
• The architecture leverages higher-order information like sibling and grandparent relations to enhance parsing accuracy, effectively combining advantages of both transition-based and graph-based parsers.

Stack-Pointer Networks for Dependency Parsing

The paper "Stack-Pointer Networks for Dependency Parsing" by Xuezhe Ma and colleagues introduces an innovative approach to dependency parsing using stack-pointer networks (StackPtr). This novel architecture integrates pointer networks with an internal stack to overcome the limitations of classical transition-based parsers and provides a comprehensive top-down parsing process.

Overview of Stack-Pointer Networks

Stack-pointer networks are designed to decode dependency trees in a depth-first, top-down manner from root to leaves. By doing so, the architecture benefits from access to the full sentence context and previously derived subtrees, effectively removing the traditional left-to-right restriction found in transition-based parsers. The paper highlights the unique integration of pointer networks with a stack mechanism, where the stack maintains the state of the depth-first search and pointer networks facilitate child selection for the headword at each parsing step.

Key Features and Contributions

The StackPtr parser is characterized by a linear complexity in parsing steps, maintaining competitive efficiency with transition-based models while embracing a global view of the sentence akin to graph-based methods. Empirical evaluations were conducted on 29 treebanks encompassing 20 languages and various dependency annotation schemas, with StackPtr achieving state-of-the-art results in 21 of them.

The three main contributions of the paper are:

1. Innovative Architecture: The introduction of a simple, yet effective architecture that leverages both transition-based and graph-based parsing advantages.
2. Extended Evaluations: Thorough empirical evaluation showcasing competitive results across multiple languages, demonstrating the parser’s robustness and versatility.
3. Higher-Order Information Utilization: The exploitation of higher-order dependencies via sibling and grandparent structures enhances parsing accuracy, as verified through comparative analyses against a strong graph-based baseline.

Experimental Results

The paper presents robust parsing accuracy metrics (UAS and LAS) across prominent datasets like the English Penn Treebank, Chinese Treebank, and German CoNLL 2009 corpus. Results indicate competitive performance with state-of-the-art graph-based parsers, notably outperforming them in languages like Chinese. Furthermore, the StackPtr model demonstrates superior performance on complete sentence parsing metrics such as UCM and LCM, attributed to its adept handling of global sentence structures.

Implications and Future Directions

The implications of stack-pointer networks are profound, offering a scalable parsing solution that harmonizes efficiency with sentence-wide context awareness. In terms of practical application, this architecture could significantly aid NLP tasks reliant on syntactic parsing, including sentiment analysis and machine translation.

Future developments suggested by the authors include enhancing the qualitative parsing error analysis and exploring reinforcement learning methodologies for optimizing subtree selection orders. Both directions promise to refine and expand the parser’s applicability and accuracy.

In conclusion, the stack-pointer networks stand as a promising contribution to the field of dependency parsing, providing a flexible, efficient framework that reconciles traditional parsing constraints with the growing need for comprehensive linguistic analysis methodologies.