Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (1805.04787v2)

Abstract: Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates.

• The paper introduces a novel span-based model that jointly predicts predicates and arguments, eliminating the need for pre-identified predicates.
• It leverages contextualized span embeddings and evaluates all span pairs to surpass previous F1 scores on both in-domain and out-of-domain datasets.
• The study suggests further enhancements through self-attention and higher-order inference to improve global consistency in semantic role labeling.

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

This paper introduces a novel approach for semantic role labeling (SRL), focusing on the simultaneous prediction of predicates and their associated argument spans. Unlike traditional BIO-tagging methods that require pre-identified predicates, the proposed method eliminates this dependency by incorporating mechanisms for end-to-end processing. The authors build upon previous work in coreference resolution to extend span representations and achieve state-of-the-art performance on the PropBank SRL benchmark without the reliance on gold predicates.

Model Overview

The model initiates a comprehensive mechanism for constructing semantic role graphs directly from text spans. It evaluates relationships by considering all possible word-span pairs within a sentence, leveraging contextualized span embeddings. These embeddings serve as a rich feature set for decision-making processes about predicate-argument relationships. The architecture draws inspiration from recent advances in tasks involving span-span relations, including coreference resolution, demonstrating a flexible applicability to multiple NLP domains like syntactic parsing and relation extraction.

Key Findings

The experiments presented show substantial improvements in SRL tasks compared to prior work, notably in settings without preidentified predicates. Demonstrating high performance across in-domain and out-of-domain datasets, this approach effectively addresses one of the main challenges faced by existing SRL systems: the inability to generalize across varied predicate instantiations in input text.

1. Accuracy and Efficiency:
   • The method showcases remarkable F1 scores, surpassing the performance of prior models, especially under conditions where gold predicates are not supplied.
   • Its joint prediction ability eradicates the need for sequential pipelining, mitigating error accumulation typically seen in layered SRL systems.
2. Implications for NLP:
   • The capability to determine predicates and arguments jointly suggests wider applicability, enhancing performance for broader language understanding tasks.
   • With ELMo embeddings, the model achieves further gains, highlighting the benefits of pretrained contextualized embeddings in SRL applications.
3. Scope for Future Development:
   • Though improvements are observed in predicting long-distance dependencies, there are existing challenges in maintaining global consistency. Recent architectures utilizing higher-order inference could offer pathways to address these limitations by easing independence assumptions.
   • Additionally, integrating self-attention mechanisms known for effective contextualization might provide further refinements, potentially leading to more robust and comprehensive SRL frameworks.

Conclusion

The introduction of a span-based model that discards the assumption of provided predicates represents a significant step forward in SRL task handling. This paper’s methodology not only extends the capability for SRL applications but also shows promise for enhancing a range of related NLP tasks by advancing the utilization of span-based contextual representations. As the work recommends, future exploration into more sophisticated inference strategies and architecture enhancements could yield even more refined models, charting new directions for semantic analysis in AI.