- The paper introduces a novel, fully differentiable higher-order inference method that refines span representations iteratively.
- It employs a coarse-to-fine inference technique to reduce antecedent pairs from 250 to 50, enhancing computational efficiency and accuracy.
- Experimental results on the OntoNotes benchmark demonstrate significant F1 score and recall improvements, achieving a new state-of-the-art.
Higher-order Coreference Resolution with Coarse-to-fine Inference
The paper "Higher-order Coreference Resolution with Coarse-to-fine Inference" presents an innovative approach to improving coreference resolution through higher-order inference while maintaining computational efficiency. The work is situated in the context of coreference systems that traditionally rely on first-order models, which often lead to globally inconsistent predictions due to their local decision-making constraints.
The authors propose a novel fully differentiable method for higher-order inference in coreference resolution. By employing the span-ranking architecture iteratively, the model uses antecedent distributions as an attention mechanism to refine span representations. This refinement process allows for the integration of multiple decision layers, effectively capturing complex higher-order structures in coreference clusters. The paper demonstrates that this methodology enables coreference linkages, such as between ambiguous pronouns and entities, to be globally consistent by iteratively adjusting based on previous predictions.
To manage computational expenses associated with iteratively refining span representations, the authors introduce a coarse-to-fine inference approach. This method involves a bilinear factor that performs an initial, less accurate pruning of antecedents, thereby reducing the computational burden without a significant decline in accuracy. This aspect supports more aggressive antecedent pruning compared to previous methods and significantly enhances the model's scalability, as evidenced by a reduction in processing antecedent pairs to just 50 instead of the traditional 250, while still improving overall accuracy.
Key experimental results highlight the efficacy of the proposed approach over existing models. Evaluated on the English OntoNotes benchmark, the new system achieves improved results with a significant increase in F1 scores, demonstrating both heightened performance and computational efficiency. Specifically, the second-order model, with a carefully refined span and antecedent representations, shows a notable enhancement in recall metrics and overall cluster consistency. Additional enhancements include the use of ELMo embeddings and adjusted hyperparameters, which further contribute to reaching a new state-of-the-art in coreference resolution.
Theoretical implications of the paper suggest that higher-order modeling can be effectively integrated into coreference resolution systems, providing a framework that can be extended to other NLP tasks that require capturing complex entity interactions over discourse. Practically, the reduced computational complexity opens avenues for processing longer documents and more extensive coreference structures without prohibitive computational costs.
Future research directions may explore the extension of higher-order inference techniques to other domains of NLP, including large-scale context understanding and document synthesis, leveraging the foundational work established in this paper. Additionally, the adoption of the coarse-to-fine strategy can inspire new methodologies in other computationally intensive inference tasks.
In conclusion, this paper advances coreference resolution with an effective blend of higher-order inference and efficient computation, paving the way for continued improvements in natural language understanding tasks.