Adversarial Multi-Criteria Learning for Chinese Word Segmentation (1704.07556v1)

Abstract: Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.

Adversarial Multi-Criteria Learning for Chinese Word Segmentation: A Comprehensive Overview

The paper "Adversarial Multi-Criteria Learning for Chinese Word Segmentation" addresses the complexities inherent in Chinese word segmentation (CWS) stemming from diverse linguistic segmentation criteria. Various segmented corpora reflect different linguistic perspectives, posing challenges for traditional models that are typically optimized for single-criterion datasets. This research proposes a novel framework that integrates multiple segmentation criteria through adversarial multi-criteria learning, positing that shared and distinct features across these criteria can significantly enhance segmentation performance.

Methodological Framework

The core innovation of this research lies in the introduction of adversarial multi-criteria learning to the CWS task. The authors propose a multi-task learning framework that treats each segmentation criterion as an individual task. Key to this framework is the division of the feature space into shared and private layers, employing three distinct shared-private models:

1. Model-I (Parallel Shared-Private Model): This model processes shared and private layers in parallel, concatenating features for the final task.
2. Model-II (Stacked Shared-Private Model): This model processes data through a stacked architecture where shared features inform criterion-specific layers, creating a tiered structure.
3. Model-III (Skip-Layer Shared-Private Model): Similar to Model-II, but with a direct path from the shared layer to the inference layer, allowing direct influence of shared features in the segmentation process.

To ensure that shared features are truly invariant and common across criteria, the framework employs an adversarial approach. This involves a discriminator tasked with distinguishing the segmentation criterion from shared features, while the shared layers are optimized to confuse this discriminator, thus enforcing criterion invariance.

Empirical Evaluation

The conducted experiments involved eight datasets representing both simplified and traditional Chinese. The proposed framework attained significant improvements over traditional single-criterion approaches. Notably, the introduction of adversarial training further boosted the models' performance, particularly in handling out-of-vocabulary (OOV) words. This enhancement implies that the adversarial objective successfully forces the shared layer to generalize better across diverse linguistic criteria.

Theoretical and Practical Implications

Practically, the proposed framework allows for the simultaneous leveraging of multiple annotated corpora, optimizing resource utilization. It negates the typical trade-off where incorporating additional data introduces noise due to heterogeneous segmentation guidelines. From a theoretical perspective, the adversarial model contributes to the field by demonstrating that criterion-invariant feature extraction enhances sequence labeling tasks under varied linguistic constraints.

Speculative Future Developments

Future research could explore expanding this framework to other languages with complex segmentation tasks or to cross-linguistic domains where linguistic phenomena necessitate simultaneous multi-criteria analysis. Another potential development might involve integrating this approach with pre-trained LLMs, potentially yielding even more robust solutions by leveraging the broader contextual understanding provided by these models.

In conclusion, this research provides strong evidence that adversarial multi-criteria learning significantly improves CWS performance and opens new avenues for leveraging heterogeneous data in natural language processing tasks.