- The paper presents a novel parser that uses character-based embeddings to capture intricate morphological details.
- It employs bidirectional LSTMs to replace traditional word lookups, enabling effective generalization over similar word forms.
- Experimental results demonstrate improved UAS and LAS in agglutinative languages while reducing out-of-vocabulary issues.
Improved Transition-Based Parsing by Modeling Characters instead of Words with LSTMs
The paper presents a notable advancement in transition-based dependency parsing by introducing a method that leverages character-based embeddings for word representation, utilizing Long Short-Term Memory (LSTM) networks. This approach is particularly relevant for parsing morphologically rich languages, where traditional word-based embeddings may fall short due to intricate morphological variations.
Contribution to Dependency Parsing
The primary contribution of this research is the introduction of character-based embeddings to represent words in a continuous-state transition-based parsing model. By modeling words as sequences of characters, the parser can effectively capture morphological nuances, enabling more informed parsing decisions in languages with rich morphology. This approach also enhances statistical sharing across similar word forms, which is not as feasible using conventional word embeddings.
The character-based approach involves encoding words with bidirectional LSTMs, reflecting both the forward and backward sequences of characters. These embeddings are integrated into the parser's state, replacing conventional lookup-based word representations. Notably, this allows generalization over morphologically related word forms even without explicit morphological annotations.
Numerical Outcomes
Experimental results across various languages demonstrate substantial improvements. Noteworthy improvements in unlabeled attachment scores (UAS) and labeled attachment scores (LAS) for languages like Basque, Hungarian, and Korean show the efficacy of character-based embeddings. The results are particularly striking for agglutinative languages, showcasing the model’s ability to learn subtle morphological dependencies from the data.
Theoretical and Practical Implications
This research suggests significant theoretical implications for understanding how character-based representations can capture syntactic and morphological information. The model negates the need for explicit morphological features, potentially simplifying the process of dependency parsing for new or low-resource languages. Practically, the model reduces the out-of-vocabulary (OOV) issue by providing a mechanism to represent unseen words through their character compositions, yielding parsers that are more robust and adaptable to various language contexts without extensive re-annotation of linguistic data.
Future Directions
This paper opens several avenues for future research. One promising direction is integrating these findings with pre-trained word embeddings or distributional representations, potentially further enhancing parsing results. Additionally, considering the computational efficiency concerns raised by character-based models, there remains a need to explore optimizations or hybrid models that balance accuracy with speed. Exploring applications in low-resource languages or cross-lingual transfer learning scenarios would also validate and extend the applicability of this approach further.
Overall, the proposed enhancements to transition-based parsing through character-level modeling reflect a significant step in dependency parsing research, particularly benefiting morphologically rich languages and providing insights into efficiently capturing complex linguistic patterns through neural architectures.