- The paper introduces an MDL framework that quantifies complexity and descriptive adequacy of CxGs without relying on annotated corpora.
- It applies a tabu search algorithm to iteratively refine multi-layer grammars, demonstrating superior compression rates across languages.
- Results reveal a trade-off between grammatical complexity and stability, offering scalable insights for multilingual natural language processing.
An Evaluation of Complexity and Descriptive Adequacy in Construction Grammars
The paper presents a novel approach to evaluating Construction Grammars (CxGs) using the Minimum Description Length (MDL) paradigm. This framework is employed to balance the computational complexity of CxGs and their descriptive adequacy when applied to unannotated corpora, covering multiple languages such as English, Spanish, French, German, and Italian. By operationalizing complexity as the encoding size of a grammar and descriptive adequacy as the encoding size of a corpus given this grammar, the paper seeks to discover optimal CxGs for these languages.
Complexity and Representation in CxGs
CxGs, which integrate lexical, syntactic, and semantic layers, are inherently more complex than purely syntactic grammars. The paper does not focus on the general representational capacity of CxGs but rather on the complexity of specific CxGs that describe specific languages, making use of observable corpora. Previous work often relied on introspection-based representations, which are unscalable and non-replicable. This research addresses such limitations by providing a data-driven methodology for both modeling and evaluating CxGs.
Methodology and Implementation
The MDL paradigm is central to this paper's evaluation framework, permitting a rigorous assessment of grammar quality through two key parameters: model complexity (captured by L1) and corpus description adequacy (captured by L2). These measures negate the need for a reliance on gold-standard annotations, offering a replicable and scalable approach for measuring grammar quality across different languages.
The paper discusses a multi-part experiment that involves initializing and refining grammars through a tabu search algorithm. This method navigates a space of potential CxGs to determine the optimal grammar for a given corpus, seeking an efficient trade-off between grammatical complexity and descriptive power. The grammars are developed in iterations, initially representing purely lexical constructs, followed by syntactic constructs, and culminating in full CxGs incorporating multiple layers of representation.
Empirical Results
The paper demonstrates significant generalizations in CxGs, emphasizing that grammars with access to multiple levels of representation offer greater descriptive adequacy compared to those with a single layer. The results reveal substantial compression over unencoded datasets: while purely lexical constructions offered minimal compression, syntactic grammars achieved higher compression rates. Notably, multi-level CxGs provided the highest rates of compression, aside from English, signifying their descriptive superiority.
Moreover, the stability of learned grammars was analyzed, revealing more variability with complex grammars, as opposed to simpler lexical grammars which demonstrated higher stability. This implies a potential trade-off between grammar complexity and stability, possibly reflective of genuine linguistic variations across corpora.
Implications and Future Work
The results underline the potential of the MDL-based approach in evaluating CxGs, offering a framework that is not constrained by the availability of annotated datasets. These findings carry significant implications for the development of multilingual grammars and the application of CxGs in diverse linguistic scenarios. Moreover, the research provides a foundation for applying these grammatical models to other linguistic phenomena, such as dialectometry.
Future work could explore the impact of non-dialectal variation on learned grammar stability and further refine the MDL measures to enhance the precision of construction probabilities. This could enhance the explanatory power of CxGs for modeling nuanced linguistic variations, underpinning advancements in multilingual natural language processing systems.
In summary, this paper contributes a rigorous methodology for evaluating the complexity and descriptive adequacy of construction grammars, validating this approach across multiple languages, and setting the stage for future explorations of representation in linguistic modeling.