Compositional Morphology for Word Representations and Language Modelling (1405.4273v1)

Abstract: This paper presents a scalable method for integrating compositional morphological representations into a vector-based probabilistic LLM. Our approach is evaluated in the context of log-bilinear LLMs, rendered suitably efficient for implementation inside a machine translation decoder by factoring the vocabulary. We perform both intrinsic and extrinsic evaluations, presenting results on a range of languages which demonstrate that our model learns morphological representations that both perform well on word similarity tasks and lead to substantial reductions in perplexity. When used for translation into morphologically rich languages with large vocabularies, our models obtain improvements of up to 1.2 BLEU points relative to a baseline system using back-off n-gram models.

• The paper introduces a log-bilinear model that integrates compositional morphological representations to enhance language modelling for morphologically rich languages.
• It achieves up to 21% perplexity reduction and 1.2 BLEU score improvements, outperforming traditional back-off n-gram models.
• The approach provides a scalable method that can be applied to machine translation and speech recognition, paving the way for future NLP advancements.

Compositional Morphology for Word Representations and LLMling: An Overview

The paper "Compositional Morphology for Word Representations and LLMling" by Jan A. Botha and Phil Blunsom presents a methodological advance in the domain of continuous space LLMs (CSLMs) by incorporating compositional morphological representations. This paper primarily addresses the complexities encountered with morphologically rich languages (MRLs), particularly focusing on improving word representation and LLMling through morphology-awareness in probabilistic LLMs.

Main Contributions

The authors propose a scalable approach for integrating word morphology into vector-based probabilistic LLMs. This method represents words as combinations of constituent morphemes and surface forms, linking words through shared morphological elements. Implemented within a log-bilinear model framework, their approach aims to tackle data sparsity in morphologically rich languages by leveraging linguistic structures for better probabilistic modelling.

The paper conducts comprehensive intrinsic and extrinsic evaluations across several languages, revealing significant reductions in perplexity and enhancements in translation quality. Notably, their models outperform traditional back-off n-gram models, showing improvements up to 1.2 BLEU points, especially in translation tasks involving complex languages like Russian and Czech.

Numerical Results and Impact

In terms of intrinsic evaluation, the CLBL++ model (additive class-based log-bilinear) exhibits perplexity reductions across languages, demonstrating the effectiveness of integrating morphological representations. For instance, the paper reports perplexity reductions of up to 21% for tokens occurring less frequently in training data. Notably, the Russian language, characterized by substantial morphological complexity, showed marked improvements.

The extrinsic evaluation on a machine translation setup affirms the utility of these morphology-aware CSLMs. Their integration resulted in noticeable BLEU score improvements across several language pairs, indicating better translation quality, especially for MRLs. The enhancement for Russian translation reached 1.2 BLEU points, a significant gain in machine translation effectiveness.

Theoretical and Practical Implications

Theoretically, the paper underscores the importance of incorporating linguistic features directly into the structure of LLMs. By doing so, it argues for an inherent morphological awareness that can guide LLMs in learning more generalized and robust word representations. This paradigm shift could inform future work in natural language processing, particularly in the development of LLMs tailored for complex morphological phenomena.

Practically, the integration of such CSLMs into applications that necessitate handling MRLs, such as machine translation and speech recognition, could yield performance enhancements. The paper elucidates a pathway towards scalable and efficient LLM integration in real-world systems, which could be a stepping stone for further advancements in natural language processing tasks that involve complex morphological constructs.

Future Directions

Future developments could explore the expansion and refinement of this morphology-based model by testing it across even larger datasets and more diverse languages. The potential for extending this work includes integrating syntactic and semantic information, investigating model performance in cross-linguistic applications, and exploring the combination with other advanced feature representations like syntactic parsing in LLMling.

Overall, the paper offers a significant contribution to the field, providing an empirical and methodological basis for the continued evolution of LLMs that consider morphological complexity, catering to the diverse needs of global language processing tasks.