Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (1804.08313v2)

Abstract: Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.

• The paper introduces a method that integrates PropBank-style semantic role labels into NMT models using GCNs to reduce argument switching errors.
• Experiments show BLEU score improvements of up to 1.2 points on full datasets, underlining the benefits of semantic integration.
• The results indicate that combining semantic and syntactic structures in NMT can further boost translation quality, guiding future research.

Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks

The integration of semantic information into neural machine translation (NMT) systems presents a significant advancement in enhancing the translation quality by preserving meaning across languages. In the paper, "Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks," the authors introduce a method that incorporates semantic-role information into NMT models using Graph Convolutional Networks (GCNs). This essay will provide a comprehensive analysis of the methodological framework, key findings, and implications for future research and applications.

Methodological Framework

The paper's core contribution lies in the use of GCNs to encode semantic information, specifically the predicate-argument structures of source sentences, to improve the translation process. GCNs are leveraged to encode these structures into the encoder component of standard attention-based encoder-decoder NMT models. This model is tested specifically on the English-German language pair.

The semantic structures employed are derived from PropBank-style semantic role labels, which annotate arguments of predicates with semantic roles. These annotations, such as A0 for the agent, are utilized to distinguish between different roles that arguments might play in a sentence. The incorporation of this semantic bias aims to rectify issues like argument switching, a prevalent problem in NMT systems.

Key Results

The experiments conducted across varying data scales—both small (News Commentary) and large (WMT16 full dataset)—illustrate the benefits of using semantic information. The integration of semantic GCNs shows a notable improvement in BLEU scores: a 0.7 increase with BiRNN and 0.8 with CNN encoders on the smaller dataset. More remarkably, a 1.2 BLEU score enhancement is observed on the full WMT16 dataset, suggesting that larger datasets allow for more effective modeling of semantic contributions.

Additionally, the paper compares the influence of semantic structures against syntactic structures, employing the same architecture for both semantic and syntactic GCNs. Interestingly, with the full dataset, semantics yielded superior results compared to syntax, indicating distinct advantages in encoding semantic information that complements syntactic dependencies.

The integration of both syntactic and semantic structures further enhances translation quality, with gains up to 24.9 BLEU, demonstrating their complementarity.

Implications and Future Directions

The ability of semantic GCNs to enhance translation quality opens several avenues for future research. The complementary nature of syntactic and semantic information elucidates the potential for further exploration into multi-layer GCN structures or hybrid models that can optimize different linguistic information layers. Moreover, expanding these experiments to other language pairs may provide broader insights into the generalizability and limitations of semantic GCNs in NMT.

The positive results obtained emphasize the need for deeper linguistic integration within neural architectures, advocating for models that can dynamically adapt and learn from complex linguistic structures beyond conventional sequence modeling techniques.

In conclusion, the integration of semantic structures via GCNs represents a valuable progression in the field of machine translation, underscoring the significant role of semantics in constructing meaningful and contextually accurate translations. Further investigation and expansion of these findings could lead to substantial advancements in both theoretical understanding and practical applications of NMT systems in multilingual contexts.