What do Neural Machine Translation Models Learn about Morphology? (1704.03471v3)

Abstract: Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.

• The paper demonstrates that character-based representations significantly improve morphological tagging accuracy and translation quality.
• It employs layer-specific analysis to reveal that lower encoder layers excel in structural learning while deeper layers enhance semantic performance.
• Findings suggest that translating into simpler target languages boosts source-side morphological learning, offering practical improvements for NMT systems.

Insights on Morphological Learning in Neural Machine Translation Models

The paper "What do Neural Machine Translation Models Learn about Morphology?" explores the extent to which neural machine translation (NMT) systems learn morphological features of languages during training. This research is integral as it touches upon the often opaque nature of neural models; while these systems excel in translation tasks, their learning about linguistic structure remains largely unexplored. This paper provides a detailed empirical examination of NMT models, specifically focusing on their ability to capture morphological information.

Analysis of Morphological Representations

A key focus of the paper is the evaluation of different representational strategies within NMT architectures regarding their effectiveness in capturing morphology. The paper differentiates between word-based and character-based representations and assesses the respective capability of these representations to perform part-of-speech (POS) and full morphological tagging tasks. The findings suggest that character-based representations outperform word-based models, particularly for infrequent words, pointing to their superior aptitude in capturing morphological subtleties. For observed language pairs such as Arabic and Czech, which are morphologically rich, the char-based models not only lead to higher tagging accuracy but also correlate with improved BLEU scores, indicating better overall translation quality.

Layer-Specific Analysis

The paper goes further by dissecting the role of individual layers within the NMT encoder. It was discovered that the lower layers focus more on the structural aspects of words, while deeper layers contribute to improving translation quality by focusing on semantics. Layer 1 representations, for instance, are found to be optimal for characterizing morphology, whereas layer 2 representations, despite offering better translation performance, capture less about word structure.

Influence of Target Language

The paper also analyzes how the target language influences source-side morphological learning. Interestingly, translating into morphologically simpler languages yields superior source-side morphological representations. This suggests that the difficulty inherent in translating to complex morphological systems might impede the learning of effective representations, a hypothesis supported by comparative BLEU scores across different language pair experiments.

Encoder-Decoder Comparison

In comparing encoder and decoder modules, the analysis indicates a marginal discrepancy in the quality of representations learned. Although it was somewhat expected that the encoder might prioritize syntactic and structural language features, while the decoder would focus on predictive LLMing, the differences were minimal. The introduction of the attention mechanism did not significantly alter the effectiveness of decoder representations, reaffirming the self-contained nature of learned representations in both system components.

Practical and Theoretical Implications

These findings hold both theoretical and practical implications. They underscore the potential for improvement in NMT systems by informing choices regarding representation strategies and architectural depth, particularly for languages with rich morphological structures. The results call for further exploration in joint morphological learning and translation tasks, potentially leading to enhancements in system performance by leveraging morphology-aware architectures.

Future Directions

Future research might extend beyond POS and morphological tagging to include semantically-oriented tasks, exploring the interplay between semantic and morphological learning in NMT systems. Furthermore, comparative studies involving alternative representations like byte-pair encoding could yield additional insights into optimal practices for encoding linguistic information in neural models. Such investigations promise to deepen our understanding of how NMT systems learn and utilize complex language features, potentially enabling more nuanced and accurate translation systems.

In summary, this paper provides a quantitative lens on the morphological capabilities of neural MT systems, offering grounded insights into how these models can be fine-tuned for improved performance across a diverse set of languages.