Character-based Neural Machine Translation (1511.04586v1)

Abstract: We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.

• The paper introduces a character-level NMT model that composes word representations from characters to overcome open vocabulary challenges.
• It leverages bidirectional LSTMs and joint attention mechanisms to generate accurate translations with less preprocessing.
• Empirical results on benchmark datasets show competitive BLEU scores, especially for languages with rich morphology.

Character-Based Neural Machine Translation: An Overview

The paper "Character-based Neural Machine Translation" introduces a novel approach to Neural Machine Translation (NMT) by representing and generating words at the character level rather than the traditional word level. This methodological shift addresses several limitations inherent in word-level translation models and opens new avenues for handling unseen word forms and the challenges of extensive vocabulary management.

Introduction to the Model

The authors propose a character-based NMT model that systematically composes representations of word forms using sequences of characters. This model improves upon previous character or subword-based systems by leveraging the strengths of character representations while maintaining the ability to learn meaningful word-level translations through a hierarchical structure. Key to this model is the application of a joint attention and translation mechanism, enabling effective translation generation by viewing sentences as sequences of characters.

This model stands out due to its ability to form representations for previously unseen words—thus addressing open vocabulary translation—and its reduction in reliance on extensive preprocessing/tokenization typical of word-based methodologies. Moreover, the computational advantages are notable; character models have more compact vocabularies and require fewer parameters, improving scalability in machine translation tasks.

Core Components

The proposed architecture integrates the following components:

1. Character Composition for Word Representation: The model utilizes the compositional approach where word vectors are formed from character sequences. This is achieved using bidirectional LSTMs to capture context, similar to methods seen in recent morphological and subword modeling approaches.
2. Joint Alignment and Translation through Attention: By employing an attention mechanism, the model aligns and translates sentences by focusing on relevant parts of the source sentence. Such an approach aligns with recent advancements in attention-based NMT systems and allows the model to handle linguistic phenomena such as word reorderings.
3. Character-Based Word Generation: In lieu of traditional word-based softmax output layers, this work introduces character-based generation through a neural architecture that conditions predicted characters on both source contexts and previously generated characters of the target language. This enables the model to generate novel word forms beyond those present in the training data.

Empirical Validation

The paper supports its claims with empirical data from bilingual translation tasks and reports its findings using BLEU scores. Notably, the character-based model performs comparably to—or slightly better than—word-based models on languages with rich morphology, where traditional models often struggle.

• Important Results: On the BTEC dataset, the character-based model and its word-based counterpart deliver comparable BLEU scores, suggesting competitive performance. Meanwhile, the Europarl dataset further substantiates the model’s efficacy, as the character-based approach edges out traditional models in BLEU score performance.

Implications and Future Work

The implications of this research extend to various fields of natural language processing and AI. The ability to manage open vocabularies efficiently and generate text using compact character representations is particularly appealing for languages with high morphological complexity. Additionally, the reduction in computational overhead positions this approach as advantageous for large-scale deployments.

In terms of future directions, the paper acknowledges potential enhancements through the integration of domain-specific knowledge such as morphology and rare word translation strategies, leveraging linguistic phenomena like cognates and optimal inference mechanisms for even better performance. Research could also explore hybrid models incorporating semantic information or pre-training mechanisms to extend the model’s generalization capabilities.

The scholarly community focused on machine translation and related areas will find this paper a valuable contribution, highlighting both innovative uses of character-based NMT and strategies for overcoming the challenges of traditional word-based systems. While the model presents a significant step forward, it opens up further questions on balancing computational efficiency with linguistic richness in machine translation systems.