Effective Approaches to Attention-based Neural Machine Translation (1508.04025v5)

Abstract: An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach which always attends to all source words and a local one that only looks at a subset of source words at a time. We demonstrate the effectiveness of both approaches over the WMT translation tasks between English and German in both directions. With local attention, we achieve a significant gain of 5.0 BLEU points over non-attentional systems which already incorporate known techniques such as dropout. Our ensemble model using different attention architectures has established a new state-of-the-art result in the WMT'15 English to German translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over the existing best system backed by NMT and an n-gram reranker.

Two Approaches to Attention in Neural Machine Translation

Introduction

Neural Machine Translation (NMT) has significantly advanced over the years, offering impressive results in translating languages with minimal domain expertise. This paper takes it a step further by diving into two types of attention-based NMT mechanisms: Global Attention and Local Attention. These methods aim to improve translation accuracy by focusing on certain parts of the source sentence while translating. Let’s break down the concepts and results from this paper, which explores these mechanisms and their impact on translating English to German and vice versa.

What is Attention in NMT?

Before we dive into the two approaches, it’s important to understand the concept of attention in NMT. An NMT network generally consists of an encoder and a decoder. The encoder processes the entire source sentence, while the decoder generates the translated sentence one word at a time. Attention mechanisms enhance NMT by dynamically weighting the importance of different parts of the source sentence during translation.

Global attention

The Global Attention model considers all the words in the source sentence while generating each target word. It calculates a context vector as a weighted sum of the encoder's output, where the weights are determined by an alignment score between the current target word and each source word. This model is simpler in architecture compared to previous approaches but equally effective.

Local attention

The Local Attention model, on the other hand, selectively focuses on a small subset of source words at a time. It predicts a single aligned position and considers words in a defined window around this position, which reduces computational overhead and is nearly as effective as the global approach. There are two main methods in local attention:

1. Monotonic Alignment: Assumes the source and target sequences are roughly aligned and considers a fixed window of source words.
2. Predictive Alignment: Dynamically predicts the aligned position and weighs the source words based on proximity to this position.

Experiment Results

The paper demonstrates the effectiveness of these models on the WMT translation tasks between English and German, with remarkable results. Particularly, the local attention model shows substantial improvements.

• Global Attention: This approach provided a significant gain of 5.0 BLEU points over non-attentional systems by focusing on all source positions.
• Local Attention: Achieved a gain of 5.0 BLEU points as well, outperforming others and establishing new state-of-the-art results on WMT English to German tasks.

An ensemble model using different attention architectures hit 25.9 BLEU points, improving by 1.0 BLEU over the previous best system.

Practical Implications

The advancements showcased in this paper have practical implications:

1. Improved Accuracy: The attention mechanisms, especially local attention, significantly enhance translation accuracy, which is crucial for real-world applications.
2. Efficiency: Local attention makes the process more computationally efficient by not having to consider the entire source sentence at once, making it suitable for longer texts.
3. Generalization: The models generalize well across different test sets, shown by their robust performance in the WMT'15 tasks.

Future Directions

The paper suggests numerous possibilities for future research:

• Explore More Alignment Functions: Testing various alignment functions might yield even better results.
• Apply to Different Language Pairs: Extending these models to other languages could validate their utility across different grammatical structures and vocabularies.
• Optimize Computation: Further optimizing local attention's computational efficiency could help in real-time translation applications.

Conclusion

This paper showcases the immense potential of attention mechanisms in neural machine translation, especially the local attention model. With significant improvements in BLEU scores and demonstrated efficiency, these methods pave the way for more accurate and scalable machine translation systems. By focusing on different parts of the source sentence, these attention models are setting new benchmarks in the field of NMT.