Zero-Resource Translation with Multi-Lingual Neural Machine Translation (1606.04164v1)

Abstract: In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.

Overview of "Zero-Resource Translation with Multi-Lingual Neural Machine Translation"

The paper "Zero-Resource Translation with Multi-Lingual Neural Machine Translation" investigates a novel approach to machine translation using a multi-lingual neural machine framework that incorporates zero-resource translation capabilities. The research introduces a finetuning algorithm applied to a multi-way, multilingual neural machine translation model, demonstrating its potential to perform translations between language pairs with no direct parallel corpora, termed zero-resource language pairs.

Contributions and Findings

The research builds on the existing foundation of neural machine translation, particularly focusing on integrating language transfer when multiple languages are involved. The key contributions and findings can be summarized as follows:

1. Finetuning Algorithm: The paper proposes a finetuning strategy that improves the performance of zero-resource language pair translation. This involves generating a pseudo-parallel corpus and finetuning only the attention-related parameters, which aligns with maintaining the integrity of the pre-trained model's parameters.
2. Empirical Verification: The paper conducts experiments using Spanish, French, and English, where the results indicate that the proposed finetuning strategy allows zero-resource translation to achieve performance levels comparable to single-pair models trained with substantial direct parallel data (up to 1 million sentences). This was achieved with a relatively small size of pseudo-parallel corpora.
3. Pivot-Based and Many-to-One Strategies: Unlike traditional pivot-based translation which translates the source language to an intermediary pivot language before reaching the target language, this paper demonstrates the efficacy of many-to-one translation strategies. These strategies leverage multi-source inputs to enhance translation performance without necessitating a multi-way parallel corpus during training.
4. Limitations in Direct Zero-Resource Translation: Initial attempts at direct translation without finetuning showed inadequate results, emphasizing the necessity of adjustments in the attention mechanism to bridge the compatibility gap between encoder and decoder for zero-resource paths.

Implications

The findings have significant implications for both theoretical progress and practical applications in machine translation:

• Theoretical Insights: The positive language transferability, as demonstrated in the many-to-one strategy, reinforces cross-linguistic mutual intelligibility as an asset to neural machine translation models. This understanding could drive future research to encapsulate structural linguistic synergies within translation systems.
• Practical Applications: The ability to perform zero-resource translation effectively diminishes the requirement for large-scale bilingual corpora, making the deployment of machine translation systems in low-resource linguistic contexts feasible. This is particularly beneficial for underrepresented languages in the digital data ecosystem.

Future Directions

Future research could explore the application of these methodologies across a broader spectrum of languages, including those with significant typological diversity. Assessing the robustness of these models with languages that have fewer common linguistic features will test the scalability and adaptability of the proposed mechanisms. Additionally, refining the finetuning process to reduce the increase in parameters and improve computational efficiency will be crucial. Integrating advanced strategies, such as mixing high-resource parallel data during finetuning or utilizing more sophisticated attention mechanisms, could further elevate zero-resource translation capabilities.

In conclusion, this paper contributes substantially to augmenting the methodological toolkit for neural machine translation, particularly in scenarios marked by limited direct translation data, while opening avenues for significant academic and applied innovations in multilingual communication technologies.