Advancements in Natural Language Generation Techniques

Natural Language Generation (NLG) is an area of artificial intelligence focused on creating human-like text. One challenge in NLG is finding the best way to decode or interpret the generated text so that it meets human expectations. Traditional methods, like maximum a posteriori (MAP), often fall short because they don't align well with what people prefer. To address this issue, researchers have developed more advanced methods like Minimum Bayes' Risk (MBR) and Quality Estimation (QE) reranking. These techniques often lead to better results but are difficult and costly to use, especially in real-time applications.

This work presents two new methods aimed at improving results without excessive costs: MBR finetuning and QE finetuning. These methods work during the training phase of the model to capture the benefits of advanced decoding without the expensive computation usually needed during the decoding phase.

#Background on Decoding Methods

NLG tasks often use various decoding methods. The most commonly used are beam search and greedy decoding. However, they can struggle with optimal performance. MAP decoding aims to generate outputs based on model probabilities but can lead to misalignment with how humans evaluate quality. Consequently, MBR decoding was introduced, which focuses on the prediction with the best quality based on a utility metric, rather than just the highest model probability.

MBR has shown to outperform other decoding methods, but its main drawback is the high computational cost. It requires generating many candidates for each query and performing extensive calculations to score these outputs, making it impractical for real-time applications.

QE reranking provides a faster alternative by scoring candidate outputs against a quality metric without the need for extensive computation. This method only requires a single calculation for each candidate instead of multiple ones, making it more efficient.

#MBR and QE Finetuning

To harness the advantages of MBR and QE without incurring high costs during inference, this work proposes MBR and QE finetuning. The goal is to adapt the model during training, enhancing it with data generated from MBR and QE methods. This involves two main steps: first, generating high-quality translations using MBR or QE, and second, using these translations to finetune the original model.

#Benefits of MBR and QE Finetuning

1. High-Quality Outputs: By training on translations generated from advanced decoding, the model learns to produce better outputs.

2. Cost-Effective: Training the model with these techniques allows it to perform better without needing expensive decoding methods during actual use.

3. Use of Monolingual Data: Monolingual data can be leveraged effectively, which is often more accessible than high-quality parallel data, enhancing model versatility.

#Practical Implementation

The practical implementation of MBR and QE finetuning involves several steps:

#Dataset Generation

1. Create Candidate Outputs: For a given source text, generate candidate translations using sampling techniques. This involves creating a range of possible translations that the model might produce.

2. Scoring Candidates: Use MBR scoring or QE metrics to evaluate these candidates. MBR uses a reference-based approach where quality is based on comparisons to expected outputs, while QE uses a reference-free method to score candidates based on their own merits.

#Finetuning Process

Finetuning consists of training the model using the generated data with two primary strategies:

• Self-Training: The model is trained on its own generated translations, providing a way to iteratively improve.

• Using a Teacher Model: A more advanced model serves as a "teacher," generating translations that the student model can learn from, providing a higher quality learning signal.

#Experimental Setup

Experiments were conducted using two pairs of languages: English-German and English-Japanese.

#English-German Configuration

• Base Training Data: The initial model was trained on a large dataset, filtering it for quality and relevance.

• Finetuning Data: Various datasets, including historical test sets and translations generated from the MBR and QE methods, were used for finetuning.

#English-Japanese Configuration

• Training and Finetuning Data: Similar to the English-German setup, the models were trained and finetuned using specific datasets tailored to the English-Japanese language pair.

#Results and Evaluation

#Quality Measurements

The models were evaluated using several automatic metrics that measure translation quality. For a clearer understanding of performance, human evaluations were also carried out to assess the quality more accurately.

#Comparative Performance

The results showed that both MBR and QE finetuning significantly improved the models' performance compared to the baseline. The models trained using these methods showed better results in translation tasks than those only trained with standard methods.

Key Findings:

1. Finetuning Advantage: Models finetuned with MBR and QE techniques outperformed those using traditional approaches.

2. Teacher Model Superiority: Utilizing a high-quality teacher model for generating training data led to further improvements over self-training methods.

3. Efficiency of QE Reranking: QE reranking provided a similar quality output to MBR but at a fraction of the computational cost.

#Further Insights

#Monolingual Data Exploration

Monolingual datasets, which consist of texts in a single language, can be highly beneficial in training translation models. The ability to leverage such data opens up possibilities for enhancing model quality without heavily relying on parallel data, which can be difficult to obtain.

#Impact of Candidate Sizes

The size of candidate outputs generated during the training phase can also influence model performance. Smaller candidate sizes can still yield high-quality results, demonstrating that efficiency in data generation is achievable.

#Conclusion

The introduction of MBR and QE finetuning methods provides a promising way to enhance language generation models effectively. By focusing on training-time adaptations, these techniques enable substantial improvements in output quality while maintaining efficiency during inference.

As the demand for high-quality, efficient language models increases, leveraging monolingual data and advanced training techniques will likely play a crucial role in future developments in NLG.

Continued research can explore more applications of these finetuning methods across various NLG tasks, further refining their capabilities and effectiveness.