Improving Text Generation through Curriculum Learning

Text generation systems have come a long way, helping transform structured data into readable text. This process is known as data-to-text generation (DTG). One interesting variant is cross-lingual DTG (XDTG), where the data and the generated text are in different languages. This is especially useful for low-resource languages because it allows the use of data from languages with more resources to create understandable content in those with fewer resources.

#Challenges with Noisy Data

One major issue with existing datasets is that they can be noisy. Noisy data refers to information that is incorrect or misleading. For instance, when generating text from facts, sometimes the reference text includes details that can't be inferred from the facts or misses essential points. This muddying of the waters can make the text generation task much harder and can lead to poor-quality outputs.

#A New Approach: Curriculum Learning

To combat the obstacles posed by noisy data, researchers have turned to a method called curriculum learning. This technique involves training models with samples presented in a specific order, starting with easier examples and gradually moving to more difficult ones. The goal is to help the model learn better and improve its performance over time.

So, instead of throwing a jumbled mess of examples at the model all at once, you start with a few simple cases, allowing it to build skills before tackling the trickier ones. Think of it as teaching a child to ride a bike by first letting them scoot around on a balance bike—much less chance of face-planting!

#The Experiment

In this research, two curriculum learning strategies are put to the test: the expanding schedule and the annealing schedule. The expanding schedule starts with easy samples and gradually adds harder ones, while the annealing schedule begins with all samples and then removes the least helpful ones as training goes on.

Researchers looked into various criteria for ordering samples. Among them were:

1. Length: Longer sentences are more complicated and tend to carry more room for mistakes.
2. Rarity: A measure based on how often certain words appear.
3. Alignment: A new criterion based on how closely related the input data is to the generated text.

The study utilized existing datasets and introduced a new one called xToTTo. This new dataset aimed to tackle the challenge of noisy annotations by applying a method that translates data from one language to another and back again, ensuring better quality and alignment.

#Results

The researchers measured success using several metrics. Their findings were interesting. The annealing schedule combined with the alignment criterion led to the best performance, showcasing improvements in Fluency, Faithfulness, and overall coverage of facts in the generated outputs.

In comparison, using criteria based solely on length or rarity didn't go as well, particularly when handling noisy data. The models trained without curriculum learning also performed poorly. It's clear that as data gets noisy, it's crucial to refine our training and focus on the highest-quality samples.

To add more detail, they used an evaluation tool—GPT-4—to assess the outputs. This tool effectively monitored fluency (how well the text flows), faithfulness (whether the text sticks to the facts), and coverage (how much of the given data is reflected in the text).

#Human Evaluation

The research included a human evaluation phase, where experts reviewed sample outputs. The results from human evaluators confirmed that the models using the better curriculum learning techniques produced more reliable and accurate text compared to those using standard methods.

Interestingly, the evaluations showed a disconnect between the findings from GPT-4 and human reviewers. GPT-4 tended to be stricter, marking texts as having less coverage, while humans found them more comprehensive. This highlights the complexity of measuring text generations.

#Conclusion

In summary, this study points out the importance of addressing noisy data in text generation. By adopting curriculum learning, especially using the alignment criterion, great progress can be made in improving cross-lingual data-to-text systems. The results suggest that refining training with higher-quality data leads to better outcomes, paving the way for more reliable text generation and potentially affecting other tasks requiring similar data handling.

So, the next time you wonder how a machine can write like a human, remember that it's not just about feeding it words. How you teach it plays a huge role!