Self-training Improves Pre-training for Natural Language Understanding
Facebook AI & Stanford
Most semi-supervised NLP approaches require specifically in-domain unlabeled data. It means that for the best results, the unlabeled portion of the data which we want to use for semi-supervised training must be from the same domain as the annotated dataset.
This paper proposes SenAugment - a method that constructs task-specific in-domain unannotated datasets on the fly from the large external bank of sentences. So for any new NLP task where we have only a small dataset, we don't need to bother anymore to collect a very similar unannotated dataset if we want to use semi-supervised training.
Now we can sort of
cheat to improve the performance of an NLP model on almost any downstream task
using Self-training (which is also called
Teacher-Student training):
1. We retrieve the most relevant sentences (few millions of them) for the current downstream task from the external bank. For retrieval we use the embedding space of a sentence encoder - Transformer, pre-trained with masked language modeling and finetuned to maximize cosine similarity between similar sentences.
2. We train the teacher model - a
RoBERTa-Large model finetuned on the downstream task.
3. Then we use a
teacher model to annotate the retrieved unlabeled in-domain sentences. We perform additional filtering by keeping the ones that have the high-confident predictions.
4. As our
student model, we then finetune a new
RoBERTa-Large using KL-divergence on the synthetic data by considering the post-softmax class probabilities as labels (i.e., not only the most confident class but the entire class distribution is used as a label for every sentence).
Such a
self-training procedure
significantly boosts the performance compared to the baseline. And the positive effect is higher when fewer GT annotated sentences are available.
As a large-scale external bank of unannotated sentences, authors use
CommonCrowl. In particular, they use a corpus with
5 billion sentences (100B words). Because of its scale and diversity, the sentence bank contains data from various domains and with different styles, allowing to retrieve relevant data for many downstream tasks. To retrieve the most relevant sentences for a specific downstream task, we need to obtain an embedding for the task. Several options exist: (1) average embeddings of all sentences in the training set; (2) average embeddings for every class; (3) keep original sentences embeddings.
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Code
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