Introduction
It has become clear over the last year that pretraining sentence encoder neural networks on unsupervised tasks, such as language modeling, then fine-tuning them on individual target tasks, can yield significantly better target task performance than could be achieved using target task training data alone BIBREF1 , BIBREF0 , BIBREF2 . Large-scale unsupervised pretraining in experiments like these seems to produce pretrained sentence encoders with substantial knowledge of the target language (which, so far, is generally English). These works have shown that a mostly task-agnostic, one-size-fits-all approach to fine-tuning a large pretrained model with a thin output layer for a given task can achieve results superior to individually optimized models.
However, it is not obvious that the model parameters obtained during unsupervised pretraining should be ideally suited to supporting this kind of transfer learning. Especially when only a small amount of training data is available for the target task, experiments of this kind are potentially brittle, and rely on the pretrained encoder parameters to be reasonably close to an optimal setting for the target task. During target task training, the encoder must learn and adapt enough to be able to solve the target task—potentially involving a very different input distribution and output label space than was seen in pretraining—but it must avoid adapting so much that it overfits and ceases to take advantage of what was learned during pretraining.
This work explores the possibility that the use of a second stage of pretraining with data-rich intermediate supervised tasks might mitigate this brittleness and improve both the robustness and effectiveness of the resulting target task model. We name this approach Supplementary Training on Intermediate Labeled-data Tasks (STILTs).
Experiments with sentence encoders on STILTs take the following form: (i) A model is first trained on an unlabeled-data task like language modeling that can teach it to handle data in the target language; (ii) The model is then further trained on an intermediate, labeled-data task for which ample labeled data is available; (iii) The model is finally fine-tuned further on the target task and evaluated. Our experiments evaluate STILTs as a means of improving target task performance on the GLUE benchmark suite BIBREF3 —a collection of target tasks drawn from the NLP literature—using the publicly-distributed OpenAI generatively-pretrained (GPT) Transformer language model BIBREF0 as our pretrained encoder. We follow Radford et al. in our basic mechanism for fine-tuning both for the intermediate and final tasks, and use the following intermediate tasks: the Multi-Genre NLI Corpus BIBREF4 , the Stanford NLI Corpus BIBREF5 , the Quora Question Pairs (QQP) dataset, and a custom fake-sentence-detection task based on the BooksCorpus dataset BIBREF6 using a method adapted from BIBREF7 . We show that using STILTs yields significant gains across most of the GLUE tasks.
As we expect that any kind of pretraining will be most valuable in a limited training data regime, we also conduct a set of fine-tuning experiments where the model is fine tuned on only 1k- or 5k-example sample of the target task training set. The results show that STILTs substantially improve model performance across most tasks in this downsampled data setting. For target tasks such as MRPC, using STILTs is critical to obtaining good performance.
Related Work
BIBREF8 compare several pretraining tasks for syntactic target tasks, and find that language model pretraining reliably performs well. BIBREF9 investigate the architectural choices behind ELMo-style pretraining with a fixed encoder, and find that the precise choice of encoder architecture strongly influences training speed, but has a relatively small impact on performance. In an publicly-available ICLR 2019 submission, BIBREF10 compare a variety of tasks for pretraining in an ELMo-style setting with no encoder fine-tuning. They conclude that language modeling generally works best among candidate single tasks for pretraining, but show some cases in which a cascade of a model pretrained on language modeling followed by another model pretrained on tasks like MNLI can work well. The paper introducing BERT BIBREF2 briefly mentions encouraging results in a direction similar to ours: One footnote notes that unpublished experiments show “substantial improvements on RTE from multi-task training with MNLI”.
In the area of sentence-to-vector sentence encoding, BIBREF11 offer one of the most comprehensive suites of diagnostic tasks, and higlight the importance of ensuring that these models preserve lexical content information.
In earlier work less closely tied to the unsupervised pretraining setup used here, BIBREF12 investigate the conditions under which task combinations can be productively combined in multi-task learning, and show that the success of a task combination can be determined by the shape of the learning curve during training for each task. In their words: “Multi-task gains are more likely for target tasks that quickly plateau with non-plateauing auxiliary tasks”.
In word representations, this work shares motivations with work on embedding space retrofitting BIBREF13 , in which a labeled dataset like WordNet is used to refine representations learned by an unsupervised embedding learning algorithm before those representations can then be used in a target task.
Results
Table 1 shows our results on GLUE with and without STILTs. Our addition of supplementary training boosts performance across many of the two sentence tasks. On each of our models trained with STILTs, we show improved overall average GLUE scores on the development set. For MNLI and QNLI target tasks, we observe marginal or no gains, likely owing to the two tasks already having large training sets. For the two single sentence tasks—the syntax-oriented CoLA task and the SST sentiment task—we find somewhat deteriorated performance. For CoLA, this mirrors results reported in BIBREF10 , who show that few pretraining tasks other than language modeling offer any advantage for CoLA. The Overall Best score is computed based on taking the best score for each task.
On the test set, we show similar performance gains across most tasks. Here, we compute Best based on Dev, which shows scores based on choosing the best supplementary training scheme for each task based on corresponding development set score. This is a more realistic estimate of test set performance, attaining a GLUE score of 76.9, a 2.3 point gain over the score of our baseline system adapted from Radford et al. This significantly closes the gap between Radford et al.'s model and the BERT model BIBREF2 variant with a similar number of parameters and layers, which attains a GLUE score of 78.3.
We perform the same experiment on the development set without the auxiliary language modeling objective. The results are shown in Table 3 in the Appendix. We similarly find improvements across many tasks by applying STILTs, showing that the benefits of supplementary training do not require language modeling at either the supplementary training or the fine-tuning stage.
Discussion
We find that sentence pair tasks seem to benefit more from supplementary training than single-sentence ones. This is true even for the case of supplementary training on the single-sentence fake-sentence-detection task, so the benefits cannot be wholly attributed to task similarity. We also find that data-constrained tasks benefit much more from supplementary training. Indeed, when applied to RTE, supplementary training on MNLI leads to a eleven-point increase in test set score, pushing the performance of Radford et al.'s GPT model with supplementary training above the BERT model of similar size, which achieves a test set score of 66.4. Based on the improvements seen from applying supplementary training on the fake-sentence-detection task, which is built on the same BooksCorpus dataset that the GPT model was trained on, it is also clear that the benefits from supplementary training do not entirely stem from the trained model being exposed to different textual domains.
Applying STILTs also comes with little complexity or computational overhead. The same infrastructure used to fine-tune the GPT model can be used to implement the supplementary training. The computational cost of the supplementary training phase is another phase of fine-tuning, which is small compared to the cost of training the original model.
However, using STILTs is not always beneficial. In particular, we show that most of our intermediate tasks were actually detrimental to the single-sentence tasks in GLUE. The interaction between the intermediate task, the target task, and the use of the auxiliary language modeling objective is a subject due for further investigation. Therefore, for best target task performance, we recommend experimenting with supplementary training with several closely-related data-rich tasks and use the development set to select the most promising approach for each task, as in the Best based on Dev formulation shown in Table 1 .
Conclusion
This work represents only an initial investigation into the benefits of supplementary supervised pretraining. More work remains to be done to firmly establish when methods like STILTs can be productively applied and what criteria can be used to predict which combinations of intermediate and target tasks should work well. Nevertheless, in our initial work with four example intermediate training tasks, GPT on STILTs achieves a test set GLUE score of 76.9, which markedly improves on our strong pretrained Transformer baseline. We also show that in data-constrained regimes, the benefits of using STILTs are even more pronounced.
Acknowledgments
We would like to thank Nikita Nangia for her helpful feedback.

Question: Is the new model evaluated on the tasks that BERT and ELMo are evaluated on?
Answer: Yes

Question: Does the additional training on supervised tasks hurt performance in some tasks?
Answer: Yes