Introduction
State-of-the-art models for natural language processing (NLP) tasks like translation, question answering, and parsing include components intended to extract representations for the meaning and contents of each input sentence. These sentence encoder components are typically trained directly for the target task at hand. This approach can be effective on data rich tasks and yields human performance on some narrowly-defined benchmarks BIBREF1 , BIBREF2 , but it is tenable only for the few NLP tasks with millions of examples of training data. This has prompted interest in pretraining for sentence encoding: There is good reason to believe it should be possible to exploit outside data and training signals to effectively pretrain these encoders, both because they are intended to primarily capture sentence meaning rather than any task-specific skill, and because we have seen dramatic successes with pretraining in the related domains of word embeddings BIBREF3 and image encoders BIBREF4 .
More concretely, four recent papers show that pretrained sentence encoders can yield very strong performance on NLP tasks. First, BIBREF5 show that a BiLSTM encoder from a neural machine translation (MT) system can be effectively reused elsewhere. BIBREF6 , BIBREF0 , and BIBREF7 show that various kinds of encoder pretrained in an unsupervised fashion through generative language modeling (LM) are effective as well. Each paper uses its own evaluation methods, though, making it unclear which pretraining task is most effective or whether multiple pretraining tasks can be productively combined; in the related setting of sentence-to-vector encoding, multitask learning with multiple labeled datasets has yielded a robust state of the art BIBREF8 .
This paper attempts to systematically address these questions. We train reusable sentence encoders on 17 different pretraining tasks, several simple baselines, and several combinations of these tasks, all using a single model architecture and procedure for pretraining and transfer, inspired by ELMo. We then evaluate each of these encoders on the nine target language understanding tasks in the GLUE benchmark BIBREF9 , yielding a total of 40 sentence encoders and 360 total trained models. We then measure correlation in performance across target tasks and plot learning curves evaluating the effect of training data volume on each pretraining and target tasks. Looking to the results of this experiment, we find that language modeling is the most effective single pretraining task we study, and that multitask learning during pretraining can offer further gains and a new state-of-the-art among fixed sentence encoders. We also, however, find reasons to worry that ELMo-style pretraining, in which we pretrain a model and use it on target tasks with no further fine-tuning, is brittle and seriously limiting: (i) Trivial baseline representations do nearly as well as the best pretrained encoders, and the margins between substantially different pretraining tasks can be extremely small. (ii) Different target tasks differ dramatically on what kinds of pretraining they benefit most from, and multitask pretraining is not sufficient to circumvent this problem and offer general-purpose pretrained encoders.
Related Work
Work toward learning reusable sentence encoders can be traced back at least as far as the multitask model of BIBREF10 , but has seen a recent surge in progress with the successes of CoVe BIBREF5 , ULMFit BIBREF6 , ELMo BIBREF0 , and the Transformer LM BIBREF7 . However, each uses a different model and dataset from the others, so while these works serve as existence proofs that effective reusable sentence encoders are possible, they do not address the question of what task or tasks should be used to create them.
The revival of interest in sentence encoder pretraining is recent enough that relatively little has been done to understand the relative merits of these models, though two exceptions stand out. In unpublished work, BIBREF11 offer an analysis of the relative strengths of translation and language modeling using a single architecture and training dataset. They find that encoders trained as language models reliably uncover the most syntactic structure, even when they are trained on a strict subset of the data used for a comparable translation model. BIBREF12 offer a deeper investigation of model design issues for ELMo, showing that all of the standard architectures for sentence encoding can be effectively pretrained with broadly similar performance, and that all learn reasonably good representations of the morphological and syntactic properties of sentences.
There has been a great deal of work on sentence-to-vector encoding, a setting in which the pretrained encoder produces a fixed-size vector representation for each input sentence BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 . These vectors are potentially useful for tasks that require fast similarity-based matching of sentences, but using them to replace sentence encoders trained in the conventional way on a given target text classification task does not reliably yield state-of-the art performance on that task BIBREF8 .
Multitask representation learning in NLP in general has been well studied, and again can be traced back at least as far as BIBREF10 . For example, BIBREF18 show promising results from the combination of translation and parsing, BIBREF8 show the benefits of multitask learning in sentence-to-vector encoding, and BIBREF19 and BIBREF20 offer studies of when multitask learning is helpful for lower-level NLP tasks.
Pretraining Tasks
Our main experiment compares encoders pretrained on a large number of tasks and task combinations, where a task is a dataset–objective function pair. This section lists these tasks, which we select either to serve as baselines or because they have shown promise in outside prior work, especially prior work on sentence-to-vector encoding. Appendix "Additional Pretraining Task Details" includes additional details on how we implemented some of these tasks, and names tasks we evaluated but left out.
Models and Training Procedures
We implement our models using the AllenNLP toolkit BIBREF37 , aiming to build the simplest architecture that could be reasonably expected to perform well on the target tasks under study. The design of the models roughly follows that used in the GLUE baselines and ELMo.
Results
Table 1 shows results on the GLUE dev set for all our pretrained encoders, each with and without the pretrained ELMo BiLSTM layers ( $^E$ ). The N/A baselines are untrained encoders with random intialization. The Single-Task baselines are aggregations of results from nine GLUE runs: The result in this row for a given GLUE task uses the encoder pretrained on only that task. For consistency with other runs, we treat the pretraining task and the target task as two separate tasks in all cases (including here) and give them separate task-specific parameters, despite the fact that they use identical data. We use $^S$ and $^C$ to distinguish the sequence-to-sequence and classification versions of the Reddit task, respectively.
To comply with GLUE's limits on test set access, we evaluated only three of our pretrained encoders on test data. These reflect our best models with and without the use of the pretrained ELMo encoder, and with and without the use of GLUE data during pretraining. For discussion of our limited hyperparameter tuning, see above. For roughly-comparable GLUE results in prior work, see BIBREF9 or https://www.gluebenchmark.com; we omit them here in the interest of space. The limited size of a US Letter page prevent us from including these baselines in this table. As of writing, the best test result using a comparable frozen pretrained encoder is 68.9 from BIBREF9 for a model similar to our GLUE $^E$ multitask model, and the best overall result is 72.8 from BIBREF7 with a model that is fine-tuned in its entirety for each target task.
While not feasible to run each setting multiple times, we estimate the variance of the GLUE score by re-running the random encoder and MNLI pretraining setups with and without ELMo with different random seeds. Across five runs, we recorded $\sigma =0.4$ for the random encoder (N/A in table), and $\sigma =0.2$ for MNLI $^E$ . This variation is substantial but not so high as to render results meaningless.
For the explicitly adversarial WNLI dataset BIBREF28 , only one of our models reached even the most frequent class performance of 56.3. In computing average and test set performances, we replace model predictions with the most frequent label to simulate the better performance achievable by choosing not to model that task.
Looking to other target tasks, the grammar-related CoLA task benefits dramatically from ELMo pretraining: The best result without language model pretraining is less than half the result achieved with such pretraining. In contrast, the meaning-oriented textual similarity benchmark STS sees good results with several kinds of pretraining, but does not benefit substantially from the use of ELMo.
Comparing pretraining tasks in isolation without ELMo, language modeling performs best, followed by MNLI. The remaining pretraining tasks yield performance near that of the random baseline. Even when training directly on each target task (Single-Task in table), we get less than a one point gain over this simple baseline. Adding ELMo yielded improvements in performance across all pretraining tasks. MNLI and English–German translation perform best in this setting, with SkipThought, Reddit classification, and DisSent also outperforming the ELMo-augmented random baseline.
With ELMo, a multitask model performs best, but without it, all three multitask models are tied or outperformed by models trained on one of their constituent tasks, suggesting that our approach to multitask learning is not reliably able to produce models that productively use the knowledge taught by each training task. However, of the two non-ELMo models that perform best on the development data, the multitask model generalizes better than the single-task model on test data for tasks like STS where the test set contains new out-of-domain data.
Conclusions
This paper presents a systematic comparison of tasks and task-combinations for the pretraining of sentence-level BiLSTM encoders like those seen in ELMo and CoVe. With 40 pretraining tasks and task combinations (not counting many more ruled out early) and nine target tasks, this represents a far more comprehensive study than any seen on this problem to date.
Our chief positive results are perhaps unsurprising: Language modeling works well as a pretraining task, and no other single task is consistently better. Multitask pretraining can produce results better than any single task can, and sets a new state-of-the-art among comparable models. Target task performance continues to improve with the addition of more language model data, even at large scales, suggesting that further work scaling up language model pretraining is warranted.
However, a closer look at our results suggests that the pretrain-and-freeze paradigm that underlies ELMo and CoVe might not be a sound platform for future work: Some trivial baselines do strikingly well, the margins between pretraining tasks are small, and some pretraining configurations (such as MNLI $^E$ ) yield better performance with less data. This suggests that we may be nearing an upper bound on the performance that can be reached with methods like these.
In addition, different tasks benefit from different forms of pretraining to a striking degree—with correlations between target tasks often low or negative—and multitask pretraining tasks fail to reliably produce models better than their best individual components. This suggests that if truly general-purpose sentence encoders are possible, our current methods cannot produce them.
While further work on language modeling seems straightforward and worthwhile, the author(s) of this paper believe that the future of this line of work will require a better understanding of the ways in which neural network target task models can benefit from outside knowledge and data, and new methods for pretraining and transfer learning to allow them to do so.
Hyperparameters and Optimization Details
See Section UID18 for general comments on hyperparameter tuning.
Multitask Learning Methods
Our multitask learning experiments have three somewhat distinctive properties: (i) We mix tasks with very different amounts of training data—at the extreme, under 1,000 examples for WNLI, and over 1,000,000,000 examples from LM BWB. (ii) Our goal is to optimize the quality of the shared encoder, not the performance of any one of the tasks in the multitask mix. (iii) We mix a relatively large number of tasks, up to eighteen at once in some conditions. These conditions make it challenging but important to avoid overfitting or underfitting any of our tasks.
Relatively little work has been done on this problem, so we conduct a small experiment here. All our experiments use the basic paradigm of randomly sampling a new task to train on at each step, and we experiment with two hyperparameters that can be used to control over- and underfitting: The probability with which we sample each task and the weight with which we scale the loss for each task. Our experiments follow the setup in Appendix "Hyperparameters and Optimization Details" , and do not use the ELMo BiLSTM.
Diagnostic Set Results
Table 6 , below, shows results on the four coarse-grained categories of the GLUE diagnostic set for all our pretraining experiments. This set consists of about 1000 expert-constructed examples in NLI format meant to isolate a range of relevant phenomena. Results use the target task classifier trained on the MNLI training set.
No model achieves performance anywhere close to human-level performance, suggesting that either none of our pretrained models extract features that are suitable for robust reasoning over text, or that the MNLI training set and the MNLI target-task model are not able to exploit any such features that exist. See Section "Analysis and Discussion" for further discussion.
While no model achieves near-human performance, the use of ELMo and other forms of unsupervised pretraining appears to be helpful on examples that highlight world knowledge and lexical-semantic knowledge, and less so on examples that highlight complex logical reasoning patterns or alternations in sentence structure. This relative weakness on sentence structure is somewhat surprising given the finding in BIBREF11 that language model pretraining is helpful for tasks involving sentence structure.

Question: Do some pretraining objectives perform better than others for sentence level understanding tasks?
Answer: Yes