To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

TL;DR

This paper investigates whether to fine-tune or use feature extraction when adapting pretrained NLP models to new tasks, showing that the best approach depends on task similarity.

Contribution

It provides empirical insights and guidelines on choosing between fine-tuning and feature extraction based on task similarity for NLP transfer learning.

Findings

Fine-tuning outperforms feature extraction on similar tasks.

Feature extraction is preferable for dissimilar tasks.

Guidelines help practitioners select adaptation methods.

Abstract

While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.

Tables

Table 1. Table 1: This paper’s guidelines for using feature extraction ( ) and fine-tuning ( ) with ELMo and BERT. Seq.: sequence labeling. Clas.: classification. Sent. pair: sentence pair tasks.

Conditions			Guidelines
Pretrain	Adapt.	Task
Any		Any	Add many task parameters
Any		Any	Add minimal task parameters
			Hyper-parameters
Any	Any	Seq. / clas.	and  have similar performance
ELMo	Any	Sent. pair	use
BERT	Any	Sent. pair	use

Table 2. Table 2: Test set performance of feature extraction ( ) and fine-tuning ( ) approaches for ELMo and BERT-base compared to two sentence embedding methods. Settings that are good for  are colored in red ( Δ Δ \Delta = -   > > 1.0); settings good for  are colored in blue ( Δ Δ \Delta = -   < < -1.0). Numbers for baseline methods are from respective papers, except for SST-2, MNLI, and STS-B results, which are from Wang et al. ( 2018 ) . BERT fine-tuning results (except on SICK) are from Devlin et al. ( 2018 ) . The metric varies across tasks (higher is always better): accuracy for SST-2, SICK-E, and MRPC; matched accuracy for MultiNLI; Pearson correlation for STS-B and SICK-R; and span F 1 for CoNLL 2003. For CoNLL 2003, we report the mean with five seeds; standard deviation is about 0.2%.

Pretraining	Adaptation	NER	SA	Nat. lang. inference		Semantic textual similarity
		CoNLL 2003	SST-2	MNLI	SICK-E	SICK-R	MRPC	STS-B
Skip-thoughts		-	81.8	62.9	-	86.6	75.8	71.8
ELMo		91.7	91.8	79.6	86.3	86.1	76.0	75.9
		91.9	91.2	76.4	83.3	83.3	74.7	75.5
	$\Delta$=-	0.2	-0.6	-3.2	-3.3	-2.8	-1.3	-0.4
BERT-base		92.2	93.0	84.6	84.8	86.4	78.1	82.9
		92.4	93.5	84.6	85.8	88.7	84.8	87.1
	$\Delta$=-	0.2	0.5	0.0	1.0	2.3	6.7	4.2

Table 3. Table 3: Comparison of ELMO-   cross-sentence embedding methods on dev. sets of sentence pair tasks.

	SICK-E	SICK-R	STS-B	MRPC
ELMo- +bi-attn.	83.8	84.0	80.2	77.0
w/o bi-attn.	70.9	51.8	38.5	72.3

Table 4. Table 4: Comparison of BERT-  cross-sentence embedding methods on dev. sets of sentence pair tasks.

	SICK-E	SICK-R	STS-B	MRPC
BERT-, joint enc.	85.5	86.4	88.1	83.3
separate encoding	81.2	86.8	86.8	81.4

Table 5. Table 5: Comparison of CoNLL 2003 NER development set performance (F 1 ) for ELMo for both feature extraction and fine-tuning. All results averaged over five random seeds.

Model configuration	F1
+ BiLSTM + CRF	95.5
+ CRF	91.9
+ CRF + gradual unfreeze	95.5
+ BiLSTM + CRF + gradual unfreeze	95.2
+ CRF	95.1

Table 6. Table 6: Accuracy of feature extraction ( ) and fine-tuning ( ) with BERT-base trained on training data of different MNLI domains and evaluated on corresponding dev sets. te : telephone. fi : fiction. tr : travel. go : government. sl : slate.

	te	go	tr	fi	sl
BERT-	84.4	86.7	86.1	84.5	80.9
$\Delta$=-	-1.1	-0.2	-0.6	0.4	-0.6
JS div	0.21	0.18	0.14	0.09	0.09

Full text

\star$$\starfootnotetext: The first two authors contributed equally.00footnotetext: Sebastian is now at DeepMind.

1 Introduction

Sequential inductive transfer learning Pan and Yang (2010) consists of two stages: pretraining, in which the model learns a general-purpose representation of inputs, and adaptation, in which the representation is transferred to a new task. Most previous work in NLP has focused on different pretraining objectives for learning word or sentence representations Mikolov et al. (2013); Kiros et al. (2015).

Few works, however, have focused on the adaptation phase. There are two main paradigms for adaptation: feature extraction and fine-tuning. In feature extraction () the model’s weights are ‘frozen’ and the pretrained representations are used in a downstream model similar to classic feature-based approaches Koehn et al. (2003). Alternatively, a pretrained model’s parameters can be unfrozen and fine-tuned () on a new task Dai and Le (2015). Both have benefits:  enables use of task-specific model architectures and may be computationally cheaper as features only need to be computed once. On the other hand,  is convenient as it may allow us to adapt a general-purpose representation to many different tasks.

Gaining a better understanding of the adaptation phase is key in making the most use out of pretrained representations. To this end, we compare two state-of-the-art pretrained models, ELMo Peters et al. (2018) and BERT Devlin et al. (2018) using both  and  across seven diverse tasks including named entity recognition, natural language inference (NLI), and paraphrase detection. We seek to characterize the conditions under which one approach substantially outperforms the other, and whether it is dependent on the pretraining objective or target task. We find that  and  have comparable performance in most cases, except when the source and target tasks are either highly similar or highly dissimilar. We furthermore shed light on the practical challenges of adaptation and provide a set of guidelines to the NLP practitioner, as summarized in Table 1.

2 Pretraining and Adaptation

While pretraining tasks have been designed with particular downstream tasks in mind Felbo et al. (2017), we focus on pretraining tasks that seek to induce universal representations suitable for any downstream task.

Word representations

Pretrained word vectors Turian et al. (2010); Pennington et al. (2014) have been an essential component in state-of-the-art NLP systems. Word representations are often fixed and fed into a task specific model (), although  can provide improvements Kim (2014). Recently, contextual word representations learned supervisedly (e.g., through machine translation; McCann et al., 2017) or unsupervisedly (typically through language modeling; Peters et al., 2018) have significantly improved over noncontextual vectors.

Sentence embedding methods

Such methods learn sentence representations via different pretraining objectives such as previous/next sentence prediction Kiros et al. (2015); Logeswaran and Lee (2018), NLI Conneau et al. (2017), or a combination of objectives Subramanian et al. (2018). During the adaptation phase, the sentence representation is typically provided as input to a linear classifier (). LM pretraining with  has also been successfully applied to sentence level tasks. Howard and Ruder (2018, ULMFiT) propose techniques for fine-tuning a LM, including triangular learning rate schedules and discriminative fine-tuning, which uses lower learning rates for lower layers. Radford et al. (2018) extend LM- to additional sentence and sentence-pair tasks.

Masked LM and next-sentence prediction

BERT Devlin et al. (2018) combines both word and sentence representations (via masked LM and next sentence prediction objectives) in a single very large pretrained transformer Vaswani et al. (2017). It is adapted to both word and sentence level tasks by  with task-specific layers.

3 Experimental Setup

We compare ELMo and BERT as representatives of the two best-performing pretraining settings. This section provides an overview of our methods; see the supplement for full details.

3.1 Target Tasks and Datasets

We evaluate on a diverse set of target tasks: named entity recognition (NER), sentiment analysis (SA), and three sentence pair tasks, natural language inference (NLI), paraphrase detection (PD), and semantic textual similarity (STS).

NER

We use the CoNLL 2003 dataset (Sang and Meulder, 2003), which provides token level annotations of newswire across four different entity types (PER, LOC, ORG, MISC).

SA

We use the binary version of the Stanford Sentiment Treebank (SST-2; Socher et al., 2013), providing sentiment labels (negative or positive) for phrases and sentences of movie reviews.

NLI

We use both the broad-domain MultiNLI dataset Williams et al. (2018) and Sentences Involving Compositional Knowledge (SICK-E; Marelli et al., 2014).

PD

For paraphrase detection (i.e., decide whether two sentences are semantically equivalent), we use the Microsoft Research Paraphrase Corpus (MRPC; Dolan and Brockett, 2005).

STS

We employ the Semantic Textual Similarity Benchmark (STS-B; Cer et al., 2017) and SICK-R Marelli et al. (2014). Both datasets, provide a human judged similarity value from 1 to 5 for each sentence pair.

3.2 Adaptation

We now describe how we adapt ELMo and BERT to these tasks. For  we require a task-specific architecture, while for  we need a task-specific output layer. For fair comparison, we conduct an extensive hyper-parameter search for each task.

Feature extraction ()

For both ELMo and BERT, we extract contextual representations of the words from all layers. During adaptation, we learn a linear weighted combination of the layers Peters et al. (2018) which is used as input to a task-specific model. When extracting features, it is important to expose the internal layers as they typically encode the most transferable representations. For SA, we employ a bi-attentive classification network McCann et al. (2017). For the sentence pair tasks, we use the ESIM model Chen et al. (2017). For NER, we use a BiLSTM with a CRF layer Lafferty et al. (2001); Lample et al. (2016).

Fine-tuning (): ELMo

We max-pool over the LM states and add a softmax layer for text classification. For the sentence pair tasks, we compute cross-sentence bi-attention between the LM states Chen et al. (2017), apply a pooling operation, then add a softmax layer. For NER, we add a CRF layer on top of the LSTM states.

Fine-tuning (): BERT

We feed the sentence representation into a softmax layer for text classification and sentence pair tasks following Devlin et al. (2018). For NER, we extract the representation of the first word piece for each token and add a softmax layer.

4 Results

We show results in Table 2 comparing ELMo and BERT for both  and  approaches across the seven tasks with one sentence embedding method, Skip-thoughts Kiros et al. (2015), that employs a next-sentence prediction objective similar to BERT.

Both ELMo and BERT outperform the sentence embedding method significantly, except on the semantic textual similarity tasks (STS) where Skip-thoughts is similar to ELMo. The overall performance of  and  varies from task to task, with small differences except for a few notable cases. For ELMo, we find the largest differences for sentence pair tasks where  consistently outperforms . For BERT, we obtain nearly the opposite result:  significantly outperforms  on all STS tasks, with much smaller differences for the others.

Discussion

Past work in NLP Mou et al. (2016) showed that similar pretraining tasks transfer better.222Mou et al. (2016), however, only investigate transfer between classification tasks (NLI $\rightarrow$ SICK-E/MRPC). In computer vision (CV), Yosinski et al. (2014) similarly found that the transferability of features decreases as the distance between the pretraining and target task increases. In this vein, Skip-thoughts—and Quick-thoughts Logeswaran and Lee (2018), which has similar performance—which use a next-sentence prediction objective similar to BERT, perform particularly well on STS tasks, indicating a close alignment between the pretraining and target task. This strong alignment also seems to be the reason for BERT’s strong relative performance on these tasks.

In CV,  generally outperforms  when transferring from ImageNet supervised classification pretraining to other classification tasks Kornblith et al. (2018). Recent results suggest  is less useful for more distant target tasks such as semantic segmentation He et al. (2018). This is in line with our results, which show strong performance with  between closely aligned tasks (next-sentence prediction in BERT and STS tasks) and poor performance for more distant tasks (LM in ELMo and sentence pair tasks). A confounding factor may be the suitability of the inductive bias of the model architecture for sentence pair tasks, which we will analyze next.

5 Analyses

Modelling pairwise interactions

LSTMs consider each token sequentially, while Transformers can relate each token to every other in each layer Vaswani et al. (2017). This might facilitate  with Transformers on sentence pair tasks, on which ELMo- performs comparatively poorly. To analyze this further, we compare different ways of encoding the sentence pair with ELMo and BERT. For ELMo, we compare encoding with and without cross-sentence bi-attention in Table 3. When adapting the ELMo LSTM to a sentence pair task, modeling the sentence interactions by fine-tuning through the bi-attention mechanism provides the best performance.333This is similar to text classification tasks, where we find max-pooling to outperform using the final hidden state, similar to Howard and Ruder (2018). This provides further evidence that the LSTM has difficulty modeling the pairwise interactions during sequential processing. This is in contrast to a Transformer LM that can be fine-tuned in this manner Radford et al. (2018).

For BERT-, we compare joint encoding of the sentence pair with encoding the sentences separately in Table 4. The latter leads to a drop in performance, which shows that the BERT representations encode cross-sentence relationships and are therefore particularly well-suited for sentence pair tasks.

Impact of additional parameters

We evaluate whether adding parameters is useful for both adaptation settings on NER. We add a CRF layer (as used in ) and a BiLSTM with a CRF layer (as used in ) to both and show results in Table 5. We find that additional parameters are key for , but hurt performance with . In addition,  requires gradual unfreezing Howard and Ruder (2018) to match performance of feature extraction.

ELMo fine-tuning

We found fine-tuning the ELMo LSTM to be initially difficult and required careful hyper-parameter tuning. Once tuned for one task, other tasks have similar hyper-parameters. Our best models used slanted triangular learning rates and discriminative fine-tuning Howard and Ruder (2018) and in some cases gradual unfreezing.

Impact of target domain

Pretrained language model representations are intended to be universal. However, the target domain might still impact the adaptation performance. We calculate the Jensen-Shannon divergence based on term distributions (Ruder and Plank, 2017) between the domains used to train BERT (books and Wikipedia) and each MNLI domain. We show results in Table 6. We find no significant correlation. At least for this task, the distance of the source and target domains does not seem to have a major impact on the adaptation performance.

Representations at different layers

In addition, we are interested how the information in the different layers of the models develops over the course of fine-tuning. We measure this information in two ways: a) with diagnostic classifiers (Adi et al., 2017); and b) with mutual information (MI; Noshad et al., 2018). Both methods allow us to associate the hidden activations of our model with a linguistic property. In both cases, we use the mean of the hidden activations of BERT-base444We show results for BERT as they are more inspectable due to the model having more layers. Trends for ELMo are similar. of each token / word piece of the sequence(s) as the representation.555We observed similar results when using max-pooling or the representation of the first token.

With diagnostic classifiers, for each example, we extract the pretrained and fine-tuned representation at each layer as features. We use these features as input to train a logistic regression model (linear regression for STS-B, which has real-valued outputs) on the training data of two single sentence (CoLA666The Corpus of Linguistic Acceptability (CoLA) consists of examples of expert English sentence acceptability judgments drawn from 22 books and journal articles on linguistic theory. It uses the Matthews correlation coefficient (Matthews, 1975) for evaluation and is available at: nyu-mll.github.io/CoLA and SST-2) and two pair sentence tasks (MRPC and STS-B). We show its performance on the corresponding dev sets in Figure 1.

For all tasks, diagnostic classifier performance generally is higher in higher layers of the model. Fine-tuning improves the performance of the diagnostic classifier at every layer. For the single sentence classification tasks CoLA and SST-2, pretrained performance increases gradually until the last layers. In contrast, for the sentence pair tasks MRPC and STS-B performance is mostly flat after the fourth layer. Relevant information for sentence pair tasks thus does not seem to be concentrated primarily in the upper layers of pretrained representations, which could explain why fine-tuning is particularly useful in these scenarios.

Computing the mutual information with regard to representations of deep neural networks has only become feasible recently with the development of more sophisticated MI estimators. In our experiments, we use the state-of-the-art ensemble dependency graph estimator (EDGE; Noshad et al., 2018) with default hyper-parameter values. As a sanity check, we compute the MI between hidden activations and random labels and random representations and random labels, which yields [math] in every case as we would expect.777For the same settings, we obtain non-zero values with earlier estimators (Saxe et al., 2018), which seem to be less reliable for higher numbers of dimensions.

We show the mutual information $I(H;Y)$ between the pretrained and fine-tuned mean hidden activations $H$ at each layer of BERT and the output labels $Y$ on the dev sets of CoLA, SST-2, and MRPC in Figure 2.

The MI between pretrained representations and labels is close to [math] across all tasks and layers, except for SST where the last layer shows a small non-zero value. In contrast, fine-tuned representations display much higher MI values. The MI for fine-tuned representations rises gradually through the intermediate and last layers for the sentence pair task MRPC, while for the single sentence classification tasks, the MI rises sharply in the last layers. Similar to our findings with diagnostic classifiers, knowledge for single sentence classification tasks thus seems mostly concentrated in the last layers, while pair sentence classification tasks gradually build up information in the intermediate and last layers of the model.

6 Conclusion

We have empirically analyzed fine-tuning and feature extraction approaches across diverse datasets, finding that the relative performance depends on the similarity of the pretraining and target tasks. We have explored possible explanations and provided practical recommendations for adapting pretrained representations to NLP practicioners.

Appendix A Experimental details

For fair comparison, all experiments include extensive hyper-parameter tuning. We tuned the learning rate, dropout ratio, weight decay and number of training epochs. In addition, the fine-tuning experiments also examined the impact of triangular learning rate schedules, gradual unfreezing, and discriminative learning rates. Hyper-parameters were tuned on the development sets and the best setting evaluated on the test sets.

All models were optimized with the Adam optimizer Kingma and Ba (2015) with weight decay fix Loshchilov and Hutter (2017).

We used the publicly available pretrained ELMo888https://allennlp.org/elmo and BERT999https://github.com/google-research/bert models in all experiments. For ELMo, we used the original two layer bidirectional LM. In the case of BERT, we used the BERT-base model, a 12 layer bidirectional transformer. We used the English uncased model for all tasks except for NER which used the English cased model.

A.1 Feature extraction

To isolate the effects of fine-tuning contextual word representations, all feature based models only include one type of word representation (ELMo or BERT) and do not include any other pretrained word representations.

For all tasks, all layers of pretrained representations were weighted together with learned scalar parameters following Peters et al. (2018).

NER

For the NER task, we use a two layer bidirectional LSTM in all experiments. For ELMo, the output layer is a CRF, similar to a state-of-the-art NER system Lample et al. (2016). Feature extraction for ELMo treated each sentence independently.

In the case of BERT, the output layer is a softmax to be consistent with the fine-tuned experiments presented in Devlin et al. (2018). In addition, as in Devlin et al. (2018), we used document context to extract word piece representations. When composing multiple word pieces into a single word representation, we found it beneficial to run the biLSTM layers over all word pieces before taking the LSTM states of the first word piece in each word. We experimented with other pooling operations to combine word pieces into a single word representation but they did not provide additional gains.

SA

We used the implementation of the bi-attentive classification network in AllenNLP Gardner et al. (2017) with default hyper-parameters, except for tuning those noted above. As in the fine-tuning experiments for SST-2, we used all available annotations during training, including those of sub-trees. Evaluation on the development and test sets used full sentences.

Sentence pair tasks

When extracting features from ELMo, each sentence was handled separately. For BERT, we extracted features for both sentences jointly to be consistent with the pretraining procedure. As reported in Section 5 this improved performance over extracting features for each sentence separately.

Our model is the ESIM model Chen et al. (2017), modified as needed to support regression tasks in addition to classification. We used default hyper-parameters except for those described above.

A.2 Fine-tuning

When fine-tuning ELMo, we found it beneficial to use discriminative learning rates Howard and Ruder (2018) where the learning rate decreased by $0.4\times$ in each layer (so that the learning rate for the second to last layer is $0.4\times$ the learning rate in the top layer). In addition, for SST-2 and NER, we also found it beneficial to gradually unfreeze the weights starting with the top layer. In this setting, in each epoch one additional layer of weights is unfrozen until all weights are training. These settings were chosen by tuning development set performance.

For fine-tuning BERT, we used the default learning rate schedule Devlin et al. (2018) that is similar to the schedule used by Howard and Ruder (2018).

SA

We considered several pooling operations for composing the ELMo LSTM states into a vector for prediction including max pooling, average pooling and taking the first/last states. Max pooling performed slightly better than average pooling on the development set.

Sentence pair tasks

Our bi-attentive fine-tuning mechanism is similar to the the attention mechanism in the feature based ESIM model. To apply it, we first computed the bi-attention between all words in both sentences, then applied the same “enhanced” pooling operation as in Chen et al. (2017) before predicting with a softmax. Note that this attention mechanism and pooling operation does not add any additional parameters to the network.