Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1903–1913Brussels, Belgium, October 31 - November 4, 2018. c©2018 Association for Computational Linguistics

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Deriving Machine Attention from Human Rationales

Yujia Bao1, Shiyu Chang2, Mo Yu2, Regina Barzilay11Computer Science and Artificial Intelligence Lab, MIT

2MIT-IBM Watson AI Lab, IBM Research{yujia, regina}@csail.mit.edu, shiyu.chang@ibm.com, yum@us.ibm.com

Abstract

Attention-based models are successful whentrained on large amounts of data. In this paper,we demonstrate that even in the low-resourcescenario, attention can be learned effectively.To this end, we start with discrete human-annotated rationales and map them into contin-uous attention. Our central hypothesis is thatthis mapping is general across domains, andthus can be transferred from resource-rich do-mains to low-resource ones. Our model jointlylearns a domain-invariant representation andinduces the desired mapping between ratio-nales and attention. Our empirical results val-idate this hypothesis and show that our ap-proach delivers significant gains over state-of-the-art baselines, yielding over 15% averageerror reduction on benchmark datasets.1

1 Introduction

Attention-based models have become architec-tures of choice for many NLP tasks. In addi-tion to significant performance gains, these mod-els are attractive, as attention is often used as aproxy for human interpretable rationales. Theirsuccess, however, is conditioned on access to largeamounts of training data. To make these mod-els applicable in low-resource scenarios, we utilizethis connection in the opposite direction. Specif-ically, we propose an approach to map human ra-tionales to high-performing attention, and use thisattention to guide models trained in low-resourcescenarios.

The notions of rationale and attention areclosely related. Both of them highlight word im-portance for the final prediction. In the case ofrationale, the importance is expressed as a hardselection, while attention provides a soft distribu-tion over the words. Figure 1 illustrates this re-latedness. One obvious approach to improve low-

1Our code and data are available at https://github.com/YujiaBao/R2A.

Task: Hotel location label: negative

a nice and clean hotel to stay for business and leisure. but the location is not good if you need publictransport . it took too long for transport and waitingfor bus . but the swimming pool looks good .

Task: Beer aroma label: positive

poured a deep brown color with little head thatdissipated pretty quickly . aroma is of sweetmaltiness with chocolate and caramel notes . flavoris also of chocolate and caramel maltiness . mouthfeelis good a bit on the thick side . drinkability is ok . thisis to be savored not sessioned .

Figure 1: Examples of rationales versus oracle at-tention. Words are highlighted according to theirrelative attention scores. Human rationales areshown in bold with underlines.

resource performance is to directly use human ra-tionales as a supervision for attention generation.The implicit assumption behind this method is thatmachine-generated attention should mimic humanrationales. However, rationales on their own arenot adequate substitutes for machine attention. In-stead of providing a soft distribution, human ratio-nales only provide the binary indication about rel-evance. Furthermore, rationales are subjectivelydefined and often vary across annotators. Finally,human rationales are not customized for a givenmodel architecture. In contrast, machine attentionis always derived as a part of a specific model ar-chitecture.

To further understand this connection, we em-pirically compare models informed by human ra-tionales and those by high-quality attention. Toobtain the latter, we derive an “oracle” attentionusing a large amount of annotations. This “ora-cle” attention is then used to guide a model thatonly has access to a small subset of this trainingdata. Not only does this model outperform theoracle-free variant, but it also yields substantialgains over its counterpart trained with human ra-

https://github.com/YujiaBao/R2Ahttps://github.com/YujiaBao/R2A

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tionales — 89.98 % vs 85.22 % average accuracyon three aspects of hotel review (see Section 4 fordetails). In practice, however, this “oracle” atten-tion is not available. To employ this method, weneed to find a way to obtain a substitute for the“oracle” attention.

In this paper, we show how to achieve this goalusing rationales. Specifically, we learn a map-ping from human rationales to high-quality atten-tion (R2A). We hypothesize that this mapping isgeneralizable across tasks and thus can be trans-ferred from resource-rich tasks.2 Figure 1 illus-trates that in both tasks, attention weighs ratio-nale words in a similar fashion: highlighting task-specific nouns and adjectives, while downplayingfunctional words. To learn and apply this mappingwe need access to rationales in both source and tar-get tasks. In the target task, we assume rationalesare provided by humans. In the source task(s), col-lecting rationales at scale is infeasible. Therefore,we use machine-generated rationales (Lei et al.,2016) as a proxy.

Our R2A model consists of three components.The first one is an attention-based model for thesource task(s) that provides supervision for at-tention generation. The second component fo-cuses on learning a domain-invariant representa-tion to support transfer. The third componentcombines this invariant representation and ratio-nales together to generate the attention. Thesethree components are trained jointly to optimizethe overall objective. Once the model is trained,we apply it to the target task to generate attentionfrom human rationales. This attention is conse-quently used to supervise the training of the targetclassifier.

We evaluate our approach on two transfer set-tings: aspect transfer within single domain and do-main transfer across multiple domains. Our exper-iments demonstrate that our approach delivers sig-nificant performance improvements over the base-lines. For instance, the average error reductionover the best baseline in domain transfer is over15%. In addition, both qualitative and quantitativeanalyses confirm that our R2A model is capable ofgenerating high-quality attention for target tasks.

2In this paper, we consider a more general setting whereone domain contains multiple tasks. Also, we assume havingone source domain. However, our proposed method is a gen-eral framework and can be easily adapted to problems withmultiple source domains.

2 Related Work

Attention-based models Attention has beenshown to be effective when the model is trainedon large amounts of training data (Bahdanau et al.,2014; Luong et al., 2015; Rush et al., 2015; Yanget al., 2016; Lin et al., 2017; Chen et al., 2017;Vaswani et al., 2017). In this setting, typicallyno additional supervision is required for learn-ing the attention. Nevertheless, further refiningattention by extra supervision has been shownto be beneficial. Examples include using wordalignments to learn attention in neural machinetranslation (Liu et al., 2016), employing argu-ment words to supervise attention in event de-tection (Liu et al., 2017), utilizing linguistically-motivated annotations to guide attention in con-stituency parsing (Kamigaito et al., 2017). Thesesupervision mechanisms are tailored to specificapplications. In contrast, our approach is based onthe connection between rationales and attention,and can be used for multiple applications.

Rationale-based models Zaidan et al. (2007)was the first to explore the value of rationalesin low-resource scenarios. They hypothesize thatthe model confidence should decrease when therationale words are removed from the inputs,and validate this idea for linear models. Recentwork (Zhang et al., 2016) explores the potentialof integrating rationales with more complex neu-ral classifiers. In their model, human rationalesare directly used to guide the sentence-level atten-tion for a CNN-based classifier. To reach goodperformance, their model still requires a sufficientamount of training data. Our work differs fromtheirs as we discern the intrinsic difference be-tween human rationales and machine attention.Moreover, we learn a model to map human ratio-nales into high-quality attention so as to provide aricher supervision for low-resource models.

Transfer learning When labeled data on the tar-get task is available, existing approaches typicallytransfer the knowledge by either fine-tuning anencoder trained on the source tasks(s) (Conneauet al., 2017; Peters et al., 2018) or multi-task learn-ing on all tasks with a shared encoder (Collobertet al., 2011). In this paper, we explore the trans-ferability of the task-specific attention through hu-man rationales. We believe this will further assistlearning in low-resource scenarios.

Our work is also related to unsupervised domain

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adaptation, as the R2A model has never seen anytarget annotations during training. Existing meth-ods commonly adapt the classifier by aligning therepresentations between the source and target do-mains (Glorot et al., 2011; Chen et al., 2012; Zhouet al., 2016; Ganin et al., 2016; Zhang et al., 2017).In contrast, our model adapts the mapping from ra-tionales to attention; thus after training, it can beapplied to different target tasks.

3 Method

Problem formulation We assume that we haveN source tasks {Si}Ni=1, where each of them hassufficient amounts of labeled examples. Using ex-isting methods (Lei et al., 2016), we can gener-ate rationales for each source example automati-cally (see Appendix 1 for details). In the targettask T , we only have a limited amount of labeledexamples with large amounts of unlabeled data.For those labeled examples, we assume access tohuman-annotated rationales.

Overview Our goal is to improve classificationperformance on the target task by learning a map-ping from human rationales to high-quality ma-chine attention (R2A). Given the scarcity of ourtarget data, we learn this mapping on resource richtasks where high-quality attention can be readilyobtained during training. Next, the mapping be-tween rationales and attention derived from thesource tasks is exported into the target task. Toenable this transfer, models have to operate overan invariant representation which we construct viaan adversarial objective. Once the mapping is de-rived, we can translate human rationales in the tar-get task into high-quality attention. This generatedattention is then used to provide additional trainingsignal for an attention-based classifier for the tar-get task. The overall pipeline is shown in Figure 2.

Alternatively, we can view the R2A mapping asa meta model that produces a prior over the atten-tion distribution across different tasks.

Model architecture Figure 3 illustrates the ar-chitecture of our R2A model, which consists ofthree components.

• Multi-task learning In order to learn the R2Amapping, we need annotation for the attention.This module generates high-quality attention asan intermediate result by minimizing the predic-tion error on the source tasks (Section 3.1).

unlabeled target data

R2A

labeled target datawith rationales

labeled target data withR2A-generated attention

R2A

labeled target data withR2A-generated attention

target classifier

Step 1: Training R2A

Step 2: R2A inference

Step 3: Training target classifier

labeled source datawith rationales

Figure 2: Overall pipeline of our approach (Sec-tion 3.4). The R2A mapping is learned from la-beled source data and unlabeled target data. Thenwe applied it to the target task to derive attentionbased on human rationales. Finally, a target clas-sifier is trained under the supervision of both theannotated labels and the R2A-generated attention.

• Domain-invariant encoder This moduleaims to transform the contextualized represen-tation obtained from the first module into adomain-invariant version. We achieve this goalthrough domain adversarial training over thesource data and the unlabeled target data (Sec-tion 3.2).

• Attention generation This module learns topredict the intermediate attention obtained fromthe first module based on the domain-invariantrepresentation and the rationales (Section 3.3).

3.1 Multi-task learningThe goal of the multi-task learning module is tolearn good attention for each source task. Thislearned attention will be used later to supervise theattention generation module. This module takesthe input text from the source tasks and predictsthe labels. To accomplish the previously statedgoal, we minimize the prediction error over all la-beled source data.

Let (xt, yt) be a training instance from anysource task t ∈ {S1, . . .SN}. We first en-code the input sequence xt into hidden states:ht = enc(xt), where enc is a bi-directionalLSTM (Hochreiter and Schmidhuber, 1997) thatis shared across all source tasks. For each positioni, the dense vector hti encodes the content and con-text information of the word xti. We then pass the

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(a) Multi-task learning (b) Domain-invariant Encoder

(c) Attention Generation

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Copy

Figure 3: Architecture of the R2A model. The model is comprised of (a) a multi-task learning compo-nent, (b) a domain-invariant encoder, and (c) an attention generation component. Solid arrows denotecomputations for training, while dotted arrows denote computations for inference.

sequence ht on to a task-specific attention moduleto produce attention αt = attt(ht) as follows:

h̃ti = tanh(Wtatth

ti + b

tatt),

αti =exp(〈h̃ti, qtatt〉)∑j exp(〈h̃tj , qtatt〉)

,

where 〈·, ·〉 denotes inner product and W tatt, btatt,qtatt are learnable parameters. We predict the la-bel of xt using the weighted sum of its contex-tualized representation: ŷt = predt(

∑i α

tihti),

where predt is a task-specific multi-layer percep-tron. We train this module to minimize the loss,Llbl, between the prediction and the annotated la-bel for all source tasks. We use cross entropy lossfor classification tasks and mean square loss forregression tasks.

3.2 Domain-invariant encoder

Supplied with large amounts of source data andunlabeled target data, this module has two goals:1) learning a general encoder for both source andtarget corpora, and 2) learning domain-invariantrepresentation. This module enables effectivetransfer—especially in the presence of significantvariance between the source and target domains.We achieve the first goal by optimizing a lan-guage modeling objective and the second goal byminimizing the Wasserstein distance between thesource and target distribution.

Let x be an input sequence, and h , [−→h ;←−h ]

be its corresponding contextualized representationobtained from enc. Here,

−→h and

←−h denote

the output sequence of the forward and backwardLSTM, respectively. In order to support transfer,enc should be general enough to effectively rep-resent both source and target corpora. For this rea-son, we ground the encoder by a language mod-eling component (Bengio et al., 2003; Mikolovet al., 2011). Specifically, we employ two Soft-max classifiers to predict the word xi based on−→h i−1 and

←−h i+1 respectively. We minimize the

cross-entropy lossLlm over all source data and un-labeled target data.

The representation h is domain-specific as itis trained to encode useful features for languagemodeling and the source tasks. To obtain an invari-ant representation, we employ a transformationlayer and propose to align the transformed repre-sentation so that it is not distinguishable whether itcomes from the source or the target. Specifically,we transform the representation hi at each positioni linearly and obtain

hinvi =Winvhi + binv,

where Winv and binv are learnable parameters.We minimize the Wasserstein distance (Arjovskyet al., 2017) between the distribution of hinv fromthe source and the one from the target, denoted asPS and PT , respectively. Since hinv is a sequence

1907

of variable length, L, we summarize it by its firstand last element via concatenation, [hinv1 ;h

invL ].

The training objective is defined as:

Lwd = sup‖f‖L≤K

Ehinv∼PS[f([hinv1 ;h

invL ])

]

− Ehinv∼PT[f([hinv1 ;h

invL ])

],

where the supremum is over allK-Lipschitz scalarfunctions f . Following Gulrajani et al. (2017), weapproximate f by a multi-layer perceptron, anduse gradient penalty to fulfill the Lipschitz con-straint.

3.3 Attention generationThe goal of this module is to generate high-qualityattention for each task. This module combinesthe domain-invariant representation together withtask-specific rationales as its input and predictstask-specific attention scores. We minimize thedistance between the predicted attention and theintermediate attention obtained from the multi-task learning module.

For any source task t ∈ {Si}Ni=1, we denotert as the task-specific rationales corresponding tothe input text xt, and denote hinv,t as the domain-invariant representation of xt. For each positioni, we first concatenate rti with the frequency of x

ti

occurring as a rationale from all training examplesof this task. We denote this augmented sequenceas r̃t. This frequency term provides the unigramlikelihood of each word being a rationale for thetask. Then we employ a sequence encoder encr2a

and an attention module attr2a to predict the at-tention scores:

ut = encr2a([hinv,t; r̃t]),

ũti = tanh(Wr2aatt u

ti + b

r2aatt),

α̂ti =exp(〈ũti, qr2aatt 〉)∑j exp(〈ũtj , qr2aatt 〉)

,

where W r2aatt , br2aatt and q

r2aatt are learnable param-

eters, and both encr2a and attr2a are shareableacross all tasks. We minimize the distance be-tween α̂t and the αt obtained from the first multi-task learning module over all source data:

Latt =∑

(αt,α̂t),t∈{Si}Ni=1

d(αt, α̂t),

where d(·, ·) can be any distance metric. In thispaper, we consider a soft-margin cosine distance:

d(a, b) , max(0, 1− cos(a, b)− 0.1),

where cos(·, ·) denotes the cosine similarity.

3.4 PipelineTraining R2A We train the three aforemen-tioned modules jointly using both the source dataand the unlabeled target data. The overall objec-tive is given by:

L = Llbl + λattLatt + λlmLlm + λwdLwd. (1)

The λs are hyper-parameters that control the im-portance of each training objective and Ls repre-sent the corresponding loss functions.

R2A inference Once the R2A model is trained,we can generate attention for each labeled targetexample based on its human-annotated rationales.

Training target classifier When testing the per-formance on the target task, of course, we are nei-ther provided with labels nor rationales. In orderto make predictions for the target task, we traina new classifier under the supervision of both thelabels and the R2A-generated attention. Specifi-cally, this target classifier shares the same archi-tecture as the source one in the multi-task learningmodule. We minimize the prediction loss, LTlbl, onthe labeled target data together with the cosine dis-tance, LTatt, between the R2A-generated attentionand the attention generated by this target classi-fier. The joint objective for this target classifier isdefined as

L = LTlbl + λTattLTatt, (2)

where λTatt controls the importance of LTatt. Forbetter transfer, we initialize the encoder in th