prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Were other baselines tested to compare with the neural baseline?
Context 1: Introduction
Privacy policies are the documents which disclose the ways in which a company gathers, uses, shares and manages a user's data  As legal documents, they function using the principle of notice and choice BIBREF0, where companies post their policies, and theoretically, users read the policies and decide to use a company's products or services only if they find the conditions outlined in its privacy policy acceptable  Many legal jurisdictions around the world accept this framework, including the United States and the European Union BIBREF1, BIBREF2  However, the legitimacy of this framework depends upon users actually reading and understanding privacy policies to determine whether company practices are acceptable to them BIBREF3  In practice this is seldom the case BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10  This is further complicated by the highly individual and nuanced compromises that users are willing to make with their data BIBREF11, discouraging a `one-size-fits-all' approach to notice of data practices in privacy documents 
With devices constantly monitoring our environment, including our personal space and our bodies, lack of awareness of how our data is being used easily leads to problematic situations where users are outraged by information misuse, but companies insist that users have consented  The discovery of increasingly egregious uses of data by companies, such as the scandals involving Facebook and Cambridge Analytica BIBREF12, have further brought public attention to the privacy concerns of the internet and ubiquitous computing  This makes privacy a well-motivated application domain for NLP researchers, where advances in enabling users to quickly identify the privacy issues most salient to them can potentially have large real-world impact 
[1]https://play google com/store/apps/details?id=com gotokeep keep intl [2]https://play google com/store/apps/details?id=com viber voip [3]A question might not have any supporting evidence for an answer within the privacy policy 
Motivated by this need, we contribute PrivacyQA, a corpus consisting of 1750 questions about the contents of privacy policies, paired with over 3500 expert annotations  The goal of this effort is to kickstart the development of question-answering methods for this domain, to address the (unrealistic) expectation that a large population should be reading many policies per day  In doing so, we identify several understudied challenges to our ability to answer these questions, with broad implications for systems seeking to serve users' information-seeking intent  By releasing this resource, we hope to provide an impetus to develop systems capable of language understanding in this increasingly important domain 
Related Work
Prior work has aimed to make privacy policies easier to understand  Prescriptive approaches towards communicating privacy information BIBREF21, BIBREF22, BIBREF23 have not been widely adopted by industry  Recently, there have been significant research effort devoted to understanding privacy policies by leveraging NLP techniques BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, especially by identifying specific data practices within a privacy policy  We adopt a personalized approach to understanding privacy policies, that allows users to query a document and selectively explore content salient to them  Most similar is the PolisisQA corpus BIBREF29, which examines questions users ask corporations on Twitter  Our approach differs in several ways: 1) The PrivacyQA dataset is larger, containing 10x as many questions and answers  2) Answers are formulated by domain experts with legal training  3) PrivacyQA includes diverse question types, including unanswerable and subjective questions 
Our work is also related to reading comprehension in the open domain, which is frequently based upon Wikipedia passages BIBREF16, BIBREF17, BIBREF15, BIBREF30 and news articles BIBREF20, BIBREF31, BIBREF32  Table TABREF4 presents the desirable attributes our dataset shares with past approaches  This work is also tied into research in applying NLP approaches to legal documents BIBREF33, BIBREF34, BIBREF35, BIBREF36, BIBREF37, BIBREF38, BIBREF39  While privacy policies have legal implications, their intended audience consists of the general public rather than individuals with legal expertise  This arrangement is problematic because the entities that write privacy policies often have different goals than the audience  feng2015applying, tan-EtAl:2016:P16-1 examine question answering in the insurance domain, another specialized domain similar to privacy, where the intended audience is the general public 
Data Collection
We describe the data collection methodology used to construct PrivacyQA  With the goal of achieving broad coverage across application types, we collect privacy policies from 35 mobile applications representing a number of different categories in the Google Play Store  One of our goals is to include both policies from well-known applications, which are likely to have carefully-constructed privacy policies, and lesser-known applications with smaller install bases, whose policies might be considerably less sophisticated  Thus, setting 5 million installs as a threshold, we ensure each category includes applications with installs on both sides of this threshold  All policies included in the corpus are in English, and were collected before April 1, 2018, predating many companies' GDPR-focused BIBREF41 updates  We leave it to future studies BIBREF42 to look at the impact of the GDPR (e g , to what extent GDPR requirements contribute to making it possible to provide users with more informative answers, and to what extent their disclosures continue to omit issues that matter to users) 
Data Collection ::: Crowdsourced Question Elicitation
The intended audience for privacy policies consists of the general public  This informs the decision to elicit questions from crowdworkers on the contents of privacy policies  We choose not to show the contents of privacy policies to crowdworkers, a procedure motivated by a desire to avoid inadvertent biases BIBREF43, BIBREF44, BIBREF45, BIBREF46, BIBREF47, and encourage crowdworkers to ask a variety of questions beyond only asking questions based on practices described in the document 
Instead, crowdworkers are presented with public information about a mobile application available on the Google Play Store including its name, description and navigable screenshots  Figure FIGREF9 shows an example of our user interface  Crowdworkers are asked to imagine they have access to a trusted third-party privacy assistant, to whom they can ask any privacy question about a given mobile application  We use the Amazon Mechanical Turk platform and recruit crowdworkers who have been conferred “master” status and are located within the United States of America  Turkers are asked to provide five questions per mobile application, and are paid $2 per assignment, taking ~eight minutes to complete the task 
Data Collection ::: Answer Selection
To identify legally sound answers, we recruit seven experts with legal training to construct answers to Turker questions  Experts identify relevant evidence within the privacy policy, as well as provide meta-annotation on the question's relevance, subjectivity, OPP-115 category BIBREF49, and how likely any privacy policy is to contain the answer to the question asked 
Data Collection ::: Analysis
Table TABREF17 presents aggregate statistics of the PrivacyQA dataset  1750 questions are posed to our imaginary privacy assistant over 35 mobile applications and their associated privacy documents  As an initial step, we formulate the problem of answering user questions as an extractive sentence selection task, ignoring for now background knowledge, statistical data and legal expertise that could otherwise be brought to bear  The dataset is partitioned into a training set featuring 27 mobile applications and 1350 questions, and a test set consisting of 400 questions over 8 policy documents  This ensures that documents in training and test splits are mutually exclusive  Every question is answered by at least one expert  In addition, in order to estimate annotation reliability and provide for better evaluation, every question in the test set is answered by at least two additional experts 
Table TABREF14 describes the distribution over first words of questions posed by crowdworkers  We also observe low redundancy in the questions posed by crowdworkers over each policy, with each policy receiving ~49 94 unique questions despite crowdworkers independently posing questions  Questions are on average 8 4 words long  As declining to answer a question can be a legally sound response but is seldom practically useful, answers to questions where a minority of experts abstain to answer are filtered from the dataset  Privacy policies are ~3000 words long on average  The answers to the question asked by the users typically have ~100 words of evidence in the privacy policy document 
Data Collection ::: Analysis ::: Categories of Questions
Questions are organized under nine categories from the OPP-115 Corpus annotation scheme BIBREF49:
First Party Collection/Use: What, why and how information is collected by the service provider
Third Party Sharing/Collection: What, why and how information shared with or collected by third parties
Data Security: Protection measures for user information
Data Retention: How long user information will be stored
User Choice/Control: Control options available to users
User Access, Edit and Deletion: If/how users can access, edit or delete information
Policy Change: Informing users if policy information has been changed
International and Specific Audiences: Practices pertaining to a specific group of users
Other: General text, contact information or practices not covered by other categories 
For each question, domain experts indicate one or more relevant OPP-115 categories  We mark a category as relevant to a question if it is identified as such by at least two annotators  If no such category exists, the category is marked as `Other' if atleast one annotator has identified the `Other' category to be relevant  If neither of these conditions is satisfied, we label the question as having no agreement  The distribution of questions in the corpus across OPP-115 categories is as shown in Table TABREF16  First party and third party related questions are the largest categories, forming nearly 66 4% of all questions asked to the privacy assistant 
Data Collection ::: Analysis ::: Answer Validation
When do experts disagree? We would like to analyze the reasons for potential disagreement on the annotation task, to ensure disagreements arise due to valid differences in opinion rather than lack of adequate specification in annotation guidelines  It is important to note that the annotators are experts rather than crowdworkers  Accordingly, their judgements can be considered valid, legally-informed opinions even when their perspectives differ  For the sake of this question we randomly sample 100 instances in the test data and analyze them for likely reasons for disagreements  We consider a disagreement to have occurred when more than one expert does not agree with the majority consensus  By disagreement we mean there is no overlap between the text identified as relevant by one expert and another 
We find that the annotators agree on the answer for 74% of the questions, even if the supporting evidence they identify is not identical i e full overlap  They disagree on the remaining 26%  Sources of apparent disagreement correspond to situations when different experts: have differing interpretations of question intent (11%) (for example, when a user asks 'who can contact me through the app', the questions admits multiple interpretations, including seeking information about the features of the app, asking about first party collection/use of data or asking about third party collection/use of data), identify different sources of evidence for questions that ask if a practice is performed or not (4%), have differing interpretations of policy content (3%), identify a partial answer to a question in the privacy policy (2%) (for example, when the user asks `who is allowed to use the app' a majority of our annotators decline to answer, but the remaining annotators highlight partial evidence in the privacy policy which states that children under the age of 13 are not allowed to use the app), and other legitimate sources of disagreement (6%) which include personal subjective views of the annotators (for example, when the user asks `is my DNA information used in any way other than what is specified', some experts consider the boilerplate text of the privacy policy which states that it abides to practices described in the policy document as sufficient evidence to answer this question, whereas others do not) 
Experimental Setup
We evaluate the ability of machine learning methods to identify relevant evidence for questions in the privacy domain  We establish baselines for the subtask of deciding on the answerability (§SECREF33) of a question, as well as the overall task of identifying evidence for questions from policies (§SECREF37)  We describe aspects of the question that can render it unanswerable within the privacy domain (§SECREF41) 
Experimental Setup ::: Answerability Identification Baselines
We define answerability identification as a binary classification task, evaluating model ability to predict if a question can be answered, given a question in isolation  This can serve as a prior for downstream question-answering  We describe three baselines on the answerability task, and find they considerably improve performance over a majority-class baseline 
SVM: We define 3 sets of features to characterize each question  The first is a simple bag-of-words set of features over the question (SVM-BOW), the second is bag-of-words features of the question as well as length of the question in words (SVM-BOW + LEN), and lastly we extract bag-of-words features, length of the question in words as well as part-of-speech tags for the question (SVM-BOW + LEN + POS)  This results in vectors of 200, 201 and 228 dimensions respectively, which are provided to an SVM with a linear kernel 
CNN: We utilize a CNN neural encoder for answerability prediction  We use GloVe word embeddings BIBREF50, and a filter size of 5 with 64 filters to encode questions 
BERT: BERT BIBREF51 is a bidirectional transformer-based language-model BIBREF52  We fine-tune BERT-base on our binary answerability identification task with a learning rate of 2e-5 for 3 epochs, with a maximum sequence length of 128 
Experimental Setup ::: Privacy Question Answering
Our goal is to identify evidence within a privacy policy for questions asked by a user  This is framed as an answer sentence selection task, where models identify a set of evidence sentences from all candidate sentences in each policy 
Experimental Setup ::: Privacy Question Answering ::: Evaluation Metric
Our evaluation metric for answer-sentence selection is sentence-level F1, implemented similar to BIBREF30, BIBREF16  Precision and recall are implemented by measuring the overlap between predicted sentences and sets of gold-reference sentences  We report the average of the maximum F1 from each n$-$1 subset, in relation to the heldout reference 
Experimental Setup ::: Privacy Question Answering ::: Baselines
We describe baselines on this task, including a human performance baseline 
No-Answer Baseline (NA) : Most of the questions we receive are difficult to answer in a legally-sound way on the basis of information present in the privacy policy  We establish a simple baseline to quantify the effect of identifying every question as unanswerable 
Word Count Baseline : To quantify the effect of using simple lexical matching to answer the questions, we retrieve the top candidate policy sentences for each question using a word count baseline BIBREF53, which counts the number of question words that also appear in a sentence  We include the top 2, 3 and 5 candidates as baselines 
BERT: We implement two BERT-based baselines BIBREF51 for evidence identification  First, we train BERT on each query-policy sentence pair as a binary classification task to identify if the sentence is evidence for the question or not (Bert)  We also experiment with a two-stage classifier, where we separately train the model on questions only to predict answerability  At inference time, if the answerable classifier predicts the question is answerable, the evidence identification classifier produces a set of candidate sentences (Bert + Unanswerable) 
Human Performance: We pick each reference answer provided by an annotator, and compute the F1 with respect to the remaining references, as described in section 4 2 1  Each reference answer is treated as the prediction, and the remaining n-1 answers are treated as the gold reference  The average of the maximum F1 across all reference answers is computed as the human baseline 
Results and Discussion
The results of the answerability baselines are presented in Table TABREF31, and on answer sentence selection in Table TABREF32  We observe that bert exhibits the best performance on a binary answerability identification task  However, most baselines considerably exceed the performance of a majority-class baseline  This suggests considerable information in the question, indicating it's possible answerability within this domain 
Table TABREF32 describes the performance of our baselines on the answer sentence selection task  The No-answer (NA) baseline performs at 28 F1, providing a lower bound on performance at this task  We observe that our best-performing baseline, Bert + Unanswerable achieves an F1 of 39 8  This suggest that bert is capable of making some progress towards answering questions in this difficult domain, while still leaving considerable headroom for improvement to reach human performance  Bert + Unanswerable performance suggests that incorporating information about answerability can help in this difficult domain  We examine this challenging phenomena of unanswerability further in Section  
Results and Discussion ::: Error Analysis
Disagreements are analyzed based on the OPP-115 categories of each question (Table TABREF34)  We compare our best performing BERT variant against the NA model and human performance  We observe significant room for improvement across all categories of questions but especially for first party, third party and data retention categories 
We analyze the performance of our strongest BERT variant, to identify classes of errors and directions for future improvement (Table 8)  We observe that a majority of answerability mistakes made by the BERT model are questions which are in fact answerable, but are identified as unanswerable by BERT  We observe that BERT makes 124 such mistakes on the test set  We collect expert judgments on relevance, subjectivity , silence and information about how likely the question is to be answered from the privacy policy from our experts  We find that most of these mistakes are relevant questions  However many of them were identified as subjective by the annotators, and at least one annotator marked 19 of these questions as having no answer within the privacy policy  However, only 6 of these questions were unexpected or do not usually have an answer in privacy policies  These findings suggest that a more nuanced understanding of answerability might help improve model performance in his challenging domain 
Results and Discussion ::: What makes Questions Unanswerable?
We further ask legal experts to identify potential causes of unanswerability of questions  This analysis has considerable implications  While past work BIBREF17 has treated unanswerable questions as homogeneous, a question answering system might wish to have different treatments for different categories of `unanswerable' questions  The following factors were identified to play a role in unanswerability:
Incomprehensibility: If a question is incomprehensible to the extent that its meaning is not intelligible 
Relevance: Is this question in the scope of what could be answered by reading the privacy policy 
Ill-formedness: Is this question ambiguous or vague  An ambiguous statement will typically contain expressions that can refer to multiple potential explanations, whereas a vague statement carries a concept with an unclear or soft definition 
Silence: Other policies answer this type of question but this one does not 
Atypicality: The question is of a nature such that it is unlikely for any policy policy to have an answer to the question 
Our experts attempt to identify the different `unanswerable' factors for all 573 such questions in the corpus  4 18% of the questions were identified as being incomprehensible (for example, `any difficulties to occupy the privacy assistant')  Amongst the comprehendable questions, 50% were identified as likely to have an answer within the privacy policy, 33 1% were identified as being privacy-related questions but not within the scope of a privacy policy (e g , 'has Viber had any privacy breaches in the past?') and 16 9% of questions were identified as completely out-of-scope (e g , `'will the app consume much space?')  In the questions identified as relevant, 32% were ill-formed questions that were phrased by the user in a manner considered vague or ambiguous  Of the questions that were both relevant as well as `well-formed', 95 7% of the questions were not answered by the policy in question but it was reasonable to expect that a privacy policy would contain an answer  The remaining 4 3% were described as reasonable questions, but of a nature generally not discussed in privacy policies  This suggests that the answerability of questions over privacy policies is a complex issue, and future systems should consider each of these factors when serving user's information seeking intent 
We examine a large-scale dataset of “natural” unanswerable questions BIBREF54 based on real user search engine queries to identify if similar unanswerability factors exist  It is important to note that these questions have previously been filtered, according to a criteria for bad questions defined as “(questions that are) ambiguous, incomprehensible, dependent on clear false presuppositions, opinion-seeking, or not clearly a request for factual information ” Annotators made the decision based on the content of the question without viewing the equivalent Wikipedia page  We randomly sample 100 questions from the development set which were identified as unanswerable, and find that 20% of the questions are not questions (e g , “all I want for christmas is you mariah carey tour”)  12% of questions are unlikely to ever contain an answer on Wikipedia, corresponding closely to our atypicality category  3% of questions are unlikely to have an answer anywhere (e g , `what guides Santa home after he has delivered presents?')  7% of questions are incomplete or open-ended (e g , `the south west wind blows across nigeria between')  3% of questions have an unresolvable coreference (e g , `how do i get to Warsaw Missouri from here')  4% of questions are vague, and a further 7% have unknown sources of error  2% still contain false presuppositions (e g , `what is the only fruit that does not have seeds?') and the remaining 42% do not have an answer within the document  This reinforces our belief that though they have been understudied in past work, any question answering system interacting with real users should expect to receive such unanticipated and unanswerable questions 
Conclusion
We present PrivacyQA, the first significant corpus of privacy policy questions and more than 3500 expert annotations of relevant answers  The goal of this work is to promote question-answering research in the specialized privacy domain, where it can have large real-world impact  Strong neural baselines on PrivacyQA achieve a performance of only 39 8 F1 on this corpus, indicating considerable room for future research  Further, we shed light on several important considerations that affect the answerability of questions  We hope this contribution leads to multidisciplinary efforts to precisely understand user intent and reconcile it with information in policy documents, from both the privacy and NLP communities 
Acknowledgements
This research was supported in part by grants from the National Science Foundation Secure and Trustworthy Computing program (CNS-1330596, CNS-1330214, CNS-15-13957, CNS-1801316, CNS-1914486, CNS-1914444) and a DARPA Brandeis grant on Personalized Privacy Assistants (FA8750-15-2-0277)  The US Government is authorized to reproduce and distribute reprints for Governmental purposes not withstanding any copyright notation  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the NSF, DARPA, or the US Government  The authors would like to extend their gratitude to Elias Wright, Gian Mascioli, Kiara Pillay, Harrison Kay, Eliel Talo, Alexander Fagella and N  Cameron Russell for providing their valuable expertise and insight to this effort  The authors are also grateful to Eduard Hovy, Lorrie Cranor, Florian Schaub, Joel Reidenberg, Aditya Potukuchi and Igor Shalyminov for helpful discussions related to this work, and to the three anonymous reviewers of this draft for their constructive feedback  Finally, the authors would like to thank all crowdworkers who consented to participate in this study 
Context 2: Introduction
We posses a wealth of prior knowledge about many natural language processing tasks  For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification 
A key problem arisen here, is how to leverage such knowledge to guide the learning process, an interesting problem for both NLP and machine learning communities  Previous studies addressing the problem fall into several lines  First, to leverage prior knowledge to label data BIBREF1 , BIBREF2   Second, to encode prior knowledge with a prior on parameters, which can be commonly seen in many Bayesian approaches BIBREF3 , BIBREF4   Third, to formalise prior knowledge with additional variables and dependencies BIBREF5   Last, to use prior knowledge to control the distributions over latent output variables BIBREF6 , BIBREF7 , BIBREF8 , which makes the output variables easily interpretable 
However, a crucial problem, which has rarely been addressed, is the bias in the prior knowledge that we supply to the learning model  Would the model be robust or sensitive to the prior knowledge? Or, which kind of knowledge is appropriate for the task? Let's see an example: we may be a baseball fan but unfamiliar with hockey so that we can provide a few number of feature words of baseball, but much less of hockey for a baseball-hockey classification task  Such prior knowledge may mislead the model with heavy bias to baseball  If the model cannot handle this situation appropriately, the performance may be undesirable 
In this paper, we investigate into the problem in the framework of Generalized Expectation Criteria BIBREF7   The study aims to reveal the factors of reducing the sensibility of the prior knowledge and therefore to make the model more robust and practical  To this end, we introduce auxiliary regularization terms in which our prior knowledge is formalized as distribution over output variables  Recall the example just mentioned, though we do not have enough knowledge to provide features for class hockey, it is easy for us to provide some neutral words, namely words that are not strong indicators of any class, like player here  As one of the factors revealed in this paper, supplying neutral feature words can boost the performance remarkably, making the model more robust 
More attractively, we do not need manual annotation to label these neutral feature words in our proposed approach 
More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution  For the first manner, we simply use the most common features as neutral features and assume the neutral features are distributed uniformly over class labels  For the second and third one, we assume we have some knowledge about the class distribution which will be detailed soon later 
To summarize, the main contributions of this work are as follows:
The rest of the paper is structured as follows: In Section 2, we briefly describe the generalized expectation criteria and present the proposed regularization terms  In Section 3, we conduct extensive experiments to justify the proposed methods  We survey related work in Section 4, and summarize our work in Section 5 
Method
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge  A labeled feature is a strong indicator of a specific class and is manually provided to the classifier  For example, words like amazing, exciting can be labeled features for class positive in sentiment classification 
Generalized Expectation Criteria
Generalized expectation (GE) criteria BIBREF7 provides us a natural way to directly constrain the model in the preferred direction  For example, when we know the proportion of each class of the dataset in a classification task, we can guide the model to predict out a pre-specified class distribution 
Formally, in a parameter estimation objective function, a GE term expresses preferences on the value of some constraint functions about the model's expectation  Given a constraint function $G({\rm x}, y)$ , a conditional model distribution $p_\theta (y|\rm x)$ , an empirical distribution $\tilde{p}({\rm x})$ over input samples and a score function $S$ , a GE term can be expressed as follows:
$$S(E_{\tilde{p}({\rm x})}[E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]])$$   (Eq  4)
Learning from Labeled Features
Druck et al  ge-fl proposed GE-FL to learn from labeled features using generalized expectation criteria  When given a set of labeled features $K$ , the reference distribution over classes of these features is denoted by $\hat{p}(y| x_k), k \in K$   GE-FL introduces the divergence between this reference distribution and the model predicted distribution $p_\theta (y | x_k)$ , as a term of the objective function:
$$\mathcal {O} = \sum _{k \in K} KL(\hat{p}(y|x_k) || p_\theta (y | x_k)) + \sum _{y,i} \frac{\theta _{yi}^2}{2 \sigma ^2}$$   (Eq  6)
where $\theta _{yi}$ is the model parameter which indicates the importance of word $i$ to class $y$   The predicted distribution $p_\theta (y | x_k)$ can be expressed as follows: $ p_\theta (y | x_k) = \frac{1}{C_k} \sum _{\rm x} p_\theta (y|{\rm x})I(x_k) $
in which $I(x_k)$ is 1 if feature $k$ occurs in instance ${\rm x}$ and 0 otherwise, $C_k = \sum _{\rm x} I(x_k)$ is the number of instances with a non-zero value of feature $k$ , and $p_\theta (y|{\rm x})$ takes a softmax form as follows: $ p_\theta (y|{\rm x}) = \frac{1}{Z(\rm x)}\exp (\sum _i \theta _{yi}x_i)  $
To solve the optimization problem, L-BFGS can be used for parameter estimation 
In the framework of GE, this term can be obtained by setting the constraint function $G({\rm x}, y) = \frac{1}{C_k} \vec{I} (y)I(x_k)$ , where $\vec{I}(y)$ is an indicator vector with 1 at the index corresponding to label $y$ and 0 elsewhere 
Regularization Terms
GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias  In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied  But as mentioned before, it is often the case that we are not able to provide enough knowledge for some of the classes  For the baseball-hockey classification task, as shown before, GE-FL will predict most of the instances as baseball  In this section, we will show three terms to make the model more robust 
Neutral features are features that are not informative indicator of any classes, for instance, word player to the baseball-hockey classification task  Such features are usually frequent words across all categories  When we set the preference distribution of the neutral features to be uniform distributed, these neutral features will prevent the model from biasing to the class that has a dominate number of labeled features 
Formally, given a set of neutral features $K^{^{\prime }}$ , the uniform distribution is $\hat{p}_u(y|x_k) = \frac{1}{|C|}, k \in K^{^{\prime }}$ , where $|C|$ is the number of classes  The objective function with the new term becomes
$$\mathcal {O}_{NE} = \mathcal {O} + \sum _{k \in K^{^{\prime }}} KL(\hat{p}_u(y|x_k) || p_\theta (y | x_k)) $$   (Eq  9)
Note that we do not need manual annotation to provide neutral features  One simple way is to take the most common features as neutral features  Experimental results show that this strategy works successfully 
Another way to prevent the model from drifting from the desired direction is to constrain the predicted class distribution on unlabeled data  When lacking knowledge about the class distribution of the data, one feasible way is to take maximum entropy principle, as below:
$$\mathcal {O}_{ME} = \mathcal {O} + \lambda \sum _{y} p(y) \log p(y)$$   (Eq  11)
where $p(y)$ is the predicted class distribution, given by $ p(y) = \frac{1}{|X|} \sum _{\rm x} p_\theta (y | \rm x)  $ To control the influence of this term on the overall objective function, we can tune $\lambda $ according to the difference in the number of labeled features of each class  In this paper, we simply set $\lambda $ to be proportional to the total number of labeled features, say $\lambda = \beta |K|$  
This maximum entropy term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$   Therefore, $E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]$ is just the model distribution $p_\theta (y|{\rm x})$ and its expectation with the empirical distribution $\tilde{p}(\rm x)$ is simply the average over input samples, namely $p(y)$   When $S$ takes the maximum entropy form, we can derive the objective function as above 
Sometimes, we have already had much knowledge about the corpus, and can estimate the class distribution roughly without labeling instances  Therefore, we introduce the KL divergence between the predicted and reference class distributions into the objective function 
Given the preference class distribution $\hat{p}(y)$ , we modify the objective function as follows:
$$\mathcal {O}_{KL} &= \mathcal {O} + \lambda KL(\hat{p}(y) || p(y))$$   (Eq  13)
Similarly, we set $\lambda = \beta |K|$  
This divergence term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$ and setting the score function to $S(\hat{p}, p) = \sum _i \hat{p}_i \log \frac{\hat{p}_i}{p_i}$ , where $p$ and $\hat{p}$ are distributions  Note that this regularization term involves the reference class distribution which will be discussed later 
Experiments
In this section, we first justify the approach when there exists unbalance in the number of labeled features or in class distribution  Then, to test the influence of $\lambda $ , we conduct some experiments with the method which incorporates the KL divergence of class distribution  Last, we evaluate our approaches in 9 commonly used text classification datasets  We set $\lambda = 5|K|$ by default in all experiments unless there is explicit declaration  The baseline we choose here is GE-FL BIBREF0 , a method based on generalization expectation criteria 
Data Preparation
We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare  We use bag-of-words feature and remove stopwords in the preprocess stage  Though we have labels of all documents, we do not use them during the learning process, instead, we use the label of features 
The movie dataset, in which the task is to classify the movie reviews as positive or negtive, is used for testing the proposed approaches with unbalanced labeled features, unbalanced datasets or different $\lambda $ parameters  All unbalanced datasets are constructed based on the movie dataset by randomly removing documents of the positive class  For each experiment, we conduct 10-fold cross validation 
As described in BIBREF0 , there are two ways to obtain labeled features  The first way is to use information gain  We first calculate the mutual information of all features according to the labels of the documents and select the top 20 as labeled features for each class as a feature pool  Note that using information gain requires the document label, but this is only to simulate how we human provide prior knowledge to the model  The second way is to use LDA BIBREF9 to select features  We use the same selection process as BIBREF0 , where they first train a LDA on the dataset, and then select the most probable features of each topic (sorted by $P(w_i|t_j)$ , the probability of word $w_i$ given topic $t_j$ ) 
Similar to BIBREF10 , BIBREF0 , we estimate the reference distribution of the labeled features using a heuristic strategy  If there are $|C|$ classes in total, and $n$ classes are associated with a feature $k$ , the probability that feature $k$ is related with any one of the $n$ classes is $\frac{0 9}{n}$ and with any other class is $\frac{0 1}{|C| - n}$  
Neutral features are the most frequent words after removing stop words, and their reference distributions are uniformly distributed  We use the top 10 frequent words as neutral features in all experiments 
With Unbalanced Labeled Features
In this section, we evaluate our approach when there is unbalanced knowledge on the categories to be classified  The labeled features are obtained through information gain  Two settings are chosen:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
As shown in Figure 1 , Maximum entropy principle shows improvement only on the balanced case  An obvious reason is that maximum entropy only favors uniform distribution 
Incorporating Neutral features performs similarly to maximum entropy since we assume that neutral words are uniformly distributed  Its accuracy decreases slowly when the number of labeled features becomes larger ( $t>4$ ) (Figure 1 (a)), suggesting that the model gradually biases to the class with more labeled features, just like GE-FL 
Incorporating the KL divergence of class distribution performs much better than GE-FL on both balanced and unbalanced datasets  This shows that it is effective to control the unbalance in labeled features and in the dataset 
With Balanced Labeled Features
We also compare with the baseline when the labeled features are balanced  Similar to the experiment above, the labeled features are obtained by information gain  Two settings are experimented with:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for each class, and conduct comparisons on the original balanced movie dataset (positive:negtive=1:1) 
(b) Similar to (a), but the class distribution is unbalanced, by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 2   When the dataset is balanced (Figure 2 (a)), there is little difference between GE-FL and our methods  The reason is that the proposed regularization terms provide no additional knowledge to the model and there is no bias in the labeled features  On the unbalanced dataset (Figure 2 (b)), incorporating KL divergence is much better than GE-FL since we provide additional knowledge(the true class distribution), but maximum entropy and neutral features are much worse because forcing the model to approach the uniform distribution misleads it 
With Unbalanced Class Distributions
Our methods are also evaluated on datasets with different unbalanced class distributions  We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents  The original balanced movie dataset is used as a control group  We test with both balanced and unbalanced labeled features  For the balanced case, we randomly select 10 features from the feature pool for each class, and for the unbalanced case, we select 10 features for one class, and 1 feature for the other  Results are shown in Figure 3  
Figure 3 (a) shows that when the dataset and the labeled features are both balanced, there is little difference between our methods and GE-FL(also see Figure 2 (a))  But when the class distribution becomes more unbalanced, the difference becomes more remarkable  Performance of neutral features and maximum entropy decrease significantly but incorporating KL divergence increases remarkably  This suggests if we have more accurate knowledge about class distribution, KL divergence can guide the model to the right direction 
Figure 3 (b) shows that when the labeled features are unbalanced, our methods significantly outperforms GE-FL  Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive 
The Influence of λ\lambda 
We present the influence of $\lambda $ on the method that incorporates KL divergence in this section  Since we simply set $\lambda = \beta |K|$ , we just tune $\beta $ here  Note that when $\beta = 0$ , the newly introduced regularization term is disappeared, and thus the model is actually GE-FL  Again, we test the method with different $\lambda $ in two settings:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other class  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 4   As expected, $\lambda $ reflects how strong the regularization is  The model tends to be closer to our preferences with the increasing of $\lambda $ on both cases 
Using LDA Selected Features
We compare our methods with GE-FL on all the 9 datasets in this section  Instead of using features obtained by information gain, we use LDA to select labeled features  Unlike information gain, LDA does not employ any instance labels to find labeled features  In this setting, we can build classification models without any instance annotation, but just with labeled features 
Table 1 shows that our three methods significantly outperform GE-FL  Incorporating neutral features performs better than GE-FL on 7 of the 9 datasets, maximum entropy is better on 8 datasets, and KL divergence better on 7 datasets 
LDA selects out the most predictive features as labeled features without considering the balance among classes  GE-FL does not exert any control on such an issue, so the performance is severely suffered  Our methods introduce auxiliary regularization terms to control such a bias problem and thus promote the model significantly 
Related Work
There have been much work that incorporate prior knowledge into learning, and two related lines are surveyed here  One is to use prior knowledge to label unlabeled instances and then apply a standard learning algorithm  The other is to constrain the model directly with prior knowledge 
Liu et al text manually labeled features which are highly predictive to unsupervised clustering assignments and use them to label unlabeled data  Chang et al guiding proposed constraint driven learning  They first used constraints and the learned model to annotate unlabeled instances, and then updated the model with the newly labeled data  Daumé daume2008cross proposed a self training method in which several models are trained on the same dataset, and only unlabeled instances that satisfy the cross task knowledge constraints are used in the self training process 
MaCallum et al gec proposed generalized expectation(GE) criteria which formalised the knowledge as constraint terms about the expectation of the model into the objective function Graça et al pr proposed posterior regularization(PR) framework which projects the model's posterior onto a set of distributions that satisfy the auxiliary constraints  Druck et al ge-fl explored constraints of labeled features in the framework of GE by forcing the model's predicted feature distribution to approach the reference distribution  Andrzejewski et al andrzejewski2011framework proposed a framework in which general domain knowledge can be easily incorporated into LDA  Altendorf et al altendorf2012learning explored monotonicity constraints to improve the accuracy while learning from sparse data  Chen et al chen2013leveraging tried to learn comprehensible topic models by leveraging multi-domain knowledge 
Mann and McCallum simple,generalized incorporated not only labeled features but also other knowledge like class distribution into the objective function of GE-FL  But they discussed only from the semi-supervised perspective and did not investigate into the robustness problem, unlike what we addressed in this paper 
There are also some active learning methods trying to use prior knowledge  Raghavan et al feedback proposed to use feedback on instances and features interlacedly, and demonstrated that feedback on features boosts the model much  Druck et al active proposed an active learning method which solicits labels on features rather than on instances and then used GE-FL to train the model 
Conclusion and Discussions
This paper investigates into the problem of how to leverage prior knowledge robustly in learning models  We propose three regularization terms on top of generalized expectation criteria  As demonstrated by the experimental results, the performance can be considerably improved when taking into account these factors  Comparative results show that our proposed methods is more effective and works more robustly against baselines  To the best of our knowledge, this is the first work to address the robustness problem of leveraging knowledge, and may inspire other research 
We then present more detailed discussions about the three regularization methods  Incorporating neutral features is the simplest way of regularization, which doesn't require any modification of GE-FL but just finding out some common features  But as Figure 1 (a) shows, only using neutral features are not strong enough to handle extremely unbalanced labeled features 
The maximum entropy regularization term shows the strong ability of controlling unbalance 
This method doesn't need any extra knowledge, and is thus suitable when we know nothing about the corpus  But this method assumes that the categories are uniformly distributed, which may not be the case in practice, and it will have a degraded performance if the assumption is violated (see Figure 1 (b), Figure 2 (b), Figure 3 (a)) 
The KL divergence performs much better on unbalanced corpora than other methods  The reason is that KL divergence utilizes the reference class distribution and doesn't make any assumptions  The fact suggests that additional knowledge does benefit the model 
However, the KL divergence term requires providing the true class distribution  Sometimes, we may have the exact knowledge about the true distribution, but sometimes we may not  Fortunately, the model is insensitive to the true distribution and therefore a rough estimation of the true distribution is sufficient  In our experiments, when the true class distribution is 1:2, where the reference class distribution is set to 1:1 5/1:2/1:2 5, the accuracy is 0 755/0 756/0 760 respectively  This provides us the possibility to perform simple computing on the corpus to obtain the distribution in reality  Or, we can set the distribution roughly with domain expertise 
Context 3: Introduction
Automatically answering questions, especially in the open-domain setting (i e , where minimal or no contextual knowledge is explicitly provided), requires bringing to bear considerable amount of background knowledge and reasoning abilities  For example, knowing the answers to the two questions in Figure FIGREF1 requires identifying a specific ISA relation (i e , that cooking is a type of learned behavior) as well as recalling the definition of a concept (i e , that global warming is defined as a worldwide increase in temperature)  In the multiple-choice setting, which is the variety of question-answering (QA) that we focus on in this paper, there is also pragmatic reasoning involved in selecting optimal answer choices (e g , while greenhouse effect might in some other context be a reasonable answer to the second question in Figure FIGREF1, global warming is a preferable candidate) 
Recent successes in QA, driven largely by the creation of new resources BIBREF2, BIBREF3, BIBREF4, BIBREF5 and advances in model pre-training BIBREF6, BIBREF7, raise a natural question: do state-of-the-art multiple-choice QA (MCQA) models that excel at standard tasks really have basic knowledge and reasoning skills?
Most existing MCQA datasets are constructed through either expensive crowd-sourcing BIBREF8 or hand engineering effort, in the former case making it possible to collect large amounts of data at the cost of losing systematic control over the semantics of the target questions  Hence, doing a controlled experiment to answer such a question for QA is difficult given a lack of targeted challenge datasets 
Having definitive empirical evidence of model competence on any given phenomenon requires constructing a wide range of systematic tests  For example, in measuring competence of definitions, not only do we want to see that the model can handle individual questions such as Figure FIGREF1 1 inside of benchmark tasks, but that it can answer a wider range of questions that exhaustively cover a broad set of concepts and question perturbations (i e , systematic adjustments to how the questions are constructed)  The same applies to ISA reasoning; not only is it important to recognize in the question in Figure FIGREF1 1 that cooking is a learned behavior, but also that cooking is a general type of behavior or, through a few more inferential steps, a type of human activity 
In this paper, we look at systematically constructing such tests by exploiting the vast amounts of structured information contained in various types of expert knowledge such as knowledge graphs and lexical taxonomies  Our general methodology works as illustrated in Figure FIGREF1: given any MCQA model trained on a set of benchmark tasks, we systematically generate a set of synthetic dataset probes (i e , MCQA renderings of the target information) from information in expert knowledge sources  We then use these probes to ask two empirical questions: 1) how well do models trained on benchmark tasks perform on these probing tasks and; 2) can such models be re-trained to master new challenges with minimal performance loss on their original tasks?
While our methodology is amenable to any knowledge source and set of models/benchmark tasks, we focus on probing state-of-the-art transformer models BIBREF7, BIBREF9 in the domain of science MCQA  For sources of expert knowledge, we use WordNet, a comprehensive lexical ontology, and other publicly available dictionary resources  We devise probes that measure model competence in definition and taxonomic knowledge in different settings (including hypernymy, hyponymy, and synonymy detection, and word sense disambiguation)  This choice is motivated by fact that the science domain is considered particularly challenging for QA BIBREF10, BIBREF11, BIBREF12, and existing science benchmarks are known to involve widespread use of such knowledge (see BIBREF1, BIBREF13 for analysis), which is also arguably fundamental to more complex forms of reasoning 
We show that accurately probing QA models via synthetic datasets is not straightforward, as unexpected artifacts can easily arise in such data  This motivates our carefully constructed baselines and close data inspection to ensure probe quality 
Our results confirm that transformer-based QA models have a remarkable ability to recognize certain types of knowledge captured in our probes—even without additional fine-tuning  Such models can even outperform strong task-specific models trained directly on our probing tasks (e g , on definitions, our best model achieves 77% test accuracy without specialized training, as opposed to 51% for a task-specific LSTM-based model)  We also show that the same models can be effectively re-fine-tuned on small samples (even 100 examples) of probe data, and that high performance on the probes tends to correlate with a smaller drop in the model's performance on the original QA task 
Our comprehensive assessment reveals several interesting nuances to the overall positive trend  For example, the performance of even the best QA models degrades substantially on our hyponym probes (by 8-15%) when going from 1-hop links to 2-hops  Further, the accuracy of even our best models on the WordNetQA probe drops by 14-44% under our cluster-based analysis, which assesses whether a model knows several facts about each individual concept, rather than just being good at answering isolated questions  State-of-the-art QA models thus have much room to improve even in some fundamental building blocks, namely definitions and taxonomic hierarchies, of more complex forms of reasoning 
Related Work
We follow recent work on constructing challenge datasets for probing neural models, which has primarily focused on the task of natural language inference (NLI) BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF18  Most of this work looks at constructing data through adversarial generation methods, which have also been found useful for creating stronger models BIBREF19  There has also been work on using synthetic data of the type we consider in this paper BIBREF20, BIBREF21, BIBREF22  We closely follow the methodology of BIBREF22, who use hand-constructed linguistic fragments to probe NLI models and study model re-training using a variant of the inoculation by fine-tuning strategy of BIBREF23  In contrast, we focus on probing open-domain MCQA models (see BIBREF24 for a related study in the reading comprehension setting) as well as constructing data from much larger sources of structured knowledge 
Our main study focuses on probing the BERT model and fine-tuning approach of BIBREF7, and other variants thereof, which are all based on the transformer architecture of BIBREF25  Related to our efforts, there have been recent studies into the types of relational knowledge contained in large-scale knowledge models BIBREF26, BIBREF27, BIBREF28, which, similar to our work, probe models using structured knowledge sources  This prior work, however, primarily focuses on unearthing the knowledge contained in the underlying language models as is without further training, using simple (single token) cloze-style probing tasks and templates (similar to what we propose in Section SECREF3)  In contrast, we focus on understanding the knowledge contained in language models after they have been trained for a QA end-task using benchmark datasets in which such knowledge is expected to be widespread  Further, our evaluation is done before and after these models are fine-tuned on our probe QA tasks, using a more complex set of QA templates and target inferences 
The use of lexical resources and knowledge graphs such as WordNet to construct datasets has a long history, and has recently appeared in work on adversarial attacks BIBREF14, BIBREF29 and general task construction BIBREF30, BIBREF31  In the area of MCQA, there is related work on constructing questions from tuples BIBREF32, BIBREF3, both of which involve standard crowd annotation to elicit question-answer pairs (see also BIBREF33, BIBREF34)  In contrast to this work, we focus on generating data in an entirely automatic fashion, which obviates the need for expensive annotation and gives us the flexibility to construct much larger datasets that control a rich set of semantic aspects of the target questions 
Dataset Probes and Construction
Our probing methodology starts by constructing challenge datasets (Figure FIGREF1, yellow box) from a target set of knowledge resources  Each of our probing datasets consists of multiple-choice questions that include a question $\textbf {q}$ and a set of answer choices or candidates $\lbrace a_{1},   a_{N}\rbrace $  This section describes in detail the 5 different datasets we build, which are drawn from two sources of expert knowledge, namely WordNet BIBREF35 and the GNU Collaborative International Dictionary of English (GCIDE)  We describe each resource in turn, and explain how the resulting dataset probes, which we call WordNetQA and DictionaryQA, are constructed 
For convenience, we will describe each source of expert knowledge as a directed, edge-labeled graph $G$  The nodes of this graph are $\mathcal {V} = \mathcal {C} \cup \mathcal {W} \cup \mathcal {S} \cup \mathcal {D}$, where $\mathcal {C}$ is a set of atomic concepts, $\mathcal {W}$ a set of words, $\mathcal {S}$ a set of sentences, and $\mathcal {D}$ a set of definitions (see Table TABREF4 for details for WordNet and GCIDE)  Each edge of $G$ is directed from an atomic concept in $\mathcal {C}$ to another node in $V$, and is labeled with a relation, such as hypernym or isa$^\uparrow $, from a set of relations $\mathcal {R}$ (see Table TABREF4) 
When defining our probe question templates, it will be useful to view $G$ as a set of (relation, source, target) triples $\mathcal {T} \subseteq \mathcal {R} \times \mathcal {C} \times \mathcal {V}$  Due to their origin in an expert knowledge source, such triples preserve semantic consistency  For instance, when the relation in a triple is def, the corresponding edge maps a concept in $\mathcal {C}$ to a definition in $\mathcal {D}$ 
To construct probe datasets, we rely on two heuristic functions, defined below for each individual probe: $\textsc {gen}_{\mathcal {Q}}(\tau )$, which generates gold question-answer pairs $(\textbf {q},\textbf {a})$ from a set of triples $\tau \subseteq \mathcal {T}$ and question templates $\mathcal {Q}$, and $\textsc {distr}(\tau ^{\prime })$, which generates distractor answers choices $\lbrace a^{\prime }_{1},   a^{\prime }_{N-1} \rbrace $ based on another set of triples $\tau ^{\prime }$ (where usually $\tau \subset \tau ^{\prime }$)  For brevity, we will use $\textsc {gen}(\tau )$ to denote $\textsc {gen}_{\mathcal {Q}}(\tau )$, leaving question templates $\mathcal {Q}$ implicit 
Dataset Probes and Construction ::: WordNetQA
WordNet is an English lexical database consisting of around 117k concepts, which are organized into groups of synsets that each contain a gloss (i e , a definition of the target concept), a set of representative English words (called lemmas), and, in around 33k synsets, example sentences  In addition, many synsets have ISA links to other synsets that express complex taxonomic relations  Figure FIGREF6 shows an example and Table TABREF4 summarizes how we formulate WordNet as a set of triples $\mathcal {T}$ of various types  These triples together represent a directed, edge-labeled graph $G$  Our main motivation for using WordNet, as opposed to a resource such as ConceptNet BIBREF36, is the availability of glosses ($\mathcal {D}$) and example sentences ($\mathcal {S}$), which allows us to construct natural language questions that contextualize the types of concepts we want to probe 
Dataset Probes and Construction ::: WordNetQA ::: Example Generation @!START@$\textsc {gen}(\tau )$@!END@ 
We build 4 individual datasets based on semantic relations native to WordNet (see BIBREF37): hypernymy (i e , generalization or ISA reasoning up a taxonomy, ISA$^\uparrow $), hyponymy (ISA$^{\downarrow }$), synonymy, and definitions  To generate a set of questions in each case, we employ a number of rule templates $\mathcal {Q}$ that operate over tuples  A subset of such templates is shown in Table TABREF8  The templates were designed to mimic naturalistic questions we observed in our science benchmarks 
For example, suppose we wish to create a question $\textbf {q}$ about the definition of a target concept $c \in \mathcal {C}$  We first select a question template from $\mathcal {Q}$ that first introduces the concept $c$ and its lemma $l \in \mathcal {W}$ in context using the example sentence $s \in \mathcal {S}$, and then asks to identify the corresponding WordNet gloss $d \in \mathcal {D}$, which serves as the gold answer $\textbf {a}$  The same is done for ISA reasoning; each question about a hypernym/hyponym relation between two concepts $c \rightarrow ^{\uparrow /\downarrow } c^{\prime } \in \mathcal {T}_{i}$ (e g , $\texttt {dog} \rightarrow ^{\uparrow /\downarrow } \texttt {animal/terrier}$) first introduces a context for $c$ and then asks for an answer that identifies $c^{\prime }$ (which is also provided with a gloss so as to contain all available context) 
In the latter case, the rules $(\texttt {isa}^{r},c,c^{\prime }) \in \mathcal {T}_i$ in Table TABREF8 cover only direct ISA links from $c$ in direction $r \in \lbrace \uparrow ,\downarrow \rbrace $  In practice, for each $c$ and direction $r$, we construct tests that cover the set HOPS$(c,r)$ of all direct as well as derived ISA relations of $c$:
This allows us to evaluate the extent to which models are able to handle complex forms of reasoning that require several inferential steps or hops 
Dataset Probes and Construction ::: WordNetQA ::: Distractor Generation: @!START@$\textsc {distr}(\tau ^{\prime })$@!END@ 
An example of how distractors are generated is shown in Figure FIGREF6, which relies on similar principles as above  For each concept $c$, we choose 4 distractor answers that are close in the WordNet semantic space  For example, when constructing hypernymy tests for $c$ from the set hops$(c,\uparrow )$, we build distractors by drawing from $\textsc {hops}(c,\downarrow )$ (and vice versa), as well as from the $\ell $-deep sister family of $c$, defined as follows  The 1-deep sister family is simply $c$'s siblings or sisters, i

ground_truth: SVM, No-Answer Baseline (NA) , Word Count Baseline, Human Performance

No-Answer Baseline (NA), Word Count Baseline, Human Performance

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What NLP tasks do they consider?
Context 3: e , the other children $\tilde{c} \ne c$ of the parent node $c^{\prime }$ of $c$  For $\ell > 1$, the $\ell $-deep sister family also includes all descendants of each $\tilde{c}$ up to $\ell -1$ levels deep, denoted $\textsc {hops}_{\ell -1}(\tilde{c},\downarrow )$  Formally:
For definitions and synonyms we build distractors from all of these sets (with a similar restriction on the depth of sister distractors as noted above)  In doing this, we can systematically investigate model performance on a wide range of distractor sets 
Dataset Probes and Construction ::: WordNetQA ::: Perturbations and Semantic Clusters
Based on how we generate data, for each concept $c$ (i e , atomic WordNet synset) and probe type (i e , definitions, hypernymy, etc ), we have a wide variety of questions related to $c$ that manipulate 1) the complexity of reasoning that is involved (e g , the number of inferential hops) and; 2) the types of distractors (or distractor perturbations) that are employed  We call such sets semantic clusters  As we describe in the next section, semantic clusters allow us to devise new types of evaluation that reveal whether models have comprehensive and consistent knowledge of target concepts (e g , evaluating whether a model can correctly answer several questions associated with a concept, as opposed to a few disjoint instances) 
Details of the individual datasets are shown in Table TABREF12  From these sets, we follow BIBREF22 in allocating a maximum of 3k examples for training and reserve the rest for development and testing  Since we are interested in probing, having large held-out sets allows us to do detailed analysis and cluster-based evaluation 
Dataset Probes and Construction ::: DictionaryQA
The DictionaryQA dataset is created from the GCIDE dictionary, which is a comprehensive open-source English dictionary built largely from the Webster's Revised Unabridged Dictionary BIBREF38  Each entry consists of a word, its part-of-speech, its definition, and an optional example sentence (see Table TABREF14)  Overall, 33k entries (out of a total of 155k) contain example sentences/usages  As with the WordNet probes, we focus on this subset so as to contextualize each word being probed  In contrast to WordNet, GCIDE does not have ISA relations or explicit synsets, so we take each unique entry to be a distinct sense  We then use the dictionary entries to create a probe that centers around word-sense disambiguation, as described below 
Dataset Probes and Construction ::: DictionaryQA ::: Example and Distractor Generation 
To generate gold questions and answers, we use the same generation templates for definitions exemplified in Figure TABREF8 for WordNetQA  To generate distractors, we simply take alternative definitions for the target words that represent a different word sense (e g , the alternative definitions of gift shown in Table TABREF14), as well as randomly chosen definitions if needed to create a 5-way multiple choice question  As above, we reserve a maximum of 3k examples for training  Since we have only 9k examples in total in this dataset (see WordSense in Table TABREF12), we also reserve 3k each for development and testing 
We note that initial attempts to build this dataset through standard random splitting gave rise to certain systematic biases that were exploited by the choice-only baseline models described in the next section, and hence inflated overall model scores  After several efforts at filtering we found that, among other factors, using definitions from entries without example sentences as distractors (e g , the first two entries in Table TABREF14) had a surprising correlation with such biases  This suggests that possible biases involving differences between dictionary entries with and without examples can taint the resulting automatically generated MCQA dataset (for more discussion on the pitfalls involved with automatic dataset construction, see Section SECREF5) 
Probing Methodology and Modeling
Given the probes above, we now can start to answer the empirical questions posed at the beginning  Our main focus is on looking at transformer-based MCQA models trained in the science domain (using the benchmarks shown in Table TABREF21)  In this section, we provide details of MCQA and the target models, as well as several baselines that we use to sanity check our new datasets  To evaluate model competence, we look at a combination of model performance after science pre-training and after additional model fine-tuning using the lossless inoculation strategy of BIBREF22 (Section SECREF22)  In Section SECREF24, we also discuss a cluster-level accuracy metric for measuring performance over semantic clusters 
Probing Methodology and Modeling ::: Task Definition and Modeling
Given a dataset $D =\lbrace (\textbf {q}^{(d)}, \lbrace a_{1}^{(d)},   , a_{N}^{(d)}\rbrace ) \rbrace _{d}^{\mid D \mid }$ consisting of pairs of questions stems $\textbf {q}$ and answer choices $a_{i}$, the goal is to find the correct answer $a_{i^{*}}$ that correctly answers each $\textbf {q}$  Throughout this paper, we look at 5-way multiple-choice problems (i e , where each $N=5$) 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Question+Answer Encoder 
To model this, our investigation centers around the use of the transformer-based BIBREF25 BERT encoder and fine-tuning approach of BIBREF7 (see also BIBREF6)  For each question and individual answer pair $q^{(j)}_{a_{i}}$, we assume the following rendering of this input:
which is run through the pre-trained BERT encoder to generate a representation for $ q^{(j)}_{a_{i}}$ using the hidden state representation for CLS (i e , the classifier token) $\textbf {c}_{i}$:
The probability of a given answer $p^{(j)}_{i}$ is then computed as $p^{(j)}_{i} \propto e^{\textbf {v}\cdot \textbf {c}^{(j)}_{i}}$, which uses an additional set of classification parameters $\textbf {v} \in \mathbb {R}^{H}$ that are optimized (along with the full transformer network) by taking the final loss of the probability of each correct answer $p_{i^{*}}$ over all answer choices:
We specifically use BERT-large uncased with whole-word masking, as well as the RoBERTa-large model from BIBREF9, which is a more robustly trained version of the original BERT model  Our system uses the implementations provided in AllenNLP BIBREF39 and Huggingface BIBREF40 
Probing Methodology and Modeling ::: Task Definition and Modeling ::: Baselines and Sanity Checks 
When creating synthetic datasets, it is important to ensure that systematic biases, or annotation artifacts BIBREF41, are not introduced into the resulting probes and that the target datasets are sufficiently challenging (or good, in the sense of BIBREF42)  To test for this, we use several of the MCQA baseline models first introduced in BIBREF0, which take inspiration from the LSTM-based models used in BIBREF43 for NLI and various partial-input baselines based on these models 
Following the notation from BIBREF0, for any given sequence $s$ of tokens in $\lbrace q^{(j)}, a_{1}^{(j)},   ,a_{N}^{(j)}\rbrace $ in $D$, an encoding of $s$ is given as $h_{s}^{(j)} = \textbf {BiLSTM}(\textsc {embed}(s)) \in \mathbb {R}^{|s| \times 2h}$ (where $h$ is the dimension of the hidden state in each directional network, and embed$(\cdot )$ is an embedding function that assigns token-level embeddings to each token in $s$)  A contextual representation for each $s$ is then built by applying an element-wise max operation over $h_{s}$ as follows:
With these contextual representations, different baseline models can be constructed  For example, a Choice-Only model, which is a variant of the well-known hypothesis-only baseline used in NLI BIBREF46, scores each choice $c_{i}$ in the following way:
for $\textbf {W}^{T} \in \mathbb {R}^{2h}$ independently of the question and assigns a probability to each answer $p_{i}^{(j)} \propto e^{\alpha _{i}^{(j)}}$ 
A slight variant of this model, the Choice-to-choice model, tries to single out a given answer choice relative to other choices by scoring all choice pairs $\alpha _{i,i^{\prime }}^{(j)} = \textsc {Att}(r^{(j)}_{c_{i}},r^{(j)}_{c_{i^{\prime }}}) \in \mathbb {R}$ using a learned attention mechanism Att and finding the choice with the minimal similarity to other options (for full details, see their original paper)  In using these partial-input baselines, which we train directly on each target probe, we can check whether systematic biases related to answer choices were introduced into the data creation process 
A Question-to-choice model, in contrast, uses the contextual representations for each question and individual choice and an attention model Att model to get a score $\alpha ^{(j)}_{q,i} = \textsc {Att}(r^{(j)}_{q},r^{(j)}_{c_{i}}) \in \mathbb {R}$ as above  Here we also experiment with using ESIM BIBREF47 to generate the contextual representations $r$, as well as a simpler VecSimilarity model that measures the average vector similarity between question and answer tokens: $\alpha ^{(j)}_{q,i} = \textsc {Sim}(\textsc {embed}(q^{(j)}),\textsc {embed}(c^{(j)}_{i}))$  In contrast to the models above, these sets of baselines are used to check for artifacts between questions and answers that are not captured in the partial-input baselines (see discussion in BIBREF49) and ensure that the overall MCQA tasks are sufficiently difficult for our transformer models 
Probing Methodology and Modeling ::: Inoculation and Pre-training
Using the various models introduced above, we train these models on benchmark tasks in the science domain and look at model performance on our probes with and without additional training on samples of probe data, building on the idea of inoculation from BIBREF23  Model inoculation is the idea of continuing to train models on new challenge tasks (in our cases, separately for each probe) using only a small amount of examples  Unlike in ordinary fine-tuning, the goal is not to learn an entirely re-purposed model, but to improve on (or vaccinate against) particular phenomena (e g , our synthetic probes) that potentially deviate from a model's original training distribution (but that nonetheless might involve knowledge already contained in the model) 
In the variant proposed in BIBREF22, for each pre-trained (science) model and architecture $M_{a}$ we continue training the model on $k$ new probe examples (with a maximum of $k=$ 3k) under a set of different hyper-parameter configurations $j \in \lbrace 1,    , J\rbrace $ and identify, for each $k$, the model $M_{*}^{a,k}$ with the best aggregate performance $S$ on the original (orig) and new task:
As in BIBREF22, we found all models to be especially sensitive to different learning rates, and performed comprehensive hyper-parameters searches that also manipulate the number of iterations and random seeds used 
Using this methodology, we can see how much exposure to new data it takes for a given model to master a new task, and whether there are phenomena that stress particular models (e g , lead to catastrophic forgetting of the original task)  Given the restrictions on the number of fine-tuning examples, our assumption is that when models are able to maintain good performance on their original task during inoculation, the quickness with which they are able to learn the inoculated task provides evidence of prior competence, which is precisely what we aim to probe  To measure past performance, we define a model's inoculation cost as the difference in the performance of this model on its original task before and after inoculation 
We pre-train on an aggregated training set of the benchmark science exams detailed in Table TABREF21, and created an aggregate development set of around 4k science questions for evaluating overall science performance and inoculation costs  To handle the mismatch between number of answer choices in these sets, we made all sets 5-way by adding empty answers as needed  We also experimented with a slight variant of inoculation, called add-some inoculation, which involves balancing the inoculation training sets with naturalistic science questions  We reserve the MCQL dataset in Table TABREF21 for this purpose, and experiment with balancing each probe example with a science example (x1 matching) and adding twice as many science questions (x2 matching, up to 3k) for each new example 
Probing Methodology and Modeling ::: Evaluating Model Competence
The standard way to evaluate our MCQA models is by looking at the overall accuracy of the correct answer prediction, or what we call instance-level accuracy (as in Table TABREF25)  Given the nature of our data and the existence of semantic clusters as detailed in Section SECREF11 (i e , sets of questions and answers under different distractor choices and inference complexity), we also measure a model's cluster-level (or strict cluster) accuracy, which requires correctly answering all questions in a cluster  Example semantic clusters are shown in Table TABREF30; in the first case, there are 6 ISA$^\uparrow $ questions (including perturbations) about the concept trouser n 01 (e g

ground_truth: text classification for themes including sentiment, web-page, science, medical and healthcare

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Are the automatically constructed datasets subject to quality control?
Context 3: , involving knowing that trousers are a type of consumer good and garment/clothing), which a model must answer in order to receive full credit 
Our cluster-based analysis is motivated by the idea that if a model truly knows the meaning of a given concept, such as the concept of trousers, then it should be able to answer arbitrary questions about this concept without sensitivity to varied distractors  While our strict cluster metric is simplistic, it takes inspiration from work on visual QA BIBREF53, and allows us to evaluate how consistent and robust models are across our different probes, and to get insight into whether errors are concentrated on a small set of concepts or widespread across clusters 
Results and Findings
In this section, we provide the results of the empirical questions first introduced in Figure FIGREF1, starting with the results of our baseline models 
Results and Findings ::: Are our Probes Sufficiently Challenging?
As shown in Table TABREF25, most of our partial-input baselines (i e , Choice-Only and Choice-to-Choice models) failed to perform well on our dataset probes across a wide range of models, showing that such probes are generally immune from biases relating to how distractors were generated  As already discussed in Section SECREF13, however, initial versions of the DictionaryQA dataset had unforeseen biases partly related to whether distractors were sampled from entries without example sentences, which resulted in high Choice-Only-GloVe scores ranging around 56% accuracy before a filtering step was applied to remove these distractors 
We had similar issues with the hypernymy probe which, even after a filtering step that used our Choice-to-Choice-GloVe model, still leads to high results on the BERT and RoBERTa choice-only models  Given that several attempts were made to entirely de-duplicate the different splits (both in terms of gold answers and distractor types), the source of these biases is not at all obvious, which shows how easy it is for unintended biases in expert knowledge to appear in the resulting datasets and the importance of having rigorous baselines  We also note the large gap in some cases between the BERT and RoBERTa versus GloVe choice-only models, which highlights the need for having partial-input baselines that use the best available models 
Using a more conventional set of Task-Specific QA models (i e , the LSTM-based Question-to-Choice models trained directly on the probes), we can see that results are not particularly strong on any of the datasets, suggesting that our probes are indeed sufficiently challenging and largely immune from overt artifacts  The poor performance of the VecSimilarity (which uses pre-trained Word2Vec embeddings without additional training) provides additional evidence that elementary lexical matching strategies are insufficient for solving any of the probing tasks 
Results and Findings ::: How well do pre-trained MCQA models do?
Science models that use non-transformer based encoders, such as the ESIM model with GloVe and ELMO, perform poorly across all probes, in many cases scoring near random chance, showing limits to how well they generalize from science to other tasks even with pre-trained GloVe and ELMO embeddings  In sharp contrast, the transformer models have mixed results, the most striking result being the RoBERTa models on the definitions and synonymy probes (achieving a test accuracy of 77% and 61%, respectively), which outperform several of the task-specific LSTM models trained directly on the probes  At first glance, this suggests that RoBERTa, which generally far outpaces even BERT across most probes, has high competence of definitions and synonyms even without explicit training on our new tasks 
Given the controlled nature of our probes, we can get a more detailed view of how well the science models are performing across different reasoning and distractor types, as shown in the first column of Figure FIGREF28 for ESIM and RoBERTa  The ESIM science model without training has uniformly poor performance across all categories, whereas the performance of RoBERTa is more varied  Across all datasets and number of hops (i e , the rows in the heat maps), model performance for RoBERTa is consistently highest among examples with random distractors (i e , the first column), and lowest in cases involving distractors that are closest in WordNet space (e g , sister and ISA, or up/down, distractors of distance $k^{\prime }=1$)  This is not surprising, given that, in the first case, random distractors are likely to be the easiest category (and the opposite for distractors close in space), but suggests that RoBERTa might only be getting the easiest cases correct 
Model performance also clearly degrades for hypernymy and hyponymy across all models as the number of hops $k$ increases (see red dashed boxes)  For example, problems that involve hyponym reasoning with sister distractors of distance $k^{\prime }=1$ (i e , the second column) degrades from 47% to 15% when the number of hops $k$ increases from 1 to 4  This general tendency persists even after additional fine-tuning, as we discuss next, and gives evidence that models are limited in their capacity for certain types of multi-hop inferences 
As discussed by BIBREF26, the choice of generation templates can have a significant effect on model performance  The results so far should therefore be regarded as a lower bound on model competence  It is possible that model performance is high for definitions, for example, because the associated templates best align with the science training distribution (which we know little about)  For this reason, the subsequent inoculation step is important—it gives the model an opportunity to learn about our target templates and couple this learned knowledge with its general knowledge acquired during pre-training and science training (which is, again, what we aim to probe) 
Results and Findings ::: Can Models Be Effectively Inoculated?
Model performance after additional fine-tuning, or inoculation, is shown in the last 3 rows of Table TABREF25, along with learning curves shown in Figure FIGREF29 for a selection of probes and models  In the former case, the performance represents the model (and inoculation amount) with the highest aggregate performance over the old task and new probe  Here we again see the transformer-based models outperform non-transformer models, and that better models correlate with lower inoculation costs  For example, when inoculating on synonymy, the cost for ESIM is around 7% reduced accuracy on its original task, as opposed to $< 1$% and around 1% for BERT and RoBERTa, respectively  This shows the high capacity for transformer models to absorb new tasks with minimal costs, as also observed in BIBREF22 for NLI 
As shown in Figure FIGREF29, transformer models tend to learn most tasks fairly quickly while keeping constant scores on their original tasks (i e , the flat dashed lines observed in plots 1-4), which gives evidence of high competence  In both cases, add-some inoculation proves to be a cheap and easy way to 1) improve scores on the probing tasks (i e , the solid black and blue lines in plot 1) and; 2) minimize loss on science (e g , the blue and black dashed lines in plots 2-4)  The opposite is the case for ESIM (plots 5-6); models are generally unable to simultaneously learn individual probes without degrading on their original task, and adding more science data during inoculation confuses models on both tasks 
As shown in Figure FIGREF28, RoBERTa is able to significantly improve performance across most categories even after inoculation with a mere 100 examples (the middle plot), which again provides strong evidence of prior competence  As an example, RoBERTa improves on 2-hop hyponymy inference with random distractors by 18% (from 59% to 77%)  After 3k examples, the model has high performance on virtually all categories (the same score increases from 59% to 87%), however results still tends to degrade as a function of hop and distractor complexity, as discussed above 
Despite the high performance of our transformer models after inoculation, model performance on most probes (with the exception of Definitions) averages around 80% for our best models  This suggests that there is still considerable room for improvement, especially for synonymy and word sense, which is a topic that we discuss more in Section SECREF6 
Results and Findings ::: Are Models Consistent across Clusters?
Table TABREF32 shows cluster-level accuracies for the different WordNetQA probes  As with performance across the different inference/distractor categories, these results are mixed  For some probes, such as definitions, our best models appear to be rather robust; e g , our RoBERTa model has a cluster accuracy of $75\%$, meaning that it can answer all questions perfectly for 75% of the target concepts and that errors are concentrated on a small minority (25%) of concepts  On synonymy and hypernymy, both BERT and RoBERTa appear robust on the majority of concepts, showing that errors are similarly concentrated  In contrast, our best model on hyponymy has an accuracy of 36%, meaning that its errors are spread across many concepts, thus suggesting less robustness 
Table TABREF30 shows a selection of semantic clusters involving ISA reasoning, as well as the model performance over different answers (shown symbolically) and perturbations  For example, in the the second case, the cluster is based around the concept/synset oppose v 06 and involves 4 inferences and a total 24 questions (i e , inferences with perturbations)  Our weakest model, ESIM, answers only 5 out of 24 questions correctly, whereas RoBERTa gets 21/24  In the other cases, RoBERTa gets all clusters correct, whereas BERT and ESIM get none of them correct 
We emphasize that these results only provide a crude look into model consistency and robustness  Recalling again the details in Table TABREF12, probes differ in terms of average size of clusters  Hyponymy, in virtue of having many more questions per cluster, might simply be a much more difficult dataset  In addition, such a strict evaluation does not take into account potential errors inside of clusters, which is an important issue that we discuss in the next section  We leave addressing such issues and coming up with more insightful cluster-based metrics for future work 
Discussion and Conclusion
We presented several new challenge datasets and a novel methodology for automatically building such datasets from knowledge graphs and taxonomies  We used these to probe state-of-the-art open-domain QA models (centering around models based on variants of BERT)  While our general methodology is amendable to any target knowledge resource or QA model/domain, we focus on probing definitions and ISA knowledge using open-source dictionaries and MCQA models trained in the science domain 
We find, consistent with recent probing studies BIBREF26, that transformer-based models have a remarkable ability to answer questions that involve complex forms of relational knowledge, both with and without explicit exposure to our new target tasks  In the latter case, a newer RoBERTa model trained only on benchmark science tasks is able to outperform several task-specific LSTM-based models trained directly on our probing data  When re-trained on small samples (e g , 100 examples) of probing data using variations of the lossless inoculation strategy from BIBREF22, RoBERTa is able to master many aspects of our probes with virtually no performance loss on its original QA task 
These positive results suggest that transformer-based models, especially models additionally fine-tuned on small samples of synthetic data, can be used in place of task-specific models used for querying relational knowledge, as has already been done for targeted tasks such as word sense disambiguation BIBREF54  Since models seem to already contain considerable amounts of relational knowledge, our simple inoculation strategy, which tries to nudge models to bring out this knowledge explicitly, could serve as a cheaper alternative to recent attempts to build architectures that explicitly incorporate structured knowledge BIBREF55; we see many areas where our inoculation strategy could be improved for such purposes, including having more complex loss functions that manage old and new information, as well as using techniques that take into account network plasticity BIBREF56 
The main appeal of using automatically generate datasets is the ability to systematically manipulate and control the complexity of target questions, which allows for more controlled experimentation and new forms of evaluation  Despite the positive results described above, results that look directly at the effect of different types of distractors and the complexity of reasoning show that our best models, even after additional fine-tuning, struggle with certain categories of hard distractors and multi-hop inferences  For some probes, our cluster-based analysis also reveals that errors are widespread across concept clusters, suggesting that models are not always consistent and robust  These results, taken together with our findings about the vulnerability of synthetic datasets to systematic biases, suggest that there is much room for improvement and that the positive results should be taken with a grain of salt  Developing better ways to evaluate semantic clusters and model robustness would be a step in this direction 
We emphasize that using synthetic versus naturalistic QA data comes with important trade-offs  While we are able to generate large amounts of systematically controlled data at virtually no cost or need for manual annotation, it is much harder to validate the quality of such data at such a scale and such varying levels of complexity  Conversely, with benchmark QA datasets, it is much harder to perform the type of careful manipulations and cluster-based analyses we report here  While we assume that the expert knowledge we employ, in virtue of being hand-curated by human experts, is generally correct, we know that such resources are fallible and error-prone  Initial crowd-sourcing experiments that look at validating samples of our data show high agreement across probes and that human scores correlate with the model trends across the probe categories  More details of these studies are left for future work 

ground_truth: No

No