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 were the baselines?
Context 1: Introduction
The task of speculation detection and scope resolution is critical in distinguishing factual information from speculative information  This has multiple use-cases, like systems that determine the veracity of information, and those that involve requirement analysis  This task is particularly important to the biomedical domain, where patient reports and medical articles often use this feature of natural language  This task is commonly broken down into two subtasks: the first subtask, speculation cue detection, is to identify the uncertainty cue in a sentence, while the second subtask: scope resolution, is to identify the scope of that cue  For instance, consider the example:
It might rain tomorrow 
The speculation cue in the sentence above is ‘might’ and the scope of the cue ‘might’ is ‘rain tomorrow’  Thus, the speculation cue is the word that expresses the speculation, while the words affected by the speculation are in the scope of that cue 
This task was the CoNLL-2010 Shared Task (BIBREF0), which had 3 different subtasks  Task 1B was speculation cue detection on the BioScope Corpus, Task 1W was weasel identification from Wikipedia articles, and Task 2 was speculation scope resolution from the BioScope Corpus  For each task, the participants were provided the train and test set, which is henceforth referred to as Task 1B CoNLL and Task 2 CoNLL throughout this paper 
For our experimentation, we use the sub corpora of the BioScope Corpus (BIBREF1), namely the BioScope Abstracts sub corpora, which is referred to as BA, and the BioScope Full Papers sub corpora, which is referred to as BF  We also use the SFU Review Corpus (BIBREF2), which is referred to as SFU 
This subtask of natural language processing, along with another similar subtask, negation detection and scope resolution, have been the subject of a body of work over the years  The approaches used to solve them have evolved from simple rule-based systems (BIBREF3) based on linguistic information extracted from the sentences, to modern deep-learning based methods  The Machine Learning techniques used varied from Maximum Entropy Classifiers (BIBREF4) to Support Vector Machines (BIBREF5,BIBREF6,BIBREF7,BIBREF8), while the deep learning approaches included Recursive Neural Networks (BIBREF9,BIBREF10), Convolutional Neural Networks (BIBREF11) and most recently transfer learning-based architectures like Bidirectional Encoder Representation from Transformers (BERT) (BIBREF12)  Figures FIGREF1 and FIGREF1 contain a summary of the papers addressing speculation detection and scope resolution (BIBREF13, BIBREF5, BIBREF9, BIBREF3, BIBREF14, BIBREF15, BIBREF16, BIBREF17, BIBREF6, BIBREF11, BIBREF18, BIBREF10, BIBREF19, BIBREF7, BIBREF4, BIBREF8) 
Inspired by the most recent approach of applying BERT to negation detection and scope resolution (BIBREF12), we take this approach one step further by performing a comparative analysis of three popular transformer-based architectures: BERT (BIBREF20), XLNet (BIBREF21) and RoBERTa (BIBREF22), applied to speculation detection and scope resolution  We evaluate the performance of each model across all datasets via the single dataset training approach, and report all scores including inter-dataset scores (i e  train on one dataset, evaluate on another) to test the generalizability of the models  This approach outperforms all existing systems on the task of speculation detection and scope resolution  Further, we jointly train on multiple datasets and obtain improvements over the single dataset training approach on most datasets 
Contrary to results observed on benchmark GLUE tasks, we observe XLNet consistently outperforming RoBERTa  To confirm this observation, we apply these models to the negation detection and scope resolution task, and observe a continuity in this trend, reporting state-of-the-art results on three of four datasets on the negation scope resolution task 
The rest of the paper is organized as follows: In Section 2, we provide a detailed description of our methodology and elaborate on the experimentation details  In Section 3, we present our results and analysis on the speculation detection and scope resolution task, using the single dataset and the multiple dataset training approach  In Section 4, we show the results of applying XLNet and RoBERTa on negation detection and scope resolution and propose a few reasons to explain why XLNet performs better than RoBERTa  Finally, the future scope and conclusion is mentioned in Section 5 
Methodology and Experimental Setup
We use the methodology by Khandelwal and Sawant (BIBREF12), and modify it to support experimentation with multiple models 
For Speculation Cue Detection:
Input Sentence: It might rain tomorrow 
True Labels: Not-A-Cue, Cue, Not-A-Cue, Not-A-Cue 
First, this sentence is preprocessed to get the target labels as per the following annotation schema:
1 – Normal Cue 2 – Multiword Cue 3 – Not a cue 4 – Pad token
Thus, the preprocessed sequence is as follows:
Input Sentence: [It, might, rain, tomorrow]
True Labels: [3,1,3,3]
Then, we preprocess the input using the tokenizer for the model being used (BERT, XLNet or RoBERTa): splitting each word into one or more tokens, and converting each token to it’s corresponding tokenID, and padding it to the maximum input length of the model  Thus,
Input Sentence: [wtt(It), wtt(might), wtt(rain), wtt(tom), wtt(## or), wtt(## row), wtt(〈pad 〉),wtt(〈pad 〉)   ]
True Labels: [3,1,3,3,3,3,4,4,4,4,   ]
The word ‘tomorrow' has been split into 3 tokens, ‘tom', ‘##or' and ‘##row'  The function to convert the word to tokenID is represented by wtt 
For Speculation Scope Resolution:
If a sentence has multiple cues, each cue's scope will be resolved individually 
Input Sentence: It might rain tomorrow 
True Labels: Out-Of-Scope, Out-Of-Scope, In-Scope, In-Scope 
First, this sentence is preprocessed to get the target labels as per the following annotation schema:
0 – Out-Of-Scope 1 – In-Scope
Thus, the preprocessed sequence is as follows:
True Scope Labels: [0,0,1,1]
As for cue detection, we preprocess the input using the tokenizer for the model being used  Additionally, we need to indicate which cue's scope we want to find in the input sentence  We do this by inserting a special token representing the token type (according to the cue detection annotation schema) before the cue word whose scope is being resolved  Here, we want to find the scope of the cue ‘might'  Thus,
Input Sentence: [wtt(It), wtt(〈token[1]〉), wtt(might), wtt(rain), wtt(tom), wtt(##or), wtt(##row), wtt(〈pad〉), wtt(〈pad〉)   ]
True Scope Labels: [0,0,1,1,1,1,0,0,0,0,   ]
Now, the preprocessed input for cue detection and similarly for scope detection is fed as input to our model as follows:
X = Model (Input)
Y = W*X + b
The W matrix is a matrix of size n_hidden x num_classes (n_hidden is the size of the representation of a token within the model)  These logits are fed to the loss function 
We use the following variants of each model:
BERT: bert-base-uncaseds3 amazonaws com/models huggingface co/bert/bert-base-uncased tar gz (The model used by BIBREF12)
RoBERTa: roberta-bases3 amazonaws com/models huggingface co/bert/roberta-base-pytorch_model bin (RoBERTa-base does not have an uncased variant)
XLNet: xlnet-base-caseds3 amazonaws com/models huggingface co/bert/xlnet-base-cased-pytorch_model bin (XLNet-base does not have an uncased variant)
The output of the model is a vector of probabilities per token  The loss is calculated for each token, by using the output vector and the true label for that token  We use class weights for the loss function, setting the weight for label 4 to 0 and all other labels to 1 (for cue detection only) to avoid training on padding token’s output 
We calculate the scores (Precision, Recall, F1) for the model per word of the input sentence, not per token that was fed to the model, as the tokens could be different for different models leading to inaccurate scores  For the above example, we calculate the output label for the word ‘tomorrow', not for each token it was split into (‘tom', ‘##or' and ‘##row')  To find the label for each word from the tokens it was split into, we experiment with 2 methods:
Average: We average the output vectors (softmax probabilities) for each token that the word was split into by the model's tokenizer  In the example above, we average the output of ‘tom', ‘##or' and ‘##row' to get the output for ‘tomorrow'  Then, we take an argmax over the resultant vector  This is then compared with the true label for the original word 
First Token: Here, we only consider the first token's probability vector (among all tokens the word was split into) as the output for that word, and get the label by an argmax over this vector  In the example above, we would consider the output vector corresponding to the token ‘tom' as the output for the word ‘tomorrow' 
For cue detection, the results are reported for the Average method only, while we report the scores for both Average and First Token for Scope Resolution 
For fair comparison, we use the same hyperparameters for the entire architecture for all 3 models  Only the tokenizer and the model are changed for each model  All other hyperparameters are kept same  We finetune the models for 60 epochs, using early stopping with a patience of 6 on the F1 score (word level) on the validation dataset  We use an initial learning rate of 3e-5, with a batch size of 8  We use the Categorical Cross Entropy loss function 
We use the Huggingface’s Pytorch Transformer library (BIBREF23) for the models and train all our models on Google Colaboratory 
Results: Speculation Cue Detection and Scope Resolution
We use a default train-validation-test split of 70-15-15 for each dataset  For the speculation detection and scope resolution subtasks using single-dataset training, we report the results as an average of 5 runs of the model  For training the model on multiple datasets, we perform a 70-15-15 split of each training dataset, after which the train and validation part of the individual datasets are merged while the scores are reported for the test part of the individual datasets, which is not used for training or validation  We report the results as an average of 3 runs of the model  Figure FIGREF8 contains results for speculation cue detection and scope resolution when trained on a single dataset  All models perform the best when trained on the same dataset as they are evaluated on, except for BF, which gets the best results when trained on BA  This is because of the transfer learning capabilities of the models and the fact that BF is a smaller dataset than BA (BF: 2670 sentences, BA: 11871 sentences)  For speculation cue detection, there is lesser generalizability for models trained on BF or BA, while there is more generalizability for models trained on SFU  This could be because of the different nature of the biomedical domain 
Figure FIGREF11 contains the results for speculation detection and scope resolution for models trained jointly on multiple datasets  We observe that training on multiple datasets helps the performance of all models on each dataset, as the quantity of data available to train the model increases  We also observe that XLNet consistently outperforms BERT and RoBERTa  To confirm this observation, we apply the 2 models to the related task of negation detection and scope resolution
Negation Cue Detection and Scope Resolution
We use a default train-validation-test split of 70-15-15 for each dataset, and use all 4 datasets (BF, BA, SFU and Sherlock)  The results for BERT are taken from BIBREF12  The results for XLNet and RoBERTa are averaged across 5 runs for statistical significance  Figure FIGREF14 contains results for negation cue detection and scope resolution  We report state-of-the-art results on negation scope resolution on BF, BA and SFU datasets  Contrary to popular opinion, we observe that XLNet is better than RoBERTa for the cue detection and scope resolution tasks  A few possible reasons for this trend are:
Domain specificity, as both negation and speculation are closely related subtasks  Further experimentation on different tasks is needed to verify this 
Most benchmark tasks are sentence classification tasks, whereas the subtasks we experiment on are sequence labelling tasks  Given the pre-training objective of XLNet (training on permutations of the input), it may be able to capture long-term dependencies better, essential for sequence labelling tasks 
We work with the base variants of the models, while most results are reported with the large variants of the models 
Conclusion and Future Scope
In this paper, we expanded on the work of Khandelwal and Sawant (BIBREF12) by looking at alternative transfer-learning models and experimented with training on multiple datasets  On the speculation detection task, we obtained a gain of 0 42 F1 points on BF, 1 98 F1 points on BA and 0 29 F1 points on SFU, while on the scope resolution task, we obtained a gain of 8 06 F1 points on BF, 4 27 F1 points on BA and 11 87 F1 points on SFU, when trained on a single dataset  While training on multiple datasets, we observed a gain of 10 6 F1 points on BF and 1 94 F1 points on BA on the speculation detection task and 2 16 F1 points on BF and 0 25 F1 points on SFU on the scope resolution task over the single dataset training approach  We thus significantly advance the state-of-the-art for speculation detection and scope resolution  On the negation scope resolution task, we applied the XLNet and RoBERTa and obtained a gain of 3 16 F1 points on BF, 0 06 F1 points on BA and 0 3 F1 points on SFU  Thus, we demonstrated the usefulness of transformer-based architectures in the field of negation and speculation detection and scope resolution  We believe that a larger and more general dataset would go a long way in bolstering future research and would help create better systems that are not domain-specific 
Context 2: 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
Context 3: Introduction
This work is licensed under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/
Entity linking (EL), mapping entity mentions in texts to a given knowledge base (KB), serves as a fundamental role in many fields, such as question answering BIBREF0 , semantic search BIBREF1 , and information extraction BIBREF2 , BIBREF3   However, this task is non-trivial because entity mentions are usually ambiguous  As shown in Figure FIGREF1 , the mention England refers to three entities in KB, and an entity linking system should be capable of identifying the correct entity as England cricket team rather than England and England national football team 
Entity linking is typically broken down into two main phases: (i) candidate generation obtains a set of referent entities in KB for each mention, and (ii) named entity disambiguation selects the possible candidate entity by solving a ranking problem  The key challenge lies in the ranking model that computes the relevance between candidates and the corresponding mentions based on the information both in texts and KBs BIBREF4   In terms of the features used for ranking, we classify existing EL models into two groups: local models to resolve mentions independently relying on textual context information from the surrounding words BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , and global (collective) models, which are the main focus of this paper, that encourage the target entities of all mentions in a document to be topically coherent BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14  
Global models usually build an entity graph based on KBs to capture coherent entities for all identified mentions in a document, where the nodes are entities, and edges denote their relations  The graph provides highly discriminative semantic signals (e g , entity relatedness) that are unavailable to local model BIBREF15   For example (Figure FIGREF1 ), an EL model seemly cannot find sufficient disambiguation clues for the mention England from its surrounding words, unless it utilizes the coherence information of consistent topic “cricket" among adjacent mentions England, Hussain, and Essex  Although the global model has achieved significant improvements, its limitation is threefold:
To mitigate the first limitation, recent EL studies introduce neural network (NN) models due to its amazing feature abstraction and generalization ability  In such models, words/entities are represented by low dimensional vectors in a continuous space, and features for mention as well as candidate entities are automatically learned from data BIBREF4   However, existing NN-based methods for EL are either local models BIBREF16 , BIBREF17 or merely use word/entity embeddings for feature extraction and rely on another modules for collective disambiguation, which thus cannot fully utilize the power of NN models for collective EL BIBREF18 , BIBREF19 , BIBREF20  
The second drawback of the global approach has been alleviated through approximate optimization techniques, such as PageRank/random walks BIBREF21 , graph pruning BIBREF22 , ranking SVMs BIBREF23 , or loopy belief propagation (LBP) BIBREF18 , BIBREF24   However, these methods are not differentiable and thus difficult to be integrated into neural network models (the solution for the first limitation) 
To overcome the third issue of inadequate training data, BIBREF17 has explored a massive amount of hyperlinks in Wikipedia, but these potential annotations for EL contain much noise, which may distract a naive disambiguation model BIBREF6  
In this paper, we propose a novel Neural Collective Entity Linking model (NCEL), which performs global EL combining deep neural networks with Graph Convolutional Network (GCN) BIBREF25 , BIBREF26 that allows flexible encoding of entity graphs  It integrates both local contextual information and global interdependence of mentions in a document, and is efficiently trainable in an end-to-end fashion  Particularly, we introduce attention mechanism to robustly model local contextual information by selecting informative words and filtering out the noise  On the other hand, we apply GCNs to improve discriminative signals of candidate entities by exploiting the rich structure underlying the correct entities  To alleviate the global computations, we propose to convolute on the subgraph of adjacent mentions  Thus, the overall coherence shall be achieved in a chain-like way via a sliding window over the document  To the best of our knowledge, this is the first effort to develop a unified model for neural collective entity linking 
In experiments, we first verify the efficiency of NCEL via theoretically comparing its time complexity with other collective alternatives  Afterwards, we train our neural model using collected Wikipedia hyperlinks instead of dataset-specific annotations, and perform evaluations on five public available benchmarks  The results show that NCEL consistently outperforms various baselines with a favorable generalization ability  Finally, we further present the performance on a challenging dataset WW BIBREF19 as well as qualitative results, investigating the effectiveness of each key module 
Preliminaries and Framework
We denote INLINEFORM0 as a set of entity mentions in a document INLINEFORM1 , where INLINEFORM2 is either a word INLINEFORM3 or a mention INLINEFORM4   INLINEFORM5 is the entity graph for document INLINEFORM6 derived from the given knowledge base, where INLINEFORM7 is a set of entities, INLINEFORM8 denotes the relatedness between INLINEFORM9 and higher values indicate stronger relations  Based on INLINEFORM10 , we extract a subgraph INLINEFORM11 for INLINEFORM12 , where INLINEFORM13 denotes the set of candidate entities for INLINEFORM14   Note that we don't include the relations among candidates of the same mention in INLINEFORM15 because these candidates are mutually exclusive in disambiguation 
Formally, we define the entity linking problem as follows: Given a set of mentions INLINEFORM0 in a document INLINEFORM1 , and an entity graph INLINEFORM2 , the goal is to find an assignment INLINEFORM3  
To collectively find the best assignment, NCEL aims to improve the discriminability of candidates' local features by using entity relatedness within a document via GCN, which is capable of learning a function of features on the graph through shared parameters over all nodes  Figure FIGREF10 shows the framework of NCEL including three main components:
Example As shown in Figure FIGREF10 , for the current mention England, we utilize its surrounding words as local contexts (e g , surplus), and adjacent mentions (e g , Hussian) as global information  Collectively, we utilize the candidates of England INLINEFORM0 as well as those entities of its adjacencies INLINEFORM1 to construct feature vectors for INLINEFORM2 and the subgraph of relatedness as inputs of our neural model  Let darker blue indicate higher probability of being predicted, the correct candidate INLINEFORM3 becomes bluer due to its bluer neighbor nodes of other mentions INLINEFORM4   The dashed lines denote entity relations that have indirect impacts through the sliding adjacent window , and the overall structure shall be achieved via multiple sub-graphs by traversing all mentions 
Before introducing our model, we first describe the component of candidate generation 
Candidate Generation
Similar to previous work BIBREF24 , we use the prior probability INLINEFORM0 of entity INLINEFORM1 conditioned on mention INLINEFORM2 both as a local feature and to generate candidate entities: INLINEFORM3   We compute INLINEFORM4 based on statistics of mention-entity pairs from: (i) Wikipedia page titles, redirect titles and hyperlinks, (ii) the dictionary derived from a large Web Corpus BIBREF27 , and (iii) the YAGO dictionary with a uniform distribution BIBREF22   We pick up the maximal prior if a mention-entity pair occurs in different resources  In experiments, to optimize for memory and run time, we keep only top INLINEFORM5 entities based on INLINEFORM6   In the following two sections, we will present the key components of NECL, namely feature extraction and neural network for collective entity linking 
Feature Extraction
The main goal of NCEL is to find a solution for collective entity linking using an end-to-end neural model, rather than to improve the measurements of local textual similarity or global mention/entity relatedness  Therefore, we use joint embeddings of words and entities at sense level BIBREF28 to represent mentions and its contexts for feature extraction  In this section, we give a brief description of our embeddings followed by our features used in the neural model 
Learning Joint Embeddings of Word and Entity
Following BIBREF28 , we use Wikipedia articles, hyperlinks, and entity outlinks to jointly learn word/mention and entity embeddings in a unified vector space, so that similar words/mentions and entities have similar vectors  To address the ambiguity of words/mentions, BIBREF28 represents each word/mention with multiple vectors, and each vector denotes a sense referring to an entity in KB  The quality of the embeddings is verified on both textual similarity and entity relatedness tasks 
Formally, each word/mention has a global embedding INLINEFORM0 , and multiple sense embeddings INLINEFORM1   Each sense embedding INLINEFORM2 refers to an entity embedding INLINEFORM3 , while the difference between INLINEFORM4 and INLINEFORM5 is that INLINEFORM6 models the co-occurrence information of an entity in texts (via hyperlinks) and INLINEFORM7 encodes the structured entity relations in KBs  More details can be found in the original paper 
Local Features
Local features focus on how compatible the entity is mentioned in a piece of text (i e , the mention and the context words)  Except for the prior probability (Section SECREF9 ), we define two types of local features for each candidate entity INLINEFORM0 :
String Similarity Similar to BIBREF16 , we define string based features as follows: the edit distance between mention's surface form and entity title, and boolean features indicating whether they are equivalent, whether the mention is inside, starts with or ends with entity title and vice versa 
Compatibility We also measure the compatibility of INLINEFORM0 with the mention's context words INLINEFORM1 by computing their similarities based on joint embeddings: INLINEFORM2 and INLINEFORM3 , where INLINEFORM4 is the context embedding of INLINEFORM5 conditioned on candidate INLINEFORM6 and is defined as the average sum of word global vectors weighted by attentions: INLINEFORM7
where INLINEFORM0 is the INLINEFORM1 -th word's attention from INLINEFORM2   In this way, we automatically select informative words by assigning higher attention weights, and filter out irrelevant noise through small weights  The attention INLINEFORM3 is computed as follows: INLINEFORM4
where INLINEFORM0 is the similarity measurement, and we use cosine similarity in the presented work  We concatenate the prior probability, string based similarities, compatibility similarities and the embeddings of contexts as well as the entity as the local feature vectors 
Global Features
The key idea of collective EL is to utilize the topical coherence throughout the entire document  The consistency assumption behind it is that: all mentions in a document shall be on the same topic  However, this leads to exhaustive computations if the number of mentions is large  Based on the observation that the consistency attenuates along with the distance between two mentions, we argue that the adjacent mentions might be sufficient for supporting the assumption efficiently 
Formally, we define neighbor mentions as INLINEFORM0 adjacent mentions before and after current mention INLINEFORM1 : INLINEFORM2 , where INLINEFORM3 is the pre-defined window size  Thus, the topical coherence at document level shall be achieved in a chain-like way  As shown in Figure FIGREF10 ( INLINEFORM4 ), mentions Hussain and Essex, a cricket player and the cricket club, provide adequate disambiguation clues to induce the underlying topic “cricket" for the current mention England, which impacts positively on identifying the mention surrey as another cricket club via the common neighbor mention Essex 
A degraded case happens if INLINEFORM0 is large enough to cover the entire document, and the mentions used for global features become the same as the previous work, such as BIBREF21   In experiments, we heuristically found a suitable INLINEFORM1 which is much smaller than the total number of mentions  The benefits of efficiency are in two ways: (i) to decrease time complexity, and (ii) to trim the entity graph into a fixed size of subgraph that facilitates computation acceleration through GPUs and batch techniques, which will be discussed in Section SECREF24  
Given neighbor mentions INLINEFORM0 , we extract two types of vectorial global features and structured global features for each candidate INLINEFORM1 :
Neighbor Mention Compatibility Suppose neighbor mentions are topical coherent, a candidate entity shall also be compatible with neighbor mentions if it has a high compatibility score with the current mention, otherwise not  That is, we extract the vectorial global features by computing the similarities between INLINEFORM0 and all neighbor mentions: INLINEFORM1 , where INLINEFORM2 is the mention embedding by averaging the global vectors of words in its surface form: INLINEFORM3 , where INLINEFORM4 are tokenized words of mention INLINEFORM5  
Subgraph Structure The above features reflect the consistent semantics in texts (i e , mentions)  We now extract structured global features using the relations in KB, which facilitates the inference among candidates to find the most topical coherent subset  For each document, we obtain the entity graph INLINEFORM0 by taking candidate entities of all mentions INLINEFORM1 as nodes, and using entity embeddings to compute their similarities as edges INLINEFORM2   Then, we extract the subgraph structured features INLINEFORM3 for each entity INLINEFORM4 for efficiency 
Formally, we define the subgraph as: INLINEFORM0 , where INLINEFORM1   For example (Figure FIGREF1 ), for entity England cricket team, the subgraph contains the relation from it to all candidates of neighbor mentions: England cricket team, Nasser Hussain (rugby union), Nasser Hussain, Essex, Essex County Cricket Club and Essex, New York  To support batch-wise acceleration, we represent INLINEFORM2 in the form of adjacency table based vectors: INLINEFORM3 , where INLINEFORM4 is the number of candidates per mention 
Finally, for each candidate INLINEFORM0 , we concatenate local features and neighbor mention compatibility scores as the feature vector INLINEFORM1 , and construct the subgraph structure representation INLINEFORM2 as the inputs of NCEL 
Neural Collective Entity Linking
NCEL incorporates GCN into a deep neural network to utilize structured graph information for collectively feature abstraction, while differs from conventional GCN in the way of applying the graph  Instead of the entire graph, only a subset of nodes is “visible" to each node in our proposed method, and then the overall structured information shall be reached in a chain-like way  Fixing the size of the subset, NCEL is further speeded up by batch techniques and GPUs, and is efficient to large-scale data 
Graph Convolutional Network
GCNs are a type of neural network model that deals with structured data  It takes a graph as an input and output labels for each node  As a simplification of spectral graph convolutions, the main idea of BIBREF26 is similar to a propagation model: to enhance the features of a node according to its neighbor nodes  The formulation is as follows: INLINEFORM0
where INLINEFORM0 is a normalized adjacent matrix of the input graph with self-connection, INLINEFORM1 and INLINEFORM2 are the hidden states and weights in the INLINEFORM3 -th layer, and INLINEFORM4 is a non-linear activation, such as ReLu 
Model Architecture
As shown in Figure FIGREF10 , NCEL identifies the correct candidate INLINEFORM0 for the mention INLINEFORM1 by using vectorial features as well as structured relatedness with candidates of neighbor mentions INLINEFORM2   Given feature vector INLINEFORM3 and subgraph representation INLINEFORM4 of each candidate INLINEFORM5 , we stack them as inputs for mention INLINEFORM6 : INLINEFORM7 , and the adjacent matrix INLINEFORM8 , where INLINEFORM9 denotes the subgraph with self-connection  We normalize INLINEFORM10 such that all rows sum to one, denoted as INLINEFORM11 , avoiding the change in the scale of the feature vectors 
Given INLINEFORM0 and INLINEFORM1 , the goal of NCEL is to find the best assignment: INLINEFORM2
where INLINEFORM0 is the output variable of candidates, and INLINEFORM1 is a probability function as follows: INLINEFORM2
where INLINEFORM0 is the score function parameters by INLINEFORM1   NCEL learns the mapping INLINEFORM2 through a neural network including three main modules: encoder, sub-graph convolution network (sub-GCN) and decoder  Next, we introduce them in turn 
Encoder The function of this module is to integrate different features by a multi-layer perceptron (MLP): INLINEFORM0
where INLINEFORM0 is the hidden states of the current mention, INLINEFORM1 and INLINEFORM2 are trainable parameters and bias  We use ReLu as the non-linear activation INLINEFORM3  
Sub-Graph Convolution Network Similar to GCN, this module learns to abstract features from the hidden state of the mention itself as well as its neighbors  Suppose INLINEFORM0 is the hidden states of the neighbor INLINEFORM1 , we stack them to expand the current hidden states of INLINEFORM2 as INLINEFORM3 , such that each row corresponds to that in the subgraph adjacent matrix INLINEFORM4   We define sub-graph convolution as: INLINEFORM5
where INLINEFORM0 is a trainable parameter 
Decoder After INLINEFORM0 iterations of sub-graph convolution, the hidden states integrate both features of INLINEFORM1 and its neighbors  A fully connected decoder maps INLINEFORM2 to the number of candidates as follows: INLINEFORM3
where INLINEFORM0  
Training
The parameters of network are trained to minimize cross-entropy of the predicted and ground truth INLINEFORM0 : INLINEFORM1
Suppose there are INLINEFORM0 documents in training corpus, each document has a set of mentions INLINEFORM1 , leading to totally INLINEFORM2 mention sets  The overall objective function is as follows: INLINEFORM3
Experiments
To avoid overfitting with some dataset, we train NCEL using collected Wikipedia hyperlinks instead of specific annotated data  We then evaluate the trained model on five different benchmarks to verify the linking precision as well as the generalization ability  Furthermore, we investigate the effectiveness of key modules in NCEL and give qualitative results for comprehensive analysis 
Baselines and Datasets
We compare NCEL with the following state-of-the-art EL methods including three local models and three types of global models:
Local models: He BIBREF29 and Chisholm BIBREF6 beat many global models by using auto-encoders and web links, respectively, and NTEE BIBREF16 achieves the best performance based on joint embeddings of words and entities 
Iterative model: AIDA BIBREF22 links entities by iteratively finding a dense subgraph 
Loopy Belief Propagation: Globerson BIBREF18 and PBoH BIBREF30 introduce LBP BIBREF31 techniques for collective inference, and Ganea BIBREF24 solves the global training problem via truncated fitting LBP 
PageRank/Random Walk: Boosting BIBREF32 , AGDISTISG BIBREF33 , Babelfy BIBREF34 , WAT BIBREF35 , xLisa BIBREF36 and WNED BIBREF19 performs PageRank BIBREF37 or random walk BIBREF38 on the mention-entity graph and use the convergence score for disambiguation 
For fairly comparison, we report the original scores of the baselines in the papers  Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents  (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing  (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk  (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies  (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation  It has 6374 mentions in 310 documents automatically extracted from Wikipedia 
Training Details and Running Time Analysis
Training We collect 50,000 Wikipedia articles according to the number of its hyperlinks as our training data  For efficiency, we trim the articles to the first three paragraphs leading to 1,035,665 mentions in total  Using CoNLL-Test A as the development set, we evaluate the trained NCEL on the above benchmarks  We set context window to 20, neighbor mention window to 6, and top INLINEFORM0 candidates for each mention  We use two layers with 2000 and 1 hidden units in MLP encoder, and 3 layers in sub-GCN  We use early stop and fine tune the embeddings  With a batch size of 16, nearly 3 epochs cost less than 15 minutes on the server with 20 core CPU and the GeForce GTX 1080Ti GPU with 12Gb memory  We use standard Precision, Recall and F1 at mention level (Micro) and at the document level (Macro) as measurements 
Complexity Analysis Compared with local methods, the main disadvantage of collective methods is high complexity and expensive costs  Suppose there are INLINEFORM0 mentions in documents on average, among these global models, NCEL not surprisingly has the lowest time complexity INLINEFORM1 since it only considers adjacent mentions, where INLINEFORM2 is the number of sub-GCN layers indicating the iterations until convergence  AIDA has the highest time complexity INLINEFORM3 in worst case due to exhaustive iteratively finding and sorting the graph  The LBP and PageRank/random walk based methods achieve similar high time complexity of INLINEFORM4 mainly because of the inference on the entire graph 
Results on GERBIL
GERBIL BIBREF41 is a benchmark entity annotation framework that aims to provide a unified comparison among different EL methods across datasets including ACE2004, AQUAINT and CoNLL  We compare NCEL with the global models that report the performance on GERBIL 
As shown in Table TABREF26 , NCEL achieves the best performance in most cases with an average gain of 2% on Micro F1 and 3% Macro F1  The baseline methods also achieve competitive results on some datasets but fail to adapt to the others  For example, AIDA and xLisa perform quite well on ACE2004 but poorly on other datasets, or WAT, PBoH, and WNED have a favorable performance on CoNLL but lower values on ACE2004 and AQUAINT  Our proposed method performs consistently well on all datasets that demonstrates the good generalization ability 
Results on TAC2010 and WW
In this section, we investigate the effectiveness of NCEL in the “easy" and “hard" datasets, respectively  Particularly, TAC2010, which has two mentions per document on average (Section SECREF19 ) and high prior probabilities of correct candidates (Figure FIGREF28 ), is regarded as the “easy" case for EL, and WW is the “hard" case since it has the most mentions with balanced prior probabilities BIBREF19   Besides, we further compare the impact of key modules by removing the following part from NCEL: global features (NCEL-local), attention (NCEL-noatt), embedding features (NCEL-noemb), and the impact of the prior probability (prior) 
The results are shown in Table FIGREF28 and Table FIGREF28   We can see the average linking precision (Micro) of WW is lower than that of TAC2010, and NCEL outperforms all baseline methods in both easy and hard cases  In the “easy" case, local models have similar performance with global models since only little global information is available (2 mentions per document)  Besides, NN-based models, NTEE and NCEL-local, perform significantly better than others including most global models, demonstrating that the effectiveness of neural models deals with the first limitation in the introduction 
Impact of NCEL Modules
As shown in Figure FIGREF28 , the prior probability performs quite well in TAC2010 but poorly in WW  Compared with NCEL-local, the global module in NCEL brings more improvements in the “hard" case than that for “easy" dataset, because local features are discriminative enough in most cases of TAC2010, and global information becomes quite helpful when local features cannot handle  That is, our propose collective model is robust and shows a good generalization ability to difficult EL  The improvements by each main module are relatively small in TAC2010, while the modules of attention and embedding features show non-negligible impacts in WW (even worse than local model), mainly because WW contains much noise, and these two modules are effective in improving the robustness to noise and the ability of generalization by selecting informative words and providing more accurate semantics, respectively 
Qualitative Analysis
The results of example in Figure FIGREF1 are shown in Table TABREF30 , which is from CoNLL testa dataset  For mention Essex, although both NCEL and NCEL-local correctly identify entity Essex County Cricket Club, NCEL outputs higher probability due to the enhancement of neighbor mentions  Moreover, for mention England, NCEL-local cannot find enough disambiguation clues from its context words, such as surplus and requirements, and thus assigns a higher probability of 0 42 to the country England according to the prior probability  Collectively, NCEL correctly identifies England cricket team with a probability of 0 72 as compared with 0 20 in NCEL-local with the help of its neighbor mention Essex 
Conclusion
In this paper, we propose a neural model for collective entity linking that is end-to-end trainable  It applies GCN on subgraphs instead of the entire entity graph to efficiently learn features from both local and global information  We design an attention mechanism that endows NCEL robust to noisy data  Trained on collected Wikipedia hyperlinks, NCEL outperforms the state-of-the-art collective methods across five different datasets  Besides, further analysis of the impacts of main modules as well as qualitative results demonstrates its effectiveness 
In the future, we will extend our method into cross-lingual settings to help link entities in low-resourced languages by exploiting rich knowledge from high-resourced languages, and deal with NIL entities to facilitate specific applications 
Acknowledgments
The work is supported by National Key Research and Development Program of China (2017YFB1002101), NSFC key project (U1736204, 61661146007), and THUNUS NExT Co-Lab 

ground_truth: varied from Maximum Entropy Classifiers (BIBREF4) to Support Vector Machines (BIBREF5,BIBREF6,BIBREF7,BIBREF8), Recursive Neural Networks (BIBREF9,BIBREF10), Convolutional Neural Networks (BIBREF11) and most recently transfer learning-based architectures like Bidirectional Encoder Representation from Transformers (BERT) (BIBREF12)

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 2: 
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 

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. How effective is their NCEL approach overall?
Context 4:  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 

ground_truth: NCEL consistently outperforms various baselines with a favorable generalization ability