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 are the source and target domains?
Context 1: Introduction
In practice, it is often difficult and costly to annotate sufficient training data for diverse application domains on-the-fly  We may have sufficient labeled data in an existing domain (called the source domain), but very few or no labeled data in a new domain (called the target domain)  This issue has motivated research on cross-domain sentiment classification, where knowledge in the source domain is transferred to the target domain in order to alleviate the required labeling effort 
One key challenge of domain adaptation is that data in the source and target domains are drawn from different distributions  Thus, adaptation performance will decline with an increase in distribution difference  Specifically, in sentiment analysis, reviews of different products have different vocabulary  For instance, restaurants reviews would contain opinion words such as “tender”, “tasty”, or “undercooked” and movie reviews would contain “thrilling”, “horrific”, or “hilarious”  The intersection between these two sets of opinion words could be small which makes domain adaptation difficult 
Several techniques have been proposed for addressing the problem of domain shifting  The aim is to bridge the source and target domains by learning domain-invariant feature representations so that a classifier trained on a source domain can be adapted to another target domain  In cross-domain sentiment classification, many works BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 utilize a key intuition that domain-specific features could be aligned with the help of domain-invariant features (pivot features)  For instance, “hilarious” and “tasty” could be aligned as both of them are relevant to “good” 
Despite their promising results, these works share two major limitations  First, they highly depend on the heuristic selection of pivot features, which may be sensitive to different applications  Thus the learned new representations may not effectively reduce the domain difference  Furthermore, these works only utilize the unlabeled target data for representation learning while the sentiment classifier was solely trained on the source domain  There have not been many studies on exploiting unlabeled target data for refining the classifier, even though it may contain beneficial information  How to effectively leverage unlabeled target data still remains an important challenge for domain adaptation 
In this work, we argue that the information from unlabeled target data is beneficial for domain adaptation and we propose a novel Domain Adaptive Semi-supervised learning framework (DAS) to better exploit it  Our main intuition is to treat the problem as a semi-supervised learning task by considering target instances as unlabeled data, assuming the domain distance can be effectively reduced through domain-invariant representation learning  Specifically, the proposed approach jointly performs feature adaptation and semi-supervised learning in a multi-task learning setting  For feature adaptation, it explicitly minimizes the distance between the encoded representations of the two domains  On this basis, two semi-supervised regularizations – entropy minimization and self-ensemble bootstrapping – are jointly employed to exploit unlabeled target data for classifier refinement 
We evaluate our method rigorously under multiple experimental settings by taking label distribution and corpus size into consideration  The results show that our model is able to obtain significant improvements over strong baselines  We also demonstrate through a series of analysis that the proposed method benefits greatly from incorporating unlabeled target data via semi-supervised learning, which is consistent with our motivation  Our datasets and source code can be obtained from https://github com/ruidan/DAS 
Related Work
Domain Adaptation: The majority of feature adaptation methods for sentiment analysis rely on a key intuition that even though certain opinion words are completely distinct for each domain, they can be aligned if they have high correlation with some domain-invariant opinion words (pivot words) such as “excellent” or “terrible”  Blitzer et al  ( BIBREF0 ) proposed a method based on structural correspondence learning (SCL), which uses pivot feature prediction to induce a projected feature space that works well for both the source and the target domains  The pivot words are selected in a way to cover common domain-invariant opinion words  Subsequent research aims to better align the domain-specific words BIBREF1 , BIBREF5 , BIBREF3 such that the domain discrepancy could be reduced  More recently, Yu and Jiang ( BIBREF4 ) borrow the idea of pivot feature prediction from SCL and extend it to a neural network-based solution with auxiliary tasks  In their experiment, substantial improvement over SCL has been observed due to the use of real-valued word embeddings  Unsupervised representation learning with deep neural networks (DNN) such as denoising autoencoders has also been explored for feature adaptation BIBREF6 , BIBREF7 , BIBREF8   It has been shown that DNNs could learn transferable representations that disentangle the underlying factors of variation behind data samples 
Although the aforementioned methods aim to reduce the domain discrepancy, they do not explicitly minimize the distance between distributions, and some of them highly rely on the selection of pivot features  In our method, we formally construct an objective for this purpose  Similar ideas have been explored in many computer vision problems, where the representations of the underlying domains are encouraged to be similar through explicit objectives BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 such as maximum mean discrepancy (MMD) BIBREF14   In NLP tasks, Li et al  ( BIBREF15 ) and Chen et al  ( BIBREF16 ) both proposed using adversarial training framework for reducing domain difference  In their model, a sub-network is added as a domain discriminator while deep features are learned to confuse the discriminator  The feature adaptation component in our model shares similar intuition with MMD and adversary training  We will show a detailed comparison with them in our experiments 
Semi-supervised Learning: We attempt to treat domain adaptation as a semi-supervised learning task by considering the target instances as unlabeled data  Some efforts have been initiated on transfer learning from unlabeled data BIBREF17 , BIBREF18 , BIBREF19   In our model, we reduce the domain discrepancy by feature adaptation, and thereafter adopt semi-supervised learning techniques to learn from unlabeled data  Primarily motivated by BIBREF20 and BIBREF21 , we employed entropy minimization and self-ensemble bootstrapping as regularizations to incorporate unlabeled data  Our experimental results show that both methods are effective when jointly trained with the feature adaptation objective, which confirms to our motivation 
Notations and Model Overview
We conduct most of our experiments under an unsupervised domain adaptation setting, where we have no labeled data from the target domain  Consider two sets INLINEFORM0 and INLINEFORM1   INLINEFORM2 is from the source domain with INLINEFORM3 labeled examples, where INLINEFORM4 is a one-hot vector representation of sentiment label and INLINEFORM5 denotes the number of classes  INLINEFORM6 is from the target domain with INLINEFORM7 unlabeled examples  INLINEFORM8 denotes the total number of training documents including both labeled and unlabeled  We aim to learn a sentiment classifier from INLINEFORM13 and INLINEFORM14 such that the classifier would work well on the target domain  We also present some results under a setting where we assume that a small number of labeled target examples are available (see Figure FIGREF27 ) 
For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier  We aim to learn feature representations that are domain-invariant and at the same time discriminative on both domains, thus we simultaneously consider three factors in our objective: (1) minimize the classification error on the labeled source examples; (2) minimize the domain discrepancy; and (3) leverage unlabeled data via semi-supervised learning 
Suppose we already have the encoded features of documents INLINEFORM0 (see Section SECREF10 ), the objective function for purpose (1) is thus the cross entropy loss on the labeled source examples DISPLAYFORM0
where INLINEFORM0 denotes the predicted label distribution  In the following subsections, we will explain how to perform feature adaptation and domain adaptive semi-supervised learning in details for purpose (2) and (3) respectively 
Feature Adaptation
Unlike prior works BIBREF0 , BIBREF4 , our method does not attempt to align domain-specific words through pivot words  In our preliminary experiments, we found that word embeddings pre-trained on a large corpus are able to adequately capture this information  As we will later show in our experiments, even without adaptation, a naive neural network classifier with pre-trained word embeddings can already achieve reasonably good results 
We attempt to explicitly minimize the distance between the source and target feature representations ( INLINEFORM0 and INLINEFORM1 )  A few methods from literature can be applied such as Maximum Mean Discrepancy (MMD) BIBREF14 or adversary training BIBREF15 , BIBREF16   The main idea of MMD is to estimate the distance between two distributions as the distance between sample means of the projected embeddings in Hilbert space  MMD is implicitly computed through a characteristic kernel, which is used to ensure that the sample mean is injective, leading to the MMD being zero if and only if the distributions are identical  In our implementation, we skip the mapping procedure induced by a characteristic kernel for simplifying the computation and learning  We simply estimate the distribution distance as the distance between the sample means in the current embedding space  Although this approximation cannot preserve all statistical features of the underlying distributions, we find it performs comparably to MMD on our problem  The following equations formally describe the feature adaptation loss INLINEFORM2 : DISPLAYFORM0
INLINEFORM0 normalization is applied on the mean representations INLINEFORM1 and INLINEFORM2 , rescaling the vectors such that all entries sum to 1  We adopt a symmetric version of KL divergence BIBREF12 as the distance function  Given two distribution vectors INLINEFORM3 , INLINEFORM4  
Domain Adaptive Semi-supervised Learning (DAS)
We attempt to exploit the information in target data through semi-supervised learning objectives, which are jointly trained with INLINEFORM0 and INLINEFORM1   Normally, to incorporate target data, we can minimize the cross entropy loss between the true label distributions INLINEFORM2 and the predicted label distributions INLINEFORM3 over target samples  The challenge here is that INLINEFORM4 is unknown, and thus we attempt to estimate it via semi-supervised learning  We use entropy minimization and bootstrapping for this purpose  We will later show in our experiments that both methods are effective, and jointly employing them overall yields the best results 
Entropy Minimization: In this method, INLINEFORM0 is estimated as the predicted label distribution INLINEFORM1 , which is a function of INLINEFORM2 and INLINEFORM3   The loss can thus be written as DISPLAYFORM0
Assume the domain discrepancy can be effectively reduced through feature adaptation, by minimizing the entropy penalty, training of the classifier is influenced by the unlabeled target data and will generally maximize the margins between the target examples and the decision boundaries, increasing the prediction confidence on the target domain 
Self-ensemble Bootstrapping: Another way to estimate INLINEFORM0 corresponds to bootstrapping  The idea is to estimate the unknown labels as the predictions of the model learned from the previous round of training  Bootstrapping has been explored for domain adaptation in previous works BIBREF18 , BIBREF19   However, in their methods, domain discrepancy was not explicitly minimized via feature adaptation  Applying bootstrapping or other semi-supervised learning techniques in this case may worsen the results as the classifier can perform quite bad on the target data 
[t] Pseudocode for training DAS INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 INLINEFORM4 = ensembling momentum, INLINEFORM5 INLINEFORM6 = weight ramp-up function INLINEFORM7 INLINEFORM8 INLINEFORM9 each minibatch INLINEFORM10 , INLINEFORM11 , INLINEFORM12 in
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 compute loss INLINEFORM3 on INLINEFORM4 compute loss INLINEFORM5 on INLINEFORM6 compute loss INLINEFORM7 on INLINEFORM8 compute loss INLINEFORM9 on INLINEFORM10 INLINEFORM11
update network parameters INLINEFORM0 , for INLINEFORM1 INLINEFORM2 INLINEFORM3
Inspired by the ensembling method proposed in BIBREF21 , we estimate INLINEFORM0 by forming ensemble predictions of labels during training, using the outputs on different training epochs  The loss is formulated as follows: DISPLAYFORM0
where INLINEFORM0 denotes the estimated labels computed on the ensemble predictions from different epochs  The loss is applied on all documents  It serves for bootstrapping on the unlabeled target data, and it also serves as a regularization that encourages the network predictions to be consistent in different training epochs  INLINEFORM1 is jointly trained with INLINEFORM2 , INLINEFORM3 , and INLINEFORM4   Algorithm SECREF6 illustrates the overall training process of the proposed domain adaptive semi-supervised learning (DAS) framework 
In Algorithm SECREF6 , INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are weights to balance the effects of INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 respectively  INLINEFORM6 and INLINEFORM7 are constant hyper-parameters  We set INLINEFORM8 as a Gaussian curve to ramp up the weight from 0 to INLINEFORM9   This is to ensure the ramp-up of the bootstrapping loss component is slow enough in the beginning of the training  After each training epoch, we compute INLINEFORM10 which denotes the predictions made by the network in current epoch, and then the ensemble prediction INLINEFORM11 is updated as a weighted average of the outputs from previous epochs and the current epoch, with recent epochs having larger weight  For generating estimated labels INLINEFORM12 , INLINEFORM13 is converted to a one-hot vector where the entry with the maximum value is set to one and other entries are set to zeros  The self-ensemble bootstrapping is a generalized version of bootstrappings that only use the outputs from the previous round of training BIBREF18 , BIBREF19   The ensemble prediction is likely to be closer to the correct, unknown labels of the target data 
CNN Encoder Implementation
We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered  In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks  Given a review document INLINEFORM1 consisting of INLINEFORM2 words, we begin by associating each word with a continuous word embedding BIBREF23 INLINEFORM3 from an embedding matrix INLINEFORM4 , where INLINEFORM5 is the vocabulary size and INLINEFORM6 is the embedding dimension  INLINEFORM7 is jointly updated with other network parameters during training  Given a window of dense word embeddings INLINEFORM8 , the convolution layer first concatenates these vectors to form a vector INLINEFORM9 of length INLINEFORM10 and then the output vector is computed by Equation ( EQREF11 ): DISPLAYFORM0
INLINEFORM0 , INLINEFORM1 is the parameter set of the encoder INLINEFORM2 and is shared across all windows of the sequence  INLINEFORM3 is an element-wise non-linear activation function  The convolution operation can capture local contextual dependencies of the input sequence and the extracted feature vectors are similar to INLINEFORM4 -grams  After the convolution operation is applied to the whole sequence, we obtain a list of hidden vectors INLINEFORM5   A max-over-time pooling layer is applied to obtain the final vector representation INLINEFORM6 of the input document 
Datasets and Experimental Settings
Existing benchmark datasets such as the Amazon benchmark BIBREF0 typically remove reviews with neutral labels in both domains  This is problematic as the label information of the target domain is not accessible in an unsupervised domain adaptation setting  Furthermore, removing neutral instances may bias the dataset favorably for max-margin-based algorithms like ours, since the resulting dataset has all uncertain labels removed, leaving only high confidence examples  Therefore, we construct new datasets by ourselves  The results on the original Amazon benchmark is qualitatively similar, and we present them in Appendix SECREF6 for completeness since most of previous works reported results on it 
Small-scale datasets: Our new dataset was derived from the large-scale Amazon datasets released by McAuley et al  ( BIBREF24 )  It contains four domains: Book (BK), Electronics (E), Beauty (BT), and Music (M)  Each domain contains two datasets  Set 1 contains 6000 instances with exactly balanced class labels, and set 2 contains 6000 instances that are randomly sampled from the large dataset, preserving the original label distribution, which we believe better reflects the label distribution in real life  The examples in these two sets do not overlap  Detailed statistics of the generated datasets are given in Table TABREF9  
In all our experiments on the small-scale datasets, we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain  Since we cannot control the label distribution of unlabeled data during training, we consider two different settings:
Setting (1): Only set 1 of the target domain is used as the unlabeled set  This tells us how the method performs in a condition when the target domain has a close-to-balanced label distribution  As we also evaluate on set 1 of the target domain, this is also considered as a transductive setting 
Setting (2): Set 2 from both the source and target domains are used as unlabeled sets  Since set 2 is directly sampled from millions of reviews, it better reflects real-life sentiment distribution 
Large-scale datasets: We further conduct experiments on four much larger datasets: IMDB (I), Yelp2014 (Y), Cell Phone (C), and Baby (B)  IMDB and Yelp2014 were previously used in BIBREF25 , BIBREF26   Cell phone and Baby are from the large-scale Amazon dataset BIBREF24 , BIBREF27   Detailed statistics are summarized in Table TABREF9   We keep all reviews in the original datasets and consider a transductive setting where all target examples are used for both training (without label information) and evaluation  We perform sampling to balance the classes of labeled source data in each minibatch INLINEFORM3 during training 
Selection of Development Set
Ideally, the development set should be drawn from the same distribution as the test set  However, under the unsupervised domain adaptation setting, we do not have any labeled target data at training phase which could be used as development set  In all of our experiments, for each pair of domains, we instead sample 1000 examples from the training set of the source domain as development set  We train the network for a fixed number of epochs, and the model with the minimum classification error on this development set is saved for evaluation  This approach works well on most of the problems since the target domain is supposed to behave like the source domain if the domain difference is effectively reduced 
Another problem is how to select the values for hyper-parameters  If we tune INLINEFORM0 and INLINEFORM1 directly on the development set from the source domain, most likely both of them will be set to 0, as unlabeled target data is not helpful for improving in-domain accuracy of the source domain  Other neural network models also have the same problem for hyper-parameter tuning  Therefore, our strategy is to use the development set from the target domain to optimize INLINEFORM2 and INLINEFORM3 for one problem (e g , we only do this on E INLINEFORM4 BK), and fix their values on the other problems  This setting assumes that we have at least two labeled domains such that we can optimize the hyper-parameters, and then we fix them for other new unlabeled domains to transfer to 
Training Details and Hyper-parameters
We initialize word embeddings using the 300-dimension GloVe vectors supplied by Pennington et al , ( BIBREF28 ), which were trained on 840 billion tokens from the Common Crawl  For each pair of domains, the vocabulary consists of the top 10000 most frequent words  For words in the vocabulary but not present in the pre-trained embeddings, we randomly initialize them 
We set hyper-parameters of the CNN encoder following previous works BIBREF22 , BIBREF4 without specific tuning on our datasets  The window size is set to 3 and the size of the hidden layer is set to 300  The nonlinear activation function is Relu  For regularization, we also follow their settings and employ dropout with probability set to 0 5 on INLINEFORM0 before feeding it to the output layer INLINEFORM1 , and constrain the INLINEFORM2 -norm of the weight vector INLINEFORM3 , setting its max norm to 3 
On the small-scale datasets and the Aamzon benchmark, INLINEFORM0 and INLINEFORM1 are set to 200 and 1, respectively, tuned on the development set of task E INLINEFORM2 BK under setting 1  On the large-scale datasets, INLINEFORM3 and INLINEFORM4 are set to 500 and 0 2, respectively, tuned on I INLINEFORM5 Y  We use a Gaussian curve INLINEFORM6 to ramp up the weight of the bootstrapping loss INLINEFORM7 from 0 to INLINEFORM8 , where INLINEFORM9 denotes the maximum number of training epochs  We train 30 epochs for all experiments  We set INLINEFORM10 to 3 and INLINEFORM11 to 0 5 for all experiments 
The batch size is set to 50 on the small-scale datasets and the Amazon benchmark  We increase the batch size to 250 on the large-scale datasets to reduce the number of iterations  RMSProp optimizer with learning rate set to 0 0005 is used for all experiments 
Models for Comparison
We compare with the following baselines:
(1) Naive: A non-domain-adaptive baseline with bag-of-words representations and SVM classifier trained on the source domain 
(2) mSDA BIBREF7 : This is the state-of-the-art method based on discrete input features  Top 1000 bag-of-words features are kept as pivot features  We set the number of stacked layers to 3 and the corruption probability to 0 5 
(3) NaiveNN: This is a non-domain-adaptive CNN trained on source domain, which is a variant of our model by setting INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 to zeros 
(4) AuxNN BIBREF4 : This is a neural model that exploits auxiliary tasks, which has achieved state-of-the-art results on cross-domain sentiment classification  The sentence encoder used in this model is the same as ours 
(5) ADAN BIBREF16 : This method exploits adversarial training to reduce representation difference between domains  The original paper uses a simple feedforward network as encoder  For fair comparison, we replace it with our CNN-based encoder  We train 5 iterations on the discriminator per iteration on the encoder and sentiment classifier as suggested in their paper 
(6) MMD: MMD has been widely used for minimizing domain discrepancy on images  In those works BIBREF9 , BIBREF13 , variants of deep CNNs are used for encoding images and the MMDs of multiple layers are jointly minimized  In NLP, adding more layers of CNNs may not be very helpful and thus those models from image-related tasks can not be directly applied to our problem  To compare with MMD-based method, we train a model that jointly minimize the classification loss INLINEFORM0 on the source domain and MMD between INLINEFORM1 and INLINEFORM2   For computing MMD, we use a Gaussian RBF which is a common choice for characteristic kernel 
In addition to the above baselines, we also show results of different variants of our model  DAS as shown in Algorithm SECREF6 denotes our full model  DAS-EM denotes the model with only entropy minimization for semi-supervised learning (set INLINEFORM0 )  DAS-SE denotes the model with only self-ensemble bootstrapping for semi-supervised learning (set INLINEFORM1 )  FANN (feature-adaptation neural network) denotes the model without semi-supervised learning performed (set both INLINEFORM2 and INLINEFORM3 to zeros) 
Main Results
Figure FIGREF17 shows the comparison of adaptation results (see Appendix SECREF7 for the exact numerical numbers)  We report classification accuracy on the small-scale dataset  For the large-scale dataset, macro-F1 is instead used since the label distribution in the test set is extremely unbalanced  Key observations are summarized as follows  (1) Both DAS-EM and DAS-SE perform better in most cases compared with ADAN, MDD, and FANN, in which only feature adaptation is performed  This demonstrates the effectiveness of the proposed domain adaptive semi-supervised learning framework  DAS-EM is more effective than DAS-SE in most cases, and the full model DAS with both techniques jointly employed overall has the best performance  (2) When comparing the two settings on the small-scale dataset, all domain-adaptive methods generally perform better under setting 1  In setting 1, the target examples are balanced in classes, which can provide more diverse opinion-related features  However, when considering unsupervised domain adaptation, we should not presume the label distribution of the unlabeled data  Thus, it is necessary to conduct experiments using datasets that reflect real-life sentiment distribution as what we did on setting2 and the large-scale dataset  Unfortunately, this is ignored by most of previous works  (3) Word-embeddings are very helpful, as we can see even NaiveNN can substantially outperform mSDA on most tasks 
To see the effect of semi-supervised learning alone, we also conduct experiments by setting INLINEFORM0 to eliminate the effect of feature adaptation  Both entropy minimization and bootstrapping perform very badly in this setting  Entropy minimization gives almost random predictions with accuracy below 0 4, and the results of bootstrapping are also much lower compared to NaiveNN  This suggests that the feature adaptation component is essential  Without it, the learned target representations are less meaningful and discriminative  Applying semi-supervised learning in this case is likely to worsen the results 
Further Analysis
In Figure FIGREF23 , we show the change of accuracy with respect to the percentage of unlabeled data used for training on three particular problems under setting 1  The value at INLINEFORM0 denotes the accuracies of NaiveNN which does not utilize any target data  For DAS, we observe a nonlinear increasing trend where the accuracy quickly improves at the beginning, and then gradually stabilizes  For other methods, this trend is less obvious, and adding more unlabeled data sometimes even worsen the results  This finding again suggests that the proposed approach can better exploit the information from unlabeled data 
We also conduct experiments under a setting with a small number of labeled target examples available  Figure FIGREF27 shows the change of accuracy with respect to the number of labeled target examples added for training  We can observe that DAS is still more effective under this setting, while the performance differences to other methods gradually decrease with the increasing number of labeled target examples 
CNN Filter Analysis
In this subsection, we aim to better understand DAS by analyzing sentiment-related CNN filters  To do that, 1) we first select a list of the most related CNN filters for predicting each sentiment label (positive, negative neutral)  Those filters can be identified according to the learned weights INLINEFORM0 of the output layer INLINEFORM1   Higher weight indicates stronger relatedness  2) Recall that in our implementation, each CNN filter has a window size of 3 with Relu activation  We can thus represent each selected filter as a ranked list of trigrams with highest activation values 
We analyze the CNN filters learned by NaiveNN, FANN and DAS respectively on task E INLINEFORM0 BT under setting 1  We focus on E INLINEFORM1 BT for study because electronics and beauty are very different domains and each of them has a diverse set of domain-specific sentiment expressions  For each method, we identify the top 10 most related filters for each sentiment label, and extract the top trigrams of each selected filter on both source and target domains  Since labeled source examples are used for training, we find the filters learned by the three methods capture similar expressions on the source domain, containing both domain-invariant and domain-specific trigrams  On the target domain, DAS captures more target-specific expressions compared to the other two methods  Due to space limitation, we only present a small subset of positive-sentiment-related filters in Table TABREF34   The complete results are provided in Appendix SECREF8   From Table TABREF34 , we can observe that the filters learned by NaiveNN are almost unable to capture target-specific sentiment expressions, while FANN is able to capture limited target-specific words such as “clean” and “scent”  The filters learned by DAS are more domain-adaptive, capturing diverse sentiment expressions in the target domain 
Conclusion
In this work, we propose DAS, a novel framework that jointly performs feature adaptation and semi-supervised learning  We have demonstrated through multiple experiments that DAS can better leverage unlabeled data, and achieve substantial improvements over baseline methods  We have also shown that feature adaptation is an essential component, without which, semi-supervised learning is not able to function properly  The proposed framework could be potentially adapted to other domain adaptation tasks, which is the focus of our future studies 
Results on Amazon Benchmark
Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al  ( BIBREF0 )  The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K)  Following their experimental settings, we consider the binary classification task to predict whether a review is positive or negative on the target domain  Each domain consists of 1000 positive and 1000 negative reviews respectively  We also allow 4000 unlabeled reviews to be used for both the source and the target domains, of which the positive and negative reviews are balanced as well, following the settings in previous works  We construct 12 cross-domain sentiment classification tasks and split the labeled data in each domain into a training set of 1600 reviews and a test set of 400 reviews  The classifier is trained on the training set of the source domain and is evaluated on the test set of the target domain  The comparison results are shown in Table TABREF37  
Numerical Results of Figure 
Due to space limitation, we only show results in figures in the paper  All numerical numbers used for plotting Figure FIGREF17 are presented in Table TABREF38   We can observe that DAS-EM, DAS-SE, and DAS all achieve substantial improvements over baseline methods under different settings 
CNN Filter Analysis Full Results
As mentioned in Section SECREF36 , we conduct CNN filter analysis on NaiveNN, FANN, and DAS  For each method, we identify the top 10 most related filters for positive, negative, neutral sentiment labels respectively, and then represent each selected filter as a ranked list of trigrams with the highest activation values on it  Table TABREF39 , TABREF40 , TABREF41 in the following pages illustrate the trigrams from the target domain (beauty) captured by the selected filters learned on E INLINEFORM0 BT for each method 
We can observe that compared to NaiveNN and FANN, DAS is able to capture a more diverse set of relevant sentiment expressions on the target domain for each sentiment label  This observation is consistent with our motivation  Since NaiveNN, FANN and other baseline methods solely train the sentiment classifier on the source domain, the learned encoder is not able to produce discriminative features on the target domain  DAS addresses this problem by refining the classifier on the target domain with semi-supervised learning, and the overall objective forces the encoder to learn feature representations that are not only domain-invariant but also discriminative on both domains 
Context 3: Introduction
Question answering (QA) has been a blooming research field for the last decade  Selection-based QA implies a family of tasks that find answer contexts from large data given questions in natural language  Three tasks have been proposed for selection-based QA  Given a document, answer extraction BIBREF0 , BIBREF1 finds answer phrases whereas answer selection BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 and answer triggering BIBREF6 , BIBREF7 find answer sentences instead, although the presence of the answer context is not assumed within the provided document for answer triggering but it is for the other two tasks  Recently, various QA tasks that are not selection-based have been proposed BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 ; however, selection-based QA remains still important because of its practical value to real applications (e g , IBM Watson, MIT Start) 
Several datasets have been released for selection-based QA  wang:07a created the QASent dataset consisting of 277 questions, which has been widely used for benchmarking the answer selection task  feng:15a presented InsuranceQA comprising 16K+ questions on insurance contexts  yang:15a introduced WikiQA for answer selection and triggering  jurczyk:16 created SelQA for large real-scale answer triggering  rajpurkar2016squad presented SQuAD for answer extraction and selection as well as for reading comprehension  Finally, morales-EtAl:2016:EMNLP2016 provided InfoboxQA for answer selection 
These corpora make it possible to evaluate the robustness of statistical question answering learning  Although all of these corpora target on selection-based QA, they are designed for different purposes such that it is important to understand the nature of these corpora so a better use of them can be made  In this paper, we make both intrinsic and extrinsic analyses of four latest corpora based on Wikipedia, WikiQA, SelQA, SQuAD, and InfoboxQA  We first give a thorough intrinsic analysis regarding contextual similarities, question types, and answer categories (Section SECREF2 )  We then map questions in all corpora to the current version of English Wikipedia and benchmark another selection-based QA task, answer retrieval (Section SECREF3 )  Finally, we present an extrinsic analysis through a set of experiments cross-testing these corpora using a convolutional neural network architecture (Section SECREF4 ) 
Intrinsic Analysis
Four publicly available corpora are selected for our analysis  These corpora are based on Wikipedia, so more comparable than the others, and have already been used for the evaluation of several QA systems 
WikiQA BIBREF6 comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles  The abstracts of these articles are then extracted to create answer candidates  The assumption is made that if many queries lead to the same article, it must contain the answer context; however, this assumption fails for some occasions, which makes this dataset more challenging  Since the existence of answer contexts is not guaranteed in this task, it is called answer triggering instead of answer selection 
SelQA BIBREF7 is a product of five annotation tasks through crowdsourcing  It consists of about 8K questions where a half of the questions are paraphrased from the other half, aiming to reduce contextual similarities between questions and answers  Each question is associated with a section in Wikipedia where the answer context is guaranteed, and also with five sections selected from the entire Wikipedia where the selection is made by the Lucene search engine  This second dataset does not assume the existence of the answer context, so can be used for the evaluation of answer triggering 
SQuAD BIBREF12 presents 107K+ crowdsourced questions on 536 Wikipedia articles, where the answer contexts are guaranteed to exist within the provided paragraph  It contains annotation of answer phrases as well as the pointers to the sentences including the answer phrases; thus, it can be used for both answer extraction and selection  This corpus also provides human accuracy on those questions, setting up a reasonable upper bound for machines  To avoid overfitting, the evaluation set is not publicly available although system outputs can be evaluated by their provided script 
InfoboxQA BIBREF13 gives 15K+ questions based on the infoboxes from 150 articles in Wikipedia  Each question is crowdsourced and associated with an infobox, where each line of the infobox is considered an answer candidate  This corpus emphasizes the gravity of infoboxes, which summary arguably the most commonly asked information about those articles  Although the nature of this corpus is different from the others, it can also be used to evaluate answer selection 
Analysis
All corpora provide datasets/splits for answer selection, whereas only (WikiQA, SQuAD) and (WikiQA, SelQA) provide datasets for answer extraction and answer triggering, respectively  SQuAD is much larger in size although questions in this corpus are often paraphrased multiple times  On the contrary, SQuAD's average candidates per question ( INLINEFORM0 ) is the smallest because SQuAD extracts answer candidates from paragraphs whereas the others extract them from sections or infoboxes that consist of bigger contexts  Although InfoboxQA is larger than WikiQA or SelQA, the number of token types ( INLINEFORM1 ) in InfoboxQA is smaller than those two, due to the repetitive nature of infoboxes 
All corpora show similar average answer candidate lengths ( INLINEFORM0 ), except for InfoboxQA where each line in the infobox is considered a candidate  SelQA and SQuAD show similar average question lengths ( INLINEFORM1 ) because of the similarity between their annotation schemes  It is not surprising that WikiQA's average question length is the smallest, considering their questions are taken from search queries  InfoboxQA's average question length is relatively small, due to the restricted information that can be asked from the infoboxes  InfoboxQA and WikiQA show the least question-answer word overlaps over questions and answers ( INLINEFORM2 and INLINEFORM3 in Table TABREF2 ), respectively  In terms of the F1-score for overlapping words ( INLINEFORM4 ), SQuAD gives the least portion of overlaps between question-answer pairs although WikiQA comes very close 
Fig

ground_truth: Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen

we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics (E), Beauty (BT), and Music (M)

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 is the source of the data?
Context 2: Introduction
Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks  Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets  In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0 
In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1  Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases  The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3  The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64)  Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task 
Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems  Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects  While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian  To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian 
The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods  The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017  The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019  In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics  In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part  Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups  Additional baseline results are also reported for Persian speech recognition  However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions  They will be defined and reported in the database technical documentation and in a future journal paper 
Data Collection
DeepMine is publicly available for everybody with a variety of licenses for different users  It was collected using crowdsourcing BIBREF4  The data collection was done using an Android application  Each respondent installed the application on his/her personal device and recorded several phrases in different sessions  The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase  For more information about data collection scenario, please refer to BIBREF4 
Data Collection ::: Post-Processing
In order to clean-up the database, the main post-processing step was to filter out problematic utterances  Possible problems include speaker word insertions (e g  repeating some part of a phrase), deletions, substitutions, and involuntary disfluencies  To detect these, we implemented an alignment stage, similar to the second alignment stage in the LibriSpeech project BIBREF5  In this method, a custom decoding graph was generated for each phrase  The decoding graph allows for word skipping and word insertion in the phrase 
For text-dependent and text-prompted parts of the database, such errors are not allowed  Hence, any utterances with errors were removed from the enrollment and test lists  For the speech recognition part, a sub-part of the utterance which is correctly aligned to the corresponding transcription is kept  After the cleaning step, around 190 thousand utterances with full transcription and 10 thousand with sub-part alignment have remained in the database 
Data Collection ::: Statistics
After processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female  297 of the respondents could not read English and have therefore read only the Persian prompts  About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i e  women are over-represented in terms of sessions, even though their number is 17% smaller than that of males  Other useful statistics related to the database are shown in Table TABREF4 
The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples 
DeepMine Database Parts
The DeepMine database consists of three parts  The first one contains fixed common phrases to perform text-dependent speaker verification  The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots)  This part can also serve for Persian ASR training  Each part is described in more details below  Table TABREF11 shows the number of unique phrases in each part of the database  For the English text-dependent part, the following phrases were selected from part1 of the RedDots database, hence the RedDots can be used as an additional training set for this part:
“My voice is my password ”
“OK Google ”
“Artificial intelligence is for real ”
“Actions speak louder than words ”
“There is no such thing as a free lunch ”
DeepMine Database Parts ::: Part1 - Text-dependent (TD)
This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode  Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded 
We have created three experimental setups with different numbers of speakers in the evaluation set  For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data)  The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set  Note that, for English, we have filtered the (Persian native) speakers by the ability to read English  Therefore, there are fewer speakers in each set for English than for Persian  There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set 
For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data  All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance  All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session  From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition  In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV)  Note that for Imposter-Wrong (IW) trials (i e  imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case  So, the number of trials for IC and IW cases are the same 
DeepMine Database Parts ::: Part2 - Text-prompted (TP)
For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment  The second mode contains a sequence of 3 month names that will be used as a test utterance  In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words  For a respondent who can read English, 3 random sequences of English digits are also recorded in each session  In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits 
Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16)  However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i e  3 to 9 utterances)  Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i e  same words as in enrollment but in different order)  From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case  In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV  Again, we just create one IW trial for each IC trial 
DeepMine Database Parts ::: Part3 - Text-independent (TI)
In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent  These phrases are chosen mostly from news and Persian Wikipedia  If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session 
This part can be useful at least for three potential applications  First, it can be used for text-independent speaker verification  The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words)  Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section) 
Based on the recording sessions, we created two experimental setups for speaker verification  In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data)  In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set  Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case 
For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test  The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions  The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case  In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances)  From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case  Table TABREF19 shows number of trials for these setups 
For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used 
DeepMine Database Parts ::: Part3 - Speech Recognition
As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition  There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7  Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian  Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training 
We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers  For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set  The remaining utterances of the other speakers are included in the training set  The test-set, large-test-set and train-set contain 5 9, 28 5 and 450 hours of speech respectively 
There are about 8300 utterances in Part3 which contain only Persian full names (i e  first and family name pairs)  Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P)  These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model 
Experiments and Results
Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition 
Experiments and Results ::: Speaker Verification Experiments
We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in BIBREF9, BIBREF10 and applied it to the Persian portion of Part1  In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16 kHz signals using HTK BIBREF11 with 25 ms Hamming windowed frames with 15 ms overlap 
The reported results are obtained with a 400-dimensional gender independent i-vector based system  The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN) BIBREF10  Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores  For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used BIBREF10  We only model the phonemes which appear in the 5 Persian text-dependent phrases 
For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm {NDCF_{0 01}^{min}}$) and NIST SRE10 ($\mathrm {NDCF_{0 001}^{min}}$)  As shown in Table TABREF22, in text-dependent SV there are 4 types of trials: Target-Correct and Imposter-Correct refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, Target-Wrong and Imposter-Wrong refer to trials when speakers uttered a wrong pass-phrase  In this paper, only the correct trials (i e  Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV BIBREF8, BIBREF12 
Table TABREF23 shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup  For filtering trials, the respondents' mobile brand and model were used in this experiment  In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i e  right side of the colon) relate for non-target trials  For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker  The second Y letter means both recordings were done using exactly the same device model  Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i e  manufacturer)  The second Y means that, in addition to the same brand, both devices have the same model  So, the most difficult target trials are “NN”, where the speaker has used different a device at the test time  In the same manner, the most difficult non-target trials which should be rejected by the system are “YY” where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device)  Hence, the similarity in the recording channel makes rejection more difficult 
The first row in Table TABREF23 shows the results for all trials  By comparing the results with the best published results on RSR2015 and RedDots BIBREF10, BIBREF8, BIBREF12, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases  For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0 3% for both genders (Table VI in BIBREF10)  The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i e  without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1 5% (Table XI in BIBREF10)  It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2 1 and 3 7% for males and females respectively 
One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device  This is allows us to analyse the effects of channel compensation methods  The second row in Table TABREF23 corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models  It is clear that severe degradation was caused by this kind of channel effects (i e  decreasing within-speaker similarities while increasing between-speaker similarities), especially for females 
The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment  Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker 
The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system  So, in this case, there is no device mismatch in all trials  By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device 
The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model  Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties 
The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials  For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case 
Looking at the first two and the last row of Table TABREF23, one can notice the significantly worse performance obtained for the female trials as compared to males  Note that these three rows include target trials where the devices used for enrollment do not necessarily match the devices used for recording test utterances  On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable  This suggest that the degraded results for females are caused by some problematic trials with device mismatch  The exact reason for this degradation is so far unclear and needs a further investigation 
In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand  In this case, as was expected, the performance degradation is smaller than in the second row 
Experiments and Results ::: Speech Recognition Experiments
In addition to speaker verification, we present several speech recognition experiments on Part3  The experiments were performed with the Kaldi toolkit BIBREF13  For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used  The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes)  The network is shown in Table TABREF25: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256  The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization BIBREF14  Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words 
Table TABREF26 shows the results in terms of word error rate (WER) for different evaluated methods  As can be seen, the created database can be used to train well performing and practically usable Persian ASR models 
Conclusions
In this paper, we have described the final version of a large speech corpus, the DeepMine database  It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English  In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions  Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition 
We provided several evaluation protocols for each part of the database  The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance  We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method 
As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part  Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance  Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases 
Acknowledgments
The data collection project was mainly supported by Sharif DeepMine company  The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No  19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602" 

ground_truth: Android application

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. Do they employ their indexing-based method to create a sample of a QA Wikipedia dataset?
Context 3:  FIGREF4 shows the distributions of seven question types grouped deterministically from the lexicons  Although these corpora have been independently developed, a general trend is found, where the what question type dominates, followed by how and who, followed by when and where, and so on 
Fig  FIGREF6 shows the distributions of answer categories automatically classified by our Convolutional Neural Network model trained on the data distributed by li:02a  Interestingly, each corpus focuses on different categories, Numeric for WikiQA and SelQA, Entity for SQuAD, and Person for InfoboxQA, which gives enough diversities for statistical learning to build robust models 
Answer Retrieval
This section describes another selection-based QA task, called answer retrieval, that finds the answer context from a larger dataset, the entire Wikipedia  SQuAD provides no mapping of the answer contexts to Wikipedia, whereas WikiQA and SelQA provide mappings; however, their data do not come from the same version of Wikipedia  We propose an automatic way of mapping the answer contexts from all corpora to the same version of Wikipeda so they can be coherently used for answer retrieval 
Each paragraph in Wikipedia is first indexed by Lucene using {1,2,3}-grams, where the paragraphs are separated by WikiExtractor and segmented by NLP4J (28 7M+ paragraphs are indexed)  Each answer sentence from the corpora in Table TABREF3 is then queried to Lucene, and the top-5 ranked paragraphs are retrieved  The cosine similarity between each sentence in these paragraphs and the answer sentence is measured for INLINEFORM0 -grams, say INLINEFORM1   A weight is assigned to each INLINEFORM2 -gram score, say INLINEFORM3 , and the weighted sum is measured: INLINEFORM4   The fixed weights of INLINEFORM5 are used for our experiments, which can be improved 
If there exists a sentence whose INLINEFORM0 , the paragraph consisting of that sentence is considered the silver-standard answer passage  Table TABREF3 shows how robust these silver-standard passages are based on human judgement ( INLINEFORM1 ) and how many passages are collected ( INLINEFORM2 ) for INLINEFORM3 , where the human judgement is performed on 50 random samples for each case  For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively  Finally, each question is queried to Lucene and the top- INLINEFORM7 paragraphs are retrieved from the entire Wikipedia  If the answer sentence exists within those retrieved paragraphs according to the silver-standard, it is considered correct 
Finding a paragraph that includes the answer context out of the entire Wikipedia is an extremely difficult task (128 7M)  The last row of Table TABREF3 shows results from answer retrieval  Given INLINEFORM0 , SelQA and SQuAD show about 34% and 35% accuracy, which are reasonable  However, WikiQA shows a significantly lower accuracy of 12 47%; this is because the questions in WikiQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search 
Answer Selection
Answer selection is evaluated by two metrics, mean average precision (MAP) and mean reciprocal rank (MRR)  The bigram CNN introduced by yu:14a is used to generate all the results in Table TABREF11 , where models are trained on either single or combined datasets  Clearly, the questions in WikiQA are the most challenging, and adding more training data from the other corpora hurts accuracy due to the uniqueness of query-based questions in this corpus  The best model is achieved by training on W+S+Q for SelQA; adding InfoboxQA hurts accuracy for SelQA although it gives a marginal gain for SQuAD  Just like WikiQA, InfoboxQA performs the best when it is trained on only itself  From our analysis, we suggest that to use models trained on WikiQA and InfoboxQA for short query-like questions, whereas to use ones trained on SelQA and SQuAD for long natural questions 
Answer Triggering
The results of INLINEFORM0 from the answer retrieval task in Section SECREF13 are used to create the datasets for answer triggering, where about 65% of the questions are not expected to find their answer contexts from the provided paragraphs for SelQA and SQuAD and 87 5% are not expected for WikiQA  Answer triggering is evaluated by the F1 scores as presented in Table TABREF11 , where three corpora are cross validated  The results on WikiQA are pretty low as expected from the poor accuracy on the answer retrieval task  Training on SelQA gives the best models for both WikiQA and SelQA  Training on SQuAD gives the best model for SQuAD although the model trained on SelQA is comparable  Since the answer triggering datasets are about 5 times larger than the answer selection datasets, it is computationally too expensive to combine all data for training  We plan to find a strong machine to perform this experiment in near future 
Related work
Lately, several deep learning approaches have been proposed for question answering  yu:14a presented a CNN model that recognizes the semantic similarity between two sentences  wang-nyberg:2015:ACL-IJCNLP presented a stacked bidirectional LSTM approach to read words in sequence, then outputs their similarity scores  feng:15a applied a general deep learning framework to non-factoid question answering  santos:16a introduced an attentive pooling mechanism that led to further improvements in selection-based QA 
Conclusion
We present a comprehensive comparison study of the existing corpora for selection-based question answering  Our intrinsic analysis provides a better understanding of the uniqueness or similarity between these corpora  Our extrinsic analysis shows the strength or weakness of combining these corpora together for statistical learning  Additionally, we create a silver-standard dataset for answer retrieval and triggering, which will be publicly available  In the future, we will explore different ways of improving the quality of our silver-standard datasets by fine-tuning the hyper-parameters 

ground_truth: Yes

No