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 invertibility condition?
Context 1: Introduction
Data annotation is a major bottleneck for the application of supervised learning approaches to many problems  As a result, unsupervised methods that learn directly from unlabeled data are increasingly important  For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent years – for example, for both part-of-speech (POS) induction BIBREF0 , BIBREF1 and unsupervised dependency parsing BIBREF2 , BIBREF3 , BIBREF4   While similar models have had success on a range of unsupervised tasks, they have mostly ignored the apparent utility of continuous word representations evident from supervised NLP applications BIBREF5 , BIBREF6   In this work, we focus on leveraging and explicitly representing continuous word embeddings within unsupervised models of syntactic structure 
Pre-trained word embeddings from massive unlabeled corpora offer a compact way of injecting a prior notion of word similarity into models that would otherwise treat words as discrete, isolated categories  However, the specific properties of language captured by any particular embedding scheme can be difficult to control, and, further, may not be ideally suited to the task at hand  For example, pre-trained skip-gram embeddings BIBREF7 with small context window size are found to capture the syntactic properties of language well BIBREF8 , BIBREF9   However, if our goal is to separate syntactic categories, this embedding space is not ideal – POS categories correspond to overlapping interspersed regions in the embedding space, evident in Figure SECREF4  
In our approach, we propose to learn a new latent embedding space as a projection of pre-trained embeddings (depicted in Figure SECREF5 ), while jointly learning latent syntactic structure – for example, POS categories or syntactic dependencies  To this end, we introduce a new generative model (shown in Figure FIGREF6 ) that first generates a latent syntactic representation (e g  a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function  The latent embeddings can be jointly learned with the structured syntax model in a completely unsupervised fashion 
By choosing an invertible neural network as our non-linear projector, and then parameterizing our model in terms of the projection's inverse, we are able to derive tractable exact inference and marginal likelihood computation procedures so long as inference is tractable in the underlying syntax model  In sec:learn-with-inv we show that this derivation corresponds to an alternate view of our approach whereby we jointly learn a mapping of observed word embeddings to a new embedding space that is more suitable for the syntax model, but include an additional Jacobian regularization term to prevent information loss 
Recent work has sought to take advantage of word embeddings in unsupervised generative models with alternate approaches BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12   BIBREF9 build an HMM with Gaussian emissions on observed word embeddings, but they do not attempt to learn new embeddings  BIBREF10 , BIBREF11 , and BIBREF12 extend HMM or dependency model with valence (DMV) BIBREF2 with multinomials that use word (or tag) embeddings in their parameterization  However, they do not represent the embeddings as latent variables 
In experiments, we instantiate our approach using both a Markov-structured syntax model and a tree-structured syntax model – specifically, the DMV  We evaluate on two tasks: part-of-speech (POS) induction and unsupervised dependency parsing without gold POS tags  Experimental results on the Penn Treebank BIBREF13 demonstrate that our approach improves the basic HMM and DMV by a large margin, leading to the state-of-the-art results on POS induction, and state-of-the-art results on unsupervised dependency parsing in the difficult training scenario where neither gold POS annotation nor punctuation-based constraints are available 
Model
As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations  Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector  Lastly, we extend our approach to more general syntactic structures 
Example: Gaussian HMM
We start by describing the Gaussian hidden Markov model introduced by BIBREF9 , which is a locally normalized model with multinomial transitions and Gaussian emissions  Given a sentence of length INLINEFORM0 , we denote the latent POS tags as INLINEFORM1 , observed (pre-trained) word embeddings as INLINEFORM2 , transition parameters as INLINEFORM3 , and Gaussian emission parameters as INLINEFORM4   The joint distribution of data and latent variables factors as:
DISPLAYFORM0
where INLINEFORM0 is the multinomial transition probability and INLINEFORM1 is the multivariate Gaussian emission probability 
While the observed word embeddings do inform this model with a notion of word similarity – lacking in the basic multinomial HMM – the Gaussian emissions may not be sufficiently flexible to separate some syntactic categories in the complex pre-trained embedding space – for example the skip-gram embedding space as visualized in Figure SECREF4 where different POS categories overlap  Next we introduce a new approach that adds flexibility to the emission distribution by incorporating new latent embedding variables 
Markov Structure with Neural Projector
To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space  We denote the latent embedding of the INLINEFORM0 word in a sentence as INLINEFORM1 , and the neural projection function as INLINEFORM2 , parameterized by INLINEFORM3   In the case of sequential Markov structure, our new model corresponds to the following generative process:
For each time step INLINEFORM0 ,
[noitemsep, leftmargin=*]
Draw the latent state INLINEFORM0
Draw the latent embedding INLINEFORM0
Deterministically produce embedding
INLINEFORM0
The graphical model is depicted in Figure FIGREF6   The deterministic projection can also be viewed as sampling each observation from a point mass at INLINEFORM0   The joint distribution of our model is: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, and INLINEFORM1 is the Dirac delta function centered at INLINEFORM2 : DISPLAYFORM0
General Structure with Neural Projector
Our approach can be applied to a broad family of structured syntax models  We denote latent embedding variables as INLINEFORM0 , discrete latent variables in the syntax model as INLINEFORM1 ( INLINEFORM2 ), where INLINEFORM3 are conditioned to generate INLINEFORM4   The joint probability of our model factors as:
DISPLAYFORM0
where INLINEFORM0 represents the probability of the syntax model, and can encode any syntactic structure – though, its factorization structure will determine whether inference is tractable in our full model  As shown in Figure FIGREF6 , we focus on two syntax models for syntactic analysis in this paper  The first is Markov-structured, which we use for POS induction, and the second is DMV-structured, which we use to learn dependency parses without supervision 
The marginal data likelihood of our model is: DISPLAYFORM0
While the discrete variables INLINEFORM0 can be marginalized out with dynamic program in many cases, it is generally intractable to marginalize out the latent continuous variables, INLINEFORM1 , for an arbitrary projection INLINEFORM2 in Eq  ( EQREF17 ), which means inference and learning may be difficult  In sec:opt, we address this issue by constraining INLINEFORM3 to be invertible, and show that this constraint enables tractable exact inference and marginal likelihood computation 
Learning & Inference
In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge  Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists  Invertible transformations have been explored before in independent components analysis BIBREF14 , gaussianization BIBREF15 , and deep density models BIBREF16 , BIBREF17 , BIBREF18 , for unstructured data  Here, we generalize this style of approach to structured learning, and augment it with discrete latent variables ( INLINEFORM2 )  Under the invertibility condition, we derive a learning algorithm and give another view of our approach revealed by the objective function  Then, we present the architecture of a neural projector we use in experiments: a volume-preserving invertible neural network proposed by BIBREF16 for independent components estimation 
Learning with Invertibility
For ease of exposition, we explain the learning algorithm in terms of Markov structure without loss of generality  As shown in Eq  ( EQREF17 ), the optimization challenge in our approach comes from the intractability of the marginalized emission factor INLINEFORM0   If we can marginalize out INLINEFORM1 and compute INLINEFORM2 , then the posterior and marginal likelihood of our Markov-structured model can be computed with the forward-backward algorithm  We can apply Eq  ( EQREF14 ) and obtain : INLINEFORM3
By using the change of variable rule to the integration, which allows the integration variable INLINEFORM0 to be replaced by INLINEFORM1 , the marginal emission factor can be computed in closed-form when the invertibility condition is satisfied: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, INLINEFORM1 is the Jacobian matrix of function INLINEFORM2 at INLINEFORM3 , and INLINEFORM4 represents the absolute value of its determinant  This Jacobian term is nonzero and differentiable if and only if INLINEFORM5 exists 
Eq  ( EQREF19 ) shows that we can directly calculate the marginal emission distribution INLINEFORM0   Denote the marginal data likelihood of Gaussian HMM as INLINEFORM1 , then the log marginal data likelihood of our model can be directly written as: DISPLAYFORM0
where INLINEFORM0 represents the new sequence of embeddings after applying INLINEFORM1 to each INLINEFORM2   Eq  ( EQREF20 ) shows that the training objective of our model is simply the Gaussian HMM log likelihood with an additional Jacobian regularization term  From this view, our approach can be seen as equivalent to reversely projecting the data through INLINEFORM3 to another manifold INLINEFORM4 that is directly modeled by the Gaussian HMM, with a regularization term  Intuitively, we optimize the reverse projection INLINEFORM5 to modify the INLINEFORM6 space, making it more appropriate for the syntax model  The Jacobian regularization term accounts for the volume expansion or contraction behavior of the projection  Maximizing it can be thought of as preventing information loss  In the extreme case, the Jacobian determinant is equal to zero, which means the projection is non-invertible and thus information is being lost through the projection  Such “information preserving” regularization is crucial during optimization, otherwise the trivial solution of always projecting data to the same single point to maximize likelihood is viable 
More generally, for an arbitrary syntax model the data likelihood of our approach is: DISPLAYFORM0
If the syntax model itself allows for tractable inference and marginal likelihood computation, the same dynamic program can be used to marginalize out INLINEFORM0   Therefore, our joint model inherits the tractability of the underlying syntax model 
Invertible Volume-Preserving Neural Net
For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice  However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive  To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19   Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”  This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property) 
From Eq  ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1   Thus, we directly define the architecture of INLINEFORM2   As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer  The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively  A single coupling layer is defined as: DISPLAYFORM0
where INLINEFORM0 is the coupling function and can be any nonlinear form  This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal  Thus the Jacobian determinant is always equal to one (i e  volume-preserving) and the invertibility condition is naturally satisfied 
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16   Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24   For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same  Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties  Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20  
Experiments
In this section, we first describe our datasets and experimental setup  We then instantiate our approach with Markov and DMV-structured syntax models, and report results on POS tagging and dependency grammar induction respectively  Lastly, we analyze the learned latent embeddings 
Data
For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank  To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9   Following BIBREF9 , the dimensionality INLINEFORM0 is set to 100, and the training context window size is set to 1 to encode more syntactic information  The skip-gram embeddings are trained on the one billion word language modeling benchmark dataset BIBREF21 in addition to the WSJ corpus 
General Experimental Setup
For the neural projector, we employ rectified networks as coupling function INLINEFORM0 following BIBREF16   We use a rectified network with an input layer, one hidden layer, and linear output units, the number of hidden units is set to the same as the number of input units  The number of coupling layers are varied as 4, 8, 16 for both tasks  We optimize marginal data likelihood directly using Adam BIBREF22   For both tasks in the fully unsupervised setting, we do not tune the hyper-parameters using supervised data 
Unsupervised POS tagging
For unsupervised POS tagging, we use a Markov-structured syntax model in our approach, which is a popular structure for unsupervised tagging tasks BIBREF9 , BIBREF10  
Following existing literature, we train and test on the entire WSJ corpus (49208 sentences, 1M tokens)  We use 45 tag clusters, the number of POS tags that appear in WSJ corpus  We train the discrete HMM and the Gaussian HMM BIBREF9 as baselines  For the Gaussian HMM, mean vectors of Gaussian emissions are initialized with the empirical mean of all word vectors with an additive noise  We assume diagonal covariance matrix for INLINEFORM0 and initialize it with the empirical variance of the word vectors  Following BIBREF9 , the covariance matrix is fixed during training  The multinomial probabilities are initialized as INLINEFORM1 , where INLINEFORM2   For our approach, we initialize the syntax model and Gaussian parameters with the pre-trained Gaussian HMM  The weights of layers in the rectified network are initialized from a uniform distribution with mean zero and a standard deviation of INLINEFORM3 , where INLINEFORM4 is the input dimension  We evaluate the performance of POS tagging with both Many-to-One (M-1) accuracy BIBREF23 and V-Measure (VM) BIBREF24   Given a model we found that the tagging performance is well-correlated with the training data likelihood, thus we use training data likelihood as a unsupervised criterion to select the trained model over 10 random restarts after training 50 epochs  We repeat this process 5 times and report the mean and standard deviation of performance 
We compare our approach with basic HMM, Gaussian HMM, and several state-of-the-art systems, including sophisticated HMM variants and clustering techniques with hand-engineered features  The results are presented in Table TABREF32   Through the introduced latent embeddings and additional neural projection, our approach improves over the Gaussian HMM by 5 4 points in M-1 and 5 6 points in VM  Neural HMM (NHMM) BIBREF10 is a baseline that also learns word representation jointly  Both their basic model and extended Conv version does not outperform the Gaussian HMM  Their best model incorporates another LSTM to model long distance dependency and breaks the Markov assumption, yet our approach still achieves substantial improvement over it without considering more context information  Moreover, our method outperforms the best published result that benefits from hand-engineered features BIBREF27 by 2 0 points on VM 
We found that most tagging errors happen in noun subcategories  Therefore, we do the one-to-one mapping between gold POS tags and induced clusters and plot the normalized confusion matrix of noun subcategories in Figure FIGREF35   The Gaussian HMM fails to identify “NN” and “NNS” correctly for most cases, and it often recognizes “NNPS” as “NNP”  In contrast, our approach corrects these errors well 
Unsupervised Dependency Parsing without gold POS tags
For the task of unsupervised dependency parse induction, we employ the Dependency Model with Valence (DMV) BIBREF2 as the syntax model in our approach  DMV is a generative model that defines a probability distribution over dependency parse trees and syntactic categories, generating tokens and dependencies in a head-outward fashion  While, traditionally, DMV is trained using gold POS tags as observed syntactic categories, in our approach, we treat each tag as a latent variable, as described in sec:general-neural 
Most existing approaches to this task are not fully unsupervised since they rely on gold POS tags following the original experimental setup for DMV  This is partially because automatically parsing from words is difficult even when using unsupervised syntactic categories BIBREF29   However, inducing dependencies from words alone represents a more realistic experimental condition since gold POS tags are often unavailable in practice  Previous work that has trained from words alone often requires additional linguistic constraints (like sentence internal boundaries) BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , acoustic cues BIBREF33 , additional training data BIBREF4 , or annotated data from related languages BIBREF34   Our approach is naturally designed to train on word embeddings directly, thus we attempt to induce dependencies without using gold POS tags or other extra linguistic information 
Like previous work we use sections 02-21 of WSJ corpus as training data and evaluate on section 23, we remove punctuations and train the models on sentences of length INLINEFORM0 , “head-percolation” rules BIBREF39 are applied to obtain gold dependencies for evaluation  We train basic DMV, extended DMV (E-DMV) BIBREF35 and Gaussian DMV (which treats POS tag as unknown latent variables and generates observed word embeddings directly conditioned on them following Gaussian distribution) as baselines  Basic DMV and E-DMV are trained with Viterbi EM BIBREF40 on unsupervised POS tags induced from our Markov-structured model described in sec:pos  Multinomial parameters of the syntax model in both Gaussian DMV and our model are initialized with the pre-trained DMV baseline  Other parameters are initialized in the same way as in the POS tagging experiment  The directed dependency accuracy (DDA) is used for evaluation and we report accuracy on sentences of length INLINEFORM1 and all lengths  We train the parser until training data likelihood converges, and report the mean and standard deviation over 20 random restarts 
Our model directly observes word embeddings and does not require gold POS tags during training  Thus, results from related work trained on gold tags are not directly comparable  However, to measure how these systems might perform without gold tags, we run three recent state-of-the-art systems in our experimental setting: UR-A E-DMV BIBREF36 , Neural E-DMV BIBREF11 , and CRF Autoencoder (CRFAE) BIBREF37   We use unsupervised POS tags (induced from our Markov-structured model) in place of gold tags  We also train basic DMV on gold tags and include several state-of-the-art results on gold tags as reference points 
As shown in Table TABREF39 , our approach is able to improve over the Gaussian DMV by 4 8 points on length INLINEFORM0 and 4 8 points on all lengths, which suggests the additional latent embedding layer and neural projector are helpful  The proposed approach yields, to the best of our knowledge, state-of-the-art performance without gold POS annotation and without sentence-internal boundary information  DMV, UR-A E-DMV, Neural E-DMV, and CRFAE suffer a large decrease in performance when trained on unsupervised tags – an effect also seen in previous work BIBREF29 , BIBREF34   Since our approach induces latent POS tags jointly with dependency trees, it may be able to learn POS clusters that are more amenable to grammar induction than the unsupervised tags  We observe that CRFAE underperforms its gold-tag counterpart substantially  This may largely be a result of the model's reliance on prior linguistic rules that become unavailable when gold POS tag types are unknown  Many extensions to DMV can be considered orthogonal to our approach – they essentially focus on improving the syntax model  It is possible that incorporating these more sophisticated syntax models into our approach may lead to further improvements 
Sensitivity Analysis
In the above experiments we initialize the structured syntax components with the pre-trained Gaussian or discrete baseline, which is shown as a useful technique to help train our deep models  We further study the results with fully random initialization  In the POS tagging experiment, we report the results in Table TABREF48   While the performance with 4 layers is comparable to the pre-trained Gaussian initialization, deeper projections (8 or 16 layers) result in a dramatic drop in performance  This suggests that the structured syntax model with very deep projections is difficult to train from scratch, and a simpler projection might be a good compromise in the random initialization setting 
Different from the Markov prior in POS tagging experiments, our parsing model seems to be quite sensitive to the initialization  For example, directed accuracy of our approach on sentences of length INLINEFORM0 is below 40 0 with random initialization  This is consistent with previous work that has noted the importance of careful initialization for DMV-based models such as the commonly used harmonic initializer BIBREF2   However, it is not straightforward to apply the harmonic initializer for DMV directly in our model without using some kind of pre-training since we do not observe gold POS 
We investigate the effect of the choice of pre-trained embedding on performance while using our approach  To this end, we additionally include results using fastText embeddings BIBREF41 – which, in contrast with skip-gram embeddings, include character-level information  We set the context windows size to 1 and the dimension size to 100 as in the skip-gram training, while keeping other parameters set to their defaults  These results are summarized in Table TABREF50 and Table TABREF51   While fastText embeddings lead to reduced performance with our model, our approach still yields an improvement over the Gaussian baseline with the new observed embeddings space 
Qualitative Analysis of Embeddings
We perform qualitative analysis to understand how the latent embeddings help induce syntactic structures  First we filter out low-frequency words and punctuations in WSJ, and visualize the rest words (10k) with t-SNE BIBREF42 under different embeddings  We assign each word with its most likely gold POS tags in WSJ and color them according to the gold POS tags 
For our Markov-structured model, we have displayed the embedding space in Figure SECREF5 , where the gold POS clusters are well-formed  Further, we present five example target words and their five nearest neighbors in terms of cosine similarity  As shown in Table TABREF53 , the skip-gram embedding captures both semantic and syntactic aspects to some degree, yet our embeddings are able to focus especially on the syntactic aspects of words, in an unsupervised fashion without using any extra morphological information 
In Figure FIGREF54 we depict the learned latent embeddings with the DMV-structured syntax model  Unlike the Markov structure, the DMV structure maps a large subset of singular and plural nouns to the same overlapping region  However, two clusters of singular and plural nouns are actually separated  We inspect the two clusters and the overlapping region in Figure FIGREF54 , it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model  In contrast, the nouns in the overlapping region are typically objects  This demonstrates that the latent embeddings are focusing on aspects of language that are specifically important for modeling dependency without ever having seen examples of dependency parses  Some previous work has deliberately created embeddings to capture different notions of similarity BIBREF43 , BIBREF44 , while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora 
Related Work
Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18   This relevant work mostly adopts simple (e g  Gaussian) and fixed priors and does not attempt to learn interpretable latent structures  Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47   Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48   Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models  However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures 
Conclusion
In this work, we define a novel generative approach to leverage continuous word representations for unsupervised learning of syntactic structure  Experiments on both POS induction and unsupervised dependency parsing tasks demonstrate the effectiveness of our proposed approach  Future work might explore more sophisticated invertible projections, or recurrent projections that jointly transform the entire input sequence 
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: The neural projector must be invertible.

we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists

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 accents are present in the corpus?
Context 3: Introduction
Reading Comprehension (RC) has become a central task in natural language processing, with great practical value in various industries  In recent years, many large-scale RC datasets in English BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 have nourished the development of numerous powerful and diverse RC models BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11  The state-of-the-art model BIBREF12 on SQuAD, one of the most widely used RC benchmarks, even surpasses human-level performance  Nonetheless, RC on languages other than English has been limited due to the absence of sufficient training data  Although some efforts have been made to create RC datasets for Chinese BIBREF13, BIBREF14 and Korean BIBREF15, it is not feasible to collect RC datasets for every language since annotation efforts to collect a new RC dataset are often far from trivial  Therefore, the setup of transfer learning, especially zero-shot learning, is of extraordinary importance 
Existing methods BIBREF16 of cross-lingual transfer learning on RC datasets often count on machine translation (MT) to translate data from source language into target language, or vice versa  These methods may not require a well-annotated RC dataset for the target language, whereas a high-quality MT model is needed as a trade-off, which might not be available when it comes to low-resource languages 
In this paper, we leverage pre-trained multilingual language representation, for example, BERT learned from multilingual un-annotated sentences (multi-BERT), in cross-lingual zero-shot RC  We fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair to explore the cross-lingual ability of multi-BERT  Surprisingly, we find that the models have the ability to transfer between low lexical similarity language pair, such as English and Chinese  Recent studies BIBREF17, BIBREF12, BIBREF18 show that cross-lingual language models have the ability to enable preliminary zero-shot transfer on simple natural language understanding tasks, but zero-shot transfer of RC has not been studied  To our knowledge, this is the first work systematically exploring the cross-lingual transferring ability of multi-BERT on RC tasks 
Zero-shot Transfer with Multi-BERT
Multi-BERT has showcased its ability to enable cross-lingual zero-shot learning on the natural language understanding tasks including XNLI BIBREF19, NER, POS, Dependency Parsing, and so on  We now seek to know if a pre-trained multi-BERT has ability to solve RC tasks in the zero-shot setting 
Zero-shot Transfer with Multi-BERT ::: Experimental Setup and Data
We have training and testing sets in three different languages: English, Chinese and Korean  The English dataset is SQuAD BIBREF2  The Chinese dataset is DRCD BIBREF14, a Chinese RC dataset with 30,000+ examples in the training set and 10,000+ examples in the development set  The Korean dataset is KorQuAD BIBREF15, a Korean RC dataset with 60,000+ examples in the training set and 10,000+ examples in the development set, created in exactly the same procedure as SQuAD  We always use the development sets of SQuAD, DRCD and KorQuAD for testing since the testing sets of the corpora have not been released yet 
Next, to construct a diverse cross-lingual RC dataset with compromised quality, we translated the English and Chinese datasets into more languages, with Google Translate  An obvious issue with this method is that some examples might no longer have a recoverable span  To solve the problem, we use fuzzy matching to find the most possible answer, which calculates minimal edit distance between translated answer and all possible spans  If the minimal edit distance is larger than min(10, lengths of translated answer - 1), we drop the examples during training, and treat them as noise when testing  In this way, we can recover more than 95% of examples  The following generated datasets are recovered with same setting 
The pre-trained multi-BERT is the official released one  This multi-lingual version of BERT were pre-trained on corpus in 104 languages  Data in different languages were simply mixed in batches while pre-training, without additional effort to align between languages  When fine-tuning, we simply adopted the official training script of BERT, with default hyperparameters, to fine-tune each model until training loss converged 
Zero-shot Transfer with Multi-BERT ::: Experimental Results
Table TABREF6 shows the result of different models trained on either Chinese or English and tested on Chinese  In row (f), multi-BERT is fine-tuned on English but tested on Chinese, which achieves competitive performance compared with QANet trained on Chinese  We also find that multi-BERT trained on English has relatively lower EM compared with the model with comparable F1 scores  This shows that the model learned with zero-shot can roughly identify the answer spans in context but less accurate  In row (c), we fine-tuned a BERT model pre-trained on English monolingual corpus (English BERT) on Chinese RC training data directly by appending fastText-initialized Chinese word embeddings to the original word embeddings of English-BERT  Its F1 score is even lower than that of zero-shot transferring multi-BERT (rows (c) v s  (e))  The result implies multi-BERT does acquire better cross-lingual capability through pre-training on multilingual corpus  Table TABREF8 shows the results of multi-BERT fine-tuned on different languages and then tested on English , Chinese and Korean  The top half of the table shows the results of training data without translation  It is not surprising that when the training and testing sets are in the same language, the best results are achieved, and multi-BERT shows transfer capability when training and testing sets are in different languages, especially between Chinese and Korean 
In the lower half of Table TABREF8, the results are obtained by the translated training data  First, we found that when testing on English and Chinese, translation always degrades the performance (En v s  En-XX, Zh v s  Zh-XX)  Even though we translate the training data into the same language as testing data, using the untranslated data still yield better results  For example, when testing on English, the F1 score of the model training on Chinese (Zh) is 53 8, while the F1 score is only 44 1 for the model training on Zh-En  This shows that translation degrades the quality of data  There are some exceptions when testing on Korean  Translating the English training data into Chinese, Japanese and Korean still improve the performance on Korean  We also found that when translated into the same language, the English training data is always better than the Chinese data (En-XX v s  Zh-XX), with only one exception (En-Fr v s  Zh-Fr when testing on KorQuAD)  This may be because we have less Chinese training data than English  These results show that the quality and the size of dataset are much more important than whether the training and testing are in the same language or not 
Zero-shot Transfer with Multi-BERT ::: Discussion ::: The Effect of Machine Translation
Table TABREF8 shows that fine-tuning on un-translated target language data achieves much better performance than data translated into the target language  Because the above statement is true across all the languages, it is a strong evidence that translation degrades the performance We notice that the translated corpus and untranslated corpus are not the same  This may be another factor that influences the results  Conducting an experiment between un-translated and back-translated data may deal with this problem 
Zero-shot Transfer with Multi-BERT ::: Discussion ::: The Effect of Other Factors
Here we discuss the case that the training data are translated  We consider each result is affected by at least three factors: (1) training corpus, (2) data size, (3) whether the source corpus is translated into the target language  To study the effect of data-size, we conducted an extra experiment where we down-sampled the size of English data to be the same as Chinese corpus, and used the down-sampled corpus to train  Then We carried out one-way ANOVA test and found out the significance of the three factors are ranked as below: (1) > (2) >> (3)  The analysis supports that the characteristics of training data is more important than translated into target language or not  Therefore, although translation degrades the performance, whether translating the corpus into the target language is not critical 
What Does Zero-shot Transfer Model Learn? ::: Unseen Language Dataset
It has been shown that extractive QA tasks like SQuAD may be tackled by some language independent strategies, for example, matching words in questions and context BIBREF20  Is zero-shot learning feasible because the model simply learns this kind of language independent strategies on one language and apply to the other?
To verify whether multi-BERT largely counts on a language independent strategy, we test the model on the languages unseen during pre-training  To make sure the languages have never been seen before, we artificially make unseen languages by permuting the whole vocabulary of existing languages  That is, all the words in the sentences of a specific language are replaced by other words in the same language to form the sentences in the created unseen language  It is assumed that if multi-BERT used to find answers by language independent strategy, then multi-BERT should also do well on unseen languages  Table TABREF14 shows that the performance of multi-BERT drops drastically on the dataset  It implies that multi-BERT might not totally rely on pattern matching when finding answers 
What Does Zero-shot Transfer Model Learn? ::: Embedding in Multi-BERT
PCA projection of hidden representations of the last layer of multi-BERT before and after fine-tuning are shown in Fig  FIGREF15  The red points represent Chinese tokens, and the blue points are for English  The results show that tokens from different languages might be embedded into the same space with close spatial distribution  Even though during the fine-tuning only the English data is used, the embedding of the Chinese token changed accordingly  We also quantitatively evaluate the similarities between the embedding of the languages  The results can be found in the Appendix 
What Does Zero-shot Transfer Model Learn? ::: Code-switching Dataset
We observe linguistic-agnostic representations in the last subsection  If tokens are represented in a language-agnostic way, the model may be able to handle code-switching data  Because there is no code-switching data for RC, we create artificial code-switching datasets by replacing some of the words in contexts or questions with their synonyms in another language  The synonyms are found by word-by-word translation with given dictionaries  We use the bilingual dictionaries collected and released in facebookresearch/MUSE GitHub repository  We substitute the words if and only if the words are in the bilingual dictionaries 
Table TABREF14 shows that on all the code-switching datasets, the EM/F1 score drops, indicating that the semantics of representations are not totally disentangled from language  However, the examples of the answers of the model (Table TABREF21) show that multi-BERT could find the correct answer spans although some keywords in the spans have been translated into another language 
What Does Zero-shot Transfer Model Learn? ::: Typology-manipulated Dataset
There are various types of typology in languages  For example, in English the typology order is subject-verb-object (SVO) order, but in Japanese and Korean the order is subject-object-verb (SOV)  We construct a typology-manipulated dataset to examine if the typology order of the training data influences the transfer learning results  If the model only learns the semantic mapping between different languages, changing English typology order from SVO to SOV should improve the transfer ability from English to Japanese  The method used to generate datasets is the same as BIBREF21 
The source code is from a GitHub repository named Shaul1321/rnn_typology, which labels given sentences to CoNLL format with StanfordCoreNLP and then re-arranges them greedily 
Table TABREF23 shows that when we change the English typology order to SOV or OSV order, the performance on Korean is improved and worsen on English and Chinese, but very slightly  The results show that the typology manipulation on the training set has little influence  It is possible that multi-BERT normalizes the typology order of different languages to some extent 
Conclusion
In this paper, we systematically explore zero-shot cross-lingual transfer learning on RC with multi-BERT  The experimental results on English, Chinese and Korean corpora show that even when the languages for training and testing are different, reasonable performance can be obtained  Furthermore, we created several artificial data to study the cross-lingual ability of multi-BERT in the presence of typology variation and code-switching  We showed that only token-level pattern matching is not sufficient for multi-BERT to answer questions and typology variation and code-switching only caused minor effects on testing performance 
Supplemental Material ::: Internal Representation of multi-BERT
The architecture of multi-BERT is a Transformer encoder BIBREF25  While fine-tuning on SQuAD-like dataset, the bottom layers of multi-BERT are initialized from Google-pretrained parameters, with an added output layer initialized from random parameters  Tokens representations from the last layer of bottom-part of multi-BERT are inputs to the output layer and then the output layer outputs a distribution over all tokens that indicates the probability of a token being the START/END of an answer span 
Supplemental Material ::: Internal Representation of multi-BERT ::: Cosine Similarity
As all translated versions of SQuAD/DRCD are parallel to each other  Given a source-target language pair, we calculate cosine similarity of the mean pooling of tokens representation within corresponding answer-span as a measure of how much they look like in terms of the internal representation of multi-BERT  The results are shown in Fig  FIGREF26 
Supplemental Material ::: Internal Representation of multi-BERT ::: SVCCA
Singular Vector Canonical Correlation Analysis (SVCCA) is a general method to compare the correlation of two sets of vector representations  SVCCA has been proposed to compare learned representations across language models BIBREF24  Here we adopt SVCCA to measure the linear similarity of two sets of representations in the same multi-BERT from different translated datasets, which are parallel to each other  The results are shown in Fig FIGREF28 
Supplemental Material ::: Improve Transfering
In the paper, we show that internal representations of multi-BERT are linear-mappable to some extent between different languages  This implies that multi-BERT model might encode semantic and syntactic information in language-agnostic ways and explains how zero-shot transfer learning could be done 
To take a step further, while transfering model from source dataset to target dataset, we align representations in two proposed way, to improve performance on target dataset 
Supplemental Material ::: Improve Transfering ::: Linear Mapping Method
Algorithms proposed in BIBREF23, BIBREF22, BIBREF26 to unsupervisedly learn linear mapping between two sets of embeddings are used here to align representations of source (training data) to those of target  We obtain the mapping generated by embeddings from one specific layer of pre-trained multi-BERT then we apply this mapping to transform the internal representations of multi-BERT while fine-tuning on training data 
Supplemental Material ::: Improve Transfering ::: Adversarial Method
In Adversarial Method, we add an additional transform layer to transform representations and a discrimination layer to discriminate between transformed representations from source language (training set) and target language (development set)  And the GAN loss is applied in the total loss of fine-tuning 
Supplemental Material ::: Improve Transfering ::: Discussion
As table TABREF33 shows, there are no improvements among above methods  Some linear mapping methods even causes devastating effect on EM/F1 scores 

ground_truth: Unanswerable

Unanswerable

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 model performance on target language reading comprehension?
Context 4: 
For example, for the sentence:
a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , have anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   (from BIBREF1 )
I feed into BERT:
[CLS] a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , [MASK] anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   and look for the score assigned to the words have and has at the masked position 
Similarly, for the pair
the game that the guard hates is bad  
the game that the guard hates are bad  
(from BIBREF3 ), I feed into BERT:
[CLS] the game that the guard hates [MASK] bad  
and compare the scores predicted for is and are 
This differs from BIBREF1 and BIBREF2 by considering the entire sentence (excluding the verb) and not just its prefix leading to the verb, and differs from BIBREF3 by conditioning the focus verb on bidirectional context 
I use the PyTorch implementation of BERT, with the pre-trained models supplied by Google  I experiment with the bert-large-uncased and bert-base-uncased models 
The bi-directional setup precludes using using the NPI stimuli of BIBREF3 , in which the minimal pair differs in two words position, which I discard from the evaluation  I also discard the agreement cases involving the verbs is or are in BIBREF1 and in BIBREF2 , because some of them are copular construction, in which strong agreement hints can be found also on the object following the verb  This is not an issue in the manually constructed BIBREF3 stimuli due to the patterns they chose 
Finally, I discard stimuli in which the focus verb or its plural/singular inflection does not appear as a single word in the BERT word-piece-based vocabulary (and hence cannot be predicted by the model)  This include discarding BIBREF3 stimuli involving the words swims or admires, resulting in 23,368 discarded pairs (out of 152,300)  I similarly discard 680 sentences from BIBREF1 where the focus verb or its inflection were one of 108 out-of-vocabulary tokens, and 28 sentence-pairs (8 tokens) from BIBREF2  
The BERT results are not directly comparable to the numbers reported in previous work  Beyond the differences due to bidirectionality and the discarded stimuli, the BERT models are also trained on a different and larger corpus (covering both wikipedia and books) 
Code is available at https://github com/yoavg/bert-syntax 
Results
Tables 1 , 2 and 3 show the results  All cases exhibit high scores—in the vast majority of the cases substantially higher than reported in previous work  As discussed above, the results are not directly comparable to previous work: the BERT models are trained on different (and larger) data, are allowed to access the suffix of the sentence in addition to its prefix, and are evaluated on somewhat different data due to discarding OOV items  Still, taken together, the high performance numbers indicate that the purely attention-based BERT models are likely capable of capturing the same kind of syntactic regularities that LSTM-based models are capable of capturing, at least as well as the LSTM models and probably better 
Another noticeable and interesting trend is that larger is not necessarily better: the BERT-Base model outperforms the BERT-Large model on many of the syntactic conditions 
Discussion
The BERT models perform remarkably well on all the syntactic test cases  I expected the attention-based mechanism to fail on these (compared to the LSTM-based models), and am surprised by these results  The BIBREF2 and BIBREF3 conditions rule out the possibility of overly relying on selectional preference cues or memorizing the wikipedia training data, and suggest real syntactic generalization is taking place  Exploring the extent to which deep purely-attention-based architectures such as BERT are capable of capturing hierarchy-sensitive and syntactic dependencies—as well as the mechanisms by which this is achieved—is a fascinating area for future research 

ground_truth: Table TABREF6, Table TABREF8

when testing on English, the F1 score of the model training on Chinese (Zh) is 53.8,  F1 score is only 44.1 for the model training on Zh-En