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. which lstm models did they compare with?
Context 1: Introduction
Recently, deep neural network has been widely employed in various recognition tasks  Increasing the depth of neural network is a effective way to improve the performance, and convolutional neural network (CNN) has benefited from it in visual recognition task BIBREF0   Deeper long short-term memory (LSTM) recurrent neural networks (RNNs) are also applied in large vocabulary continuous speech recognition (LVCSR) task, because LSTM networks have shown better performance than Fully-connected feed-forward deep neural network BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4  
Training neural network becomes more challenge when it goes deep  A conceptual tool called linear classifier probe is introduced to better understand the dynamics inside a neural network BIBREF5   The discriminating features of linear classifier is the hidden units of a intermediate layer  For deep neural networks, it is observed that deeper layer's accuracy is lower than that of shallower layers  Therefore, the tool shows the difficulty of deep neural model training visually 
Layer-wise pre-training is a successful method to train very deep neural networks BIBREF6   The convergence becomes harder with increasing the number of layers, even though the model is initialized with Xavier or its variants BIBREF7 , BIBREF8   But the deeper network which is initialized with a shallower trained network could converge well 
The size of LVCSR training dataset goes larger and training with only one GPU becomes high time consumption inevitably  Therefore, parallel training with multi-GPUs is more suitable for LVCSR system  Mini-batch based stochastic gradient descent (SGD) is the most popular method in neural network training procedure  Asynchronous SGD is a successful effort for parallel training based on it BIBREF9 , BIBREF10   It can many times speed up the training time without decreasing the accuracy  Besides, synchronous SGD is another effective effort, where the parameter server waits for every works to finish their computation and sent their local models to it, and then it sends updated model back to all workers BIBREF11   Synchronous SGD converges well in parallel training with data parallelism, and is also easy to implement 
In order to further improve the performance of deep neural network with parallel training, several methods are proposed  Model averaging method achieves linear speedup, as the final model is averaged from all parameters of local models in different workers BIBREF12 , BIBREF13 , but the accuracy decreases compared with single GPU training  Moreover, blockwise model-updating filter (BMUF) provides another almost linear speedup approach with multi-GPUs on the basis of model averaging  It can achieve improvement or no-degradation of recognition performance compared with mini-batch SGD on single GPU BIBREF14  
Moving averaged (MA) approaches are also proposed for parallel training  It is demonstrated that the moving average of the parameters obtained by SGD performs as well as the parameters that minimize the empirical cost, and moving average parameters can be used as the estimator of them, if the size of training data is large enough BIBREF15   One pass learning is then proposed, which is the combination of learning rate schedule and averaged SGD using moving average BIBREF16   Exponential moving average (EMA) is proposed as a non-interference method BIBREF17   EMA model is not broadcasted to workers to update their local models, and it is applied as the final model of entire training process  EMA method is utilized with model averaging and BMUF to further decrease the character error rate (CER)  It is also easy to implement in existing parallel training systems 
Frame stacking can also speed up the training time BIBREF18   The super frame is stacked by several regular frames, and it contains the information of them  Thus, the network can see multiple frames at a time, as the super frame is new input  Frame stacking can also lead to faster decoding 
For streaming voice search service, it needs to display intermediate recognition results while users are still speaking  As a result, the system needs to fulfill high real-time requirement, and we prefer unidirectional LSTM network rather than bidirectional one  High real-time requirement means low real time factor (RTF), but the RTF of deep LSTM model is higher inevitably  The dilemma of recognition accuracy and real-time requirement is an obstacle to the employment of deep LSTM network  Deep model outperforms because it contains more knowledge, but it is also cumbersome  As a result, the knowledge of deep model can be distilled to a shallow model BIBREF19   It provided a effective way to employ the deep model to the real-time system 
In this paper, we explore a entire deep LSTM RNN training framework, and employ it to real-time application  The deep learning systems benefit highly from a large quantity of labeled training data  Our first and basic speech recognition system is trained on 17000 hours of Shenma voice search dataset  It is a generic dataset sampled from diverse aspects of search queries  The requirement of speech recognition system also addressed by specific scenario, such as map and navigation task  The labeled dataset is too expensive, and training a new model with new large dataset from the beginning costs lots of time  Thus, it is natural to think of transferring the knowledge from basic model to new scenario's model  Transfer learning expends less data and less training time than full training  In this paper, we also introduce a novel transfer learning strategy with segmental Minimum Bayes-Risk (sMBR)  As a result, transfer training with only 1000 hours data can match equivalent performance for full training with 7300 hours data 
Our deep LSTM training framework for LVCSR is presented in Section 2  Section 3 describes how the very deep models does apply in real world applications, and how to transfer the model to another task  The framework is analyzed and discussed in Section 4, and followed by the conclusion in Section 5 
Layer-wise Training with Soft Target and Hard Target
Gradient-based optimization of deep LSTM network with random initialization get stuck in poor solution easily  Xavier initialization can partially solve this problem BIBREF7 , so this method is the regular initialization method of all training procedure  However, it does not work well when it is utilized to initialize very deep model directly, because of vanishing or exploding gradients  Instead, layer-wise pre-training method is a effective way to train the weights of very deep architecture BIBREF6 , BIBREF20   In layer-wise pre-training procedure, a one-layer LSTM model is firstly trained with normalized initialization  Sequentially, two-layers LSTM model's first layer is initialized by trained one-layer model, and its second layer is regularly initialized  In this way, a deep architecture is layer-by-layer trained, and it can converge well 
In conventional layer-wise pre-training, only parameters of shallower network are transfered to deeper one, and the learning targets are still the alignments generated by HMM-GMM system  The targets are vectors that only one state's probability is one, and the others' are zeros  They are known as hard targets, and they carry limited knowledge as only one state is active  In contrast, the knowledge of shallower network should be also transfered to deeper one  It is obtained by the softmax layer of existing model typically, so each state has a probability rather than only zero or one, and called as soft target  As a result, the deeper network which is student network learns the parameters and knowledge from shallower one which is called teacher network  When training the student network from the teacher network, the final alignment is the combination of hard target and soft target in our layer-wise training phase  The final alignment provides various knowledge which transfered from teacher network and extracted from true labels  If only soft target is learned, student network perform no better than teacher network, but it could outperform teacher network as it also learns true labels 
The deeper network spends less time to getting the same level of original network than the network trained from the beginning, as a period of low performance is skipped  Therefore, training with hard and soft target is a time saving method  For large training dataset, training with the whole dataset still spends too much time  A network firstly trained with only a small part of dataset could go deeper as well, and so the training time reducing rapidly  When the network is deep enough, it then trained on the entire dataset to get further improvement  There is no gap of accuracy between these two approaches, but latter one saves much time 
Differential Saturation Check
The objects of conventional saturation check are gradients and the cell activations BIBREF4   Gradients are clipped to range [-5, 5], while the cell activations clipped to range [-50, 50]  Apart from them, the differentials of recurrent layers is also limited  If the differentials go beyond the range, corresponding back propagation is skipped, while if the gradients and cell activations go beyond the bound, values are set as the boundary values  The differentials which are too large or too small will lead to the gradients easily vanishing, and it demonstrates the failure of this propagation  As a result, the parameters are not updated, and next propagation  
Sequence Discriminative Training
Cross-entropy (CE) is widely used in speech recognition training system as a frame-wise discriminative training criterion  However, it is not well suited to speech recognition, because speech recognition training is a sequential learning problem  In contrast, sequence discriminative training criterion has shown to further improve performance of neural network first trained with cross-entropy BIBREF21 , BIBREF22 , BIBREF23   We choose state-level minimum bayes risk (sMBR) BIBREF21 among a number of sequence discriminative criterion is proposed, such as maximum mutual information (MMI) BIBREF24 and minimum phone error (MPE) BIBREF25   MPE and sMBR are designed to minimize the expected error of different granularity of labels, while CE aims to minimizes expected frame error, and MMI aims to minimizes expected sentence error  State-level information is focused on by sMBR 
a frame-level accurate model is firstly trained by CE loss function, and then sMBR loss function is utilized for further training to get sequence-level accuracy  Only a part of training dataset is needed in sMBR training phase on the basis of whole dataset CE training 
Parallel Training
It is demonstrated that training with larger dataset can improve recognition accuracy  However, larger dataset means more training samples and more model parameters  Therefore, parallel training with multiple GPUs is essential, and it makes use of data parallelism BIBREF9   The entire training data is partitioned into several split without overlapping and they are distributed to different GPUs  Each GPU trains with one split of training dataset locally  All GPUs synchronize their local models with model average method after a mini-batch optimization BIBREF12 , BIBREF13  
Model average method achieves linear speedup in training phase, but the recognition accuracy decreases compared with single GPU training  Block-wise model updating filter (BMUF) is another successful effort in parallel training with linear speedup as well  It can achieve no-degradation of recognition accuracy with multi-GPUs BIBREF14   In the model average method, aggregated model INLINEFORM0 is computed and broadcasted to GPUs  On the basis of it, BMUF proposed a novel model updating strategy: INLINEFORM1 INLINEFORM2
Where INLINEFORM0 denotes model update, and INLINEFORM1 is the global-model update  There are two parameters in BMUF, block momentum INLINEFORM2 , and block learning rate INLINEFORM3   Then, the global model is updated as INLINEFORM4
Consequently, INLINEFORM0 is broadcasted to all GPUs to initial their local models, instead of INLINEFORM1 in model average method 
Averaged SGD is proposed to further accelerate the convergence speed of SGD  Averaged SGD leverages the moving average (MA) INLINEFORM0 as the estimator of INLINEFORM1 BIBREF15 : INLINEFORM2
Where INLINEFORM0 is computed by model averaging or BMUF  It is shown that INLINEFORM1 can well converge to INLINEFORM2 , with the large enough training dataset in single GPU training  It can be considered as a non-interference strategy that INLINEFORM3 does not participate the main optimization process, and only takes effect after the end of entire optimization  However, for the parallel training implementation, each INLINEFORM4 is computed by model averaging and BMUF with multiple models, and moving average model INLINEFORM5 does not well converge, compared with single GPU training 
Model averaging based methods are employed in parallel training of large scale dataset, because of their faster convergence, and especially no-degradation implementation of BMUF  But combination of model averaged based methods and moving average does not match the expectation of further enhance performance and it is presented as INLINEFORM0
The weight of each INLINEFORM0 is equal in moving average method regardless the effect of temporal order  But INLINEFORM1 closer to the end of training achieve higher accuracy in the model averaging based approach, and thus it should be with more proportion in final INLINEFORM2   As a result, exponential moving average(EMA) is appropriate, which the weight for each older parameters decrease exponentially, and never reaching zero  After moving average based methods, the EMA parameters are updated recursively as INLINEFORM3
Here INLINEFORM0 represents the degree of weight decrease, and called exponential updating rate  EMA is also a non-interference training strategy that is implemented easily, as the updated model is not broadcasted  Therefore, there is no need to add extra learning rate updating approach, as it can be appended to existing training procedure directly 
Deployment
There is a high real time requirement in real world application, especially in online voice search system  Shenma voice search is one of the most popular mobile search engines in China, and it is a streaming service that intermediate recognition results displayed while users are still speaking  Unidirectional LSTM network is applied, rather than bidirectional one, because it is well suited to real-time streaming speech recognition 
Distillation
It is demonstrated that deep neural network architecture can achieve improvement in LVCSR  However, it also leads to much more computation and higher RTF, so that the recognition result can not be displayed in real time  It should be noted that deeper neural network contains more knowledge, but it is also cumbersome  the knowledge is key to improve the performance  If it can be transfered from cumbersome model to a small model, the recognition ability can also be transfered to the small model  Knowledge transferring to small model is called distillation BIBREF19   The small model can perform as well as cumbersome model, after distilling  It provide a way to utilize high-performance but high RTF model in real time system  The class probability produced by the cumbersome model is regarded as soft target, and the generalization ability of cumbersome model is transfered to small model with it  Distillation is model's knowledge transferring approach, so there is no need to use the hard target, which is different with the layer-wise training method 
9-layers unidirectional LSTM model achieves outstanding performance, but meanwhile it is too computationally expensive to allow deployment in real time recognition system  In order to ensure real-time of the system, the number of layers needs to be reduced  The shallower network can learn the knowledge of deeper network with distillation  It is found that RTF of 2-layers network is acceptable, so the knowledge is distilled from 9-layers well-trained model to 2-layers model  Table TABREF16 shows that distillation from 9-layers to 2-layers brings RTF decrease of relative 53%, while CER only increases 5%  The knowledge of deep network is almost transfered with distillation, Distillation brings promising RTF reduction, but only little knowledge of deep network is lost  Moreover, CER of 2-layers distilled LSTM decreases relative 14%, compared with 2-layers regular-trained LSTM 
Transfer Learning with sMBR
For a certain specific scenario, the model trained with the data recorded from it has better adaptation than the model trained with generic scenario  But it spends too much time training a model from the beginning, if there is a well-trained model for generic scenarios  Moreover, labeling a large quantity of training data in new scenario is both costly and time consuming  If a model transfer trained with smaller dataset can obtained the similar recognition accuracy compared with the model directly trained with larger dataset, it is no doubt that transfer learning is more practical  Since specific scenario is a subset of generic scenario, some knowledge can be shared between them  Besides, generic scenario consists of various conditions, so its model has greater robustness  As a result, not only shared knowledge but also robustness can be transfered from the model of generic scenario to the model of specific one 
As the model well trained from generic scenario achieves good performance in frame level classification, sequence discriminative training is required to adapt new model to specific scenario additionally  Moreover, it does not need alignment from HMM-GMM system, and it also saves amount of time to prepare alignment 
Training Data
A large quantity of labeled data is needed for training a more accurate acoustic model  We collect the 17000 hours labeled data from Shenma voice search, which is one of the most popular mobile search engines in China  The dataset is created from anonymous online users' search queries in Mandarin, and all audio file's sampling rate is 16kHz, recorded by mobile phones  This dataset consists of many different conditions, such as diverse noise even low signal-to-noise, babble, dialects, accents, hesitation and so on 
In the Amap, which is one of the most popular web mapping and navigation services in China, users can search locations and navigate to locations they want though voice search  To present the performance of transfer learning with sequence discriminative training, the model trained from Shenma voice search which is greneric scenario transfer its knowledge to the model of Amap voice search  7300 hours labeled data is collected in the similar way of Shenma voice search data collection 
Two dataset is divided into training set, validation set and test set separately, and the quantity of them is shown in Table TABREF10   The three sets are split according to speakers, in order to avoid utterances of same speaker appearing in three sets simultaneously  The test sets of Shenma and Amap voice search are called Shenma Test and Amap Test 
Experimental setup
LSTM RNNs outperform conventional RNNs for speech recognition system, especially deep LSTM RNNs, because of its long-range dependencies more accurately for temporal sequence conditions BIBREF26 , BIBREF23   Shenma and Amap voice search is a streaming service that intermediate recognition results displayed while users are still speaking  So as for online recognition in real time, we prefer unidirectional LSTM model rather than bidirectional one  Thus, the training system is unidirectional LSTM-based 
A 26-dimensional filter bank and 2-dimensional pitch feature is extracted for each frame, and is concatenated with first and second order difference as the final input of the network  The super frame are stacked by 3 frames without overlapping  The architecture we trained consists of two LSTM layers with sigmoid activation function, followed by a full-connection layer  The out layer is a softmax layer with 11088 hidden markov model (HMM) tied-states as output classes, the loss function is cross-entropy (CE)  The performance metric of the system in Mandarin is reported with character error rate (CER)  The alignment of frame-level ground truth is obtained by GMM-HMM system  Mini-batched SGD is utilized with momentum trick and the network is trained for a total of 4 epochs  The block learning rate and block momentum of BMUF are set as 1 and 0 9  5-gram language model is leveraged in decoder, and the vocabulary size is as large as 760000  Differentials of recurrent layers is limited to range [-10000,10000], while gradients are clipped to range [-5, 5] and cell activations clipped to range [-50, 50]  After training with CE loss, sMBR loss is employed to further improve the performance 
It has shown that BMUF outperforms traditional model averaging method, and it is utilized at the synchronization phase  After synchronizing with BMUF, EMA method further updates the model in non-interference way  The training system is deployed on the MPI-based HPC cluster where 8 GPUs  Each GPU processes non-overlap subset split from the entire large scale dataset in parallel 
Local models from distributed workers synchronize with each other in decentralized way  In the traditional model averaging and BMUF method, a parameter server waits for all workers to send their local models, aggregate them, and send the updated model to all workers  Computing resource of workers is wasted until aggregation of the parameter server done  Decentralized method makes full use of computing resource, and we employ the MPI-based Mesh AllReduce method  It is mesh topology as shown in Figure FIGREF12   There is no centralized parameter server, and peer to peer communication is used to transmit local models between workers  Local model INLINEFORM0 of INLINEFORM1 -th worker in INLINEFORM2 workers cluster is split to INLINEFORM3 pieces INLINEFORM4 , and send to corresponding worker  In the aggregation phase, INLINEFORM5 -th worker computed INLINEFORM6 splits of model INLINEFORM7 and send updated model INLINEFORM8 back to workers  As a result, all workers participate in aggregation and no computing resource is dissipated  It is significant to promote training efficiency, when the size of neural network model is too large  The EMA model is also updated additionally, but not broadcasting it 
Results
In order to evaluate our system, several sets of experiments are performed  The Shenma test set including about 9000 samples and Amap test set including about 7000 samples contain various real world conditions  It simulates the majority of user scenarios, and can well evaluates the performance of a trained model  Firstly, we show the results of models trained with EMA method  Secondly, for real world applications, very deep LSTM is distilled to a shallow one, so as for lower RTF  The model of Amap is also needed to train for map and navigation scenarios  The performance of transfer learning from Shenma voice search to Amap voice search is also presented 
Layer-wise Training
In layer-wise training, the deeper model learns both parameters and knowledge from the shallower model  The deeper model is initialized by the shallower one, and its alignment is the combination of hard target and soft target of shallower one  Two targets have the same weights in our framework  The teacher model is trained with CE  For each layer-wise trained CE model, corresponding sMBR model is also trained, as sMBR could achieve additional improvement  In our framework, 1000 hours data is randomly selected from the total dataset for sMBR training  There is no obvious performance enhancement when the size of sMBR training dataset increases 
For very deep unidirectional LSTM initialized with Xavier initialization algorithm, 6-layers model converges well, but there is no further improvement with increasing the number of layers  Therefore, the first 6 layers of 7-layers model is initialized by 6-layers model, and soft target is provided by 6-layers model  Consequently, deeper LSTM is also trained in the same way  It should be noticed that the teacher model of 9-layers model is the 8-layers model trained by sMBR, while the other teacher model is CE model  As shown in Table TABREF15 , the layer-wise trained models always performs better than the models with Xavier initialization, as the model is deep  Therefore, for the last layer training, we choose 8-layers sMBR model as the teacher model instead of CE model  A comparison between 6-layers and 9-layers sMBR models shows that 3 additional layers of layer-wise training brings relative 12 6% decreasing of CER  It is also significant that the averaged CER of sMBR models with different layers decreases absolute 0 73% approximately compared with CE models, so the improvement of sequence discriminative learning is promising 
Transfer Learning
2-layers distilled model of Shenma voice search has shown a impressive performance on Shenma Test, and we call it Shenma model  It is trained for generic search scenario, but it has less adaptation for specific scenario like Amap voice search  Training with very large dataset using CE loss is regarded as improvement of frame level recognition accuracy, and sMBR with less dataset further improves accuracy as sequence discriminative training  If robust model of generic scenario is trained, there is no need to train a model with very large dataset, and sequence discriminative training with less dataset is enough  Therefore, on the basis of Shenma model, it is sufficient to train a new Amap model with small dataset using sMBR  As shown in Table TABREF18 , Shenma model presents the worst performance among three methods, since it does not trained for Amap scenario  2-layers Shenma model further trained with sMBR achieves about 8 1% relative reduction, compared with 2-layers regular-trained Amap model  Both training sMBR datasets contain the same 1000 hours data  As a result, with the Shenma model, only about 14% data usage achieves lower CER, and it leads to great time and cost saving with less labeled data  Besides, transfer learning with sMBR does not use the alignment from the HMM-GMM system, so it also saves huge amount of time 
Conclusion
We have presented a whole deep unidirectional LSTM parallel training system for LVCSR  The recognition performance improves when the network goes deep  Distillation makes it possible that deep LSTM model transfer its knowledge to shallow model with little loss  The model could be distilled to 2-layers model with very low RTF, so that it can display the immediate recognition results  As a result, its CER decrease relatively 14%, compared with the 2-layers regular trained model  In addition, transfer learning with sMBR is also proposed  If a great model has well trained from generic scenario, only 14% of the size of training dataset is needed to train a more accuracy acoustic model for specific scenario  Our future work includes 1) finding more effective methods to reduce the CER by increasing the number of layers; 2) applying this training framework to Connectionist Temporal Classification (CTC) and attention-based neural networks 

ground_truth: Unidirectional LSTM networks with 2, 6, 7, 8, and  9 layers.

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 model did they use for their system?
Context 2: Introduction
Over the past two decades, the emergence of social media has enabled the proliferation of traceable human behavior  The content posted by users can reflect who their friends are, what topics they are interested in, or which company they are working for  At the same time, users are listing a number of profile fields to define themselves to others  The utilization of such metadata has proven important in facilitating further developments of applications in advertising BIBREF0 , personalization BIBREF1 , and recommender systems BIBREF2   However, profile information can be limited, depending on the platform, or it is often deliberately omitted BIBREF3   To uncloak this information, a number of studies have utilized social media users' footprints to approximate their profiles 
This paper explores the potential of predicting a user's industry –the aggregate of enterprises in a particular field– by identifying industry indicative text in social media  The accurate prediction of users' industry can have a big impact on targeted advertising by minimizing wasted advertising BIBREF4 and improved personalized user experience  A number of studies in the social sciences have associated language use with social factors such as occupation, social class, education, and income BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8   An additional goal of this paper is to examine such findings, and in particular the link between language and occupational class, through a data-driven approach 
In addition, we explore how meaning changes depending on the occupational context  By leveraging word embeddings, we seek to quantify how, for example, cloud might mean a separate concept (e g , condensed water vapor) in the text written by users that work in environmental jobs while it might be used differently by users in technology occupations (e g , Internet-based computing) 
Specifically, this paper makes four main contributions  First, we build a large, industry-annotated dataset that contains over 20,000 blog users  In addition to their posted text, we also link a number of user metadata including their gender, location, occupation, introduction and interests 
Second, we build content-based classifiers for the industry prediction task and study the effect of incorporating textual features from the users' profile metadata using various meta-classification techniques, significantly improving both the overall accuracy and the average per industry accuracy 
Next, after examining which words are indicative for each industry, we build vector-space representations of word meanings and calculate one deviation for each industry, illustrating how meaning is differentiated based on the users' industries  We qualitatively examine the resulting industry-informed semantic representations of words by listing the words per industry that are most similar to job related and general interest terms 
Finally, we rank the different industries based on the normalized relative frequencies of emotionally charged words (positive and negative) and, in addition, discover that, for both genders, these frequencies do not statistically significantly correlate with an industry's gender dominance ratio 
After discussing related work in Section SECREF2 , we present the dataset used in this study in Section SECREF3   In Section SECREF4 we evaluate two feature selection methods and examine the industry inference problem using the text of the users' postings  We then augment our content-based classifier by building an ensemble that incorporates several metadata classifiers  We list the most industry indicative words and expose how each industrial semantic field varies with respect to a variety of terms in Section SECREF5   We explore how the frequencies of emotionally charged words in each gender correlate with the industries and their respective gender dominance ratio and, finally, conclude in Section SECREF6  
Related Work
Alongside the wide adoption of social media by the public, researchers have been leveraging the newly available data to create and refine models of users' behavior and profiling  There exists a myriad research that analyzes language in order to profile social media users  Some studies sought to characterize users' personality BIBREF9 , BIBREF10 , while others sequenced the expressed emotions BIBREF11 , studied mental disorders BIBREF12 , and the progression of health conditions BIBREF13   At the same time, a number of researchers sought to predict the social media users' age and/or gender BIBREF14 , BIBREF15 , BIBREF16 , while others targeted and analyzed the ethnicity, nationality, and race of the users BIBREF17 , BIBREF18 , BIBREF19   One of the profile fields that has drawn a great deal of attention is the location of a user  Among others, Hecht et al  Hecht11 predicted Twitter users' locations using machine learning on nationwide and state levels  Later, Han et al  Han14 identified location indicative words to predict the location of Twitter users down to the city level 
As a separate line of research, a number of studies have focused on discovering the political orientation of users BIBREF15 , BIBREF20 , BIBREF21   Finally, Li et al  Li14a proposed a way to model major life events such as getting married, moving to a new place, or graduating  In a subsequent study, BIBREF22 described a weakly supervised information extraction method that was used in conjunction with social network information to identify the name of a user's spouse, the college they attended, and the company where they are employed 
The line of work that is most closely related to our research is the one concerned with understanding the relation between people's language and their industry  Previous research from the fields of psychology and economics have explored the potential for predicting one's occupation from their ability to use math and verbal symbols BIBREF23 and the relationship between job-types and demographics BIBREF24   More recently, Huang et al  Huang15 used machine learning to classify Sina Weibo users to twelve different platform-defined occupational classes highlighting the effect of homophily in user interactions  This work examined only users that have been verified by the Sina Weibo platform, introducing a potential bias in the resulting dataset  Finally, Preotiuc-Pietro et al  Preoctiuc15 predicted the occupational class of Twitter users using the Standard Occupational Classification (SOC) system, which groups the different jobs based on skill requirements  In that work, the data collection process was limited to only users that specifically mentioned their occupation in their self-description in a way that could be directly mapped to a SOC occupational class  The mapping between a substring of their self-description and a SOC occupational class was done manually  Because of the manual annotation step, their method was not scalable; moreover, because they identified the occupation class inside a user self-description, only a very small fraction of the Twitter users could be included (in their case, 5,191 users) 
Both of these recent studies are based on micro-blogging platforms, which inherently restrict the number of characters that a post can have, and consequently the way that users can express themselves 
Moreover, both studies used off-the-shelf occupational taxonomies (rather than self-declared occupation categories), resulting in classes that are either too generic (e g , media, welfare and electronic are three of the twelve Sina Weibo categories), or too intermixed (e g , an assistant accountant is in a different class from an accountant in SOC)  To address these limitations, we investigate the industry prediction task in a large blog corpus consisting of over 20K American users, 40K web-blogs, and 560K blog posts 
Dataset
We compile our industry-annotated dataset by identifying blogger profiles located in the U S  on the profile finder on http://www blogger com, and scraping only those users that had the industry profile element completed 
For each of these bloggers, we retrieve all their blogs, and for each of these blogs we download the 21 most recent blog postings  We then clean these blog posts of HTML tags and tokenize them, and drop those bloggers whose cumulative textual content in their posts is less than 600 characters  Following these guidelines, we identified all the U S  bloggers with completed industry information 
Traditionally, standardized industry taxonomies organize economic activities into groups based on similar production processes, products or services, delivery systems or behavior in financial markets  Following such assumptions and regardless of their many similarities, a tomato farmer would be categorized into a distinct industry from a tobacco farmer  As demonstrated in Preotiuc-Pietro et al  Preoctiuc15 such groupings can cause unwarranted misclassifications 
The Blogger platform provides a total of 39 different industry options  Even though a completed industry value is an implicit text annotation, we acknowledge the same problem noted in previous studies: some categories are too broad, while others are very similar  To remedy this and following Guibert et al  Guibert71, who argued that the denominations used in a classification must reflect the purpose of the study, we group the different Blogger industries based on similar educational background and similar technical terminology  To do that, we exclude very general categories and merge conceptually similar ones  Examples of broad categories are the Education and the Student options: a teacher could be teaching in any concentration, while a student could be enrolled in any discipline  Examples of conceptually similar categories are the Investment Banking and the Banking options 
The final set of categories is shown in Table TABREF1 , along with the number of users in each category  The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts  Table TABREF2 presents additional statistics of our dataset 
Text-based Industry Modeling
After collecting our dataset, we split it into three sets: a train set, a development set, and a test set  The sizes of these sets are 17,880, 2,500, and 2,500 users, respectively, with users randomly assigned to these sets  In all the experiments that follow, we evaluate our classifiers by training them on the train set, configure the parameters and measure performance on the development set, and finally report the prediction accuracy and results on the test set  Note that all the experiments are performed at user level, i e , all the data for one user is compiled into one instance in our data sets 
To measure the performance of our classifiers, we use the prediction accuracy  However, as shown in Table TABREF1 , the available data is skewed across categories, which could lead to somewhat distorted accuracy numbers depending on how well a model learns to predict the most populous classes  Moreover, accuracy alone does not provide a great deal of insight into the individual performance per industry, which is one of the main objectives in this study  Therefore, in our results below, we report: (1) micro-accuracy ( INLINEFORM0 ), calculated as the percentage of correctly classified instances out of all the instances in the development (test) data; and (2) macro-accuracy ( INLINEFORM1 ), calculated as the average of the per-category accuracies, where the per-category accuracy is the percentage of correctly classified instances out of the instances belonging to one category in the development (test) data 
Leveraging Blog Content
In this section, we seek the effectiveness of using solely textual features obtained from the users' postings to predict their industry 
The industry prediction baseline Majority is set by discovering the most frequently featured class in our training set and picking that class in all predictions in the respective development or testing set 
After excluding all the words that are not used by at least three separate users in our training set, we build our AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier  As seen in Figure FIGREF3 , we can far exceed the Majority baseline performance by incorporating basic language signals into machine learning algorithms (173% INLINEFORM0 improvement) 
We additionally explore the potential of improving our text classification task by applying a number of feature ranking methods and selecting varying proportions of top ranked features in an attempt to exclude noisy features  We start by ranking the different features, w, according to their Information Gain Ratio score (IGR) with respect to every industry, i, and training our classifier using different proportions of the top features  INLINEFORM0 INLINEFORM1
Even though we find that using the top 95% of all the features already exceeds the performance of the All Words model on the development data, we further experiment with ranking our features with a more aggressive formula that heavily promotes the features that are tightly associated with any industry category  Therefore, for every word in our training set, we define our newly introduced ranking method, the Aggressive Feature Ranking (AFR), as: INLINEFORM0
In Figure FIGREF3 we illustrate the performance of all four methods in our industry prediction task on the development data  Note that for each method, we provide both the accuracy ( INLINEFORM0 ) and the average per-class accuracy ( INLINEFORM1 )  The Majority and All Words methods apply to all the features; therefore, they are represented as a straight line in the figure  The IGR and AFR methods are applied to varying subsets of the features using a 5% step 
Our experiments demonstrate that the word choice that the users make in their posts correlates with their industry  The first observation in Figure FIGREF3 is that the INLINEFORM0 is proportional to INLINEFORM1 ; as INLINEFORM2 increases, so does INLINEFORM3   Secondly, the best result on the development set is achieved by using the top 90% of the features using the AFR method  Lastly, the improvements of the IGR and AFR feature selections are not substantially better in comparison to All Words (at most 5% improvement between All Words and AFR), which suggest that only a few noisy features exist and most of the words play some role in shaping the “language" of an industry 
As a final evaluation, we apply on the test data the classifier found to work best on the development data (AFR feature selection, top 90% features), for an INLINEFORM0 of 0 534 and INLINEFORM1 of 0 477 
Leveraging User Metadata
Together with the industry information and the most recent postings of each blogger, we also download a number of accompanying profile elements  Using these additional elements, we explore the potential of incorporating users' metadata in our classifiers 
Table TABREF7 shows the different user metadata we consider together with their coverage percentage (not all users provide a value for all of the profile elements)  With the exception of the gender field, the remaining metadata elements shown in Table TABREF7 are completed by the users as a freely editable text field  This introduces a considerable amount of noise in the set of possible metadata values  Examples of noise in the occupation field include values such as “Retired”, “I work ”, or “momma” which are not necessarily informative for our industry prediction task 
To examine whether the metadata fields can help in the prediction of a user's industry, we build classifiers using the different metadata elements  For each metadata element that has a textual value, we use all the words in the training set for that field as features  The only two exceptions are the state field, which is encoded as one feature that can take one out of 50 different values representing the 50 U S  states; and the gender field, which is encoded as a feature with a distinct value for each user gender option: undefined, male, or female 
As shown in Table TABREF9 , we build four different classifiers using the multinomial NB algorithm: Occu (which uses the words found in the occupation profile element), Intro (introduction), Inter (interests), and Gloc (combined gender, city, state) 
In general, all the metadata classifiers perform better than our majority baseline ( INLINEFORM0 of 18 88%)  For the Gloc classifier, this result is in alignment with previous studies BIBREF24   However, the only metadata classifier that outperforms the content classifier is the Occu classifier, which despite missing and noisy occupation values exceeds the content classifier's performance by an absolute 3 2% 
To investigate the promise of combining the five different classifiers we have built so far, we calculate their inter-prediction agreement using Fleiss's Kappa BIBREF25 , as well as the lower prediction bounds using the double fault measure BIBREF26   The Kappa values, presented in the lower left side of Table TABREF10 , express the classification agreement for categorical items, in this case the users' industry  Lower values, especially values below 30%, mean smaller agreement  Since all five classifiers have better-than-baseline accuracy, this low agreement suggests that their predictions could potentially be combined to achieve a better accumulated result 
Moreover, the double fault measure values, which are presented in the top-right hand side of Table TABREF10 , express the proportion of test cases for which both of the two respective classifiers make false predictions, essentially providing the lowest error bound for the pairwise ensemble classifier performance  The lower those numbers are, the greater the accuracy potential of any meta-classification scheme that combines those classifiers  Once again, the low double fault measure values suggest potential gain from a combination of the base classifiers into an ensemble of models 
After establishing the promise of creating an ensemble of classifiers, we implement two meta-classification approaches  First, we combine our classifiers using features concatenation (or early fusion)  Starting with our content-based classifier (Text), we successively add the features derived from each metadata element  The results, both micro- and macro-accuracy, are presented in Table TABREF12   Even though all these four feature concatenation ensembles outperform the content-based classifier in the development set, they fail to outperform the Occu classifier 
Second, we explore the potential of using stacked generalization (or late fusion) BIBREF27   The base classifiers, referred to as L0 classifiers, are trained on different folds of the training set and used to predict the class of the remaining instances  Those predictions are then used together with the true label of the training instances to train a second classifier, referred to as the L1 classifier: this L1 is used to produce the final prediction on both the development data and the test data  Traditionally, stacking uses different machine learning algorithms on the same training data  However in our case, we use the same algorithm (multinomial NB) on heterogeneous data (i e , different types of data such as content, occupation, introduction, interests, gender, city and state) in order to exploit all available sources of information 
The ensemble learning results on the development set are shown in Table TABREF12   We notice a constant improvement for both metrics when adding more classifiers to our ensemble except for the Gloc classifier, which slightly reduces the performance  The best result is achieved using an ensemble of the Text, Occu, Intro, and Inter L0 classifiers; the respective performance on the test set is an INLINEFORM0 of 0 643 and an INLINEFORM1 of 0 564  Finally, we present in Figure FIGREF11 the prediction accuracy for the final classifier for each of the different industries in our test dataset  Evidently, some industries are easier to predict than others  For example, while the Real Estate and Religion industries achieve accuracy figures above 80%, other industries, such as the Banking industry, are predicted correctly in less than 17% of the time  Anecdotal evidence drawn from the examination of the confusion matrix does not encourage any strong association of the Banking class with any other  The misclassifications are roughly uniform across all other classes, suggesting that the users in the Banking industry use language in a non-distinguishing way 
Qualitative Analysis
In this section, we provide a qualitative analysis of the language of the different industries 
Top-Ranked Words
To conduct a qualitative exploration of which words indicate the industry of a user, Table TABREF14 shows the three top-ranking content words for the different industries using the AFR method 
Not surprisingly, the top ranked words align well with what we would intuitively expect for each industry  Even though most of these words are potentially used by many users regardless of their industry in our dataset, they are still distinguished by the AFR method because of the different frequencies of these words in the text of each industry 
Industry-specific Word Similarities
Next, we examine how the meaning of a word is shaped by the context in which it is uttered  In particular, we qualitatively investigate how the speakers' industry affects meaning by learning vector-space representations of words that take into account such contextual information  To achieve this, we apply the contextualized word embeddings proposed by Bamman et al  Bamman14, which are based on an extension of the “skip-gram" language model BIBREF28  
In addition to learning a global representation for each word, these contextualized embeddings compute one deviation from the common word embedding representation for each contextual variable, in this case, an industry option  These deviations capture the terms' meaning variations (shifts in the INLINEFORM0 -dimensional space of the representations, where INLINEFORM1 in our experiments) in the text of the different industries, however all the embeddings are in the same vector space to allow for comparisons to one another 
Using the word representations learned for each industry, we present in Table TABREF16 the terms in the Technology and the Tourism industries that have the highest cosine similarity with a job-related word, customers  Similarly, Table TABREF17 shows the words in the Environment and the Tourism industries that are closest in meaning to a general interest word, food  More examples are given in the Appendix SECREF8  
The terms that rank highest in each industry are noticeably different  For example, as seen in Table TABREF17 , while food in the Environment industry is similar to nutritionally and locally, in the Tourism industry the same word relates more to terms such as delicious and pastries  These results not only emphasize the existing differences in how people in different industries perceive certain terms, but they also demonstrate that those differences can effectively be captured in the resulting word embeddings 
Emotional Orientation per Industry and Gender
As a final analysis, we explore how words that are emotionally charged relate to different industries  To quantify the emotional orientation of a text, we use the Positive Emotion and Negative Emotion categories in the Linguistic Inquiry and Word Count (LIWC) dictionary BIBREF29   The LIWC dictionary contains lists of words that have been shown to correlate with the psychological states of people that use them; for example, the Positive Emotion category contains words such as “happy,” “pretty,” and “good ”
For the text of all the users in each industry we measure the frequencies of Positive Emotion and Negative Emotion words normalized by the text's length  Table TABREF20 presents the industries' ranking for both categories of words based on their relative frequencies in the text of each industry 
We further perform a breakdown per-gender, where we once again calculate the proportion of emotionally charged words in each industry, but separately for each gender  We find that the industry rankings of the relative frequencies INLINEFORM0 of emotionally charged words for the two genders are statistically significantly correlated, which suggests that regardless of their gender, users use positive (or negative) words with a relative frequency that correlates with their industry  (In other words, even if e g , Fashion has a larger number of women users, both men and women working in Fashion will tend to use more positive words than the corresponding gender in another industry with a larger number of men users such as Automotive )
Finally, motivated by previous findings of correlations between job satisfaction and gender dominance in the workplace BIBREF30 , we explore the relationship between the usage of Positive Emotion and Negative Emotion words and the gender dominance in an industry  Although we find that there are substantial gender imbalances in each industry (Appendix SECREF9 ), we did not find any statistically significant correlation between the gender dominance ratio in the different industries and the usage of positive (or negative) emotional words in either gender in our dataset 
Conclusion
In this paper, we examined the task of predicting a social media user's industry  We introduced an annotated dataset of over 20,000 blog users and applied a content-based classifier in conjunction with two feature selection methods for an overall accuracy of up to 0 534, which represents a large improvement over the majority class baseline of 0 188 
We also demonstrated how the user metadata can be incorporated in our classifiers  Although concatenation of features drawn both from blog content and profile elements did not yield any clear improvements over the best individual classifiers, we found that stacking improves the prediction accuracy to an overall accuracy of 0 643, as measured on our test dataset  A more in-depth analysis showed that not all industries are equally easy to predict: while industries such as Real Estate and Religion are clearly distinguishable with accuracy figures over 0 80, others such as Banking are much harder to predict 
Finally, we presented a qualitative analysis to provide some insights into the language of different industries, which highlighted differences in the top-ranked words in each industry, word semantic similarities, and the relative frequency of emotionally charged words 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation 
Additional Examples of Word Similarities
Context 3: Introduction
Cancer is one of the leading causes of death in the world, with over 80,000 deaths registered in Canada in 2017 (Canadian Cancer Statistics 2017)  A computer-aided system for cancer diagnosis usually involves a pathologist rendering a descriptive report after examining the tissue glass slides obtained from the biopsy of a patient  A pathology report contains specific analysis of cells and tissues, and other histopathological indicators that are crucial for diagnosing malignancies  An average sized laboratory may produces a large quantity of pathology reports annually (e g , in excess of 50,000), but these reports are written in mostly unstructured text and with no direct link to the tissue sample  Furthermore, the report for each patient is a personalized document and offers very high variability in terminology due to lack of standards and may even include misspellings and missing punctuation, clinical diagnoses interspersed with complex explanations, different terminology to label the same malignancy, and information about multiple carcinoma appearances included in a single report BIBREF0  
In Canada, each Provincial and Territorial Cancer Registry (PTCR) is responsible for collecting the data about cancer diseases and reporting them to Statistics Canada (StatCan)  Every year, Canadian Cancer Registry (CCR) uses the information sources of StatCan to compile an annual report on cancer and tumor diseases  Many countries have their own cancer registry programs  These programs rely on the acquisition of diagnostic, treatment, and outcome information through manual processing and interpretation from various unstructured sources (e g , pathology reports, autopsy/laboratory reports, medical billing summaries)  The manual classification of cancer pathology reports is a challenging, time-consuming task and requires extensive training BIBREF0  
With the continued growth in the number of cancer patients, and the increase in treatment complexity, cancer registries face a significant challenge in manually reviewing the large quantity of reports BIBREF1 , BIBREF0   In this situation, Natural Language Processing (NLP) systems can offer a unique opportunity to automatically encode the unstructured reports into structured data  Since, the registries already have access to the large quantity of historically labeled and encoded reports, a supervised machine learning approach of feature extraction and classification is a compelling direction for making their workflow more effective and streamlined  If successful, such a solution would enable processing reports in much lesser time allowing trained personnel to focus on their research and analysis  However, developing an automated solution with high accuracy and consistency across wide variety of reports is a challenging problem 
For cancer registries, an important piece of information in a pathology report is the associated ICD-O code which describes the patient's histological diagnosis, as described by the World Health Organization's (WHO) International Classification of Diseases for Oncology BIBREF2   Prediction of the primary diagnosis from a pathology report provides a valuable starting point for exploration of machine learning techniques for automated cancer surveillance  A major application for this purpose would be “auto-reporting” based on analysis of whole slide images, the digitization of the biopsy glass slides  Structured, summarized and categorized reports can be associated with the image content when searching in large archives  Such as system would be able to drastically increase the efficiency of diagnostic processes for the majority of cases where in spite of obvious primary diagnosis, still time and effort is required from the pathologists to write a descriptive report 
The primary objective of our study is to analyze the efficacy of existing machine learning approaches for the automated classification of pathology reports into different diagnosis categories  We demonstrate that TF-IDF feature vectors combined with linear SVM or XGBoost classifier can be an effective method for classification of the reports, achieving up to 83% accuracy  We also show that TF-IDF features are capable of identifying important keywords within a pathology report  Furthermore, we have created a new dataset consisting of 1,949 pathology reports across 37 primary diagnoses  Taken together, our exploratory experiments with a newly introduced dataset on pathology reports opens many new opportunities for researchers to develop a scalable and automatic information extraction from unstructured pathology reports 
Background
NLP approaches for information extraction within the biomedical research areas range from rule-based systems BIBREF3 , to domain-specific systems using feature-based classification BIBREF1 , to the recent deep networks for end-to-end feature extraction and classification BIBREF0   NLP has had varied degree of success with free-text pathology reports BIBREF4   Various studies have acknowledge the success of NLP in interpreting pathology reports, especially for classification tasks or extracting a single attribute from a report BIBREF4 , BIBREF5  
The Cancer Text Information Extraction System (caTIES) BIBREF6 is a framework developed in a caBIG project focuses on information extraction from pathology reports  Specifically, caTIES extracts information from surgical pathology reports (SPR) with good precision as well as recall 
Another system known as Open Registry BIBREF7 is capable of filtering the reports with disease codes containing cancer  In BIBREF8 , an approach called Automated Retrieval Console (ARC) is proposed which uses machine learning models to predict the degree of association of a given pathology or radiology with the cancer  The performance ranges from an F-measure of 0 75 for lung cancer to 0 94 for colorectal cancer  However, ARC uses domain-specific rules which hiders with the generalization of the approach to variety of pathology reports 
This research work is inspired by themes emerging in many of the above studies  Specifically, we are evaluating the task of predicting the primary diagnosis from the pathology report  Unlike previous approaches, the system does not rely on custom rule-based knowledge, domain specific features, balanced dataset with fewer number of classes 
Materials and Methods
We assembled a dataset of 1,949 cleaned pathology reports  Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes  The reports are collected from four different body parts or primary sites from multiple patients  The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution  The dataset was developed in three steps as follows 
Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9   Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney  The selection was primarily made based on the quality of PDF files 
Cleaning reports: The next step was to extract the text content from these reports  Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset  We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files  Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system 
Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively  This split resulted in 1,364 training, and 585 testing reports 
Pre-Processing of Reports
We pre-processed the reports by setting their text content to lowercase and filtering out any non-alphanumeric characters  We used NLTK library to remove stopping words, e g , `the', `an', `was', `if' and so on BIBREF10   We then analyzed the reports to find common bigrams, such as “lung parenchyma”, “microscopic examination”, “lymph node” etc  We joined the biagrams with a hyphen, converting them into a single word  We further removed the words that occur less than 2% in each of the diagnostic category  As well, we removed the words that occur more than 90% across all the categories  We stored each pre-processed report in a separate text file 
TF-IDF features
TF-IDF stands for Term Frequency-Inverse Document Frequency, and it is a useful weighting scheme in information retrieval and text mining  TF-IDF signifies the importance of a term in a document within a corpus  It is important to note that a document here refers to a pathology report, a corpus refers to the collection of reports, and a term refers to a single word in a report  The TF-IDF weight for a term INLINEFORM0 in a document INLINEFORM1 is given by DISPLAYFORM0
We performed the following steps to transform a pathology report into a feature vector:
Create a set of vocabulary containing all unique words from all the pre-processed training reports 
Create a zero vector INLINEFORM0 of the same length as the vocabulary 
For each word INLINEFORM0 in a report INLINEFORM1 , set the corresponding index in INLINEFORM2 to INLINEFORM3  
The resultant INLINEFORM0 is a feature vector for the report INLINEFORM1 and it is a highly sparse vector 
Keyword extraction and topic modelling
The keyword extraction involves identifying important words within reports that summarizes its content  In contrast, the topic modelling allows grouping these keywords using an intelligent scheme, enabling users to further focus on certain aspects of a document  All the words in a pathology report are sorted according to their TF-IDF weights  The top INLINEFORM0 sorted words constitute the top INLINEFORM1 keywords for the report  The INLINEFORM2 is empirically set to 50 within this research  The extracted keywords are further grouped into different topics by using latent Dirichlet allocation (LDA) BIBREF11   The keywords in a report are highlighted using the color theme based on their topics 
Evaluation metrics
Each model is evaluated using two standard NLP metrics—micro and macro averaged F-scores, the harmonic mean of related metrics precision and recall  For each diagnostic category INLINEFORM0 from a set of 37 different classes INLINEFORM1 , the number of true positives INLINEFORM2 , false positives INLINEFORM3 , and false negatives INLINEFORM4 , the micro F-score is defined as DISPLAYFORM0
whereas macro F-score is given by DISPLAYFORM0
In summary, micro-averaged metrics have class representation roughly proportional to their test set representation (same as accuracy for classification problem with a single label per data point), whereas macro-averaged metrics are averaged by class without weighting by class prevalence BIBREF12  
Experimental setting
In this study, we performed two different series of experiments: i) evaluating the performance of TF-IDF features and various machine learning classifiers on the task of predicting primary diagnosis from the text content of a given report, and ii) using TF-IDF and LDA techniques to highlight the important keywords within a report  For the first experiment series, training reports are pre-processed, then their TF-IDF features are extracted  The TF-IDF features and the training labels are used to train different classification models  These different classification models and their hyper-parameters are reported in tab:classifier  The performance of classifiers is measured quantitatively on the test dataset using the evaluation metrics discussed in the previous section  For the second experiment series, a random report is selected and its top 50 keywords are extracted using TF-IDF weights  These 50 keywords are highlighted using different colors based on their associated topic, which are extracted through LDA  A non-expert based qualitative inspection is performed on the extracted keywords and their corresponding topics 
Experiment Series 1
A classification model is trained to predict the primary diagnosis given the content of the cancer pathology report  The performance results on this task are reported in tab:results  We can observe that the XGBoost classifier outperformed all other models for both the micro F-score metric, with a score of 0 92, and the macro F-score metric, with a score of 0 31  This was an improvement of 7% for the micro F-score over the next best model, SVM-L, and a marginal improvement of 5% for macro F-score  It is interesting to note that SVM with linear kernels performs much better than SVM with RBF kernel, scoring 9% on the macro F-score and 12% more on the micro F-score  It is suspected that since words used in primary diagnosis itself occur in some reports, thus enabling the linear models to outperform complex models 
Experiment Series 2
fig:keywords shows the top 50 keywords highlighted using TF-IDF and LDA  The proposed approach has performed well in highlighting the important regions, for example the topic highlighted with a red color containing “presence range tumor necrosis” provides useful biomarker information to readers 
Conclusions
We proposed a simple yet efficient TF-IDF method to extract and corroborate useful keywords from pathology cancer reports  Encoding a pathology report for cancer and tumor surveillance is a laborious task, and sometimes it is subjected to human errors and variability in the interpretation  One of the most important aspects of encoding a pathology report involves extracting the primary diagnosis  This may be very useful for content-based image retrieval to combine with visual information  We used existing classification model and TF-IDF features to predict the primary diagnosis  We achieved up to 92% accuracy using XGBoost classifier  The prediction accuracy empowers the adoption of machine learning methods for automated information extraction from pathology reports 

ground_truth: AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier

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 features are used?
Context 4:  Since IWSLT participators use different data pre-processing methods, we reproduce several competitive baselines based on the ESPnet BIBREF12 for a fair comparison 
Experiments ::: Dataset ::: Speech translation data:
The organizer provides a speech translation corpus extracting from the TED talk (ST-TED), which consists of raw English wave files, English transcriptions, and aligned German translations  The corpus contains 272 hours of English speech with 171k segments  We split 2k segments from the corpus as dev set and tst2010, tst2013, tst2014, tst2015 are used as test sets 
Speech recognition data: Aside from ST-TED, TED-LIUM2 corpus BIBREF13 is provided as speech recognition data, which contains 207 hours of English speech and 93k transcript sentences 
Text translation data: We use transcription and translation pairs in the ST-TED corpus and WIT3 as in-domain MT data, which contains 130k and 200k sentence pairs respectively  WMT2018 is used as out-of-domain training data which consists of 41M sentence pairs 
Data preprocessing: For speech data, the utterances are segmented into multiple frames with a 25 ms window size and a 10 ms step size  Then we extract 80-channel log-Mel filter bank and 3-dimensional pitch features using Kaldi BIBREF14, resulting in 83-dimensional input features  We normalize them by the mean and the standard deviation on the whole training set  The utterances with more than 3000 frames are discarded  The transcripts in ST-TED are in true-case with punctuation while in TED-LIUM2, transcripts are in lower-case and unpunctuated  Thus, we lowercase all the sentences and remove the punctuation to keep consistent  To increase the amount of training data, we perform speed perturbation on the raw signals with speed factors 0 9 and 1 1  For the text translation data, sentences longer than 80 words or shorter than 10 words are removed  Besides, we discard pairs whose length ratio between source and target sentence is smaller than 0 5 or larger than 2 0  Word tokenization is performed using the Moses scripts and both English and German words are in lower-case 
We use two different sets of vocabulary for our experiments  For the subword experiments, both English and German vocabularies are generated using sentencepiece BIBREF15 with a fixed size of 5k tokens  BIBREF9 inaguma2018speech show that increasing the vocabulary size is not helpful for ST task  For the character experiments, both English and German sentences are represented in the character level 
For evaluation, we segment each audio with the LIUM SpkDiarization tool BIBREF16 and then perform MWER segmentation with RWTH toolkit BIBREF17  We use lowercase BLEU as evaluation metric 
Experiments ::: Baseline Models and Implementation
We compare our method with following baselines 
Vanilla ST baseline: The vanilla ST BIBREF9 has only a speech encoder and a decoder  It is trained from scratch on the ST-TED corpus 
Pre-training baselines: We conduct three pre-training baseline experiments: 1) encoder pre-training, in which the ST encoder is initialized from an ASR model; 2) decoder pre-training, in which the ST decoder is initialized from an MT model; and 3) encoder-decoder pre-training, where both the encoder and decoder are pre-trained  The ASR model has the same architecture with vanilla ST model, trained on the mixture of ST-TED and TED-LIUM2 corpus  The MT model has a text encoder and decoder with the same architecture of which in TCEN  It is first trained on WMT data (out-of-domain) and then fine-tuned on in-domain data 
Multi-task baselines: We also conduct three multi-task baseline experiments including one-to-many setting, many-to-one setting, and many-to-many setting  In the first two settings, we train the model with $\alpha _{st}=0 75$ while $\alpha _{asr}=0 25$ or $\alpha _{mt}=0 25$  For many-to-many setting, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$   For MT task, we use only in-domain data 
Many-to-many+pre-training: We train a many-to-many multi-task model where the encoders and decoders are derived from pre-trained ASR and MT models  Triangle+pre-train: BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 proposed a triangle multi-task strategy for speech translation  Their model solves the subnet waste issue by concatenating an ST decoder to an ASR encoder-decoder model  Notably, their ST decoder can consume representations from the speech encoder as well as the ASR decoder  For a fair comparison, the speech encoder and the ASR decoder are initialized from the pre-trained ASR model  The Triangle model is fine-tuned under their multi-task manner 
All our baselines as well as TCEN are implemented based on ESPnet BIBREF12, the RNN size is set as $d=1024$ for all models  We use a dropout of 0 3 for embeddings and encoders, and train using Adadelta with initial learning rate of 1 0 for a maximum of 10 epochs 
For training of TCEN, we set $\alpha _{asr}=0 2$ and $\alpha _{mt}=0 8$ in the pre-training stage, since the MT dataset is much larger than ASR dataset  For fine-tune, we use $\alpha _{st}=0 6, \alpha _{asr}=0 2$ and $\alpha _{mt}=0 2$, same as the `many-to-many' baseline 
For testing, we select the model with the best accuracy on speech translation task on dev set  At inference time, we use a beam size of 10, and the beam scores include length normalization with a weight of 0 2 
Experiments ::: Experimental Results
Table TABREF29 shows the results on four test sets as well as the average performance  Our method significantly outperforms the strong `many-to-many+pretrain' baseline by 3 6 and 2 2 BLEU scores respectively, indicating the proposed method is very effective that substantially improves the translation quality  Besides, both pre-training and multi-task learning can improve translation quality, and the pre-training settings (2nd-4th rows) are more effective compared to multi-task settings (5th-8th rows)  We observe a performance degradation in the `triangle+pretrain' baseline  Compared to our method, where the decoder receives higher-level syntactic and semantic linguistic knowledge extracted from text encoder, their ASR decoder can only provide lower word-level linguistic information  Besides, since their model lacks text encoder and the architecture of ST decoder is different from MT decoder, their model cannot utilize the large-scale MT data in all the training stages  Interestingly, we find that the char-level models outperform the subword-level models in all settings, especially in vanilla baseline  A similar phenomenon is observed by BIBREF6 berard2018end  A possible explanation is that learning the alignments between speech frames and subword units in another language is notoriously difficult  Our method can bring more gains in the subword setting since our model is good at learning the text-to-text alignment and the subword-level alignment is more helpful to the translation quality 
Experiments ::: Discussion ::: Ablation Study
To better understand the contribution of each component, we perform an ablation study on subword-level experiments  The results are shown in Table TABREF37  In `-MT noise' setting, we do not add noise to source sentences for MT  In `-weight sharing' setting, we use different parameters in CTC classification layer and source embedding layer  These two experiments prove that both weight sharing and using noisy MT input benefit to the final translation quality  Performance degrades more in `-weight sharing', indicating the semantic consistency contributes more to our model  In the `-pretrain' experiment, we remove the pre-training stage and directly update the model on three tasks, leading to a dramatic decrease on BLEU score, indicating the pre-training is an indispensable step for end-to-end ST 
Experiments ::: Discussion ::: Learning Curve
It is interesting to investigate why our method is superior to baselines  We find that TCEN achieves a higher final result owing to a better start-point in fine-tuning  Figure FIGREF39 provides learning curves of subword accuracy on validation set  The x-axis denotes the fine-tuning training steps  The vanilla model starts at a low accuracy, because its networks are not pre-trained on the ASR and MT data  The trends of our model and `many-to-many+pretrain' are similar, but our model outperforms it about five points in the whole fine-tuning process  It indicates that the gain comes from bridging the gap between pre-training and fine-tuning rather than a better fine-tuning process 
Experiments ::: Discussion ::: Compared with a Cascaded System
Table TABREF29 compares our model with end-to-end baselines  Here, we compare our model with cascaded systems  We build a cascaded system by combining the ASR model and MT model used in pre-training baseline  Word error rate (WER) of the ASR system and BLEU score of the MT system are reported in the supplementary material  In addition to a simple combination of the ASR and MT systems, we also re-segment the ASR outputs before feeding to the MT system, denoted as cascaded+re-seg  Specifically, we train a seq2seq model BIBREF19 on the MT dataset, where the source side is a no punctuation sentence and the target side is a natural sentence  After that, we use the seq2seq model to add sentence boundaries and punctuation on ASR outputs  Experimental results are shown in Table TABREF41  Our end-to-end model outperforms the simple cascaded model over 2 BLEU scores, and it achieves a comparable performance with the cascaded model combining with a sentence re-segment model 
Related Work
Early works conduct speech translation in a pipeline manner BIBREF2, BIBREF20, where the ASR output lattices are fed into an MT system to generate target sentences  HMM BIBREF21, DenseNet BIBREF22, TDNN BIBREF23 are commonly used ASR systems, while RNN with attention BIBREF19 and Transformer BIBREF10 are top choices for MT  To enhance the robustness of the NMT model towards ASR errors, BIBREF24 DBLP:conf/eacl/TsvetkovMD14 and BIBREF25 DBLP:conf/asru/ChenHHL17 propose to simulate the noise in training and inference 
To avoid error propagation and high latency issues, recent works propose translating the acoustic speech into text in target language without yielding the source transcription BIBREF4  Since ST data is scarce, pre-training BIBREF7, multi-task learning BIBREF4, BIBREF6, curriculum learning BIBREF26, attention-passing BIBREF27, and knowledge distillation BIBREF28, BIBREF29 strategies have been explored to utilize ASR data and MT data  Specifically, BIBREF5 DBLP:conf/interspeech/WeissCJWC17 show improvements of performance by training the ST model jointly with the ASR and the MT model  BIBREF6 berard2018end observe faster convergence and better results due to pre-training and multi-task learning on a larger dataset  BIBREF7 DBLP:conf/naacl/BansalKLLG19 show that pre-training a speech encoder on one language can improve ST quality on a different source language  All of them follow the traditional multi-task training strategies  BIBREF26 DBLP:journals/corr/abs-1802-06003 propose to use curriculum learning to improve ST performance on syntactically distant language pairs  To effectively leverage transcriptions in ST data, BIBREF18 DBLP:conf/naacl/AnastasopoulosC18 augment the multi-task model where the target decoder receives information from the source decoder and they show improvements on low-resource speech translation  Their model just consumes ASR and ST data, in contrast, our work sufficiently utilizes the large-scale MT data to capture the rich semantic knowledge  BIBREF30 DBLP:conf/icassp/JiaJMWCCALW19 use pre-trained MT and text-to-speech (TTS) synthesis models to convert weakly supervised data into ST pairs and demonstrate that an end-to-end MT model can be trained using only synthesised data 
Conclusion
This paper has investigated the end-to-end method for ST  It has discussed why there is a huge gap between pre-training and fine-tuning in previous methods  To alleviate these issues, we have proposed a method, which is capable of reusing every sub-net and keeping the role of sub-net consistent between pre-training and fine-tuning  Empirical studies have demonstrated that our model significantly outperforms baselines 

ground_truth: Unanswerable