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 
Context 3: Introduction
Distributed word representations, commonly referred to as word embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , serve as elementary building blocks in the course of algorithm design for an expanding range of applications in natural language processing (NLP), including named entity recognition BIBREF4 , BIBREF5 , parsing BIBREF6 , sentiment analysis BIBREF7 , BIBREF8 , and word-sense disambiguation BIBREF9   Although the empirical utility of word embeddings as an unsupervised method for capturing the semantic or syntactic features of a certain word as it is used in a given lexical resource is well-established BIBREF10 , BIBREF11 , BIBREF12 , an understanding of what these features mean remains an open problem BIBREF13 , BIBREF14 and as such word embeddings mostly remain a black box  It is desirable to be able to develop insight into this black box and be able to interpret what it means, while retaining the utility of word embeddings as semantically-rich intermediate representations  Other than the intrinsic value of this insight, this would not only allow us to explain and understand how algorithms work BIBREF15 , but also set a ground that would facilitate the design of new algorithms in a more deliberate way 
Recent approaches to generating word embeddings (e g  BIBREF0 , BIBREF2 ) are rooted linguistically in the field of distributed semantics BIBREF16 , where words are taken to assume meaning mainly by their degree of interaction (or lack thereof) with other words in the lexicon BIBREF17 , BIBREF18   Under this paradigm, dense, continuous vector representations are learned in an unsupervised manner from a large corpus, using the word cooccurrence statistics directly or indirectly, and such an approach is shown to result in vector representations that mathematically capture various semantic and syntactic relations between words BIBREF0 , BIBREF2 , BIBREF3   However, the dense nature of the learned embeddings obfuscate the distinct concepts encoded in the different dimensions, which renders the resulting vectors virtually uninterpretable  The learned embeddings make sense only in relation to each other and their specific dimensions do not carry explicit information that can be interpreted  However, being able to interpret a word embedding would illuminate the semantic concepts implicitly represented along the various dimensions of the embedding, and reveal its hidden semantic structures 
In the literature, researchers tackled interpretability problem of the word embeddings using different approaches  Several researchers BIBREF19 , BIBREF20 , BIBREF21 proposed algorithms based on non-negative matrix factorization (NMF) applied to cooccurrence variant matrices  Other researchers suggested to obtain interpretable word vectors from existing uninterpretable word vectors by applying sparse coding BIBREF22 , BIBREF23 , by training a sparse auto-encoder to transform the embedding space BIBREF24 , by rotating the original embeddings BIBREF25 , BIBREF26 or by applying transformations based on external semantic datasets BIBREF27  
Although the above-mentioned approaches provide better interpretability that is measured using a particular method such as word intrusion test, usually the improved interpretability comes with a cost of performance in the benchmark tests such as word similarity or word analogy  One possible explanation for this performance decrease is that the proposed transformations from the original embedding space distort the underlying semantic structure constructed by the original embedding algorithm  Therefore, it can be claimed that a method that learns dense and interpretable word embeddings without inflicting any damage to the underlying semantic learning mechanism is the key to achieve both high performing and interpretable word embeddings 
Especially after the introduction of the word2vec algorithm by Mikolov BIBREF0 , BIBREF1 , there has been a growing interest in algorithms that generate improved word representations under some performance metric  Significant effort is spent on appropriately modifying the objective functions of the algorithms in order to incorporate knowledge from external resources, with the purpose of increasing the performance of the resulting word representations BIBREF28 , BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36 , BIBREF37   Inspired by the line of work reported in these studies, we propose to use modified objective functions for a different purpose: learning more interpretable dense word embeddings  By doing this, we aim to incorporate semantic information from an external lexical resource into the word embedding so that the embedding dimensions are aligned along predefined concepts  This alignment is achieved by introducing a modification to the embedding learning process  In our proposed method, which is built on top of the GloVe algorithm BIBREF2 , the cost function for any one of the words of concept word-groups is modified by the introduction of an additive term to the cost function  Each embedding vector dimension is first associated with a concept  For a word belonging to any one of the word-groups representing these concepts, the modified cost term favors an increase for the value of this word's embedding vector dimension corresponding to the concept that the particular word belongs to  For words that do not belong to any one of the word-groups, the cost term is left untouched  Specifically, Roget's Thesaurus BIBREF38 , BIBREF39 is used to derive the concepts and concept word-groups to be used as the external lexical resource for our proposed method  We quantitatively demonstrate the increase in interpretability by using the measure given in BIBREF27 , BIBREF40 as well as demonstrating qualitative results  We also show that the semantic structure of the original embedding has not been harmed in the process since there is no performance loss with standard word-similarity or word-analogy tests 
The paper is organized as follows  In Section SECREF2 , we discuss previous studies related to our work under two main categories: interpretability of word embeddings and joint-learning frameworks where the objective function is modified  In Section SECREF3 , we present the problem framework and provide the formulation within the GloVe BIBREF2 algorithm setting  In Section SECREF4 where our approach is proposed, we motivate and develop a modification to the original objective function with the aim of increasing representation interpretability  In Section SECREF5 , experimental results are provided and the proposed method is quantitatively and qualitatively evaluated  Additionally, in Section SECREF5 , results demonstrating the extent to which the original semantic structure of the embedding space is affected are presented by using word-analogy and word-similarity tests  We conclude the paper in Section SECREF6  
Related Work
Methodologically, our work is related to prior studies that aim to obtain “improved” word embeddings using external lexical resources, under some performance metric  Previous work in this area can be divided into two main categories: works that i) modify the word embedding learning algorithm to incorporate lexical information, ii) operate on pre-trained embeddings with a post-processing step 
Among works that follow the first approach, BIBREF28 extend the Skip-Gram model by incorporating the word similarity relations extracted from the Paraphrase Database (PPDB) and WordNet BIBREF29 , into the Skip-Gram predictive model as an additional cost term  In BIBREF30 , the authors extend the CBOW model by considering two types of semantic information, termed relational and categorical, to be incorporated into the embeddings during training  For the former type of semantic information, the authors propose the learning of explicit vectors for the different relations extracted from a semantic lexicon such that the word pairs that satisfy the same relation are distributed more homogeneously  For the latter, the authors modify the learning objective such that some weighted average distance is minimized for words under the same semantic category  In BIBREF31 , the authors represent the synonymy and hypernymy-hyponymy relations in terms of inequality constraints, where the pairwise similarity rankings over word triplets are forced to follow an order extracted from a lexical resource  Following their extraction from WordNet, the authors impose these constraints in the form of an additive cost term to the Skip-Gram formulation  Finally, BIBREF32 builds on top of the GloVe algorithm by introducing a regularization term to the objective function that encourages the vector representations of similar words as dictated by WordNet to be similar as well 
Turning our attention to the post-processing approach for enriching word embeddings with external lexical knowledge, BIBREF33 has introduced the retrofitting algorithm that acts on pre-trained embeddings such as Skip-Gram or GloVe  The authors propose an objective function that aims to balance out the semantic information captured in the pre-trained embeddings with the constraints derived from lexical resources such as WordNet, PPDB and FrameNet  One of the models proposed in BIBREF34 extends the retrofitting approach to incorporate the word sense information from WordNet  Similarly, BIBREF35 creates multi-sense embeddings by gathering the word sense information from a lexical resource and learning to decompose the pre-trained embeddings into a convex combination of sense embeddings  In BIBREF36 , the authors focus on improving word embeddings for capturing word similarity, as opposed to mere relatedness  To this end, they introduce the counter-fitting technique which acts on the input word vectors such that synonymous words are attracted to one another whereas antonymous words are repelled, where the synonymy-antonymy relations are extracted from a lexical resource  More recently, the ATTRACT-REPEL algorithm proposed by BIBREF37 improves on counter-fitting by a formulation which imparts the word vectors with external lexical information in mini-batches 
Most of the studies discussed above ( BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF36 , BIBREF37 ) report performance improvements in benchmark tests such as word similarity or word analogy, while BIBREF29 uses a different analysis method (mean reciprocal rank)  In sum, the literature is rich with studies aiming to obtain word embeddings that perform better under specific performance metrics  However, less attention has been directed to the issue of interpretability of the word embeddings  In the literature, the problem of interpretability has been tackled using different approaches  BIBREF19 proposed non-negative matrix factorization (NMF) for learning sparse, interpretable word vectors from co-occurrence variant matrices where the resulting vector space is called non-negative sparse embeddigns (NNSE)  However, since NMF methods require maintaining a global matrix for learning, they suffer from memory and scale issue  This problem has been addressed in BIBREF20 where an online method of learning interpretable word embeddings from corpora using a modified version of skip-gram model BIBREF0 is proposed  As a different approach, BIBREF21 combined text-based similarity information among words with brain activity based similarity information to improve interpretability using joint non-negative sparse embedding (JNNSE) 
A common alternative approach for learning interpretable embeddings is to learn transformations that map pre-trained state-of-the-art embeddings to new interpretable semantic spaces  To obtain sparse, higher dimensional and more interpretable vector spaces, BIBREF22 and BIBREF23 use sparse coding on conventional dense word embeddings  However, these methods learn the projection vectors that are used for the transformation from the word embeddings without supervision  For this reason, labels describing the corresponding semantic categories cannot be provided  An alternative approach was proposed in BIBREF25 , where orthogonal transformations were utilized to increase interpretability while preserving the performance of the underlying embedding  However, BIBREF25 has also shown that total interpretability of an embedding is kept constant under any orthogonal transformation and it can only be redistributed across the dimensions  Rotation algorithms based on exploratory factor analysis (EFA) to preserve the performance of the original word embeddings while improving their interpretability was proposed in BIBREF26   BIBREF24 proposed to deploy a sparse auto-encoder using pre-trained dense word embeddings to improve interpretability  More detailed investigation of semantic structure and interpretability of word embeddings can be found in BIBREF27 , where a metric was proposed to quantitatively measure the degree of interpretability already present in the embedding vector spaces 
Previous works on interpretability mentioned above, except BIBREF21 , BIBREF27 and our proposed method, do not need external resources, utilization of which has both advantages and disadvantages  Methods that do not use external resources require fewer resources but they also lack the aid of information extracted from these resources 
Problem Description
For the task of unsupervised word embedding extraction, we operate on a discrete collection of lexical units (words) INLINEFORM0 that is part of an input corpus INLINEFORM1 , with number of tokens INLINEFORM2 , sourced from a vocabulary INLINEFORM3 of size INLINEFORM4   In the setting of distributional semantics, the objective of a word embedding algorithm is to maximize some aggregate utility over the entire corpus so that some measure of “closeness” is maximized for pairs of vector representations INLINEFORM14 for words which, on the average, appear in proximity to one another  In the GloVe algorithm BIBREF2 , which we base our improvements upon, the following objective function is considered: DISPLAYFORM0
In ( EQREF6 ), INLINEFORM0 and INLINEFORM1 stand for word and context vector representations, respectively, for words INLINEFORM2 and INLINEFORM3 , while INLINEFORM4 represents the (possibly weighted) cooccurrence count for the word pair INLINEFORM5   Intuitively, ( EQREF6 ) represents the requirement that if some word INLINEFORM6 occurs often enough in the context (or vicinity) of another word INLINEFORM7 , then the corresponding word representations should have a large enough inner product in keeping with their large INLINEFORM8 value, up to some bias terms INLINEFORM9 ; and vice versa  INLINEFORM10 in ( EQREF6 ) is used as a discounting factor that prohibits rare cooccurrences from disproportionately influencing the resulting embeddings 
The objective ( EQREF6 ) is minimized using stochastic gradient descent by iterating over the matrix of cooccurrence records INLINEFORM0   In the GloVe algorithm, for a given word INLINEFORM1 , the final word representation is taken to be the average of the two intermediate vector representations obtained from ( EQREF6 ); i e, INLINEFORM2   In the next section, we detail the enhancements made to ( EQREF6 ) for the purposes of enhanced interpretability, using the aforementioned framework as our basis 
Imparting Interpretability
Our approach falls into a joint-learning framework where the distributional information extracted from the corpus is allowed to fuse with the external lexicon-based information  Word-groups extracted from Roget's Thesaurus are directly mapped to individual dimensions of word embeddings

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. Was PolyReponse evaluated against some baseline?
Context 2: Introduction and Background
Task-oriented dialogue systems are primarily designed to search and interact with large databases which contain information pertaining to a certain dialogue domain: the main purpose of such systems is to assist the users in accomplishing a well-defined task such as flight booking BIBREF0, tourist information BIBREF1, restaurant search BIBREF2, or booking a taxi BIBREF3  These systems are typically constructed around rigid task-specific ontologies BIBREF1, BIBREF4 which enumerate the constraints the users can express using a collection of slots (e g , price range for restaurant search) and their slot values (e g , cheap, expensive for the aforementioned slots)  Conversations are then modelled as a sequence of actions that constrain slots to particular values  This explicit semantic space is manually engineered by the system designer  It serves as the output of the natural language understanding component as well as the input to the language generation component both in traditional modular systems BIBREF5, BIBREF6 and in more recent end-to-end task-oriented dialogue systems BIBREF7, BIBREF8, BIBREF9, BIBREF3 
Working with such explicit semantics for task-oriented dialogue systems poses several critical challenges on top of the manual time-consuming domain ontology design  First, it is difficult to collect domain-specific data labelled with explicit semantic representations  As a consequence, despite recent data collection efforts to enable training of task-oriented systems across multiple domains BIBREF0, BIBREF3, annotated datasets are still few and far between, as well as limited in size and the number of domains covered  Second, the current approach constrains the types of dialogue the system can support, resulting in artificial conversations, and breakdowns when the user does not understand what the system can and cannot support  In other words, training a task-based dialogue system for voice-controlled search in a new domain always implies the complex, expensive, and time-consuming process of collecting and annotating sufficient amounts of in-domain dialogue data 
In this paper, we present a demo system based on an alternative approach to task-oriented dialogue  Relying on non-generative response retrieval we describe the PolyResponse conversational search engine and its application in the task of restaurant search and booking  The engine is trained on hundreds of millions of real conversations from a general domain (i e , Reddit), using an implicit representation of semantics that directly optimizes the task at hand  It learns what responses are appropriate in different conversational contexts, and consequently ranks a large pool of responses according to their relevance to the current user utterance and previous dialogue history (i e , dialogue context) 
The technical aspects of the underlying conversational search engine are explained in detail in our recent work BIBREF11, while the details concerning the Reddit training data are also available in another recent publication BIBREF12  In this demo, we put focus on the actual practical usefulness of the search engine by demonstrating its potential in the task of restaurant search, and extending it to deal with multi-modal data  We describe a PolyReponse system that assists the users in finding a relevant restaurant according to their preference, and then additionally helps them to make a booking in the selected restaurant  Due to its retrieval-based design, with the PolyResponse engine there is no need to engineer a structured ontology, or to solve the difficult task of general language generation  This design also bypasses the construction of dedicated decision-making policy modules  The large ranking model already encapsulates a lot of knowledge about natural language and conversational flow 
Since retrieved system responses are presented visually to the user, the PolyResponse restaurant search engine is able to combine text responses with relevant visual information (e g , photos from social media associated with the current restaurant and related to the user utterance), effectively yielding a multi-modal response  This setup of using voice as input, and responding visually is becoming more and more prevalent with the rise of smart screens like Echo Show and even mixed reality  Finally, the PolyResponse restaurant search engine is multilingual: it is currently deployed in 8 languages enabling search over restaurants in 8 cities around the world  System snapshots in four different languages are presented in Figure FIGREF16, while screencast videos that illustrate the dialogue flow with the PolyResponse engine are available at: https://tinyurl com/y3evkcfz 
PolyResponse: Conversational Search
The PolyResponse system is powered by a single large conversational search engine, trained on a large amount of conversational and image data, as shown in Figure FIGREF2  In simple words, it is a ranking model that learns to score conversational replies and images in a given conversational context  The highest-scoring responses are then retrieved as system outputs  The system computes two sets of similarity scores: 1) $S(r,c)$ is the score of a candidate reply $r$ given a conversational context $c$, and 2) $S(p,c)$ is the score of a candidate photo $p$ given a conversational context $c$  These scores are computed as a scaled cosine similarity of a vector that represents the context ($h_c$), and a vector that represents the candidate response: a text reply ($h_r$) or a photo ($h_p$)  For instance, $S(r,c)$ is computed as $S(r,c)=C cos(h_r,h_c)$, where $C$ is a learned constant  The part of the model dealing with text input (i e , obtaining the encodings $h_c$ and $h_r$) follows the architecture introduced recently by Henderson:2019acl  We provide only a brief recap here; see the original paper for further details 
PolyResponse: Conversational Search ::: Text Representation 
The model, implemented as a deep neural network, learns to respond by training on hundreds of millions context-reply $(c,r)$ pairs  First, similar to Henderson:2017arxiv, raw text from both $c$ and $r$ is converted to unigrams and bigrams  All input text is first lower-cased and tokenised, numbers with 5 or more digits get their digits replaced by a wildcard symbol #, while words longer than 16 characters are replaced by a wildcard token LONGWORD  Sentence boundary tokens are added to each sentence  The vocabulary consists of the unigrams that occur at least 10 times in a random 10M subset of the Reddit training set (see Figure FIGREF2) plus the 200K most frequent bigrams in the same random subset 
During training, we obtain $d$-dimensional feature representations ($d=320$) shared between contexts and replies for each unigram and bigram jointly with other neural net parameters  A state-of-the-art architecture based on transformers BIBREF13 is then applied to unigram and bigram vectors separately, which are then averaged to form the final 320-dimensional encoding  That encoding is then passed through three fully-connected non-linear hidden layers of dimensionality $1,024$  The final layer is linear and maps the text into the final $l$-dimensional ($l=512$) representation: $h_c$ and $h_r$  Other standard and more sophisticated encoder models can also be used to provide final encodings $h_c$ and $h_r$, but the current architecture shows a good trade-off between speed and efficacy with strong and robust performance in our empirical evaluations on the response retrieval task using Reddit BIBREF14, OpenSubtitles BIBREF15, and AmazonQA BIBREF16 conversational test data, see BIBREF12 for further details 
In training the constant $C$ is constrained to lie between 0 and $\sqrt{l}$  Following Henderson:2017arxiv, the scoring function in the training objective aims to maximise the similarity score of context-reply pairs that go together, while minimising the score of random pairings: negative examples  Training proceeds via SGD with batches comprising 500 pairs (1 positive and 499 negatives) 
PolyResponse: Conversational Search ::: Photo Representation 
Photos are represented using convolutional neural net (CNN) models pretrained on ImageNet BIBREF17  We use a MobileNet model with a depth multiplier of 1 4, and an input dimension of $224 \times 224$ pixels as in BIBREF18  This provides a $1,280 \times 1 4 = 1,792$-dimensional representation of a photo, which is then passed through a single hidden layer of dimensionality $1,024$ with ReLU activation, before being passed to a hidden layer of dimensionality 512 with no activation to provide the final representation $h_p$ 
PolyResponse: Conversational Search ::: Data Source 1: Reddit 
For training text representations we use a Reddit dataset similar to AlRfou:2016arxiv  Our dataset is large and provides natural conversational structure: all Reddit data from January 2015 to December 2018, available as a public BigQuery dataset, span almost 3 7B comments BIBREF12  We preprocess the dataset to remove uninformative and long comments by retaining only sentences containing more than 8 and less than 128 word tokens  After pairing all comments/contexts $c$ with their replies $r$, we obtain more than 727M context-reply $(c,r)$ pairs for training, see Figure FIGREF2 
PolyResponse: Conversational Search ::: Data Source 2: Yelp 
Once the text encoding sub-networks are trained, a photo encoder is learned on top of a pretrained MobileNet CNN, using data taken from the Yelp Open dataset: it contains around 200K photos and their captions  Training of the multi-modal sub-network then maximises the similarity of captions encoded with the response encoder $h_r$ to the photo representation $h_p$  As a result, we can compute the score of a photo given a context using the cosine similarity of the respective vectors  A photo will be scored highly if it looks like its caption would be a good response to the current context 
PolyResponse: Conversational Search ::: Index of Responses 
The Yelp dataset is used at inference time to provide text and photo candidates to display to the user at each step in the conversation  Our restaurant search is currently deployed separately for each city, and we limit the responses to a given city  For instance, for our English system for Edinburgh we work with 396 restaurants, 4,225 photos (these include additional photos obtained using the Google Places API without captions), 6,725 responses created from the structured information about restaurants that Yelp provides, converted using simple templates to sentences of the form such as “Restaurant X accepts credit cards ”, 125,830 sentences extracted from online reviews 
PolyResponse: Conversational Search ::: PolyResponse in a Nutshell 
The system jointly trains two encoding functions (with shared word embeddings) $f(context)$ and $g(reply)$ which produce encodings $h_c$ and $h_r$, so that the similarity $S(c,r)$ is high for all $(c,r)$ pairs from the Reddit training data and low for random pairs  The encoding function $g()$ is then frozen, and an encoding function $t(photo)$ is learnt which makes the similarity between a photo and its associated caption high for all (photo, caption) pairs from the Yelp dataset, and low for random pairs  $t$ is a CNN pretrained on ImageNet, with a shallow one-layer DNN on top  Given a new context/query, we then provide its encoding $h_c$ by applying $f()$, and find plausible text replies and photo responses according to functions $g()$ and $t()$, respectively  These should be responses that look like answers to the query, and photos that look like they would have captions that would be answers to the provided query 
At inference, finding relevant candidates given a context reduces to computing $h_c$ for the context $c$ , and finding nearby $h_r$ and $h_p$ vectors  The response vectors can all be pre-computed, and the nearest neighbour search can be further optimised using standard libraries such as Faiss BIBREF19 or approximate nearest neighbour retrieval BIBREF20, giving an efficient search that scales to billions of candidate responses 
The system offers both voice and text input and output  Speech-to-text and text-to-speech conversion in the PolyResponse system is currently supported by the off-the-shelf Google Cloud tools 
Dialogue Flow
The ranking model lends itself to the one-shot task of finding the most relevant responses in a given context  However, a restaurant-browsing system needs to support a dialogue flow where the user finds a restaurant, and then asks questions about it  The dialogue state for each search scenario is represented as the set of restaurants that are considered relevant  This starts off as all the restaurants in the given city, and is assumed to monotonically decrease in size as the conversation progresses until the user converges to a single restaurant  A restaurant is only considered valid in the context of a new user input if it has relevant responses corresponding to it  This flow is summarised here:
S1  Initialise $R$ as the set of all restaurants in the city  Given the user's input, rank all the responses in the response pool pertaining to restaurants in $R$ 
S2  Retrieve the top $N$ responses $r_1, r_2, \ldots , r_N$ with corresponding (sorted) cosine similarity scores: $s_1 \ge s_2 \ge \ldots \ge s_N$ 
S3  Compute probability scores $p_i \propto \exp (a \cdot s_i)$ with $\sum _{i=1}^N p_i$, where $a>0$ is a tunable constant 
S4  Compute a score $q_e$ for each restaurant/entity $e \in R$, $q_e = \sum _{i: r_i \in e} p_i$ 
S5  Update $R$ to the smallest set of restaurants with highest $q$ whose $q$-values sum up to more than a predefined threshold $t$ 
S6  Display the most relevant responses associated with the updated $R$, and return to S2 
If there are multiple relevant restaurants, one response is shown from each  When only one restaurant is relevant, the top $N$ responses are all shown, and relevant photos are also displayed  The system does not require dedicated understanding, decision-making, and generation modules, and this dialogue flow does not rely on explicit task-tailored semantics  The set of relevant restaurants is kept internally while the system narrows it down across multiple dialogue turns  A simple set of predefined rules is used to provide a templatic spoken system response: e g , an example rule is “One review of $e$ said $r$”, where $e$ refers to the restaurant, and $r$ to a relevant response associated with $e$  Note that while the demo is currently focused on the restaurant search task, the described “narrowing down” dialogue flow is generic and applicable to a variety of applications dealing with similar entity search 
The system can use a set of intent classifiers to allow resetting the dialogue state, or to activate the separate restaurant booking dialogue flow  These classifiers are briefly discussed in §SECREF4 
Other Functionality ::: Multilinguality 
The PolyResponse restaurant search is currently available in 8 languages and for 8 cities around the world: English (Edinburgh), German (Berlin), Spanish (Madrid), Mandarin (Taipei), Polish (Warsaw), Russian (Moscow), Korean (Seoul), and Serbian (Belgrade)  Selected snapshots are shown in Figure FIGREF16, while we also provide videos demonstrating the use and behaviour of the systems at: https://tinyurl com/y3evkcfz  A simple MT-based translate-to-source approach at inference time is currently used to enable the deployment of the system in other languages: 1) the pool of responses in each language is translated to English by Google Translate beforehand, and pre-computed encodings of their English translations are used as representations of each foreign language response; 2) a provided user utterance (i e , context) is translated to English on-the-fly and its encoding $h_c$ is then learned  We plan to experiment with more sophisticated multilingual models in future work 
Other Functionality ::: Voice-Controlled Menu Search 
An additional functionality enables the user to get parts of the restaurant menu relevant to the current user utterance as responses  This is achieved by performing an additional ranking step of available menu items and retrieving the ones that are semantically relevant to the user utterance using exactly the same methodology as with ranking other responses  An example of this functionality is shown in Figure FIGREF21 
Other Functionality ::: Resetting and Switching to Booking 
The restaurant search system needs to support the discrete actions of restarting the conversation (i e , resetting the set $R$), and should enable transferring to the slot-based table booking flow  This is achieved using two binary intent classifiers, that are run at each step in the dialogue  These classifiers make use of the already-computed $h_c$ vector that represents the user's latest text  A single-layer neural net is learned on top of the 512-dimensional encoding, with a ReLU activation and 100 hidden nodes  To train the classifiers, sets of 20 relevant paraphrases (e g , “Start again”) are provided as positive examples  Finally, when the system successfully switches to the booking scenario, it proceeds to the slot filling task: it aims to extract all the relevant booking information from the user (e g , date, time, number of people to dine)  The entire flow of the system illustrating both the search phase and the booking phase is provided as the supplemental video material 
Conclusion and Future Work
This paper has presented a general approach to search-based dialogue that does not rely on explicit semantic representations such as dialogue acts or slot-value ontologies, and allows for multi-modal responses  In future work, we will extend the current demo system to more tasks and languages, and work with more sophisticated encoders and ranking functions  Besides the initial dialogue flow from this work (§SECREF3), we will also work with more complex flows dealing, e g , with user intent shifts 
Context 3:  Specifically, the vector representations of words that belong to a particular group are encouraged to have deliberately increased values in a particular dimension that corresponds to the word-group under consideration  This can be achieved by modifying the objective function of the embedding algorithm to partially influence vector representation distributions across their dimensions over an input vocabulary  To do this, we propose the following modification to the GloVe objective in ( EQREF6 ): rCl J = i,j=1V f(Xij)[ (wiTwj + bi + bj -Xij)2
+ k(l=1D INLINEFORM0 iFl g(wi,l) + l=1D INLINEFORM1 j Fl g(wj,l) ) ]  In ( SECREF4 ), INLINEFORM2 denotes the indices for the elements of the INLINEFORM3 th concept word-group which we wish to assign in the vector dimension INLINEFORM4   The objective ( SECREF4 ) is designed as a mixture of two individual cost terms: the original GloVe cost term along with a second term that encourages embedding vectors of a given concept word-group to achieve deliberately increased values along an associated dimension INLINEFORM5   The relative weight of the second term is controlled by the parameter INLINEFORM6   The simultaneous minimization of both objectives ensures that words that are similar to, but not included in, one of these concept word-groups are also “nudged” towards the associated dimension INLINEFORM7   The trained word vectors are thus encouraged to form a distribution where the individual vector dimensions align with certain semantic concepts represented by a collection of concept word-groups, one assigned to each vector dimension  To facilitate this behaviour, ( SECREF4 ) introduces a monotone decreasing function INLINEFORM8 defined as INLINEFORM9
which serves to increase the total cost incurred if the value of the INLINEFORM0 th dimension for the two vector representations INLINEFORM1 and INLINEFORM2 for a concept word INLINEFORM3 with INLINEFORM4 fails to be large enough  INLINEFORM5 is also shown in Fig  FIGREF7  
The objective ( SECREF4 ) is minimized using stochastic gradient descent over the cooccurrence records INLINEFORM0   Intuitively, the terms added to ( SECREF4 ) in comparison with ( EQREF6 ) introduce the effect of selectively applying a positive step-type input to the original descent updates of ( EQREF6 ) for concept words along their respective vector dimensions, which influences the dimension value in the positive direction  The parameter INLINEFORM1 in ( SECREF4 ) allows for the adjustment of the magnitude of this influence as needed 
In the next section, we demonstrate the feasibility of this approach by experiments with an example collection of concept word-groups extracted from Roget's Thesaurus 
Experiments and Results
We first identified 300 concepts, one for each dimension of the 300-dimensional vector representation, by employing Roget's Thesaurus  This thesaurus follows a tree structure which starts with a Root node that contains all the words and phrases in the thesaurus  The root node is successively split into Classes and Sections, which are then (optionally) split into Subsections of various depths, finally ending in Categories, which constitute the smallest unit of word/phrase collections in the structure  The actual words and phrases descend from these Categories, and make up the leaves of the tree structure  We note that a given word typically appears in multiple categories corresponding to the different senses of the word  We constructed concept word-groups from Roget's Thesaurus as follows: We first filtered out the multi-word phrases and the relatively obscure terms from the thesaurus  The obscure terms were identified by checking them against a vocabulary extracted from Wikipedia  We then obtained 300 word-groups as the result of a partitioning operation applied to the subtree that ends with categories as its leaves  The partition boundaries, hence the resulting word-groups, can be chosen in many different ways  In our proposed approach, we have chosen to determine this partitioning by traversing this tree structure from the root node in breadth-first order, and by employing a parameter INLINEFORM0 for the maximum size of a node  Here, the size of a node is defined as the number of unique words that ever-descend from that node  During the traversal, if the size of a given node is less than this threshold, we designate the words that ultimately descend from that node as a concept word-group  Otherwise, if the node has children, we discard the node, and queue up all its children for further consideration  If this node does not have any children, on the other hand, the node is truncated to INLINEFORM1 elements with the highest frequency-ranks, and the resulting words are designated as a concept word-group  We note that the choice of INLINEFORM2 greatly affects the resulting collection of word-groups: Excessively large values result in few word-groups that greatly overlap with one another, while overly small values result in numerous tiny word-groups that fail to adequately represent a concept  We experimentally determined that a INLINEFORM3 value of 452 results in the most healthy number of relatively large word-groups (113 groups with size INLINEFORM4 100), while yielding a preferably small overlap amongst the resulting word-groups (with average overlap size not exceeding 3 words)  A total of 566 word-groups were thus obtained  259 smallest word-groups (with size INLINEFORM5 38) were discarded to bring down the number of word-groups to 307  Out of these, 7 groups with the lowest median frequency-rank were further discarded, which yields the final 300 concept word-groups used in the experiments  We present some of the resulting word-groups in Table TABREF9  
By using the concept word-groups, we have trained the GloVe algorithm with the proposed modification given in Section SECREF4 on a snapshot of English Wikipedia measuring 8GB in size, with the stop-words filtered out  Using the parameters given in Table TABREF10 , this resulted in a vocabulary size of 287,847  For the weighting parameter in Eq  SECREF4 , we used a value of INLINEFORM0   The algorithm was trained over 20 iterations  The GloVe algorithm without any modifications was also trained as a baseline with the same parameters  In addition to the original GloVe algorithm, we compare our proposed method with previous studies that aim to obtain interpretable word vectors  We train the improved projected gradient model proposed in BIBREF20 to obtain word vectors (called OIWE-IPG) using the same corpus we use to train GloVe and our proposed method  Using the methods proposed in BIBREF23 , BIBREF26 , BIBREF24 on our baseline GloVe embeddings, we obtain SOV, SPINE and Parsimax (orthogonal) word representations, respectively  We train all the models with the proposed parameters  However, in BIBREF26 , the authors show results for a relatively small vocabulary of 15,000 words  When we trained their model on our baseline GloVe embeddings with a large vocabulary of size 287,847, the resulting vectors performed significantly poor on word similarity tasks compared to the results presented in their paper  In addition, Parsimax (orthogonal) word vectors obtained using method in BIBREF26 are nearly identical to the baseline vectors (i e  learned orthogonal transformation matrix is very close to identity)  Therefore, Parsimax (orthogonal) yields almost same results with baseline vectors in all evaluations  We evaluate the interpretability of the resulting embeddings qualitatively and quantitatively  We also test the performance of the embeddings on word similarity and word analogy tests 
In our experiments, vocabulary size is close to 300,000 while only 16,242 unique words of the vocabulary are present in the concept groups  Furthermore, only dimensions that correspond to the concept group of the word will be updated due to the additional cost term  Given that these concept words can belong to multiple concept groups (2 on average), only 33,319 parameters are updated  There are 90 million individual parameters present for the 300,000 word vectors of size 300  Of these parameters, only approximately 33,000 are updated by the additional cost term 
Qualitative Evaluation for Interpretability
In Fig  FIGREF13 , we demonstrate the particular way in which the proposed algorithm ( SECREF4 ) influences the vector representation distributions  Specifically, we consider, for illustration, the 32nd dimension values for the original GloVe algorithm and our modified version, restricting the plots to the top-1000 words with respect to their frequency ranks for clarity of presentation  In Fig  FIGREF13 , the words in the horizontal axis are sorted in descending order with respect to the values at the 32nd dimension of their word embedding vectors coming from the original GloVe algorithm  The dimension values are denoted with blue and red/green markers for the original and the proposed algorithms, respectively  Additionally, the top-50 words that achieve the greatest 32nd dimension values among the considered 1000 words are emphasized with enlarged markers, along with text annotations  In the presented simulation of the proposed algorithm, the 32nd dimension values are encoded with the concept JUDGMENT, which is reflected as an increase in the dimension values for words such as committee, academy, and article  We note that these words (red) are not part of the pre-determined word-group for the concept JUDGMENT, in contrast to words such as award, review and account (green) which are  This implies that the increase in the corresponding dimension values seen for these words is attributable to the joint effect of the first term in ( SECREF4 ) which is inherited from the original GloVe algorithm, in conjunction with the remaining terms in the proposed objective expression ( SECREF4 )  This experiment illustrates that the proposed algorithm is able to impart the concept of JUDGMENT on its designated vector dimension above and beyond the supplied list of words belonging to the concept word-group for that dimension  We also present the list of words with the greatest dimension value for the dimensions 11, 13, 16, 31, 36, 39, 41, 43 and 79 in Table TABREF11   These dimensions are aligned/imparted with the concepts that are given in the column headers  In Table TABREF11 , the words that are highlighted with green denote the words that exist in the corresponding word-group obtained from Roget's Thesaurus (and are thus explicitly forced to achieve increased dimension values), while the red words denote the words that achieve increased dimension values by virtue of their cooccurrence statistics with the thesaurus-based words (indirectly, without being explicitly forced)  This again illustrates that a semantic concept can indeed be coded to a vector dimension provided that a sensible lexical resource is used to guide semantically related words to the desired vector dimension via the proposed objective function in ( SECREF4 )  Even the words that do not appear in, but are semantically related to, the word-groups that we formed using Roget's Thesaurus, are indirectly affected by the proposed algorithm  They also reflect the associated concepts at their respective dimensions even though the objective functions for their particular vectors are not modified  This point cannot be overemphasized  Although the word-groups extracted from Roget's Thesaurus impose a degree of supervision to the process, the fact that the remaining words in the entire vocabulary are also indirectly affected makes the proposed method a semi-supervised approach that can handle words that are not in these chosen word-groups  A qualitative example of this result can be seen in the last column of Table TABREF11   It is interesting to note the appearance of words such as guerilla, insurgency, mujahideen, Wehrmacht and Luftwaffe in addition to the more obvious and straightforward army, soldiers and troops, all of which are not present in the associated word-group WARFARE 
Most of the dimensions we investigated exhibit similar behaviour to the ones presented in Table TABREF11   Thus generally speaking, we can say that the entries in Table TABREF11 are representative of the great majority  However, we have also specifically looked for dimensions that make less sense and determined a few such dimensions which are relatively less satisfactory

ground_truth: No

No

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

Please answer the questions based on the text provided below. Do they report results only on English data?
Context 3:  These less satisfactory examples are given in Table TABREF14   These examples are also interesting in that they shed insight into the limitations posed by polysemy and existence of very rare outlier words 
Quantitative Evaluation for Interpretability
One of the main goals of this study is to improve the interpretability of dense word embeddings by aligning the dimensions with predefined concepts from a suitable lexicon  A quantitative measure is required to reliably evaluate the achieved improvement  One of the methods proposed to measure the interpretability is the word intrusion test BIBREF41   But, this method is expensive to apply since it requires evaluations from multiple human evaluators for each embedding dimension  In this study, we use a semantic category-based approach based on the method and category dataset (SEMCAT) introduced in BIBREF27 to quantify interpretability  Specifically, we apply a modified version of the approach presented in BIBREF40 in order to consider possible sub-groupings within the categories in SEMCAT  Interpretability scores are calculated using Interpretability Score (IS) as given below:
DISPLAYFORM0
In ( EQREF17 ), INLINEFORM0 and INLINEFORM1 represents the interpretability scores in the positive and negative directions of the INLINEFORM2 dimension ( INLINEFORM3 , INLINEFORM4 number of dimensions in the embedding space) of word embedding space for the INLINEFORM5 category ( INLINEFORM6 , INLINEFORM7 is number of categories in SEMCAT, INLINEFORM8 ) in SEMCAT respectively  INLINEFORM9 is the set of words in the INLINEFORM10 category in SEMCAT and INLINEFORM11 is the number of words in INLINEFORM12   INLINEFORM13 corresponds to the minimum number of words required to construct a semantic category (i e  represent a concept)  INLINEFORM14 represents the set of INLINEFORM15 words that have the highest ( INLINEFORM16 ) and lowest ( INLINEFORM17 ) values in INLINEFORM18 dimension of the embedding space  INLINEFORM19 is the intersection operator and INLINEFORM20 is the cardinality operator (number of elements) for the intersecting set  In ( EQREF17 ), INLINEFORM21 gives the interpretability score for the INLINEFORM22 dimension and INLINEFORM23 gives the average interpretability score of the embedding space 
Fig  FIGREF18 presents the measured average interpretability scores across dimensions for original GloVe embeddings, for the proposed method and for the other four methods we compare, along with a randomly generated embedding  Results are calculated for the parameters INLINEFORM0 and INLINEFORM1   Our proposed method significantly improves the interpretability for all INLINEFORM2 compared to the original GloVe approach  Our proposed method is second to only SPINE in increasing interpretability  However, as we will experimentally demonstrate in the next subsection, in doing this, SPINE almost entirely destroys the underlying semantic structure of the word embeddings, which is the primary function of a word embedding 
The proposed method and interpretability measurements are both based on utilizing concepts represented by word-groups  Therefore it is expected that there will be higher interpretability scores for some of the dimensions for which the imparted concepts are also contained in SEMCAT  However, by design, word groups that they use are formed by using different sources and are independent  Interpretability measurements use SEMCAT while our proposed method utilizes Roget's Thesaurus 
Intrinsic Evaluation of the Embeddings
It is necessary to show that the semantic structure of the original embedding has not been damaged or distorted as a result of aligning the dimensions with given concepts, and that there is no substantial sacrifice involved from the performance that can be obtained with the original GloVe  To check this, we evaluate performances of the proposed embeddings on word similarity BIBREF42 and word analogy BIBREF0 tests  We compare the results with the original embeddings and the three alternatives excluding Parsimax BIBREF26 since orthogonal transformations will not affect the performance of the original embeddings on these tests 
Word similarity test measures the correlation between word similarity scores obtained from human evaluation (i e  true similarities) and from word embeddings (usually using cosine similarity)  In other words, this test quantifies how well the embedding space reflects human judgements in terms of similarities between different words  The correlation scores for 13 different similarity test sets are reported in Table TABREF20   We observe that, let alone a reduction in performance, the obtained scores indicate an almost uniform improvement in the correlation values for the proposed algorithm, outperforming all the alternatives in almost all test sets  Categories from Roget's thesaurus are groupings of words that are similar in some sense which the original embedding algorithm may fail to capture  These test results signify that the semantic information injected into the algorithm by the additional cost term is significant enough to result in a measurable improvement  It should also be noted that scores obtained by SPINE is unacceptably low on almost all tests indicating that it has achieved its interpretability performance at the cost of losing its semantic functions 
Word analogy test is introduced in BIBREF1 and looks for the answers of the questions that are in the form of "X is to Y, what Z is to ?" by applying simple arithmetic operations to vectors of words X, Y and Z  We present precision scores for the word analogy tests in Table TABREF21   It can be seen that the alternative approaches that aim to improve interpretability, have poor performance on the word analogy tests  However, our proposed method has comparable performance with the original GloVe embeddings  Our method outperforms GloVe in semantic analogy test set and in overall results, while GloVe performs slightly better in syntactic test set  This comparable performance is mainly due to the cost function of our proposed method that includes the original objective of the GloVe 
To investigate the effect of the additional cost term on the performance improvement in the semantic analogy test, we present Table TABREF22   In particular, we present results for the cases where i) all questions in the dataset are considered, ii) only the questions that contains at least one concept word are considered, iii) only the questions that consist entirely of concept words are considered  We note specifically that for the last case, only a subset of the questions under the semantic category family txt ended up being included  We observe that for all three scenarios, our proposed algorithm results in an improvement in the precision scores  However, the greatest performance increase is seen for the last scenario, which underscores the extent to which the semantic features captured by embeddings can be improved with a reasonable selection of the lexical resource from which the concept word-groups were derived 
Conclusion
We presented a novel approach to impart interpretability into word embeddings  We achieved this by encouraging different dimensions of the vector representation to align with predefined concepts, through the addition of an additional cost term in the optimization objective of the GloVe algorithm that favors a selective increase for a pre-specified input of concept words along each dimension 
We demonstrated the efficacy of this approach by applying qualitative and quantitative evaluations for interpretability  We also showed via standard word-analogy and word-similarity tests that the semantic coherence of the original vector space is preserved, even slightly improved  We have also performed and reported quantitative comparisons with several other methods for both interpretabilty increase and preservation of semantic coherence  Upon inspection of Fig  FIGREF18 and Tables TABREF20 , TABREF21 , and TABREF22 altogether, it should be noted that our proposed method achieves both of the objectives simultaneously, increased interpretability and preservation of the intrinsic semantic structure 
An important point was that, while it is expected for words that are already included in the concept word-groups to be aligned together since their dimensions are directly updated with the proposed cost term, it was also observed that words not in these groups also aligned in a meaningful manner without any direct modification to their cost function  This indicates that the cost term we added works productively with the original cost function of GloVe to handle words that are not included in the original concept word-groups, but are semantically related to those word-groups  The underlying mechanism can be explained as follows  While the outside lexical resource we introduce contains a relatively small number of words compared to the total number of words, these words and the categories they represent have been carefully chosen and in a sense, "densely span" all the words in the language  By saying "span", we mean they cover most of the concepts and ideas in the language without leaving too many uncovered areas  With "densely" we mean all areas are covered with sufficient strength  In other words, this subset of words is able to constitute a sufficiently strong skeleton, or scaffold  Now remember that GloVe works to align or bring closer related groups of words, which will include words from the lexical source  So the joint action of aligning the words with the predefined categories (introduced by us) and aligning related words (handled by GloVe) allows words not in the lexical groups to also be aligned meaningfully  We may say that the non-included words are "pulled along" with the included words by virtue of the "strings" or "glue" that is provided by GloVe  In numbers, the desired effect is achieved by manipulating less than only 0 05% of parameters of the entire word vectors  Thus, while there is a degree of supervision coming from the external lexical resource, the rest of the vocabulary is also aligned indirectly in an unsupervised way  This may be the reason why, unlike earlier proposed approaches, our method is able to achieve increasing interpretability without destroying underlying semantic structure, and consequently without sacrificing performance in benchmark tests 
Upon inspecting the 2nd column of Table TABREF14 , where qualitative results for concept TASTE are presented, another insight regarding the learning mechanism of our proposed approach can be made  Here it seems understandable that our proposed approach, along with GloVe, brought together the words taste and polish, and then the words Polish and, for instance, Warsaw are brought together by GloVe  These examples are interesting in that they shed insight into how GloVe works and the limitations posed by polysemy  It should be underlined that the present approach is not totally incapable of handling polysemy, but cannot do so perfectly  Since related words are being clustered, sufficiently well-connected words that do not meaningfully belong along with others will be appropriately "pulled away" from that group by several words, against the less effective, inappropriate pull of a particular word  Even though polish with lowercase "p" belongs where it is, it is attracting Warsaw to itself through polysemy and this is not meaningful  Perhaps because Warsaw is not a sufficiently well-connected word, it ends being dragged along, although words with greater connectedness to a concept group might have better resisted such inappropriate attractions 
In this study, we used the GloVe algorithm as the underlying dense word embedding scheme to demonstrate our approach  However, we stress that it is possible for our approach to be extended to other word embedding algorithms which have a learning routine consisting of iterations over cooccurrence records, by making suitable adjustments in the objective function  Since word2vec model is also based on the coocurrences of words in a sliding window through a large corpus, we expect that our approach can also be applied to word2vec after making suitable adjustments, which can be considered as an immediate future work for our approach  Although the semantic concepts are encoded in only one direction (positive) within the embedding dimensions, it might be beneficial to pursue future work that also encodes opposite concepts, such as good and bad, in two opposite directions of the same dimension 
The proposed methodology can also be helpful in computational cross-lingual studies, where the similarities are explored across the vector spaces of different languages BIBREF43 , BIBREF44  

ground_truth: Yes

Unanswerable