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 models/frameworks do they compare to?
Context 1: Introduction
Deep neural networks (DNNs), in particular convolutional and recurrent neural networks, with huge architectures have been proven successful in wide range of tasks including audio processing such as speech to text [1 - 4], emotion recognition [5 - 8], speech/non-speech (e g , of non-speech include noise, music, etc ,) classification [9 - 12], etc 
Training these deep architectures require large amount of annotated data, as a result, they cannot be used in low data resource scenarios which is common in speech-based applications [13 - 15]  Apart from collecting large data corpus, annotating the data is also very difficult, and requires manual supervision and efforts  Especially, annotation of speech for tasks like emotion recognition also suffer from lack of agreement among the annotators [16]  Hence, there is a need to build reliable systems that can work in low resource scenario 
In this work, we propose a novel approach to address the task of classification in low data resource scenarios  Our approach involves simultaneously considering more than one sample (in this work, two samples are considered) to train the classifier  We call this approach as simultaneous two sample learning (s2sL)  The proposed approach is also applicable to low resource data suffering with data imbalance  The contributions of this paper are:
Proposed approach
The s2sL approach proposed to address low data resource problem is explained in this Section  In this work, we use MLP (modified to handle our data representation) as the base classifier  Here, we explain the s2sL approach by considering two-class classification task 
Data representation
Consider a two-class classification task with INLINEFORM0 denoting the set of class labels, and let INLINEFORM1 and INLINEFORM2 be the number of samples corresponding to INLINEFORM3 and INLINEFORM4 , respectively  In general, to train a classifier, the samples in the train set are provided as input-output pairs as follows  DISPLAYFORM0
where INLINEFORM0 refers to the INLINEFORM1 -dimensional feature vector representing the INLINEFORM2 sample corresponding to INLINEFORM3 class label, and INLINEFORM4 , refers to output label of INLINEFORM5 class  In the proposed data representation format, called simultaneous two sample (s2s) representation, we will simultaneously consider two samples as follows  DISPLAYFORM0
where INLINEFORM0 , INLINEFORM1 are the INLINEFORM2 -dimensional feature vectors representing the INLINEFORM3 sample in INLINEFORM4 class and INLINEFORM5 sample in the INLINEFORM6 class, respectively; and INLINEFORM7 , INLINEFORM8 refers to the output labels of INLINEFORM9 and INLINEFORM10 class, respectively 
Hence, in s2s data representation, we will have an input feature vector of length INLINEFORM0 i e , INLINEFORM1 , and output class labels as either INLINEFORM2 or INLINEFORM3   Each sample can be combined with all the samples (i e , with ( INLINEFORM4 ) samples) in the dataset  Therefore, by representing the data in the s2s format, the number of samples in the train set increases to INLINEFORM5 from INLINEFORM6 samples  We hypothesize that the s2s format is expected to help the network not only to learn the characteristics of the two classes separately, but also the difference and similarity in characteristics of the two classes 
Classifier training
MLP, the most commonly used feed forward neural network, is considered as the base classifier to validate our proposed s2s framework  Generally, MLPs are trained using the data format given by eq  INLINEFORM0   But to train the MLP on our s2s based data representation (as in eq  INLINEFORM1 ), the following modifications are made to the MLP architecture (refer to Figure FIGREF4 ) 
We have INLINEFORM0 units (instead of INLINEFORM1 units) in the input layer to accept the two samples i e , INLINEFORM2 and INLINEFORM3 , simultaneously 
The structure of the hidden layer in this approach is similar to that of a regular MLP  The number of hidden layers and hidden units can be varied depending upon the complexity of the problem  The number of units in the hidden layer is selected empirically by varying the number of hidden units from 2 to twice the length of the input layer (i e , 2 to INLINEFORM0 ) and the unit at which the highest performance is obtained are selected  In this paper, we considered only a single hidden layer  Rectified linear units (ReLU) are used for hidden layer 
The output layer will consist of units equal to twice the considered number of classes in the classification task i e, the output layer will have four units for two-class classification task  The sigmoid activation function (not softmax) is used on the output layer units  Unlike regular MLP, we use sigmoid activation units in the output layer, with binary cross-entropy as the cost function, because the output labels in the proposed s2s based data representation will have more than one unit active at a time (not one-hot encoded output) and this condition cannot be handled using softmax function 
As can be seen from Figure FIGREF4 , the output layer in our proposed method has outputs INLINEFORM0 and INLINEFORM1 which corresponds to the outputs associated with the input feature vectors INLINEFORM2 and INLINEFORM3 , respectively  For a two-class classification problem, there will be four units in the output layer and the possible output labels are INLINEFORM4 , INLINEFORM5 , INLINEFORM6 , INLINEFORM7 corresponding to the class labels INLINEFORM8 and INLINEFORM9 , respectively  This architecture is referred to as s2s-MLP  In s2sL, s2s-MLP is trained using the s2s data representation format  Further, the s2s-MLP is trained using adam optimizer 
Classifier testing
Generally, the feature vector corresponding to the test sample is provided as input to the trained MLP in the testing phase and the class label is decided based on the obtained output 
However, in s2sL method, the feature vector corresponding to the test sample should also be converted to the s2s data representation format to test the trained s2s-MLP  We propose a testing approach, where the given test sample is combined with a set of preselected reference samples, whose class label is known a priori, to generate multiple instances of the same test sample as follows  DISPLAYFORM0
where INLINEFORM0 , INLINEFORM1 refer to the INLINEFORM2 -dimensional feature vector corresponding to the test sample and the INLINEFORM3 reference sample, respectively  INLINEFORM4 refers to the considered number of reference samples  These reference samples can be selected from any of the two classes 
For testing the s2s-MLP (as shown in Figure FIGREF4 ), each test sample INLINEFORM0 (same as ' INLINEFORM1 ' in (3)) is combined with all the INLINEFORM2 reference samples ( INLINEFORM3 ) to form INLINEFORM4 different instances of the same test sample INLINEFORM5   The corresponding outputs ( INLINEFORM6 ) obtained from s2s-MLP for the INLINEFORM7 generated instances of INLINEFORM8 are combined by voting-based decision approach to obtain the final decision INLINEFORM9   The class label that gets maximum votes is considered as the predicted output label 
Experiments
We validate the performance of the proposed s2sL by providing the preliminary results obtained on two different tasks namely, Speech/Music discrimination and emotion classification  We considered the GTZAN Music-Speech dataset [17], consisting of 120 audio files (60 speech and 60 music), for task of classifying speech and music  Each audio file (of 2 seconds duration) is represented using a 13-dimensional mel-frequency cepstral coefficient (MFCC) vector, where each MFCC vector is the average of all the frame level (frame size of 30 msec and an overlap of 10 msec) MFCC vectors  It is to be noted that our main intention for this task is not better feature selection, but to demonstrate the effectiveness of our approach, in particular for low data scenarios 
The standard Berlin speech emotion database (EMO-DB) [18] consisting of 535 utterances corresponding to 7 different emotions is considered for the task of emotion classification  Each utterance is represented by a 19-dimensional feature vector obtained by using the feature selection algorithm from WEKA toolkit [19] on the 384-dimensional utterance level feature vector obtained using openSMILE toolkit [20]  For two class classification, we considered the two most confusing emotion pairs i e , (Neutral,Sad) and (Anger, Happy)  Data corresponding to Speech/Music classification (60 speech and 60 music samples) and Neutral/Sad classification (79 neutral and 62 sad utterances) is balanced whereas Anger/Happy classification task has data imbalance, with anger forming the majority class (127 samples) and happy forming the minority class (71 samples)  Therefore, we show the performance of s2sL on both, balanced and imbalanced datasets 
All experimental results are validated using 5-fold cross validation (80% of data for training and 20% for testing in each fold)  Further, to analyze the effectiveness of s2sL in low resource scenarios, different proportions of training data, within each fold, are considered to train the system  For this analysis, we considered 4 different proportions i e , INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 of the training data to train the classifier  For instance, INLINEFORM4 means considering only half of the original training data to train the classifier, and INLINEFORM5 means considering the complete training data  5-fold cross validation is considered for all data proportions  Accuracy (in %) is used as a performance measure for balanced data classification tasks (i e , Speech/Music classification and Neutral/Sad emotion classification), whereas the more preferred INLINEFORM6 measure [21] is used as a measure for imbalanced data classification task (i e , Anger/Happy emotion classification) 
Table TABREF14 show the results obtained for proposed s2sL approach in comparison to that of MLP for the tasks of Speech/Music and Neutral/Sad classification, by considering different proportions of training data  The values in Table TABREF14 are mean accuracies (in %) obtained by 5-fold cross validation  It can be observed from Table TABREF14 that for both tasks, s2sL method outperforms MLP, especially at low resource conditions  s2sL shows an absolute improvement in accuracy of INLINEFORM0 % and INLINEFORM1 % over MLP for Speech/Music and Neutral/Sad classification tasks, respectively, when INLINEFORM2 of the original training data is used in experiments 
Table TABREF14 show the results (in terms of INLINEFORM0 values) obtained for proposed s2sL approach in comparison to that of MLP for Anger/Happy classification (data imbalance problem)  Here, state-of-the-art methods i e , Eusboost [22] and MWMOTE [23] are also considered for comparison  It can be observed from Table TABREF14 that the s2sL method outperforms MLP, and also performs better than Eusboost and MWMOTE techniques on imbalanced data (around 3 % absolute improvement in INLINEFORM1 value for s2sL compared to MWMOTE, when INLINEFORM2 of the training data is considered)  In particular, at lower amounts of training data, s2sL outperforms all the other methods, illustrating its effectiveness even for low resourced data imbalance problems  s2sL method shows an absolute improvement of 6% ( INLINEFORM3 ) in INLINEFORM4 value over the second best ( INLINEFORM5 for MWMOTE), when only INLINEFORM6 of the training data is used 
Conclusions
In this paper, we introduced a novel s2s framework to effectively learn the class discriminative characteristics, even from low data resources  In this framework, more than one sample (here, two samples) are simultaneously considered to train the classifier  Further, this framework allows to generate multiple instances of the same test sample, by considering preselected reference samples, to achieve a more profound decision making  We illustrated the significance of our approach by providing the experimental results for two different tasks namely, speech/music discrimination and emotion classification  Further, we showed that the s2s framework can also handle the low resourced data imbalance problem 
References
[1] Hinton, G , Deng, L , Yu, D , Dahl, G  E , Mohamed, A  R , Jaitly, N , Senior, A , Vanhoucke, V , Nguyen, P , Sainath, T  N  & Kingsbury, B  (2012) Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups  IEEE Signal Processing Magazine, pp  82–97 
[2] Vinyals, O , Ravuri, S  V  & Povey, D  (2012) Revisiting recurrent neural networks for robust ASR  In Proc  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp  4085–4088 
[3] Lu, L , Zhang, X , Cho K  & Renals, S  (2015) A study of the recurrent neural network encoder-decoder for large vocabulary speech recognition  In Proc  INTERSPEECH, pp  3249–3253 
[4] Zhang, Y , Pezeshki, M , Brakel, P , Zhang, S , Bengio, C L Y  & Courville, A  (2017) Towards end-to-end speech recognition with deep convolutional neural networks  arXiv preprint, arXiv:1701 02720 
[5] Han, K , Yu, D  & Tashev, I  (2014) Speech emotion recognition using deep neural network and extreme learning machine  In Proc  Interspeech, pp  223–227 
[6] Trigeorgis, G , Ringeval, F , Brueckner, R , Marchi, E , Nicolaou, M  A , Schuller, B , & Zafeiriou, S  (2016) Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network  In Proc  ICASSP, pp  5200–5204) 
[7] Huang, Che-Wei, & Shrikanth S  Narayanan  (2016) Attention Assisted Discovery of Sub-Utterance Structure in Speech Emotion Recognition  In Proc  INTERSPEECH, pp  1387–1391 
[8] Zhang, Z , Ringeval, F , Han, J , Deng, J , Marchi, E  & Schuller, B  (2016) Facing realism in spontaneous emotion recognition from speech: Feature enhancement by autoencoder with LSTM neural networks  In Proc  INTERSPEECH, pp  3593–3597 
[9] Scheirer, E  D  & Slaney, M  (2003) Multi-feature speech/music discrimination system  U S  Patent 6,570,991 
[10] Pikrakis, A  & Theodoridis S  (2014) Speech-music discrimination: A deep learning perspective  In Proc  European Signal Processing Conference (EUSIPCO), pp  616–620 
[11] Choi, K , Fazekas, G  & Sandler, M  (2016) Automatic tagging using deep convolutional neural networks  arXiv preprint, arXiv:1606 00298 
[12] Zazo Candil, R , Sainath, T N , Simko, G  & Parada, C  (2016) Feature learning with raw-waveform CLDNNs for Voice Activity Detection  In Proc  Interspeech, pp  3668–3672 
[13] Tian, L , Moore, JD  & Lai, C  (2015) Emotion recognition in spontaneous and acted dialogues  In Proc  International Conference on Affective Computing and Intelligent Interaction (ACII), pp  698–704 
[14] Dumpala, S H , Chakraborty, R  & Kopparapu, S K  (2017) k-FFNN: A priori knowledge infused feed-forward neural networks  arXiv preprint, arXiv:1704 07055 
[15] Dumpala, S H , Chakraborty, R  & Kopparapu, S K  (2018) Knowledge-driven feed-forward neural network for audio affective content analysis  To appear in AAAI-2018 workshop on Affective Content Analysis 
[16] Deng, J , Zhang, Z , Marchi, E  & Schuller, B  (2013) Sparse autoencoder-based feature transfer learning for speech emotion recognition  In Proc  Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp  511–516 
[17] George Tzanetakis, Gtzan musicspeech  Availabe on-line at http://marsyas info/download/datasets 
[18] Burkhardt, F , Paeschke, A , Rolfes, M , Sendlmeier, W F  & Weiss, B  (2005) A database of german emotional speech  In Proc  Interspeech, pp  1517–1520 
[19] Hall, M , Frank, E , Holmes, G , Pfahringer, B , Reutemann, P  & Witten, I H  (2009) The WEKA data mining software: an update  ACM SIGKDD explorations newsletter, 11(1), pp  10–18 
[20] Eyben, F , Weninger, F , Gross, F  & Schuller, B  (2013) Recent developments in opensmile, the munich open-source multimedia feature extractor  In Proc  ACM international conference on Multimedia, pp  835–838 
[21] Maratea, A , Petrosino, A  & Manzo, M  (2014) Adjusted f-measure and kernel scaling for imbalanced data learning  Information Sciences 257:331–341 
[22] Galar, M , Fernandez, A , Barrenechea, E  & Herrera, F  (2013) Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling  Pattern Recognition 46(12):3460–3471 
[23] Barua, S , Islam, M  M , Yao, X  & Murase, K  (2014) Mwmote–majority weighted minority oversampling technique for imbalanced data set learning  IEEE Transactions on Knowledge and Data Engineering 26(2):405–425 
Context 2: Introduction
Accurate language identification (LID) is the first step in many natural language processing and machine comprehension pipelines  If the language of a piece of text is known then the appropriate downstream models like parts of speech taggers and language models can be applied as required 
LID is further also an important step in harvesting scarce language resources  Harvested data can be used to bootstrap more accurate LID models and in doing so continually improve the quality of the harvested data  Availability of data is still one of the big roadblocks for applying data driven approaches like supervised machine learning in developing countries 
Having 11 official languages of South Africa has lead to initiatives (discussed in the next section) that have had positive effect on the availability of language resources for research  However, many of the South African languages are still under resourced from the point of view of building data driven models for machine comprehension and process automation 
Table TABREF2 shows the percentages of first language speakers for each of the official languages of South Africa  These are four conjunctively written Nguni languages (zul, xho, nbl, ssw), Afrikaans (afr) and English (eng), three disjunctively written Sotho languages (nso, sot, tsn), as well as tshiVenda (ven) and Xitsonga (tso)  The Nguni languages are similar to each other and harder to distinguish  The same is true of the Sotho languages 
This paper presents a hierarchical naive Bayesian and lexicon based classifier for LID of short pieces of text of 15-20 characters long  The algorithm is evaluated against recent approaches using existing test sets from previous works on South African languages as well as the Discriminating between Similar Languages (DSL) 2015 and 2017 shared tasks 
Section SECREF2 reviews existing works on the topic and summarises the remaining research problems  Section SECREF3 of the paper discusses the proposed algorithm and Section SECREF4 presents comparative results 
Related Works
The focus of this section is on recently published datasets and LID research applicable to the South African context  An in depth survey of algorithms, features, datasets, shared tasks and evaluation methods may be found in BIBREF0 
The datasets for the DSL 2015 & DSL 2017 shared tasks BIBREF1 are often used in LID benchmarks and also available on Kaggle   The DSL datasets, like other LID datasets, consists of text sentences labelled by language  The 2017 dataset, for example, contains 14 languages over 6 language groups with 18000 training samples and 1000 testing samples per language 
The recently published JW300 parallel corpus BIBREF2 covers over 300 languages with around 100 thousand parallel sentences per language pair on average  In South Africa, a multilingual corpus of academic texts produced by university students with different mother tongues is being developed BIBREF3  The WiLI-2018 benchmark dataset BIBREF4 for monolingual written natural language identification includes around 1000 paragraphs of 235 languages  A possibly useful link can also be made BIBREF5 between Native Language Identification (NLI) (determining the native language of the author of a text) and Language Variety Identification (LVI) (classification of different varieties of a single language) which opens up more datasets  The Leipzig Corpora Collection BIBREF6, the Universal Declaration of Human Rights and Tatoeba are also often used sources of data 
The NCHLT text corpora BIBREF7 is likely a good starting point for a shared LID task dataset for the South African languages BIBREF8  The NCHLT text corpora contains enough data to have 3500 training samples and 600 testing samples of 300+ character sentences per language  Researchers have recently started applying existing algorithms for tasks like neural machine translation in earnest to such South African language datasets BIBREF9 
Existing NLP datasets, models and services BIBREF10 are available for South African languages  These include an LID algorithm BIBREF11 that uses a character level n-gram language model  Multiple papers have shown that 'shallow' naive Bayes classifiers BIBREF12, BIBREF8, BIBREF13, BIBREF14, SVMs BIBREF15 and similar models work very well for doing LID  The DSL 2017 paper BIBREF1, for example, gives an overview of the solutions of all of the teams that competed on the shared task and the winning approach BIBREF16 used an SVM with character n-gram, parts of speech tag features and some other engineered features  The winning approach for DSL 2015 used an ensemble naive Bayes classifier  The fasttext classifier BIBREF17 is perhaps one of the best known efficient 'shallow' text classifiers that have been used for LID  
Multiple papers have proposed hierarchical stacked classifiers (including lexicons) that would for example first classify a piece of text by language group and then by exact language BIBREF18, BIBREF19, BIBREF8, BIBREF0  Some work has also been done on classifying surnames between Tshivenda, Xitsonga and Sepedi BIBREF20  Additionally, data augmentation BIBREF21 and adversarial training BIBREF22 approaches are potentially very useful to reduce the requirement for data 
Researchers have investigated deeper LID models like bidirectional recurrent neural networks BIBREF23 or ensembles of recurrent neural networks BIBREF24  The latter is reported to achieve 95 12% in the DSL 2015 shared task  In these models text features can include character and word n-grams as well as informative character and word-level features learnt BIBREF25 from the training data  The neural methods seem to work well in tasks where more training data is available 
In summary, LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  Increased confusion can in general be expected between shorter pieces of text and languages that are more closely related  Shallow methods still seem to work well compared to deeper models for LID  Other remaining research opportunities seem to be data harvesting, building standardised datasets and creating shared tasks for South Africa and Africa  Support for language codes that include more languages seems to be growing and discoverability of research is improving with more survey papers coming out  Paywalls also seem to no longer be a problem; the references used in this paper was either openly published or available as preprint papers 
Methodology
The proposed LID algorithm builds on the work in BIBREF8 and BIBREF26  We apply a naive Bayesian classifier with character (2, 4 & 6)-grams, word unigram and word bigram features with a hierarchical lexicon based classifier 
The naive Bayesian classifier is trained to predict the specific language label of a piece of text, but used to first classify text as belonging to either the Nguni family, the Sotho family, English, Afrikaans, Xitsonga or Tshivenda

ground_truth: MLP

Eusboost, MWMOTE

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 languages are similar to each other?
Context 2:  The scikit-learn multinomial naive Bayes classifier is used for the implementation with an alpha smoothing value of 0 01 and hashed text features 
The lexicon based classifier is then used to predict the specific language within a language group  For the South African languages this is done for the Nguni and Sotho groups  If the lexicon prediction of the specific language has high confidence then its result is used as the final label else the naive Bayesian classifier's specific language prediction is used as the final result  The lexicon is built over all the data and therefore includes the vocabulary from both the training and testing sets 
The lexicon based classifier is designed to trade higher precision for lower recall  The proposed implementation is considered confident if the number of words from the winning language is at least one more than the number of words considered to be from the language scored in second place 
The stacked classifier is tested against three public LID implementations BIBREF17, BIBREF23, BIBREF8  The LID implementation described in BIBREF17 is available on GitHub and is trained and tested according to a post on the fasttext blog  Character (5-6)-gram features with 16 dimensional vectors worked the best  The implementation discussed in BIBREF23 is available from https://github com/tomkocmi/LanideNN  Following the instructions for an OSX pip install of an old r0 8 release of TensorFlow, the LanideNN code could be executed in Python 3 7 4  Settings were left at their defaults and a learning rate of 0 001 was used followed by a refinement with learning rate of 0 0001  Only one code modification was applied to return the results from a method that previously just printed to screen  The LID algorithm described in BIBREF8 is also available on GitHub 
The stacked classifier is also tested against the results reported for four other algorithms BIBREF16, BIBREF26, BIBREF24, BIBREF15  All the comparisons are done using the NCHLT BIBREF7, DSL 2015 BIBREF19 and DSL 2017 BIBREF1 datasets discussed in Section SECREF2 
Results and Analysis
The average classification accuracy results are summarised in Table TABREF9  The accuracies reported are for classifying a piece of text by its specific language label  Classifying text only by language group or family is a much easier task as reported in BIBREF8 
Different variations of the proposed classifier were evaluated  A single NB classifier (NB), a stack of two NB classifiers (NB+NB), a stack of a NB classifier and lexicon (NB+Lex) and a lexicon (Lex) by itself  A lexicon with a 50% training token dropout is also listed to show the impact of the lexicon support on the accuracy 
From the results it seems that the DSL 2017 task might be harder than the DSL 2015 and NCHLT tasks  Also, the results for the implementation discussed in BIBREF23 might seem low, but the results reported in that paper is generated on longer pieces of text so lower scores on the shorter pieces of text derived from the NCHLT corpora is expected 
The accuracy of the proposed algorithm seems to be dependent on the support of the lexicon  Without a good lexicon a non-stacked naive Bayesian classifier might even perform better 
The execution performance of some of the LID implementations are shown in Table TABREF10  Results were generated on an early 2015 13-inch Retina MacBook Pro with a 2 9 GHz CPU (Turbo Boosted to 3 4 GHz) and 8GB RAM  The C++ implementation in BIBREF17 is the fastest  The implementation in BIBREF8 makes use of un-hashed feature representations which causes it to be slower than the proposed sklearn implementation  The execution performance of BIBREF23 might improve by a factor of five to ten when executed on a GPU 
Conclusion
LID of short texts, informal styles and similar languages remains a difficult problem which is actively being researched  The proposed algorithm was evaluated on three existing datasets and compared to the implementations of three public LID implementations as well as to reported results of four other algorithms  It performed well relative to the other methods beating their results  However, the performance is dependent on the support of the lexicon 
We would like to investigate the value of a lexicon in a production system and how to possibly maintain it using self-supervised learning  We are investigating the application of deeper language models some of which have been used in more recent DSL shared tasks  We would also like to investigate data augmentation strategies to reduce the amount of training data that is required 
Further research opportunities include data harvesting, building standardised datasets and shared tasks for South Africa as well as the rest of Africa  In general, the support for language codes that include more languages seems to be growing, discoverability of research is improving and paywalls seem to no longer be a big problem in getting access to published research 
Context 3: Introduction
There exists a class of language construction known as pun in natural language texts and utterances, where a certain word or other lexical items are used to exploit two or more separate meanings  It has been shown that understanding of puns is an important research question with various real-world applications, such as human-computer interaction BIBREF0 , BIBREF1 and machine translation BIBREF2   Recently, many researchers show their interests in studying puns, like detecting pun sentences BIBREF3 , locating puns in the text BIBREF4 , interpreting pun sentences BIBREF5 and generating sentences containing puns BIBREF6 , BIBREF7 , BIBREF8   A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another sign, for an intended humorous or rhetorical effect  Puns can be generally categorized into two groups, namely heterographic puns (where the pun and its latent target are phonologically similar) and homographic puns (where the two meanings of the pun reflect its two distinct senses) BIBREF9   Consider the following two examples:
The first punning joke exploits the sound similarity between the word “propane" and the latent target “profane", which can be categorized into the group of heterographic puns  Another categorization of English puns is homographic pun, exemplified by the second instance leveraging distinct senses of the word “gut" 
Pun detection is the task of detecting whether there is a pun residing in the given text  The goal of pun location is to find the exact word appearing in the text that implies more than one meanings  Most previous work addresses such two tasks separately and develop separate systems BIBREF10 , BIBREF5   Typically, a system for pun detection is built to make a binary prediction on whether a sentence contains a pun or not, where all instances (with or without puns) are taken into account during training  For the task of pun location, a separate system is used to make a single prediction as to which word in the given sentence in the text that trigger more than one semantic interpretations of the text, where the training data involves only sentences that contain a pun  Therefore, if one is interested in solving both problems at the same time, a pipeline approach that performs pun detection followed by pun location can be used 
Compared to the pipeline methods, joint learning has been shown effective BIBREF11 , BIBREF12 since it is able to reduce error propagation and allows information exchange between tasks which is potentially beneficial to all the tasks  In this work, we demonstrate that the detection and location of puns can be jointly addressed by a single model  The pun detection and location tasks can be combined as a sequence labeling problem, which allows us to jointly detect and locate a pun in a sentence by assigning each word a tag  Since each context contains a maximum of one pun BIBREF9 , we design a novel tagging scheme to capture this structural constraint  Statistics on the corpora also show that a pun tends to appear in the second half of a context  To capture such a structural property, we also incorporate word position knowledge into our structured prediction model  Experiments on the benchmark datasets show that detection and location tasks can reinforce each other, leading to new state-of-the-art performance on these two tasks  To the best of our knowledge, this is the first work that performs joint detection and location of English puns by using a sequence labeling approach 
Problem Definition
We first design a simple tagging scheme consisting of two tags { INLINEFORM0 }:
INLINEFORM0 tag means the current word is not a pun 
INLINEFORM0 tag means the current word is a pun 
If the tag sequence of a sentence contains a INLINEFORM0 tag, then the text contains a pun and the word corresponding to INLINEFORM1 is the pun 
The contexts have the characteristic that each context contains a maximum of one pun BIBREF9   In other words, there exists only one pun if the given sentence is detected as the one containing a pun  Otherwise, there is no pun residing in the text  To capture this interesting property, we propose a new tagging scheme consisting of three tags, namely { INLINEFORM0 } 
INLINEFORM0 tag indicates that the current word appears before the pun in the given context 
INLINEFORM0 tag highlights the current word is a pun 
INLINEFORM0 tag indicates that the current word appears after the pun 
We empirically show that the INLINEFORM0 scheme can guarantee the context property that there exists a maximum of one pun residing in the text 
Given a context from the training set, we will be able to generate its corresponding gold tag sequence using a deterministic procedure  Under the two schemes, if a sentence does not contain any puns, all words will be tagged with INLINEFORM0 or INLINEFORM1 , respectively  Exemplified by the second sentence “Some diets cause a gut reaction," the pun is given as “gut " Thus, under the INLINEFORM2 scheme, it should be tagged with INLINEFORM3 , while the words before it are assigned with the tag INLINEFORM4 and words after it are with INLINEFORM5 , as illustrated in Figure FIGREF8   Likewise, the INLINEFORM6 scheme tags the word “gut" with INLINEFORM7 , while other words are tagged with INLINEFORM8   Therefore, we can combine the pun detection and location tasks into one problem which can be solved by the sequence labeling approach 
Model
Neural models have shown their effectiveness on sequence labeling tasks BIBREF13 , BIBREF14 , BIBREF15   In this work, we adopt the bidirectional Long Short Term Memory (BiLSTM) BIBREF16 networks on top of the Conditional Random Fields BIBREF17 (CRF) architecture to make labeling decisions, which is one of the classical models for sequence labeling  Our model architecture is illustrated in Figure FIGREF8 with a running example  Given a context/sentence INLINEFORM0 where INLINEFORM1 is the length of the context, we generate the corresponding tag sequence INLINEFORM2 based on our designed tagging schemes and the original annotations for pun detection and location provided by the corpora  Our model is then trained on pairs of INLINEFORM3  
Input  The contexts in the pun corpus hold the property that each pun contains exactly one content word, which can be either a noun, a verb, an adjective, or an adverb  To capture this characteristic, we consider lexical features at the character level  Similar to the work of BIBREF15 , the character embeddings are trained by the character-level LSTM networks on the unannotated input sequences  Nonlinear transformations are then applied to the character embeddings by highway networks BIBREF18 , which map the character-level features into different semantic spaces 
We also observe that a pun tends to appear at the end of a sentence  Specifically, based on the statistics, we found that sentences with a pun that locate at the second half of the text account for around 88% and 92% in homographic and heterographic datasets, respectively  We thus introduce a binary feature that indicates if a word is located at the first or the second half of an input sentence to capture such positional information  A binary indicator can be mapped to a vector representation using a randomly initialized embedding table BIBREF19 , BIBREF20   In this work, we directly adopt the value of the binary indicator as part of the input 
The concatenation of the transformed character embeddings, the pre-trained word embeddings BIBREF21 , and the position indicators are taken as input of our model 
Tagging  The input is then fed into a BiLSTM network, which will be able to capture contextual information  For a training instance INLINEFORM0 , we suppose the output by the word-level BiLSTM is INLINEFORM1   The CRF layer is adopted to capture label dependencies and make final tagging decisions at each position, which has been included in many state-of-the-art sequence labeling models BIBREF14 , BIBREF15   The conditional probability is defined as:
where INLINEFORM0 is a set of all possible label sequences consisting of tags from INLINEFORM1 (or INLINEFORM2 ), INLINEFORM3 and INLINEFORM4 are weight and bias parameters corresponding to the label pair INLINEFORM5   During training, we minimize the negative log-likelihood summed over all training instances:
where INLINEFORM0 refers to the INLINEFORM1 -th instance in the training set  During testing, we aim to find the optimal label sequence for a new input INLINEFORM2 :
This search process can be done efficiently using the Viterbi algorithm 
Datasets and Settings
We evaluate our model on two benchmark datasets BIBREF9   The homographic dataset contains 2,250 contexts, 1,607 of which contain a pun  The heterographic dataset consists of 1,780 contexts with 1,271 containing a pun  We notice there is no standard splitting information provided for both datasets  Thus we apply 10-fold cross validation  To make direct comparisons with prior studies, following BIBREF4 , we accumulated the predictions for all ten folds and calculate the scores in the end 
For each fold, we randomly select 10% of the instances from the training set for development  Word embeddings are initialized with the 100-dimensional Glove BIBREF21   The dimension of character embeddings is 30 and they are randomly initialized, which can be fine tuned during training  The pre-trained word embeddings are not updated during training  The dimensions of hidden vectors for both char-level and word-level LSTM units are set to 300  We adopt stochastic gradient descent (SGD) BIBREF26 with a learning rate of 0 015 
For the pun detection task, if the predicted tag sequence contains at least one INLINEFORM0 tag, we regard the output (i e , the prediction of our pun detection model) for this task as true, otherwise false  For the pun location task, a predicted pun is regarded as correct if and only if it is labeled as the gold pun in the dataset  As to pun location, to make fair comparisons with prior studies, we only consider the instances that are labeled as the ones containing a pun  We report precision, recall and INLINEFORM1 score in Table TABREF11   A list of prior works that did not employ joint learning are also shown in the first block of Table TABREF11  
Results
We also implemented a baseline model based on conditional random fields (CRF), where features like POS tags produced by the Stanford POS tagger BIBREF27 , n-grams, label transitions, word suffixes and relative position to the end of the text are considered  We can see that our model with the INLINEFORM0 tagging scheme yields new state-of-the-art INLINEFORM1 scores on pun detection and competitive results on pun location, compared to baselines that do not adopt joint learning in the first block  For location on heterographic puns, our model's performance is slightly lower than the system of BIBREF25 , which is a rule-based locator  Compared to CRF, we can see that our model, either with the INLINEFORM2 or the INLINEFORM3 scheme, yields significantly higher recall on both detection and location tasks, while the precisions are relatively close  This demonstrates the effectiveness of BiLSTM, which learns the contextual features of given texts – such information appears to be helpful in recalling more puns 
Compared to the INLINEFORM0 scheme, the INLINEFORM1 tagging scheme is able to yield better performance on these two tasks  After studying outputs from these two approaches, we found that one leading source of error for the INLINEFORM2 approach is that there exist more than one words in a single instance that are assigned with the INLINEFORM3 tag  However, according to the description of pun in BIBREF9 , each context contains a maximum of one pun  Thus, such a useful structural constraint is not well captured by the simple approach based on the INLINEFORM4 tagging scheme  On the other hand, by applying the INLINEFORM5 tagging scheme, such a constraint is properly captured in the model  As a result, the results for such a approach are significantly better than the approach based on the INLINEFORM6 tagging scheme, as we can observe from the table  Under the same experimental setup, we also attempted to exclude word position features  Results are given by INLINEFORM7 - INLINEFORM8   It is expected that the performance of pun location drops, since such position features are able to capture the interesting property that a pun tends to appear in the second half of a sentence  While such knowledge is helpful for the location task, interestingly, a model without position knowledge yields improved performance on the pun detection task  One possible reason is that detecting whether a sentence contains a pun is not concerned with such word position information 
Additionally, we conduct experiments over sentences containing a pun only, namely 1,607 and 1,271 instances from homographic and heterographic pun corpora separately  It can be regarded as a “pipeline” method where the classifier for pun detection is regarded as perfect  Following the prior work of BIBREF4 , we apply 10-fold cross validation  Since we are given that all input sentences contain a pun, we only report accumulated results on pun location, denoted as Pipeline in Table TABREF11   Compared with our approaches, the performance of such an approach drops significantly  On the other hand, such a fact demonstrates that the two task, detection and location of puns, can reinforce each other  These figures demonstrate the effectiveness of our sequence labeling method to detect and locate English puns in a joint manner 
Error Analysis
We studied the outputs from our system and make some error analysis  We found the errors can be broadly categorized into several types, and we elaborate them here  1) Low word coverage: since the corpora are relatively small, there exist many unseen words in the test set  Learning the representations of such unseen words is challenging, which affects the model's performance  Such errors contribute around 40% of the total errors made by our system  2) Detection errors: we found many errors are due to the model's inability to make correct pun detection  Such inability harms both pun detection and pun location  Although our approach based on the INLINEFORM0 tagging scheme yields relatively higher scores on the detection task, we still found that 40% of the incorrectly predicted instances fall into this group  3) Short sentences: we found it was challenging for our model to make correct predictions when the given text is short  Consider the example “Superglue! Tom rejoined," here the word rejoined is the corresponding pun  However, it would be challenging to figure out the pun with such limited contextual information 
Related Work
Most existing systems address pun detection and location separately  BIBREF22 applied word sense knowledge to conduct pun detection  BIBREF24 trained a bidirectional RNN classifier for detecting homographic puns  Next, a knowledge-based approach is adopted to find the exact pun  Such a system is not applicable to heterographic puns  BIBREF28 applied Google n-gram and word2vec to make decisions  The phonetic distance via the CMU Pronouncing Dictionary is computed to detect heterographic puns  BIBREF10 used the hidden Markov model and a cyclic dependency network with rich features to detect and locate puns  BIBREF23 used a supervised approach to pun detection and a weakly supervised approach to pun location based on the position within the context and part of speech features  BIBREF25 proposed a rule-based system for pun location that scores candidate words according to eleven simple heuristics  Two systems are developed to conduct detection and location separately in the system known as UWAV BIBREF3   The pun detector combines predictions from three classifiers  The pun locator considers word2vec similarity between every pair of words in the context and position to pinpoint the pun  The state-of-the-art system for homographic pun location is a neural method BIBREF4 , where the word senses are incorporated into a bidirectional LSTM model  This method only supports the pun location task on homographic puns  Another line of research efforts related to this work is sequence labeling, such as POS tagging, chunking, word segmentation and NER  The neural methods have shown their effectiveness in this task, such as BiLSTM-CNN BIBREF13 , GRNN BIBREF29 , LSTM-CRF BIBREF30 , LSTM-CNN-CRF BIBREF14 , LM-LSTM-CRF BIBREF15  
In this work, we combine pun detection and location tasks as a single sequence labeling problem  Inspired by the work of BIBREF15 , we also adopt a LSTM-CRF with character embeddings to make labeling decisions 
Conclusion
In this paper, we propose to perform pun detection and location tasks in a joint manner from a sequence labeling perspective  We observe that each text in our corpora contains a maximum of one pun  Hence, we design a novel tagging scheme to incorporate such a constraint  Such a scheme guarantees that there is a maximum of one word that will be tagged as a pun during the testing phase  We also found the interesting structural property such as the fact that most puns tend to appear at the second half of the sentences can be helpful for such a task, but was not explored in previous works  Furthermore, unlike many previous approaches, our approach, though simple, is generally applicable to both heterographic and homographic puns  Empirical results on the benchmark datasets prove the effectiveness of the proposed approach that the two tasks of pun detection and location can be addressed by a single model from a sequence labeling perspective 
Future research includes the investigations on how to make use of richer semantic and linguistic information for detection and location of puns  Research on puns for other languages such as Chinese is still under-explored, which could also be an interesting direction for our future studies 
Acknowledgments
We would like to thank the three anonymous reviewers for their thoughtful and constructive comments  This work is supported by Singapore Ministry of Education Academic Research Fund (AcRF) Tier 2 Project MOE2017-T2-1-156, and is partially supported by SUTD project PIE-SGP-AI-2018-01 

ground_truth: Nguni languages (zul, xho, nbl, ssw), Sotho languages (nso, sot, tsn)

The Nguni languages are similar to each other, The same is true of the Sotho languages

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 baselines do they compare with?
Context 4:  We observed a speedup of 3 2x on IMDb train time per epoch compared to the optimized LSTM implementation provided in NVIDIA's cuDNN library  For specific batch sizes and sequence lengths, a 16x speed gain is possible  Figure FIGREF15 provides extensive speed comparisons 
In Figure FIGREF12 , we visualize the hidden state vectors INLINEFORM0 of the final QRNN layer on part of an example from the IMDb dataset  Even without any post-processing, changes in the hidden state are visible and interpretable in regards to the input  This is a consequence of the elementwise nature of the recurrent pooling function, which delays direct interaction between different channels of the hidden state until the computation of the next QRNN layer 
Language Modeling
We replicate the language modeling experiment of BIBREF2 and BIBREF13 to benchmark the QRNN architecture for natural language sequence prediction  The experiment uses a standard preprocessed version of the Penn Treebank (PTB) by BIBREF25  
We implemented a gated QRNN model with medium hidden size: 2 layers with 640 units in each layer  Both QRNN layers use a convolutional filter width INLINEFORM0 of two timesteps  While the “medium” models used in other work BIBREF2 , BIBREF13 consist of 650 units in each layer, it was more computationally convenient to use a multiple of 32  As the Penn Treebank is a relatively small dataset, preventing overfitting is of considerable importance and a major focus of recent research  It is not obvious in advance which of the many RNN regularization schemes would perform well when applied to the QRNN  Our tests showed encouraging results from zoneout applied to the QRNN's recurrent pooling layer, implemented as described in Section SECREF5  
The experimental settings largely followed the “medium” setup of BIBREF2   Optimization was performed by stochastic gradient descent (SGD) without momentum  The learning rate was set at 1 for six epochs, then decayed by 0 95 for each subsequent epoch, for a total of 72 epochs  We additionally used INLINEFORM0 regularization of INLINEFORM1 and rescaled gradients with norm above 10  Zoneout was applied by performing dropout with ratio 0 1 on the forget gates of the QRNN, without rescaling the output of the dropout function  Batches consist of 20 examples, each 105 timesteps 
Comparing our results on the gated QRNN with zoneout to the results of LSTMs with both ordinary and variational dropout in Table TABREF14 , we see that the QRNN is highly competitive  The QRNN without zoneout strongly outperforms both our medium LSTM and the medium LSTM of BIBREF2 which do not use recurrent dropout and is even competitive with variational LSTMs  This may be due to the limited computational capacity that the QRNN's pooling layer has relative to the LSTM's recurrent weights, providing structural regularization over the recurrence 
Without zoneout, early stopping based upon validation loss was required as the QRNN would begin overfitting  By applying a small amount of zoneout ( INLINEFORM0 ), no early stopping is required and the QRNN achieves competitive levels of perplexity to the variational LSTM of BIBREF13 , which had variational inference based dropout of 0 2 applied recurrently  Their best performing variation also used Monte Carlo (MC) dropout averaging at test time of 1000 different masks, making it computationally more expensive to run 
When training on the PTB dataset with an NVIDIA K40 GPU, we found that the QRNN is substantially faster than a standard LSTM, even when comparing against the optimized cuDNN LSTM  In Figure FIGREF15 we provide a breakdown of the time taken for Chainer's default LSTM, the cuDNN LSTM, and QRNN to perform a full forward and backward pass on a single batch during training of the RNN LM on PTB  For both LSTM implementations, running time was dominated by the RNN computations, even with the highly optimized cuDNN implementation  For the QRNN implementation, however, the “RNN” layers are no longer the bottleneck  Indeed, there are diminishing returns from further optimization of the QRNN itself as the softmax and optimization overhead take equal or greater time  Note that the softmax, over a vocabulary size of only 10,000 words, is relatively small; for tasks with larger vocabularies, the softmax would likely dominate computation time 
It is also important to note that the cuDNN library's RNN primitives do not natively support any form of recurrent dropout  That is, running an LSTM that uses a state-of-the-art regularization scheme at cuDNN-like speeds would likely require an entirely custom kernel 
Character-level Neural Machine Translation
We evaluate the sequence-to-sequence QRNN architecture described in SECREF5 on a challenging neural machine translation task, IWSLT German–English spoken-domain translation, applying fully character-level segmentation  This dataset consists of 209,772 sentence pairs of parallel training data from transcribed TED and TEDx presentations, with a mean sentence length of 103 characters for German and 93 for English  We remove training sentences with more than 300 characters in English or German, and use a unified vocabulary of 187 Unicode code points 
Our best performance on a development set (TED tst2013) was achieved using a four-layer encoder–decoder QRNN with 320 units per layer, no dropout or INLINEFORM0 regularization, and gradient rescaling to a maximum magnitude of 5  Inputs were supplied to the encoder reversed, while the encoder convolutions were not masked  The first encoder layer used convolutional filter width INLINEFORM1 , while the other encoder layers used INLINEFORM2   Optimization was performed for 10 epochs on minibatches of 16 examples using Adam BIBREF28 with INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , and INLINEFORM6   Decoding was performed using beam search with beam width 8 and length normalization INLINEFORM7   The modified log-probability ranking criterion is provided in the appendix 
Results using this architecture were compared to an equal-sized four-layer encoder–decoder LSTM with attention, applying dropout of 0 2  We again optimized using Adam; other hyperparameters were equal to their values for the QRNN and the same beam search procedure was applied  Table TABREF17 shows that the QRNN outperformed the character-level LSTM, almost matching the performance of a word-level attentional baseline 
Related Work
Exploring alternatives to traditional RNNs for sequence tasks is a major area of current research  Quasi-recurrent neural networks are related to several such recently described models, especially the strongly-typed recurrent neural networks (T-RNN) introduced by BIBREF12   While the motivation and constraints described in that work are different, BIBREF12 's concepts of “learnware” and “firmware” parallel our discussion of convolution-like and pooling-like subcomponents  As the use of a fully connected layer for recurrent connections violates the constraint of “strong typing”, all strongly-typed RNN architectures (including the T-RNN, T-GRU, and T-LSTM) are also quasi-recurrent  However, some QRNN models (including those with attention or skip-connections) are not “strongly typed”  In particular, a T-RNN differs from a QRNN as described in this paper with filter size 1 and f-pooling only in the absence of an activation function on INLINEFORM0   Similarly, T-GRUs and T-LSTMs differ from QRNNs with filter size 2 and fo- or ifo-pooling respectively in that they lack INLINEFORM1 on INLINEFORM2 and use INLINEFORM3 rather than sigmoid on INLINEFORM4  
The QRNN is also related to work in hybrid convolutional–recurrent models  BIBREF31 apply CNNs at the word level to generate INLINEFORM0 -gram features used by an LSTM for text classification  BIBREF32 also tackle text classification by applying convolutions at the character level, with a stride to reduce sequence length, then feeding these features into a bidirectional LSTM  A similar approach was taken by BIBREF10 for character-level machine translation  Their model's encoder uses a convolutional layer followed by max-pooling to reduce sequence length, a four-layer highway network, and a bidirectional GRU  The parallelism of the convolutional, pooling, and highway layers allows training speed comparable to subword-level models without hard-coded text segmentation 
The QRNN encoder–decoder model shares the favorable parallelism and path-length properties exhibited by the ByteNet BIBREF33 , an architecture for character-level machine translation based on residual convolutions over binary trees  Their model was constructed to achieve three desired properties: parallelism, linear-time computational complexity, and short paths between any pair of words in order to better propagate gradient signals 
Conclusion
Intuitively, many aspects of the semantics of long sequences are context-invariant and can be computed in parallel (e g , convolutionally), but some aspects require long-distance context and must be computed recurrently  Many existing neural network architectures either fail to take advantage of the contextual information or fail to take advantage of the parallelism  QRNNs exploit both parallelism and context, exhibiting advantages from both convolutional and recurrent neural networks  QRNNs have better predictive accuracy than LSTM-based models of equal hidden size, even though they use fewer parameters and run substantially faster  Our experiments show that the speed and accuracy advantages remain consistent across tasks and at both word and character levels 
Extensions to both CNNs and RNNs are often directly applicable to the QRNN, while the model's hidden states are more interpretable than those of other recurrent architectures as its channels maintain their independence across timesteps  We believe that QRNNs can serve as a building block for long-sequence tasks that were previously impractical with traditional RNNs 
Beam search ranking criterion
The modified log-probability ranking criterion we used in beam search for translation experiments is: DISPLAYFORM0
where INLINEFORM0 is a length normalization parameter BIBREF34 , INLINEFORM1 is the INLINEFORM2 th output character, and INLINEFORM3 is a “target length” equal to the source sentence length plus five characters  This reduces at INLINEFORM4 to ordinary beam search with probabilities: DISPLAYFORM0
and at INLINEFORM0 to beam search with probabilities normalized by length (up to the target length): DISPLAYFORM0
Conveniently, this ranking criterion can be computed at intermediate beam-search timesteps, obviating the need to apply a separate reranking on complete hypotheses 

ground_truth: They compare with the following models: by Pedersen (2017), by Pramanick and Das (2017), by Mikhalkova and Karyakin (2017),  by Vadehra (2017), Indurthi and Oota (2017), by Vechtomova (2017), by (Cai et al., 2018), and CRF.