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. Is SemCor3.0 reflective of English language data in general?
Context 1: Introduction
Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0  Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods 
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge  The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4  Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6 
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma 
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10  Recent neural-based methods are devoted to dealing with this problem  BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer  BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words  However, neither of them can totally beat the best word expert supervised methods 
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network  Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss  Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge 
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system  Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering  Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI)  We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task  In particular, our contribution is two-fold:
1  We construct context-gloss pairs and propose three BERT-based models for WSD 
2  We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems 
Methodology
In this section, we describe our method in detail 
Methodology ::: Task Definition
In WSD, a sentence $s$ usually consists of a series of words: $\lbrace w_1,\cdots ,w_m\rbrace $, and some of the words $\lbrace w_{i_1},\cdots ,w_{i_k}\rbrace $ are targets $\lbrace t_1,\cdots ,t_k\rbrace $ need to be disambiguated  For each target $t$, its candidate senses $\lbrace c_1,\cdots ,c_n\rbrace $ come from entries of its lemma in a pre-defined sense inventory (usually WordNet)  Therefore, WSD task aims to find the most suitable entry (symbolized as unique sense key) for each target in a sentence  See a sentence example in Table TABREF1 
Methodology ::: BERT
BERT BIBREF15 is a new language representation model, and its architecture is a multi-layer bidirectional Transformer encoder  BERT model is pre-trained on a large corpus and two novel unsupervised prediction tasks, i e , masked language model and next sentence prediction tasks are used in pre-training  When incorporating BERT into downstream tasks, the fine-tuning procedure is recommended  We fine-tune the pre-trained BERT model on WSD task 
Methodology ::: BERT ::: BERT(Token-CLS)
Since every target in a sentence needs to be disambiguated to find its exact sense, WSD task can be regarded as a token-level classification task  To incorporate BERT to WSD task, we take the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer for every target lemma, which is the same as the last layer of the Bi-LSTM model BIBREF11 
Methodology ::: GlossBERT
BERT can explicitly model the relationship of a pair of texts, which has shown to be beneficial to many pair-wise natural language understanding tasks  In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem 
We describe our construction method with an example (See Table TABREF1)  There are four targets in this sentence, and here we take target word research as an example:
Methodology ::: GlossBERT ::: Context-Gloss Pairs
The sentence containing target words is denoted as context sentence  For each target word, we extract glosses of all $N$ possible senses (here $N=4$) of the target word (research) in WordNet to obtain the gloss sentence  [CLS] and [SEP] marks are added to the context-gloss pairs to make it suitable for the input of BERT model  A similar idea is also used in aspect-based sentiment analysis BIBREF16 
Methodology ::: GlossBERT ::: Context-Gloss Pairs with Weak Supervision
Based on the previous construction method, we add weak supervised signals to the context-gloss pairs (see the highlighted part in Table TABREF1)  The signal in the gloss sentence aims to point out the target word, and the signal in the context sentence aims to emphasize the target word considering the situation that a target word may occur more than one time in the same sentence 
Therefore, each target word has $N$ context-gloss pair training instances ($label\in \lbrace yes, no\rbrace $)  When testing, we output the probability of $label=yes$ of each context-gloss pair and choose the sense corresponding to the highest probability as the prediction label of the target word  We experiment with three GlossBERT models:
Methodology ::: GlossBERT ::: GlossBERT(Token-CLS)
We use context-gloss pairs as input  We highlight the target word by taking the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer ($label\in \lbrace yes, no\rbrace $) 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS)
We use context-gloss pairs as input  We take the final hidden state of the first token [CLS] as the representation of the whole sequence and add a classification layer ($label\in \lbrace yes, no\rbrace $), which does not highlight the target word 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS-WS)
We use context-gloss pairs with weak supervision as input  We take the final hidden state of the first token [CLS] and add a classification layer ($label\in \lbrace yes, no\rbrace $), which weekly highlight the target word by the weak supervision 
Experiments ::: Datasets
The statistics of the WSD datasets are shown in Table TABREF12 
Experiments ::: Datasets ::: Training Dataset
Following previous work BIBREF13, BIBREF12, BIBREF10, BIBREF17, BIBREF9, BIBREF7, we choose SemCor3 0 as training corpus, which is the largest corpus manually annotated with WordNet sense for WSD 
Experiments ::: Datasets ::: Evaluation Datasets
We evaluate our method on several English all-words WSD datasets  For a fair comparison, we use the benchmark datasets proposed by BIBREF17 which include five standard all-words fine-grained WSD datasets from the Senseval and SemEval competitions: Senseval-2 (SE2), Senseval-3 (SE3), SemEval-2007 (SE07), SemEval-2013 (SE13) and SemEval-2015 (SE15)  Following BIBREF13, BIBREF12 and BIBREF10, we choose SE07, the smallest among these test sets, as the development set 
Experiments ::: Datasets ::: WordNet
Since BIBREF17 map all the sense annotations in these datasets from their original versions to WordNet 3 0, we extract word sense glosses from WordNet 3 0 
Experiments ::: Settings
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task  The number of Transformer blocks is 12, the number of the hidden layer is 768, the number of self-attention heads is 12, and the total number of parameters of the pre-trained model is 110M  When fine-tuning, we use the development set (SE07) to find the optimal settings for our experiments  We keep the dropout probability at 0 1, set the number of epochs to 4  The initial learning rate is 2e-5, and the batch size is 64 
Experiments ::: Results
Table TABREF19 shows the performance of our method on the English all-words WSD benchmark datasets  We compare our approach with previous methods 
The first block shows the MFS baseline, which selects the most frequent sense in the training corpus for each target word 
The second block shows two knowledge-based systems  Lesk$_{ext+emb}$ BIBREF4 is a variant of Lesk algorithm BIBREF2 by calculating the gloss-context overlap of the target word  Babelfy BIBREF6 is a unified graph-based approach which exploits the semantic network structure from BabelNet 
The third block shows two word expert traditional supervised systems  IMS BIBREF7 is a flexible framework which trains SVM classifiers and uses local features  And IMS$_{+emb}$ BIBREF9 is the best configuration of the IMS framework, which also integrates word embeddings as features 
The fourth block shows several recent neural-based methods  Bi-LSTM BIBREF11 is a baseline for neural models  Bi-LSTM$_{+ att  + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task  GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge  CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge 
In the last block, we report the performance of our method  BERT(Token-CLS) is our baseline, which does not incorporate gloss information, and it performs slightly worse than previous traditional supervised methods and recent neural-based methods  It proves that directly using BERT cannot obtain performance growth  The other three methods outperform other models by a substantial margin, which proves that the improvements come from leveraging BERT to better exploit gloss information  It is worth noting that our method achieves significant improvements in SE07 and Verb over previous methods, which have the highest ambiguity level among all datasets and all POS tags respectively according to BIBREF17 
Moreover, GlossBERT(Token-CLS) performs better than GlossBERT(Sent-CLS), which proves that highlighting the target word in the sentence is important  However, the weakly highlighting method GlossBERT(Sent-CLS-WS) performs best in most circumstances, which may result from its combination of the advantages of the other two methods 
Experiments ::: Discussion
There are two main reasons for the great improvements of our experimental results  First, we construct context-gloss pairs and convert WSD problem to a sentence-pair classification task which is similar to NLI tasks and train only one classifier, which is equivalent to expanding the corpus  Second, we leverage BERT BIBREF15 to better exploit the gloss information  BERT model shows its advantage in dealing with sentence-pair classification tasks by its amazing improvement on QA and NLI tasks  This advantage comes from both of its two novel unsupervised prediction tasks 
Compared with traditional word expert supervised methods, our GlossBERT shows its effectiveness to alleviate the effort of feature engineering and does not require training a dedicated classifier for every target lemma  Up to now, it can be said that the neural network method can totally beat the traditional word expert method  Compared with recent neural-based methods, our solution is more intuitive and can make better use of gloss knowledge  Besides, our approach demonstrates that when we fine-tune BERT on a downstream task, converting it into a sentence-pair classification task may be a good choice 
Conclusion
In this paper, we seek to better leverage gloss knowledge in a supervised neural WSD system  We propose a new solution to WSD by constructing context-gloss pairs and then converting WSD to a sentence-pair classification task  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task 
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments  The research work is supported by National Natural Science Foundation of China (No  61751201 and 61672162), Shanghai Municipal Science and Technology Commission (16JC1420401 and 17JC1404100), Shanghai Municipal Science and Technology Major Project (No 2018SHZDZX01) and ZJLab 
Context 2: INTRODUCTION
The idea of language identification is to classify a given audio signal into a particular class using a classification algorithm  Commonly language identification task was done using i-vector systems [1]  A very well known approach for language identification proposed by N  Dahek et al  [1] uses the GMM-UBM model to obtain utterance level features called i-vectors  Recent advances in deep learning [15,16] have helped to improve the language identification task using many different neural network architectures which can be trained efficiently using GPUs for large scale datasets  These neural networks can be configured in various ways to obtain better accuracy for language identification task  Early work on using Deep learning for language Identification was published by Pavel Matejka et al  [2], where they used stacked bottleneck features extracted from deep neural networks for language identification task and showed that the bottleneck features learned by Deep neural networks are better than simple MFCC or PLP features  Later the work by I  Lopez-Moreno et al  [3] from Google showed how to use Deep neural networks to directly map the sequence of MFCC frames into its language class so that we can apply language identification at the frame level  Speech signals will have both spatial and temporal information, but simple DNNs are not able to capture temporal information  Work done by J  Gonzalez-Dominguez et al  [4] by Google developed an LSTM based language identification model which improves the accuracy over the DNN based models  Work done by Alicia et al  [5] used CNNs to improve upon i-vector [1] and other previously developed systems  The work done by Daniel Garcia-Romero et al  [6] has used a combination of Acoustic model trained for speech recognition with Time-delay neural networks where they train the TDNN model by feeding the stacked bottleneck features from acoustic model to predict the language labels at the frame level  Recently X-vectors [7] is proposed for speaker identification task and are shown to outperform all the previous state of the art speaker identification algorithms and are also used for language identification by David Snyder et al  [8] 
In this paper, we explore multiple pooling strategies for language identification task  Mainly we propose Ghost-VLAD based pooling method for language identification  Inspired by the recent work by W  Xie et al  [9] and Y  Zhong et al  [10], we use Ghost-VLAD to improve the accuracy of language identification task for Indian languages  We explore multiple pooling strategies including NetVLAD pooling [11], Average pooling and Statistics pooling( as proposed in X-vectors [7]) and show that Ghost-VLAD pooling is the best pooling strategy for language identification  Our model obtains the best accuracy of 98 24%, and it outperforms all the other previously proposed pooling methods  We conduct all our experiments on 635hrs of audio data for 7 Indian languages collected from $\textbf {All India Radio}$ news channel  The paper is organized as follows  In section 2, we explain the proposed pooling method for language identification  In section 3, we explain our dataset  In section 4, we describe the experiments, and in section 5, we describe the results 
POOLING STRATEGIES
In any language identification model, we want to obtain utterance level representation which has very good language discriminative features  These representations should be compact and should be easily separable by a linear classifier  The idea of any pooling strategy is to pool the frame-level representations into a single utterance level representation  Previous works by [7] have used simple mean and standard deviation aggregation to pool the frame-level features from the top layer of the neural network to obtain the utterance level features  Recently [9] used VLAD based pooling strategy for speaker identification which is inspired from [10] proposed for face recognition  The NetVLAD [11] and Ghost-VLAD [10] methods are proposed for Place recognition and face recognition, respectively, and in both cases, they try to aggregate the local descriptors into global features  In our case, the local descriptors are features extracted from ResNet [15], and the global utterance level feature is obtained by using GhostVLAD pooling  In this section, we explain different pooling methods, including NetVLAD, Ghost-VLAD, Statistic pooling, and Average pooling 
POOLING STRATEGIES ::: NetVLAD pooling
The NetVLAD pooling strategy was initially developed for place recognition by R  Arandjelovic et al  [11]  The NetVLAD is an extension to VLAD [18] approach where they were able to replace the hard assignment based clustering with soft assignment based clustering so that it can be trained with neural network in an end to end fashion  In our case, we use the NetVLAD layer to map N local features of dimension D into a fixed dimensional vector, as shown in Figure 1 (Left side) 
The model takes spectrogram as an input and feeds into CNN based ResNet architecture  The ResNet is used to map the spectrogram into 3D feature map of dimension HxWxD  We convert this 3D feature map into 2D by unfolding H and W dimensions, creating a NxD dimensional feature map, where N=HxW  The NetVLAD layer is kept on top of the feature extraction layer of ResNet, as shown in Figure 1  The NetVLAD now takes N features vectors of dimension D and computes a matrix V of dimension KxD, where K is the number clusters in the NetVLAD layer, and D is the dimension of the feature vector  The matrix V is computed as follows 
Where $w_k$,$b_k$ and $c_k$ are trainable parameters for the cluster $k$ and V(j,k) represents a point in the V matrix for (j,k)th location  The matrix is constructed using the equation (1) where the first term corresponds to the soft assignment of the input $x_i$ to the cluster $c_k$, whereas the second term corresponds to the residual term which tells how far the input descriptor $x_i$ is from the cluster center $c_k$ 
POOLING STRATEGIES ::: GhostVLAD pooling
GhostVLAD is an extension of the NetVLAD approach, which we discussed in the previous section  The GhostVLAD model was proposed for face recognition by Y  Zhong [10]  GhostVLAD works exactly similar to NetVLAD except it adds Ghost clusters along with the NetVLAD clusters  So, now we will have a K+G number of clusters instead of K clusters  Where G is the number of ghost clusters, we want to add (typically 2-4)  The Ghost clusters are added to map any noisy or irrelevant content into ghost clusters and are not included during the feature aggregation stage, as shown in Figure 1 (Right side)  Which means that we compute the matrix V for both normal cluster K and ghost clusters G, but we will not include the vectors belongs to ghost cluster from V during concatenation of the features  Due to which, during feature aggregation stage the contribution of the noisy and unwanted features to normal VLAD clusters are assigned less weights while Ghost clusters absorb most of the weight  We illustrate this in Figure 1(Right Side), where the ghost clusters are shown in red color  We use Ghost clusters when we are computing the V matrix, but they are excluded during the concatenation stage
Context 3: Introduction
Neural Machine Translation (NMT) has shown its effectiveness in translation tasks when NMT systems perform best in recent machine translation campaigns BIBREF0 , BIBREF1   Compared to phrase-based Statistical Machine Translation (SMT) which is basically an ensemble of different features trained and tuned separately, NMT directly modeling the translation relationship between source and target sentences  Unlike SMT, NMT does not require much linguistic information and large monolingual data to achieve good performances 
An NMT consists of an encoder which recursively reads and represents the whole source sentence into a context vector and a recurrent decoder which takes the context vector and its previous state to predict the next target word  It is then trained in an end-to-end fashion to learn parameters which maximizes the likelihood between the outputs and the references  Recently, attention-based NMT has been featured in most state-of-the-art systems  First introduced by BIBREF2 , attention mechanism is integrated in decoder side as feedforward layers  It allows the NMT to decide which source words should take part in the predicting process of the next target words  It helps to improve NMTs significantly  Nevertheless, since the attention mechanism is specific to a particular source sentence and the considering target word, it is also specific to particular language pairs 
Some recent work has focused on extending the NMT framework to multilingual scenarios  By training such network using parallel corpora in number of different languages, NMT could benefit from additional information embedded in a common semantic space across languages  Basically, the proposed NMT are required to employ multiple encoders or multiple decoders to deal with multilinguality  Furthermore, in order to avoid the tight dependency of the attention mechanism to specific language pairs, they also need to modify their architecture to combine either the encoders or the attention layers  These modifications are specific to the purpose of the tasks as well  Thus, those multilingual NMTs are more complicated, much more free parameters to learn and more difficult to perform standard trainings compared to the original NMT 
In this paper, we introduce a unified approach to seamlessly extend the original NMT to multilingual settings  Our approach allows us to integrate any language in any side of the encoder-decoder architecture with only one encoder and one decoder for all the languages involved  Moreover, it is not necessary to do any network modification to enable attention mechanism in our NMT systems  We then apply our proprosed framework in two demanding scenarios: under-resourced translation and zero-resourced translation  The results show that bringing multilinguality to NMT helps to improve individual translations  With some insightful analyses of the results, we set our goal toward a fully multilingual NMT framework 
The paper starts with a detailed introduction to attention-based NMT  In Section SECREF3 , related work about multi-task NMT is reviewed  Section SECREF5 describes our proposed approach and thorough comparisons to the related work  It is followed by a section of evaluating our systems in two aforementioned scenarios, in which different strategies have been employed under a unified approach (Section SECREF4 )  Finally, the paper ends with conclusion and future work 
This work is licenced under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/
Neural Machine Translation: Background
An NMT system consists of an encoder which automatically learns the characteristics of a source sentence into fix-length context vectors and a decoder that recursively combines the produced context vectors with the previous target word to generate the most probable word from a target vocabulary 
More specifically, a bidirectional recurrent encoder reads every words INLINEFORM0 of a source sentence INLINEFORM1 and encodes a representation INLINEFORM2 of the sentence into a fixed-length vector INLINEFORM3 concatinated from those of the forward and backward directions: INLINEFORM4
Here INLINEFORM0 is the one-hot vector of the word INLINEFORM1 and INLINEFORM2 is the word embedding matrix which is shared across the source words  INLINEFORM3 is the recurrent unit computing the current hidden state of the encoder based on the previous hidden state  INLINEFORM4 is then called an annotation vector, which encodes the source sentence up to the time INLINEFORM5 from both forward and backward directions  Recurrent units in NMT can be a simple recurrent neural network unit (RNN), a Long Short-Term Memory unit (LSTM) BIBREF3 or a Gated Recurrent Unit (GRU) BIBREF4
Similar to the encoder, the recurrent decoder generates one target word INLINEFORM0 to form a translated target sentence INLINEFORM1 in the end  At the time INLINEFORM2 , it takes the previous hidden state of the decoder INLINEFORM3 , the previous embedded word representation INLINEFORM4 and a time-specific context vector INLINEFORM5 as inputs to calculate the current hidden state INLINEFORM6 : INLINEFORM7
Again, INLINEFORM0 is the recurrent activation function of the decoder and INLINEFORM1 is the shared word embedding matrix of the target sentences  The context vector INLINEFORM2 is calculated based on the annotation vectors from the encoder  Before feeding the annotation vectors into the decoder, an attention mechanism is set up in between, in order to choose which annotation vectors should contribute to the predicting decision of the next target word  Intuitively, a relevance between the previous target word and the annotation vectors can be used to form some attention scenario  There exists several ways to calculate the relevance as shown in BIBREF5 , but what we describe here follows the proposed method of BIBREF2 DISPLAYFORM0
In BIBREF2 , this attention mechanism, originally called alignment model, has been employed as a simple feedforward network with the first layer is a learnable layer via INLINEFORM0 , INLINEFORM1 and INLINEFORM2   The relevance scores INLINEFORM3 are then normalized into attention weights INLINEFORM4 and the context vector INLINEFORM5 is calculated as the weighted sum of all annotation vectors INLINEFORM6   Depending on how much attention the target word at time INLINEFORM7 put on the source states INLINEFORM8 , a soft alignment is learned  By being employed this way, word alignment is not a latent variable but a parametrized function, making the alignment model differentiable  Thus, it could be trained together with the whole architecture using backpropagation 
One of the most severe problems of NMT is handling of the rare words, which are not in the short lists of the vocabularies, i e  out-of-vocabulary (OOV) words, or do not appear in the training set at all  In BIBREF6 , the rare target words are copied from their aligned source words after the translation  This heuristic works well with OOV words and named entities but unable to translate unseen words  In BIBREF7 , their proposed NMT models have been shown to not only be effective on reducing vocabulary sizes but also have the ability to generate unseen words  This is achieved by segmenting the rare words into subword units and translating them  The state-of-the-art translation systems essentially employ subword NMT BIBREF7  
Universal Encoder and Decoder for Multilingual Neural Machine Translation
While the majority of previous research has focused on improving the performance of NMT on individual language pairs with individual NMT systems, recent work has started investigating potential ways to conduct the translation involved in multiple languages using a single NMT system  The possible reason explaining these efforts lies on the unique architecture of NMT  Unlike SMT, NMT consists of separated neural networks for the source and target sides, or the encoder and decoder, respectively  This allows these components to map a sentence in any language to a representation in an embedding space which is believed to share common semantics among the source languages involved  From that shared space, the decoder, with some implicit or explicit relevant constraints, could transform the representation into a concrete sentence in any desired language  In this section, we review some related work on this matter  We then describe a unified approach toward an universal attention-based NMT scheme  Our approach does not require any architecture modification and it can be trained to learn a minimal number of parameters compared to the other work 
Related Work
By extending the solution of sequence-to-sequence modeling using encoder-decoder architectures to multi-task learning, Luong2016 managed to achieve better performance on some INLINEFORM0 tasks such as translation, parsing and image captioning compared to individual tasks  Specifically in translation, the work utilizes multiple encoders to translate from multiple languages, and multiple decoders to translate to multiple languages  In this view of multilingual translation, each language in source or target side is modeled by one encoder or decoder, depending on the side of the translation  Due to the natural diversity between two tasks in that multi-task learning scenario, e g  translation and parsing, it could not feature the attention mechanism although it has proven its effectiveness in NMT  There exists two directions which proposed for multilingual translation scenarios where they leverage the attention mechanism  The first one is indicated in the work from BIBREF8 , where it introduce an one-to-many multilingual NMT system to translates from one source language into multiple target languages  Having one source language, the attention mechanism is then handed over to the corresponding decoder  The objective function is changed to adapt to multilingual settings  In testing time, the parameters specific to a desired language pair are used to perform the translation 
Firat2016 proposed another approach which genuinely delivers attention-based NMT to multilingual translation  As in BIBREF9 , their approach utilizes one encoder per source language and one decoder per target language for many-to-many translation tasks  Instead of a quadratic number of independent attention layers, however, one single attention mechanism is integrated into their NMT, performing an affine transformation between the hidden layer of INLINEFORM0 source languages and that one of INLINEFORM1 target languages  It is required to change their architecture to accomodate such a complicated shared attention mechanism 
In a separate effort to achieve multilingual NMT, the work of Zoph2016 leverages available parallel data from other language pairs to help reducing possible ambiguities in the translation process into a single target language  They employed the multi-source attention-based NMT in a way that only one attention mechanism is required despite having multiple encoders  To achieve this, the outputs of the encoders were combined before feeding to the attention layer  They implemented two types of encoder combination; One is adding a non-linear layer on the concatenation of the encoders' hidden states  The other is using a variant of LSTM taking the respective gate values from the individual LSTM units of the encoders  As a result, the combined hidden states contain information from both encoders , thus encode the common semantic of the two source languages 
Universal Encoder and Decoder
Inspired by the multi-source NMT as additional parallel data in several languages are expected to benefit single translations, we aim to develop a NMT-based approach toward an universal framework to perform multilingual translation  Our solution features two treatments: 1) Coding the words in different languages as different words in the language-mixed vocabularies and 2) Forcing the NMT to translating a representation of source sentences into the sentences in a desired target language 
Language-specific Coding  When the encoder of a NMT system considers words across languages as different words, with a well-chosen architecture, it is expected to be able to learn a good representation of the source words in an embedding space in which words carrying similar meaning would have a closer distance to each others than those are semantically different  This should hold true when the words have the same or similar surface form, such as (@de@Obama; @en@Obama) or (@de@Projektion; @en@projection)  This should also hold true when the words have the same or similar meaning across languages, such as (@en@car; @en@automobile) or (@de@Flussufer; @en@bank)  Our encoder then acts similarly to the one of multi-source approach BIBREF10 , collecting additional information from other sources for better translations, but with a much simpler embedding function  Unlike them, we need only one encoder, so we could reduce the number of parameters to learn  Furthermore, we neither need to change the network architecture nor depend on which recurrent unit (GRU, LSTM or simple RNN) is currently using in the encoder 
We could apply the same trick to the target sentences and thus enable many-to-many translation capability of our NMT system  Similar to the multi-target translation BIBREF8 , we exploit further the correlation in semantics of those target sentences across different languages  The main difference between our approach and the work of BIBREF8 is that we need only one decoder for all target languages  Given one encoder for multiple source languages and one decoder for multiple target languages, it is trivial to incorporate the attention mechanism as in the case of a regular NMT for single language translation  In training, the attention layers were directed to learn relevant alignments between words in specific language pair and forward the produced context vector to the decoder  Now we rely totally on the network to learn good alignments between source and target sides  In fact, giving more information, our system are able to form nice alignments 
In comparison to other research that could perform complete multi-task learning, e g  the work from BIBREF9 or the approach proposed by BIBREF11 , our method is able to accommodate the attention layers seemlessly and easily  It also draws a clear distinction from those works in term of the complexity of the whole network: considerably less parameters to learn, thus reduces overfitting, with a conventional attention mechanism and a standard training procedure 
Target Forcing  While language-specific coding allows us to implement a multilingual attention-based NMT, there are two issues we have to consider before training the network  The first is that the number of rare words would increase in proportion with the number of languages involved  This might be solved by applying a rare word treatment method with appropriate awareness of the vocabularies' size  The second one is more problematic: Ambiguity level in the translation process definitely increases due to the additional introduction of words having the same or similar meaning across languages at both source and target sides  We deal with the problem by explicitly forcing the attention and translation to the direction that we prefer, expecting the information would limit the ambiguity to the scope of one language instead of all target languages

ground_truth: Yes

Unanswerable

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

Please answer the questions based on the text provided below. Which 7 Indian languages do they experiment with?
Context 2:  These concatenated features are fed into the projection layer, followed by softmax to predict the language label 
POOLING STRATEGIES ::: Statistic and average pooling
In statistic pooling, we compute the first and second order statistics of the local features from the top layer of the ResNet model  The 3-D feature map is unfolded to create N features of D dimensions, and then we compute the mean and standard deviation of all these N vectors and get two D dimensional vectors, one for mean and the other for standard deviation  We then concatenate these 2 features and feed it to the projection layer for predicting the language label 
In the Average pooling layer, we compute only the first-order statistics (mean) of the local features from the top layer of the CNN model  The feature map from the top layer of CNN is unfolded to create N features of D dimensions, and then we compute the mean of all these N vectors and get D dimensional representation  We then feed this feature to the projection layer followed by softmax for predicting the language label 
DATASET
In this section, we describe our dataset collection process  We collected and curated around 635Hrs of audio data for 7 Indian languages, namely Kannada, Hindi, Telugu, Malayalam, Bengali, and English  We collected the data from the All India Radio news channel where an actor will be reading news for about 5-10 mins  To cover many speakers for the dataset, we crawled data from 2010 to 2019  Since the audio is very long to train any deep neural network directly, we segment the audio clips into smaller chunks using Voice activity detector  Since the audio clips will have music embedded during the news, we use Inhouse music detection model to remove the music segments from the dataset to make the dataset clean and our dataset contains 635Hrs of clean audio which is divided into 520Hrs of training data containing 165K utterances and 115Hrs of testing data containing 35K utterances  The amount of audio data for training and testing for each of the language is shown in the table bellow 
EXPERIMENTS
In this section, we describe the feature extraction process and network architecture in detail  We use spectral features of 256 dimensions computed using 512 point FFT for every frame, and we add an energy feature for every frame giving us total 257 features for every frame  We use a window size of 25ms and frame shift of 10ms during feature computation  We crop random 5sec audio data from each utterance during training which results in a spectrogram of size 257x500 (features x number of features)  We use these spectrograms as input to our CNN model during training  During testing, we compute the prediction score irrespective of the audio length 
For the network architecture, we use ResNet-34 architecture, as described in [9]  The model uses convolution layers with Relu activations to map the spectrogram of size 257x500 input into 3D feature map of size 1x32x512  This feature cube is converted into 2D feature map of dimension 32x512 and fed into Ghost-VLAD/NetVLAD layer to generate a representation that has more language discrimination capacity  We use Adam optimizer with an initial learning rate of 0 01 and a final learning rate of 0 00001 for training  Each model is trained for 15 epochs with early stopping criteria 
For the baseline, we train an i-vector model using GMM-UBM  We fit a small classifier on top of the generated i-vectors to measure the accuracy  This model is referred as i-vector+svm   To compare our model with the previous state of the art system, we set up the x-vector language identification system [8]  The x-vector model used time-delay neural networks (TDNN) along with statistic-pooling  We use 7 layer TDNN architecture similar to [8] for training  We refer to this model as tdnn+stat-pool   Finally, we set up a Deep LSTM based language identification system similar to [4] but with little modification where we add statistics pooling for the last layers hidden activities before classification  We use 3 layer Bi-LSTM with 256 hidden units at each layer  We refer to this model as LSTM+stat-pool  We train our i-vector+svm and TDNN+stat-pool using Kaldi toolkit  We train our NetVLAD and GhostVLAD experiments using Keras by modifying the code given by [9] for language identification  We train the LSTM+stat-pool and the remaining experiments using Pytorch [14] toolkit, and we will opensource all the codes and data soon 
RESULTS
In this section, we compare the performance of our system with the recent state of the art language identification approaches  We also compare different pooling strategies and finally, compare the robustness of our system to the length of the input spectrogram during training  We visualize the embeddings generated by the GhostVLAD method and conclude that the GhostVLAD embeddings shows very good feature discrimination capabilities 
RESULTS ::: Comparison with different approaches
We compare our system performance with the previous state of the art language identification approaches, as shown in Table 2  The i-vector+svm system is trained using GMM-UBM models to generate i-vectors as proposed in [1]  Once the i-vectors are extracted, we fit SVM classifier to classify the audio  The TDNN+stat-pool system is trained with a statistics pooling layer and is called the x-vector system as proposed by David Snyder et al  [11] and is currently the state of the art language identification approach as far as our knowledge  Our methods outperform the state of the art x-vector system by absolute 1 88% improvement in F1-score, as shown in Table 2 
RESULTS ::: Comparison with different pooling techniques
We compare our approach with different pooling strategies in Table 3  We use ResNet as our base feature extraction network  We keep the base network the same and change only the pooling layers to see which pooling approach performs better for language identification task  Our experiments show that GhostVLAD pooling outperforms all the other pooling methods by achieving 98 43% F1-Score 
RESULTS ::: Duration analysis
To observe the performance of our method with different input durations, we conducted an experiment where we train our model on different input durations  Since our model uses ResNet as the base feature extractor, we need to feed fixed-length spectrogram  We conducted 4 different experiments where we trained the model using 2sec, 3sec, 4sec and 5sec spectrograms containing 200,300,400 and 500 frames respectively  We observed that the model trained with a 5sec spectrogram is the best model, as shown in Table 4 
RESULTS ::: Visualization of embeddings
We visualize the embeddings generated by our approach to see the effectiveness  We extracted 512-dimensional embeddings for our testing data and reduced the dimensionality using t-sne projection  The t-sne plot of the embeddings space is shown in Figure 3  The plot shows that the embeddings learned by our approach has very good discriminative properties
Conclusion
In this work, we use Ghost-VLAD pooling approach that was originally proposed for face recognition to improve language identification performance for Indian languages  We collected and curated 630 hrs audio data from news All India Radio news channel for 7 Indian languages  Our experimental results shows that our approach outperforms the previous state of the art methods by an absolute 1 88% F1-score  We have also conducted experiments with different pooling strategies proposed in the past, and the GhostVLAD pooling approach turns out to be the best approach for aggregating frame-level features into a single utterance level feature  Our experiments also prove that our approach works much better even if the input during training contains smaller durations  Finally, we see that the embeddings generated by our method has very good language discriminative features and helps to improve the performance of language identification 

ground_truth: Hindi, English, Kannada, Telugu, Assamese, Bengali and Malayalam

Kannada, Hindi, Telugu, Malayalam, Bengali,  English and Assamese (in table, missing in text)

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 test their framework performance on commonly used language pairs, such as English-to-German?
Context 3:  We realize this idea by adding at the beginning and at the end of every source sentences a special symbol indicating the language they would be translated into  For example, in a multilingual NMT, when a source sentence is German and the target language is English, the original sentence (already language-specific coded) is:
@de@darum @de@geht @de@es @de@in @de@meinem @de@Vortrag
Now when we force it to be translated into English, the target-forced sentence becomes:
<E> @de@darum @de@geht @de@es @de@in @de@meinem @de@Vortrag <E>
Due to the nature of recurrent units used in the encoder and decoder, in training, those starting symbols encourage the network learning the translation of following target words in a particular language pair  In testing time, information of the target language we provided help to limit the translated candidates, hence forming the translation in the desired language 
Figure FIGREF8 illustrates the essence of our approach  With two steps in the preprocessing phase, namely language-specific coding and target forcing, we are able to employ multilingual attention-based NMT without any special treatment in training such a standard architecture  Our encoder and attention-enable decoder can be seen as a shared encoder and decoder across languages, or an universal encoder and decoder  The flexibitily of our approach allow us to integrate any language into source or target side  As we will see in Section SECREF4 , it has proven to be extremely helpful not only in low-resourced scenarios but also in translation of well-resourced language pairs as it provides a novel way to make use of large monolingual corpora in NMT 
Evaluation
In this section, we describe the evaluation of our proposed approach in comparisons with the strong baselines using NMT in two scenarios: the translation of an under-resource language pair and the translation of a language pair that does not exist any paralled data at all 
Experimental Settings
Training Data  We choose WIT3's TED corpus BIBREF12 as the basis of our experiments since it might be the only high-quality parallel data of many low-resourced language pairs  TED is also multilingual in a sense that it includes numbers of talks which are commonly translated into many languages  In addition, we use a much larger corpus provided freely by WMT organizers when we evaluate the impact of our approach in a real machine translation campaign  It includes the paralled corpus extracted from the digital corpus of European Parliament (EPPS), the News Commentary (NC) and the web-crawled parallel data (CommonCrawl)  While the number of sentences in popular TED corpora varies from 13 thousands to 17 thousands, the total number of sentences in those larger corpus is approximately 3 million sentences 
Neural Machine Translation Setup  All experiments have been conducted using NMT framework Nematus, Following the work of Sennrich2016a, subword segmentation is handled in the prepocessing phase using Byte-Pair Encoding (BPE)  Excepts stated clearly in some experiments, we set the number of BPE merging operations at 39500 on the joint of source and target data  When training all NMT systems, we take out the sentence pairs exceeding 50-word length and shuffle them inside every minibatch  Our short-list vocabularies contain 40,000 most frequent words while the others are considered as rare words and applied the subword translation  We use an 1024-cell GRU layer and 1000-dimensional embeddings with dropout at every layer with the probability of 0 2 in the embedding and hidden layers and 0 1 in the input and ourput layers  We trained our systems using gradient descent optimization with Adadelta BIBREF13 on minibatches of size 80 and the gradient is rescaled whenever its norm exceed 1 0  All the trainings last approximately seven days if the early-stopping condition could not be reached  At a certain time, an external evaluation script on BLEU BIBREF14 is conducted on a development set to decide the early-stopping condition  This evaluation script has also being used to choose the model archiving the best BLEU on the development set instead of the maximal loglikelihood between the translations and target sentences while training  In translation, the framework produces INLINEFORM0 -best candidates and we then use a beam search with the beam size of 12 to get the best translation 
Under-resourced Translation
First, we consider the translation for an under-resourced pair of languages  Here a small portion of the large parallel corpus for English-German is used as a simulation for the scenario where we do not have much parallel data: Translating texts in English to German  We perform language-specific coding in both source and target sides  By accommodating the German monolingual data as an additional input (German INLINEFORM0 German), which we called the mix-source approach, we could enrich the training data in a simple, natural way  Given this under-resourced situation, it could help our NMT obtain a better representation of the source side, hence, able to learn the translation relationship better  Including monolingual data in this way might also improve the translation of some rare word types such as named entities  Furthermore, as the ultimate goal of our work, we would like to investigate the advantages of multilinguality in NMT  We incorporate a similar portion of French-German parallel corpus into the English-German one  As discussed in Section SECREF5 , it is expected to help reducing the ambiguity in translation between one language pair since it utilizes the semantic context provided by the other source language  We name this mix-multi-source 
Table TABREF16 summarizes the performance of our systems measured in BLEU on two test sets, tst2013 and tst2014  Compared to the baseline NMT system which is solely trained on TED English-German data, our mix-source system achieves a considerable improvement of 2 6 BLEU points on tst2013 and 2 1 BLEU points on and tst2014   Adding French data to the source side and their corresponding German data to the target side in our mix-multi-source system also help to gain 2 2 and 1 6 BLEU points more on tst2013 tst2014, respectively  We observe a better improvement from our mix-source system compared to our mix-multi-source system  We speculate the reason that the mix-source encoder utilize the same information shared in two languages while the mix-multi-source receives and processes similar information in the other language but not necessarily the same  We might validate this hypothesis by comparing two systems trained on a common English-German-French corpus of TED  We put it in our future work's plan 
As we expected Figure FIGREF19 shows how different words in different languages can be close in the shared space after being learned to translate into a common language  We extract the word embeddings from the encoder of the mix-multi-source (En,Fr INLINEFORM0 De,De) after training, remove the language-specific codes (@en@ and @fr@)and project the word vectors to the 2D space using t-SNE BIBREF15  
Using large monolingual data in NMT 
A standard NMT system employs parallel data only  While good parallel corpora are limited in number, getting monolingual data of an arbitrary language is trivial  To make use of German monolingual corpus in an English INLINEFORM0 German NMT system, sennrich2016b built a separate German INLINEFORM1 English NMT using the same parallel corpus, then they used that system to translate the German monolingual corpus back to English, forming a synthesis parallel data  gulcehre2015 trained another RNN-based language model to score the monolingual corpus and integrate it to the NMT system through shallow or deep fusion  Both methods requires to train separate systems with possibly different hyperparameters for each  Conversely, by applying mix-source method to the big monolingual data, we need to train only one network  We mix the TED parallel corpus and the substantial monolingual corpus (EPPS+NC+CommonCrawl) and train a mix-source NMT system from those data 
The first result is not encouraging when its performance is even worse than the baseline NMT which is trained on the small parallel data only  Not using the same information in the source side, as we discussed in case of mix-multi-source strategy, could explain the degrading in performance of such a system  But we believe that the magnitude and unbalancing of the corpus are the main reasons  The data contains nearly four millions sentences but only around twenty thousands of them (0 5%) are the genuine parallel data  As a quick attempt, after we get the model with that big data, we continue training on the real parallel corpus for some more epochs  When this adaptation is applied, our system brings an improvement of +1 52 BLEU on tst2013 and +1 06 BLEU on tst2014 (Table TABREF21 ) 
Zero-resourced Translation
Among low-resourced scenarios, zero-resourced translation task stands in an extreme level  A zero-resourced translation task is one of the most difficult situation when there is no parallel data between the translating language pair  To the best of our knowledge, there have been yet existed a published work about using NMT for zero-resourced translation tasks up to now  In this section, we extend our strategies using the proposed multilingual NMT approach as first attempts to this extreme situation 
We employ language-specific coding and target forcing in a strategy called bridge  Unlike the strategies used in under-resourced translation task, bridge is an entire many-to-many multilingual NMT  Simulating a zero-resourced German INLINEFORM0 French translation task given the available German-English and English-French parallel corpora, after applying language-specific coding and target forcing for each corpus, we mix those data with an English-English data as a “bridge” creating some connection between German and French  We also propose a variant of this strategy that we incorporate French-French data  And we call it universal 
We evaluate bridge and universal systems on two German INLINEFORM0 French test sets  They are compared to a direct system, which is an NMT trained on German INLINEFORM1 French data, and to a pivot system, which essentially consists of two separate NMTs trained to translate from German to English and English to French  The direct system should not exist in a real zero-resourced situation  We refer it as the perfect system for comparison purpose only  In case of the pivot system, to generate a translated text in French from a German sentence, we first translate it to English, then the output sentence is fed to the English INLINEFORM2 German NMT system to obtain the French translation  Since there are more than two languages involved in those systems, we increase the number of BPE merging operations proportionally in order to reduce the number of rare words in such systems  We do not expect our proposed systems to perform well with this primitive way of building direct translating connections since this is essentially a difficult task  We report the performance of those systems in Table TABREF23  
Unsupprisingly, both bridge and universal systems perform worse than the pivot one  We consider two possible reasons:
Our target forcing mechanism is moderately primitive  Since the process is applied after language-specific coding, the target forcing symbol is the same for all source sentences in every languages  Thus, the forcing strength might not be enough to guide the decision of the next words  Once the very first word is translated into a word in wrong language, the following words tend to be translated into that wrong language again  Table TABREF24 shows some statistics of the translated words and sentences in wrong language 
Balancing of the training corpus  Although it is not severe as in the case of mix-source system for large monolingual data, the limited number of sentences in target language can affect the training  The difference of 1 07 BLEU points between bridge and universal might explain this assumption as we added more target data (French) in universal strategy, thus reducing the unbalance in training 
Those issues would be addressed in our following future work toward the multilingual attention-based NMT 
Conclusion and Future Work
In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework  By treating words in different languages as different words and force the attention and translation to the direction of desired target language, we are able to employ attention-enable NMT toward a multilingual translation system  Our proposed approach alleviates the need of complicated architecture re-designing when accommodating attention mechanism  In addition, the number of free parameters to learn in our network does not go beyond that magnitute of a single NMT system  With its universality, our approach has shown its effectiveness in an under-resourced translation task with considerable improvements  In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages 
Nevertheless, there are issues that we can continue working on to address in future work  A more balancing data would be helpful for this framework  The mechanism of forcing the NMT system to the right target language could be improved  We could conduct more detailed analyses of the various strategies under the framework to show its universarity 

ground_truth: Yes

Yes