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 evaluate only on English datasets?
Context 1: Introduction
Data annotation is a major bottleneck for the application of supervised learning approaches to many problems  As a result, unsupervised methods that learn directly from unlabeled data are increasingly important  For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent years – for example, for both part-of-speech (POS) induction BIBREF0 , BIBREF1 and unsupervised dependency parsing BIBREF2 , BIBREF3 , BIBREF4   While similar models have had success on a range of unsupervised tasks, they have mostly ignored the apparent utility of continuous word representations evident from supervised NLP applications BIBREF5 , BIBREF6   In this work, we focus on leveraging and explicitly representing continuous word embeddings within unsupervised models of syntactic structure 
Pre-trained word embeddings from massive unlabeled corpora offer a compact way of injecting a prior notion of word similarity into models that would otherwise treat words as discrete, isolated categories  However, the specific properties of language captured by any particular embedding scheme can be difficult to control, and, further, may not be ideally suited to the task at hand  For example, pre-trained skip-gram embeddings BIBREF7 with small context window size are found to capture the syntactic properties of language well BIBREF8 , BIBREF9   However, if our goal is to separate syntactic categories, this embedding space is not ideal – POS categories correspond to overlapping interspersed regions in the embedding space, evident in Figure SECREF4  
In our approach, we propose to learn a new latent embedding space as a projection of pre-trained embeddings (depicted in Figure SECREF5 ), while jointly learning latent syntactic structure – for example, POS categories or syntactic dependencies  To this end, we introduce a new generative model (shown in Figure FIGREF6 ) that first generates a latent syntactic representation (e g  a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function  The latent embeddings can be jointly learned with the structured syntax model in a completely unsupervised fashion 
By choosing an invertible neural network as our non-linear projector, and then parameterizing our model in terms of the projection's inverse, we are able to derive tractable exact inference and marginal likelihood computation procedures so long as inference is tractable in the underlying syntax model  In sec:learn-with-inv we show that this derivation corresponds to an alternate view of our approach whereby we jointly learn a mapping of observed word embeddings to a new embedding space that is more suitable for the syntax model, but include an additional Jacobian regularization term to prevent information loss 
Recent work has sought to take advantage of word embeddings in unsupervised generative models with alternate approaches BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12   BIBREF9 build an HMM with Gaussian emissions on observed word embeddings, but they do not attempt to learn new embeddings  BIBREF10 , BIBREF11 , and BIBREF12 extend HMM or dependency model with valence (DMV) BIBREF2 with multinomials that use word (or tag) embeddings in their parameterization  However, they do not represent the embeddings as latent variables 
In experiments, we instantiate our approach using both a Markov-structured syntax model and a tree-structured syntax model – specifically, the DMV  We evaluate on two tasks: part-of-speech (POS) induction and unsupervised dependency parsing without gold POS tags  Experimental results on the Penn Treebank BIBREF13 demonstrate that our approach improves the basic HMM and DMV by a large margin, leading to the state-of-the-art results on POS induction, and state-of-the-art results on unsupervised dependency parsing in the difficult training scenario where neither gold POS annotation nor punctuation-based constraints are available 
Model
As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations  Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector  Lastly, we extend our approach to more general syntactic structures 
Example: Gaussian HMM
We start by describing the Gaussian hidden Markov model introduced by BIBREF9 , which is a locally normalized model with multinomial transitions and Gaussian emissions  Given a sentence of length INLINEFORM0 , we denote the latent POS tags as INLINEFORM1 , observed (pre-trained) word embeddings as INLINEFORM2 , transition parameters as INLINEFORM3 , and Gaussian emission parameters as INLINEFORM4   The joint distribution of data and latent variables factors as:
DISPLAYFORM0
where INLINEFORM0 is the multinomial transition probability and INLINEFORM1 is the multivariate Gaussian emission probability 
While the observed word embeddings do inform this model with a notion of word similarity – lacking in the basic multinomial HMM – the Gaussian emissions may not be sufficiently flexible to separate some syntactic categories in the complex pre-trained embedding space – for example the skip-gram embedding space as visualized in Figure SECREF4 where different POS categories overlap  Next we introduce a new approach that adds flexibility to the emission distribution by incorporating new latent embedding variables 
Markov Structure with Neural Projector
To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space  We denote the latent embedding of the INLINEFORM0 word in a sentence as INLINEFORM1 , and the neural projection function as INLINEFORM2 , parameterized by INLINEFORM3   In the case of sequential Markov structure, our new model corresponds to the following generative process:
For each time step INLINEFORM0 ,
[noitemsep, leftmargin=*]
Draw the latent state INLINEFORM0
Draw the latent embedding INLINEFORM0
Deterministically produce embedding
INLINEFORM0
The graphical model is depicted in Figure FIGREF6   The deterministic projection can also be viewed as sampling each observation from a point mass at INLINEFORM0   The joint distribution of our model is: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, and INLINEFORM1 is the Dirac delta function centered at INLINEFORM2 : DISPLAYFORM0
General Structure with Neural Projector
Our approach can be applied to a broad family of structured syntax models  We denote latent embedding variables as INLINEFORM0 , discrete latent variables in the syntax model as INLINEFORM1 ( INLINEFORM2 ), where INLINEFORM3 are conditioned to generate INLINEFORM4   The joint probability of our model factors as:
DISPLAYFORM0
where INLINEFORM0 represents the probability of the syntax model, and can encode any syntactic structure – though, its factorization structure will determine whether inference is tractable in our full model  As shown in Figure FIGREF6 , we focus on two syntax models for syntactic analysis in this paper  The first is Markov-structured, which we use for POS induction, and the second is DMV-structured, which we use to learn dependency parses without supervision 
The marginal data likelihood of our model is: DISPLAYFORM0
While the discrete variables INLINEFORM0 can be marginalized out with dynamic program in many cases, it is generally intractable to marginalize out the latent continuous variables, INLINEFORM1 , for an arbitrary projection INLINEFORM2 in Eq  ( EQREF17 ), which means inference and learning may be difficult  In sec:opt, we address this issue by constraining INLINEFORM3 to be invertible, and show that this constraint enables tractable exact inference and marginal likelihood computation 
Learning & Inference
In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge  Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists  Invertible transformations have been explored before in independent components analysis BIBREF14 , gaussianization BIBREF15 , and deep density models BIBREF16 , BIBREF17 , BIBREF18 , for unstructured data  Here, we generalize this style of approach to structured learning, and augment it with discrete latent variables ( INLINEFORM2 )  Under the invertibility condition, we derive a learning algorithm and give another view of our approach revealed by the objective function  Then, we present the architecture of a neural projector we use in experiments: a volume-preserving invertible neural network proposed by BIBREF16 for independent components estimation 
Learning with Invertibility
For ease of exposition, we explain the learning algorithm in terms of Markov structure without loss of generality  As shown in Eq  ( EQREF17 ), the optimization challenge in our approach comes from the intractability of the marginalized emission factor INLINEFORM0   If we can marginalize out INLINEFORM1 and compute INLINEFORM2 , then the posterior and marginal likelihood of our Markov-structured model can be computed with the forward-backward algorithm  We can apply Eq  ( EQREF14 ) and obtain : INLINEFORM3
By using the change of variable rule to the integration, which allows the integration variable INLINEFORM0 to be replaced by INLINEFORM1 , the marginal emission factor can be computed in closed-form when the invertibility condition is satisfied: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, INLINEFORM1 is the Jacobian matrix of function INLINEFORM2 at INLINEFORM3 , and INLINEFORM4 represents the absolute value of its determinant  This Jacobian term is nonzero and differentiable if and only if INLINEFORM5 exists 
Eq  ( EQREF19 ) shows that we can directly calculate the marginal emission distribution INLINEFORM0   Denote the marginal data likelihood of Gaussian HMM as INLINEFORM1 , then the log marginal data likelihood of our model can be directly written as: DISPLAYFORM0
where INLINEFORM0 represents the new sequence of embeddings after applying INLINEFORM1 to each INLINEFORM2   Eq  ( EQREF20 ) shows that the training objective of our model is simply the Gaussian HMM log likelihood with an additional Jacobian regularization term  From this view, our approach can be seen as equivalent to reversely projecting the data through INLINEFORM3 to another manifold INLINEFORM4 that is directly modeled by the Gaussian HMM, with a regularization term  Intuitively, we optimize the reverse projection INLINEFORM5 to modify the INLINEFORM6 space, making it more appropriate for the syntax model  The Jacobian regularization term accounts for the volume expansion or contraction behavior of the projection  Maximizing it can be thought of as preventing information loss  In the extreme case, the Jacobian determinant is equal to zero, which means the projection is non-invertible and thus information is being lost through the projection  Such “information preserving” regularization is crucial during optimization, otherwise the trivial solution of always projecting data to the same single point to maximize likelihood is viable 
More generally, for an arbitrary syntax model the data likelihood of our approach is: DISPLAYFORM0
If the syntax model itself allows for tractable inference and marginal likelihood computation, the same dynamic program can be used to marginalize out INLINEFORM0   Therefore, our joint model inherits the tractability of the underlying syntax model 
Invertible Volume-Preserving Neural Net
For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice  However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive  To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19   Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”  This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property) 
From Eq  ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1   Thus, we directly define the architecture of INLINEFORM2   As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer  The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively  A single coupling layer is defined as: DISPLAYFORM0
where INLINEFORM0 is the coupling function and can be any nonlinear form  This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal  Thus the Jacobian determinant is always equal to one (i e  volume-preserving) and the invertibility condition is naturally satisfied 
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16   Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24   For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same  Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties  Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20  
Experiments
In this section, we first describe our datasets and experimental setup  We then instantiate our approach with Markov and DMV-structured syntax models, and report results on POS tagging and dependency grammar induction respectively  Lastly, we analyze the learned latent embeddings 
Data
For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank  To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9   Following BIBREF9 , the dimensionality INLINEFORM0 is set to 100, and the training context window size is set to 1 to encode more syntactic information  The skip-gram embeddings are trained on the one billion word language modeling benchmark dataset BIBREF21 in addition to the WSJ corpus 
General Experimental Setup
For the neural projector, we employ rectified networks as coupling function INLINEFORM0 following BIBREF16   We use a rectified network with an input layer, one hidden layer, and linear output units, the number of hidden units is set to the same as the number of input units  The number of coupling layers are varied as 4, 8, 16 for both tasks  We optimize marginal data likelihood directly using Adam BIBREF22   For both tasks in the fully unsupervised setting, we do not tune the hyper-parameters using supervised data 
Unsupervised POS tagging
For unsupervised POS tagging, we use a Markov-structured syntax model in our approach, which is a popular structure for unsupervised tagging tasks BIBREF9 , BIBREF10  
Following existing literature, we train and test on the entire WSJ corpus (49208 sentences, 1M tokens)  We use 45 tag clusters, the number of POS tags that appear in WSJ corpus  We train the discrete HMM and the Gaussian HMM BIBREF9 as baselines  For the Gaussian HMM, mean vectors of Gaussian emissions are initialized with the empirical mean of all word vectors with an additive noise  We assume diagonal covariance matrix for INLINEFORM0 and initialize it with the empirical variance of the word vectors  Following BIBREF9 , the covariance matrix is fixed during training  The multinomial probabilities are initialized as INLINEFORM1 , where INLINEFORM2   For our approach, we initialize the syntax model and Gaussian parameters with the pre-trained Gaussian HMM  The weights of layers in the rectified network are initialized from a uniform distribution with mean zero and a standard deviation of INLINEFORM3 , where INLINEFORM4 is the input dimension  We evaluate the performance of POS tagging with both Many-to-One (M-1) accuracy BIBREF23 and V-Measure (VM) BIBREF24   Given a model we found that the tagging performance is well-correlated with the training data likelihood, thus we use training data likelihood as a unsupervised criterion to select the trained model over 10 random restarts after training 50 epochs  We repeat this process 5 times and report the mean and standard deviation of performance 
We compare our approach with basic HMM, Gaussian HMM, and several state-of-the-art systems, including sophisticated HMM variants and clustering techniques with hand-engineered features  The results are presented in Table TABREF32   Through the introduced latent embeddings and additional neural projection, our approach improves over the Gaussian HMM by 5 4 points in M-1 and 5 6 points in VM  Neural HMM (NHMM) BIBREF10 is a baseline that also learns word representation jointly  Both their basic model and extended Conv version does not outperform the Gaussian HMM  Their best model incorporates another LSTM to model long distance dependency and breaks the Markov assumption, yet our approach still achieves substantial improvement over it without considering more context information  Moreover, our method outperforms the best published result that benefits from hand-engineered features BIBREF27 by 2 0 points on VM 
We found that most tagging errors happen in noun subcategories  Therefore, we do the one-to-one mapping between gold POS tags and induced clusters and plot the normalized confusion matrix of noun subcategories in Figure FIGREF35   The Gaussian HMM fails to identify “NN” and “NNS” correctly for most cases, and it often recognizes “NNPS” as “NNP”  In contrast, our approach corrects these errors well 
Unsupervised Dependency Parsing without gold POS tags
For the task of unsupervised dependency parse induction, we employ the Dependency Model with Valence (DMV) BIBREF2 as the syntax model in our approach  DMV is a generative model that defines a probability distribution over dependency parse trees and syntactic categories, generating tokens and dependencies in a head-outward fashion  While, traditionally, DMV is trained using gold POS tags as observed syntactic categories, in our approach, we treat each tag as a latent variable, as described in sec:general-neural 
Most existing approaches to this task are not fully unsupervised since they rely on gold POS tags following the original experimental setup for DMV  This is partially because automatically parsing from words is difficult even when using unsupervised syntactic categories BIBREF29   However, inducing dependencies from words alone represents a more realistic experimental condition since gold POS tags are often unavailable in practice  Previous work that has trained from words alone often requires additional linguistic constraints (like sentence internal boundaries) BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , acoustic cues BIBREF33 , additional training data BIBREF4 , or annotated data from related languages BIBREF34   Our approach is naturally designed to train on word embeddings directly, thus we attempt to induce dependencies without using gold POS tags or other extra linguistic information 
Like previous work we use sections 02-21 of WSJ corpus as training data and evaluate on section 23, we remove punctuations and train the models on sentences of length INLINEFORM0 , “head-percolation” rules BIBREF39 are applied to obtain gold dependencies for evaluation  We train basic DMV, extended DMV (E-DMV) BIBREF35 and Gaussian DMV (which treats POS tag as unknown latent variables and generates observed word embeddings directly conditioned on them following Gaussian distribution) as baselines  Basic DMV and E-DMV are trained with Viterbi EM BIBREF40 on unsupervised POS tags induced from our Markov-structured model described in sec:pos  Multinomial parameters of the syntax model in both Gaussian DMV and our model are initialized with the pre-trained DMV baseline  Other parameters are initialized in the same way as in the POS tagging experiment  The directed dependency accuracy (DDA) is used for evaluation and we report accuracy on sentences of length INLINEFORM1 and all lengths  We train the parser until training data likelihood converges, and report the mean and standard deviation over 20 random restarts 
Our model directly observes word embeddings and does not require gold POS tags during training  Thus, results from related work trained on gold tags are not directly comparable  However, to measure how these systems might perform without gold tags, we run three recent state-of-the-art systems in our experimental setting: UR-A E-DMV BIBREF36 , Neural E-DMV BIBREF11 , and CRF Autoencoder (CRFAE) BIBREF37   We use unsupervised POS tags (induced from our Markov-structured model) in place of gold tags  We also train basic DMV on gold tags and include several state-of-the-art results on gold tags as reference points 
As shown in Table TABREF39 , our approach is able to improve over the Gaussian DMV by 4 8 points on length INLINEFORM0 and 4 8 points on all lengths, which suggests the additional latent embedding layer and neural projector are helpful  The proposed approach yields, to the best of our knowledge, state-of-the-art performance without gold POS annotation and without sentence-internal boundary information  DMV, UR-A E-DMV, Neural E-DMV, and CRFAE suffer a large decrease in performance when trained on unsupervised tags – an effect also seen in previous work BIBREF29 , BIBREF34   Since our approach induces latent POS tags jointly with dependency trees, it may be able to learn POS clusters that are more amenable to grammar induction than the unsupervised tags  We observe that CRFAE underperforms its gold-tag counterpart substantially  This may largely be a result of the model's reliance on prior linguistic rules that become unavailable when gold POS tag types are unknown  Many extensions to DMV can be considered orthogonal to our approach – they essentially focus on improving the syntax model  It is possible that incorporating these more sophisticated syntax models into our approach may lead to further improvements 
Sensitivity Analysis
In the above experiments we initialize the structured syntax components with the pre-trained Gaussian or discrete baseline, which is shown as a useful technique to help train our deep models  We further study the results with fully random initialization  In the POS tagging experiment, we report the results in Table TABREF48   While the performance with 4 layers is comparable to the pre-trained Gaussian initialization, deeper projections (8 or 16 layers) result in a dramatic drop in performance  This suggests that the structured syntax model with very deep projections is difficult to train from scratch, and a simpler projection might be a good compromise in the random initialization setting 
Different from the Markov prior in POS tagging experiments, our parsing model seems to be quite sensitive to the initialization  For example, directed accuracy of our approach on sentences of length INLINEFORM0 is below 40 0 with random initialization  This is consistent with previous work that has noted the importance of careful initialization for DMV-based models such as the commonly used harmonic initializer BIBREF2   However, it is not straightforward to apply the harmonic initializer for DMV directly in our model without using some kind of pre-training since we do not observe gold POS 
We investigate the effect of the choice of pre-trained embedding on performance while using our approach  To this end, we additionally include results using fastText embeddings BIBREF41 – which, in contrast with skip-gram embeddings, include character-level information  We set the context windows size to 1 and the dimension size to 100 as in the skip-gram training, while keeping other parameters set to their defaults  These results are summarized in Table TABREF50 and Table TABREF51   While fastText embeddings lead to reduced performance with our model, our approach still yields an improvement over the Gaussian baseline with the new observed embeddings space 
Qualitative Analysis of Embeddings
We perform qualitative analysis to understand how the latent embeddings help induce syntactic structures  First we filter out low-frequency words and punctuations in WSJ, and visualize the rest words (10k) with t-SNE BIBREF42 under different embeddings  We assign each word with its most likely gold POS tags in WSJ and color them according to the gold POS tags 
For our Markov-structured model, we have displayed the embedding space in Figure SECREF5 , where the gold POS clusters are well-formed  Further, we present five example target words and their five nearest neighbors in terms of cosine similarity  As shown in Table TABREF53 , the skip-gram embedding captures both semantic and syntactic aspects to some degree, yet our embeddings are able to focus especially on the syntactic aspects of words, in an unsupervised fashion without using any extra morphological information 
In Figure FIGREF54 we depict the learned latent embeddings with the DMV-structured syntax model  Unlike the Markov structure, the DMV structure maps a large subset of singular and plural nouns to the same overlapping region  However, two clusters of singular and plural nouns are actually separated  We inspect the two clusters and the overlapping region in Figure FIGREF54 , it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model  In contrast, the nouns in the overlapping region are typically objects  This demonstrates that the latent embeddings are focusing on aspects of language that are specifically important for modeling dependency without ever having seen examples of dependency parses  Some previous work has deliberately created embeddings to capture different notions of similarity BIBREF43 , BIBREF44 , while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora 
Related Work
Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18   This relevant work mostly adopts simple (e g  Gaussian) and fixed priors and does not attempt to learn interpretable latent structures  Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47   Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48   Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models  However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures 
Conclusion
In this work, we define a novel generative approach to leverage continuous word representations for unsupervised learning of syntactic structure  Experiments on both POS induction and unsupervised dependency parsing tasks demonstrate the effectiveness of our proposed approach  Future work might explore more sophisticated invertible projections, or recurrent projections that jointly transform the entire input sequence 
Context 2: Introduction
Pretrained Language Models (PTLMs) such as BERT BIBREF1 have spearheaded advances on many NLP tasks  Usually, PTLMs are pretrained on unlabeled general-domain and/or mixed-domain text, such as Wikipedia, digital books or the Common Crawl corpus 
When applying PTLMs to specific domains, it can be useful to domain-adapt them  Domain adaptation of PTLMs has typically been achieved by pretraining on target-domain text  One such model is BioBERT BIBREF2, which was initialized from general-domain BERT and then pretrained on biomedical scientific publications  The domain adaptation is shown to be helpful for target-domain tasks such as biomedical Named Entity Recognition (NER) or Question Answering (QA)  On the downside, the computational cost of pretraining can be considerable: BioBERTv1 0 was adapted for ten days on eight large GPUs (see Table TABREF1), which is expensive, environmentally unfriendly, prohibitive for small research labs and students, and may delay prototyping on emerging domains 
We therefore propose a fast, CPU-only domain-adaptation method for PTLMs: We train Word2Vec BIBREF3 on target-domain text and align the resulting word vectors with the wordpiece vectors of an existing general-domain PTLM  The PTLM thus gains domain-specific lexical knowledge in the form of additional word vectors, but its deeper layers remain unchanged  Since Word2Vec and the vector space alignment are efficient models, the process requires a fraction of the resources associated with pretraining the PTLM itself, and it can be done on CPU 
In Section SECREF4, we use the proposed method to domain-adapt BERT on PubMed+PMC (the data used for BioBERTv1 0) and/or CORD-19 (Covid-19 Open Research Dataset)  We improve over general-domain BERT on eight out of eight biomedical NER tasks, using a fraction of the compute cost associated with BioBERT  In Section SECREF5, we show how to quickly adapt an existing Question Answering model to text about the Covid-19 pandemic, without any target-domain Language Model pretraining or finetuning 
Related work ::: The BERT PTLM
For our purpose, a PTLM consists of three parts: A tokenizer $\mathcal {T}_\mathrm {LM} : \mathbb {L}^+ \rightarrow \mathbb {L}_\mathrm {LM}^+$, a wordpiece embedding function $\mathcal {E}_\mathrm {LM}: \mathbb {L}_\mathrm {LM} \rightarrow \mathbb {R}^{d_\mathrm {LM}}$ and an encoder function $\mathcal {F}_\mathrm {LM}$  $\mathbb {L}_\mathrm {LM}$ is a limited vocabulary of wordpieces  All words that are not in $\mathbb {L}_\mathrm {LM}$ are tokenized into sequences of shorter wordpieces, e g , tachycardia becomes ta ##chy ##card ##ia  Given a sentence $S = [w_1, \ldots , w_T]$, tokenized as $\mathcal {T}_\mathrm {LM}(S) = [\mathcal {T}_\mathrm {LM}(w_1); \ldots ; \mathcal {T}_\mathrm {LM}(w_T)]$, $\mathcal {E}_\mathrm {LM}$ embeds every wordpiece in $\mathcal {T}_\mathrm {LM}(S)$ into a real-valued, trainable wordpiece vector  The wordpiece vectors of the entire sequence are stacked and fed into $\mathcal {F}_\mathrm {LM}$  Note that we consider position and segment embeddings to be a part of $\mathcal {F}_\mathrm {LM}$ rather than $\mathcal {E}_\mathrm {LM}$ 
In the case of BERT, $\mathcal {F}_\mathrm {LM}$ is a Transformer BIBREF4, followed by a final Feed-Forward Net  During pretraining, the Feed-Forward Net predicts the identity of masked wordpieces  When finetuning on a supervised task, it is usually replaced with a randomly initialized task-specific layer 
Related work ::: Domain-adapted PTLMs
Domain adaptation of PTLMs is typically achieved by pretraining on unlabeled target-domain text  Some examples of such models are BioBERT BIBREF2, which was pretrained on the PubMed and/or PubMed Central (PMC) corpora, SciBERT BIBREF5, which was pretrained on papers from SemanticScholar, ClinicalBERT BIBREF6, BIBREF7 and ClinicalXLNet BIBREF8, which were pretrained on clinical patient notes, and AdaptaBERT BIBREF9, which was pretrained on Early Modern English text  In most cases, a domain-adapted PTLM is initialized from a general-domain PTLM (e g , standard BERT), though BIBREF5 report better results with a model that was pretrained from scratch with a custom wordpiece vocabulary  In this paper, we focus on BioBERT, as its domain adaptation corpora are publicly available 
Related work ::: Word vectors
Word vectors are distributed representations of words that are trained on unlabeled text  Contrary to PTLMs, word vectors are non-contextual, i e , a word type is always assigned the same vector, regardless of context  In this paper, we use Word2Vec BIBREF3 to train word vectors  We will denote the Word2Vec lookup function as $\mathcal {E}_\mathrm {W2V} : \mathbb {L}_\mathrm {W2V} \rightarrow \mathbb {R}^{d_\mathrm {W2V}}$ 
Related work ::: Word vector space alignment
Word vector space alignment has most frequently been explored in the context of cross-lingual word embeddings  For instance, BIBREF10 align English and Spanish Word2Vec spaces by a simple linear transformation  BIBREF11 use a related method to align cross-lingual word vectors and multilingual BERT wordpiece vectors 
Method
In the following, we assume access to a general-domain PTLM, as described in Section SECREF2, and a corpus of unlabeled target-domain text 
Method ::: Creating new input vectors
In a first step, we train Word2Vec on the target-domain corpus  In a second step, we take the intersection of $\mathbb {L}_\mathrm {LM}$ and $\mathbb {L}_\mathrm {W2V}$  In practice, the intersection mostly contains wordpieces from $\mathbb {L}_\mathrm {LM}$ that correspond to standalone words  It also contains single characters and other noise, however, we found that filtering them does not improve alignment quality  In a third step, we use the intersection to fit an unconstrained linear transformation $\mathbf {W} \in \mathbb {R}^{d_\mathrm {LM} \times d_\mathrm {W2V}}$ via least squares:
Intuitively, $\mathbf {W}$ makes Word2Vec vectors “look like” the PTLM's native wordpiece vectors, just like cross-lingual alignment makes word vectors from one language “look like” word vectors from another language  In Table TABREF7 (top), we show examples of within-space and cross-space nearest neighbors after alignment 
Method ::: Updating the wordpiece embedding layer
Next, we redefine the wordpiece embedding layer of the PTLM  The most radical strategy would be to replace the entire layer with the aligned Word2Vec vectors:
In initial experiments, this strategy led to a drop in performance, presumably because function words are not well represented by Word2Vec, and replacing them disrupts BERT's syntactic abilities  To prevent this problem, we leave existing wordpiece vectors intact and only add new ones:
Method ::: Updating the tokenizer
In a final step, we update the tokenizer to account for the added words  Let $\mathcal {T}_\mathrm {LM}$ be the standard BERT tokenizer, and let $\hat{\mathcal {T}}_\mathrm {LM}$ be the tokenizer that treats all words in $\mathbb {L}_\mathrm {LM} \cup \mathbb {L}_\mathrm {W2V}$ as one-wordpiece tokens, while tokenizing any other words as usual 
In practice, a given word may or may not benefit from being tokenized by $\hat{\mathcal {T}}_\mathrm {LM}$ instead of $\mathcal {T}_\mathrm {LM}$  To give a concrete example, 82% of the words in the BC5CDR NER dataset that end in the suffix -ia are inside a disease entity (e g , tachycardia)  $\mathcal {T}_\mathrm {LM}$ tokenizes this word as ta ##chy ##card ##ia, thereby exposing the orthographic cue to the model  As a result, $\mathcal {T}_\mathrm {LM}$ leads to higher recall on -ia diseases  But there are many cases where wordpiece tokenization is meaningless or misleading  For instance euthymia (not a disease) is tokenized by $\mathcal {T}_\mathrm {LM}$ as e ##uth ##ym ##ia, making it likely to be classified as a disease  By contrast, $\hat{\mathcal {T}}_\mathrm {LM}$ gives euthymia a one-wordpiece representation that depends only on distributional semantics  We find that using $\hat{\mathcal {T}}_\mathrm {LM}$ improves precision on -ia diseases 
To combine these complementary strengths, we use a 50/50 mixture of $\mathcal {T}_\mathrm {LM}$-tokenization and $\hat{\mathcal {T}}_\mathrm {LM}$-tokenization when finetuning the PTLM on a task  At test time, we use both tokenizers and mean-pool the outputs  Let $o(\mathcal {T}(S))$ be some output of interest (e g , a logit), given sentence $S$ tokenized by $\mathcal {T}$  We predict:
Experiment 1: Biomedical NER
In this section, we use the proposed method to create GreenBioBERT, an inexpensive and environmentally friendly alternative to BioBERT  Recall that BioBERTv1 0 (biobert_v1 0_pubmed_pmc) was initialized from general-domain BERT (bert-base-cased) and pretrained on PubMed+PMC 
Experiment 1: Biomedical NER ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 768$ on PubMed+PMC (see Appendix for details)  Then, we follow the procedure described in Section SECREF3 to update the wordpiece embedding layer and tokenizer of general-domain BERT 
Experiment 1: Biomedical NER ::: Finetuning
We finetune GreenBioBERT on the eight publicly available NER tasks used in BIBREF2  We also do reproduction experiments with general-domain BERT and BioBERTv1 0, using the same setup as our model  We average results over eight random seeds  See Appendix for details on preprocessing, training and hyperparameters 
Experiment 1: Biomedical NER ::: Results and discussion
Table TABREF7 (bottom) shows entity-level precision, recall and F1  For ease of visualization, Figure FIGREF13 shows what portion of the BioBERT – BERT F1 delta is covered  We improve over general-domain BERT on all tasks with varying effect sizes  Depending on the points of reference, we cover an average 52% to 60% of the BioBERT – BERT F1 delta (54% for BioBERTv1 0, 60% for BioBERTv1 1 and 52% for our reproduction experiments)  Table TABREF17 (top) shows the importance of vector space alignment: If we replace the aligned Word2Vec vectors with their non-aligned counterparts (by setting $\mathbf {W} = \mathbf {1}$) or with randomly initialized vectors, F1 drops on all tasks 
Experiment 2: Covid-19 QA
In this section, we use the proposed method to quickly adapt an existing general-domain QA model to an emerging target domain: Covid-19  Our baseline model is SQuADBERT (bert-large-uncased-whole-word-masking-finetuned-squad), a version of BERT that was finetuned on general-domain SQuAD BIBREF19  We evaluate on Deepset-AI Covid-QA, a SQuAD-style dataset with 1380 questions (see Appendix for details on data and preprocessing)  We assume that there is no target-domain finetuning data, which is a realistic setup for a new domain 
Experiment 2: Covid-19 QA ::: Domain adaptation
We train Word2Vec with vector size $d_\mathrm {W2V} = d_\mathrm {LM} = 1024$ on CORD-19 (Covid-19 Open Research Dataset) and/or PubMed+PMC  The process takes less than an hour on CORD-19 and about one day on the combined corpus, again without the need for a GPU  Then, we update SQuADBERT's wordpiece embedding layer and tokenizer, as described in Section SECREF3  We refer to the resulting model as GreenCovidSQuADBERT 
Experiment 2: Covid-19 QA ::: Results and discussion
Table TABREF17 (bottom) shows that GreenCovidSQuADBERT outperforms general-domain SQuADBERT in all metrics  Most of the improvement can be achieved with just the small CORD-19 corpus, which is more specific to the target domain (compare “Cord-19 only” and “Cord-19+PubMed+PMC”) 
Conclusion
As a reaction to the trend towards high-resource models, we have proposed an inexpensive, CPU-only method for domain-adapting Pretrained Language Models: We train Word2Vec vectors on target-domain data and align them with the wordpiece vector space of a general-domain PTLM 
On eight biomedical NER tasks, we cover over 50% of the BioBERT – BERT F1 delta, at 5% of BioBERT's domain adaptation CO$_2$ footprint and 2% of its cloud compute cost  We have also shown how to rapidly adapt an existing BERT QA model to an emerging domain – the Covid-19 pandemic – without the need for target-domain Language Model pretraining or finetuning 
We hope that our approach will benefit practitioners with limited time or resources, and that it will encourage environmentally friendlier NLP 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Word2Vec training
We downloaded the PubMed, PMC and CORD-19 corpora from:
https://ftp ncbi nlm nih gov/pub/pmc/oa_bulk/ [20 January 2020, 68GB raw text]
https://ftp ncbi nlm nih gov/pubmed/baseline/ [20 January 2020, 24GB raw text]
https://pages semanticscholar org/coronavirus-research [17 April 2020, 2GB raw text]
We extract all abstracts and text bodies and apply the BERT basic tokenizer (a word tokenizer that standard BERT uses before wordpiece tokenization)  Then, we train CBOW Word2Vec with negative sampling  We use default parameters except for the vector size (which we set to $d_\mathrm {W2V} = d_\mathrm {LM}$) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Pretrained models
General-domain BERT and BioBERTv1 0 were downloaded from:
https://storage googleapis com/bert_models/2018_10_18/cased_L-12_H-768_A-12 zip
https://github com/naver/biobert-pretrained
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Data
We downloaded the NER datasets by following instructions on https://github com/dmis-lab/biobert#Datasets  For detailed dataset statistics, see BIBREF2 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Preprocessing
We cut all sentences into chunks of 30 or fewer whitespace-tokenized words (without splitting inside labeled spans)  Then, we tokenize every chunk $S$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$ and add special tokens:
Word-initial wordpieces in $\mathcal {T}(S)$ are labeled as B(egin), I(nside) or O(utside), while non-word-initial wordpieces are labeled as X(ignore) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Modeling, training and inference
We follow BIBREF2's implementation (https://github com/dmis-lab/biobert): We add a randomly initialized softmax classifier on top of the last BERT layer to predict the labels  We finetune the entire model to minimize negative log likelihood, with the standard Adam optimizer BIBREF20 and a linear learning rate scheduler (10% warmup)  Like BIBREF2, we finetune on the concatenation of the training and development set  All finetuning runs were done on a GeForce Titan X GPU (12GB) 
Since we do not have the resources for an extensive hyperparameter search, we use defaults and recommendations from the BioBERT repository: Batch size of 32, peak learning rate of $1 \cdot 10^{-5}$, and 100 epochs 
At inference time, we gather the output logits of word-initial wordpieces only  Since the number of word-initial wordpieces is the same for $\mathcal {T}_\mathrm {LM}(S)$ and $\hat{\mathcal {T}}_\mathrm {LM}(S)$, this makes mean-pooling the logits straightforward 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 1: Biomedical NER ::: Note on our reproduction experiments
We found it easier to reproduce or exceed BIBREF2's results for general-domain BERT, compared to their results for BioBERTv1 0 (see Figure FIGREF13, main paper)  While this may be due to hyperparameters, it suggests that BioBERTv1 0 was more strongly tuned than BERT in the original BioBERT paper  This observation does not affect our conclusions, as GreenBioBERT performs better than reproduced BERT as well 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Pretrained model
We downloaded the SQuADBERT baseline from:
https://huggingface co/bert-large-uncased-whole-word-masking-finetuned-squad
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Data
We downloaded the Deepset-AI Covid-QA dataset from:
https://github com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA json [24 April 2020]
At the time of writing, the dataset contains 1380 questions and gold answer spans  Every question is associated with one of 98 research papers (contexts)  We treat the entire dataset as a test set 
Note that there are some important differences between the dataset and SQuAD, which make the task challenging:
The contexts are full documents rather than single paragraphs  Thus, the correct answer may appear several times, often with slightly different wordings  Only a single one of the occurrences is annotated as correct, e g :
What was the prevalence of Coronavirus OC43 in community samples in Ilorin, Nigeria?
13 3% (95% CI 6 9-23 6%) # from main text
(13 3%, 10/75)  # from abstract
SQuAD gold answers are defined as the “shortest span in the paragraph that answered the question” BIBREF19, but many Covid-QA gold answers are longer and contain non-essential context, e g :
When was the Middle East Respiratory Syndrome Coronavirus isolated first?
(MERS-CoV) was first isolated in 2012, in a 60-year-old man who died in Jeddah, KSA due to severe acute pneumonia and multiple organ failure
2012,
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Preprocessing
We tokenize every question-context pair $(Q, C)$ with $\mathcal {T} = \mathcal {T}_\mathrm {LM}$ or $\mathcal {T} = \hat{\mathcal {T}}_\mathrm {LM}$, which yields $(\mathcal {T}(Q), \mathcal {T}(C))$  Since $\mathcal {T}(C)$ is usually too long to be digested in a single forward pass, we define a sliding window with width and stride $N = \mathrm {floor}(\frac{509 - |\mathcal {T}(Q)|}{2})$  At step $n$, the “active” window is between $a^{(l)}_n = nN$ and $a^{(r)}_n = \mathrm {min}(|C|, nN+N)$  The input is defined as:
$p^{(l)}_n$ and $p^{(r)}_n$ are chosen such that $|X^{(n)}| = 512$, and such that the active window is in the center of the input (if possible) 
Inexpensive Domain Adaptation of Pretrained Language Models (Appendix) ::: Experiment 2: Covid-19 QA ::: Modeling and inference
Feeding $X^{(n)}$ into the pretrained QA model yields start logits $\mathbf {h^{\prime }}^{(\mathrm {start}, n)} \in \mathbb {R}^{|X^{(n)}|}$ and end logits $\mathbf {h^{\prime }}^{(\mathrm {end},n)} \in \mathbb {R}^{|X^{(n)}|}$  We extract and concatenate the slices that correspond to the active windows of all steps:
Next, we map the logits from the wordpiece level to the word level  This allows us to mean-pool the outputs of $\mathcal {T}_\mathrm {LM}$ and $\hat{\mathcal {T}}_\mathrm {LM}$ even when $|\mathcal {T}_\mathrm {LM}(C)| \ne |\hat{\mathcal {T}}_\mathrm {LM}(C)|$ 
Let $c_i$ be a whitespace-delimited word in $C$  Let $\mathcal {T}(C)_{j:j+|\mathcal {T}(c_i)|}$ be the corresponding wordpieces  The start and end logits of $c_i$ are derived as:
Finally, we return the answer span $C_{k:k^{\prime }}$ that maximizes $o^{(\mathrm {start})}_k + o^{(\mathrm {end})}_{k^{\prime }}$, subject to the constraints that $k^{\prime }$ does not precede $k$ and the answer span is not longer than 500 characters 

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 eight NER tasks did they evaluate on?
Context 3: Introduction
Named Entity Recognition (NER) is a foremost NLP task to label each atomic elements of a sentence into specific categories like "PERSON", "LOCATION", "ORGANIZATION" and othersBIBREF0  There has been an extensive NER research on English, German, Dutch and Spanish language BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, and notable research on low resource South Asian languages like HindiBIBREF6, IndonesianBIBREF7 and other Indian languages (Kannada, Malayalam, Tamil and Telugu)BIBREF8  However, there has been no study on developing neural NER for Nepali language  In this paper, we propose a neural based Nepali NER using latest state-of-the-art architecture based on grapheme-level which doesn't require any hand-crafted features and no data pre-processing 
Recent neural architecture like BIBREF1 is used to relax the need to hand-craft the features and need to use part-of-speech tag to determine the category of the entity  However, this architecture have been studied for languages like English, and German and not been applied to languages like Nepali which is a low resource language i e limited data set to train the model  Traditional methods like Hidden Markov Model (HMM) with rule based approachesBIBREF9,BIBREF10, and Support Vector Machine (SVM) with manual feature-engineeringBIBREF11 have been applied but they perform poor compared to neural  However, there has been no research in Nepali NER using neural network  Therefore, we created the named entity annotated dataset partly with the help of Dataturk to train a neural model  The texts used for this dataset are collected from various daily news sources from Nepal around the year 2015-2016 
Following are our contributions:
We present a novel Named Entity Recognizer (NER) for Nepali language  To best of our knowledge we are the first to propose neural based Nepali NER 
As there are not good quality dataset to train NER we release a dataset to support future research
We perform empirical evaluation of our model with state-of-the-art models with relative improvement of upto 10%
In this paper, we present works similar to ours in Section SECREF2  We describe our approach and dataset statistics in Section SECREF3 and SECREF4, followed by our experiments, evaluation and discussion in Section SECREF5, SECREF6, and SECREF7  We conclude with our observations in Section SECREF8 
To facilitate further research our code and dataset will be made available at github com/link-yet-to-be-updated
Related Work
There has been a handful of research on Nepali NER task based on approaches like Support Vector Machine and gazetteer listBIBREF11 and Hidden Markov Model and gazetteer listBIBREF9,BIBREF10 
BIBREF11 uses SVM along with features like first word, word length, digit features and gazetteer (person, organization, location, middle name, verb, designation and others)  It uses one vs rest classification model to classify each word into different entity classes  However, it does not the take context word into account while training the model  Similarly, BIBREF9 and BIBREF10 uses Hidden Markov Model with n-gram technique for extracting POS-tags  POS-tags with common noun, proper noun or combination of both are combined together, then uses gazetteer list as look-up table to identify the named entities 
Researchers have shown that the neural networks like CNNBIBREF12, RNNBIBREF13, LSTMBIBREF14, GRUBIBREF15 can capture the semantic knowledge of language better with the help of pre-trained embbeddings like word2vecBIBREF16, gloveBIBREF17 or fasttextBIBREF18 
Similar approaches has been applied to many South Asian languages like HindiBIBREF6, IndonesianBIBREF7, BengaliBIBREF19 and In this paper, we present the neural network architecture for NER task in Nepali language, which doesn't require any manual feature engineering nor any data pre-processing during training  First we are comparing BiLSTMBIBREF14, BiLSTM+CNNBIBREF20, BiLSTM+CRFBIBREF1, BiLSTM+CNN+CRFBIBREF2 models with CNN modelBIBREF0 and Stanford CRF modelBIBREF21  Secondly, we show the comparison between models trained on general word embeddings, word embedding + character-level embedding, word embedding + part-of-speech(POS) one-hot encoding and word embedding + grapheme clustered or sub-word embeddingBIBREF22  The experiments were performed on the dataset that we created and on the dataset received from ILPRL lab  Our extensive study shows that augmenting word embedding with character or grapheme-level representation and POS one-hot encoding vector yields better results compared to using general word embedding alone 
Approach
In this section, we describe our approach in building our model  This model is partly inspired from multiple models BIBREF20,BIBREF1, andBIBREF2
Approach ::: Bidirectional LSTM
We used Bi-directional LSTM to capture the word representation in forward as well as reverse direction of a sentence  Generally, LSTMs take inputs from left (past) of the sentence and computes the hidden state  However, it is proven beneficialBIBREF23 to use bi-directional LSTM, where, hidden states are computed based from right (future) of sentence and both of these hidden states are concatenated to produce the final output as $h_t$=[$\overrightarrow{h_t}$;$\overleftarrow{h_t}$], where $\overrightarrow{h_t}$, $\overleftarrow{h_t}$ = hidden state computed in forward and backward direction respectively 
Approach ::: Features ::: Word embeddings
We have used Word2Vec BIBREF16, GloVe BIBREF17 and FastText BIBREF18 word vectors of 300 dimensions  These vectors were trained on the corpus obtained from Nepali National Corpus  This pre-lemmatized corpus consists of 14 million words from books, web-texts and news papers  This corpus was mixed with the texts from the dataset before training CBOW and skip-gram version of word2vec using gensim libraryBIBREF24  This trained model consists of vectors for 72782 unique words 
Light pre-processing was performed on the corpus before training it  For example, invalid characters or characters other than Devanagari were removed but punctuation and numbers were not removed  We set the window context at 10 and the rare words whose count is below 5 are dropped  These word embeddings were not frozen during the training session because fine-tuning word embedding help achieve better performance compared to frozen oneBIBREF20 
We have used fasttext embeddings in particular because of its sub-word representation ability, which is very useful in highly inflectional language as shown in Table TABREF25  We have trained the word embedding in such a way that the sub-word size remains between 1 and 4  We particularly chose this size because in Nepali language a single letter can also be a word, for example e, t, C, r, l, n, u and a single character (grapheme) or sub-word can be formed after mixture of dependent vowel signs with consonant letters for example, C + O + = CO, here three different consonant letters form a single sub-word 
The two-dimensional visualization of an example word npAl is shown in FIGREF14  Principal Component Analysis (PCA) technique was used to generate this visualization which helps use to analyze the nearest neighbor words of a given sample word  84 and 104 nearest neighbors were observed using word2vec and fasttext embedding respectively on the same corpus 
Approach ::: Features ::: Character-level embeddings
BIBREF20 and BIBREF2 successfully presented that the character-level embeddings, extracted using CNN, when combined with word embeddings enhances the NER model performance significantly, as it is able to capture morphological features of a word  Figure FIGREF7 shows the grapheme-level CNN used in our model, where inputs to CNN are graphemes  Character-level CNN is also built in similar fashion, except the inputs are characters  Grapheme or Character -level embeddings are randomly initialized from [0,1] with real values with uniform distribution of dimension 30 
Approach ::: Features ::: Grapheme-level embeddings
Grapheme is atomic meaningful unit in writing system of any languages  Since, Nepali language is highly morphologically inflectional, we compared grapheme-level representation with character-level representation to evaluate its effect  For example, in character-level embedding, each character of a word npAl results into n + + p + A + l has its own embedding  However, in grapheme level, a word npAl is clustered into graphemes, resulting into n + pA + l  Here, each grapheme has its own embedding  This grapheme-level embedding results good scores on par with character-level embedding in highly inflectional languages like Nepali, because graphemes also capture syntactic information similar to characters  We created grapheme clusters using uniseg package which is helpful in unicode text segmentations 
Approach ::: Features ::: Part-of-speech (POS) one hot encoding
We created one-hot encoded vector of POS tags and then concatenated with pre-trained word embeddings before passing it to BiLSTM network  A sample of data is shown in figure FIGREF13 
Dataset Statistics ::: OurNepali dataset
Since, we there was no publicly available standard Nepali NER dataset and did not receive any dataset from the previous researchers, we had to create our own dataset  This dataset contains the sentences collected from daily newspaper of the year 2015-2016  This dataset has three major classes Person (PER), Location (LOC) and Organization (ORG)  Pre-processing was performed on the text before creation of the dataset, for example all punctuations and numbers besides ',', '-', '|' and ' ' were removed  Currently, the dataset is in standard CoNLL-2003 IO formatBIBREF25 
Since, this dataset is not lemmatized originally, we lemmatized only the post-positions like Ek, kO, l, mA, m, my, jF, sg, aEG which are just the few examples among 299 post positions in Nepali language  We obtained these post-positions from sanjaalcorps and added few more to match our dataset  We will be releasing this list in our github repository  We found out that lemmatizing the post-positions boosted the F1 score by almost 10% 
In order to label our dataset with POS-tags, we first created POS annotated dataset of 6946 sentences and 16225 unique words extracted from POS-tagged Nepali National Corpus and trained a BiLSTM model with 95 14% accuracy which was used to create POS-tags for our dataset 
The dataset released in our github repository contains each word in newline with space separated POS-tags and Entity-tags  The sentences are separated by empty newline  A sample sentence from the dataset is presented in table FIGREF13 
Dataset Statistics ::: ILPRL dataset
After much time, we received the dataset from Bal Krishna Bal, ILPRL, KU  This dataset follows standard CoNLL-2003 IOB formatBIBREF25 with POS tags  This dataset is prepared by ILPRL Lab, KU and KEIV Technologies  Few corrections like correcting the NER tags had to be made on the dataset  The statistics of both the dataset is presented in table TABREF23 
Table TABREF24 presents the total entities (PER, LOC, ORG and MISC) from both of the dataset used in our experiments  The dataset is divided into three parts with 64%, 16% and 20% of the total dataset into training set, development set and test set respectively 
Experiments
In this section, we present the details about training our neural network  The neural network architecture are implemented using PyTorch framework BIBREF26  The training is performed on a single Nvidia Tesla P100 SXM2  We first run our experiment on BiLSTM, BiLSTM-CNN, BiLSTM-CRF BiLSTM-CNN-CRF using the hyper-parameters mentioned in Table TABREF30  The training and evaluation was done on sentence-level  The RNN variants are initialized randomly from $(-\sqrt{k},\sqrt{k})$ where $k=\frac{1}{hidden\_size}$ 
First we loaded our dataset and built vocabulary using torchtext library  This eased our process of data loading using its SequenceTaggingDataset class  We trained our model with shuffled training set using Adam optimizer with hyper-parameters mentioned in table TABREF30  All our models were trained on single layer of LSTM network  We found out Adam was giving better performance and faster convergence compared to Stochastic Gradient Descent (SGD)  We chose those hyper-parameters after many ablation studies  The dropout of 0 5 is applied after LSTM layer 
For CNN, we used 30 different filters of sizes 3, 4 and 5  The embeddings of each character or grapheme involved in a given word, were passed through the pipeline of Convolution, Rectified Linear Unit and Max-Pooling  The resulting vectors were concatenated and applied dropout of 0 5 before passing into linear layer to obtain the embedding size of 30 for the given word  This resulting embedding is concatenated with word embeddings, which is again concatenated with one-hot POS vector 
Experiments ::: Tagging Scheme
Currently, for our experiments we trained our model on IO (Inside, Outside) format for both the dataset, hence the dataset does not contain any B-type annotation unlike in BIO (Beginning, Inside, Outside) scheme 
Experiments ::: Early Stopping
We used simple early stopping technique where if the validation loss does not decrease after 10 epochs, the training was stopped, else the training will run upto 100 epochs  In our experience, training usually stops around 30-50 epochs 
Experiments ::: Hyper-parameters Tuning
We ran our experiment looking for the best hyper-parameters by changing learning rate from (0,1, 0 01, 0 001, 0 0001), weight decay from [$10^{-1}$, $10^{-2}$, $10^{-3}$, $10^{-4}$, $10^{-5}$, $10^{-6}$, $10^{-7}$], batch size from [1, 2, 4, 8, 16, 32, 64, 128], hidden size from [8, 16, 32, 64, 128, 256, 512 1024]  Table TABREF30 shows all other hyper-parameter used in our experiment for both of the dataset 
Experiments ::: Effect of Dropout
Figure FIGREF31 shows how we end up choosing 0 5 as dropout rate  When the dropout layer was not used, the F1 score are at the lowest  As, we slowly increase the dropout rate, the F1 score also gradually increases, however after dropout rate = 0 5, the F1 score starts falling down  Therefore, we have chosen 0 5 as dropout rate for all other experiments performed 
Evaluation
In this section, we present the details regarding evaluation and comparison of our models with other baselines 
Table TABREF25 shows the study of various embeddings and comparison among each other in OurNepali dataset  Here, raw dataset represents such dataset where post-positions are not lemmatized  We can observe that pre-trained embeddings significantly improves the score compared to randomly initialized embedding  We can deduce that Skip Gram models perform better compared CBOW models for word2vec and fasttext  Here, fastText_Pretrained represents the embedding readily available in fastText website, while other embeddings are trained on the Nepali National Corpus as mentioned in sub-section SECREF11  From this table TABREF25, we can clearly observe that model using fastText_Skip Gram embeddings outperforms all other models 
Table TABREF35 shows the model architecture comparison between all the models experimented  The features used for Stanford CRF classifier are words, letter n-grams of upto length 6, previous word and next word  This model is trained till the current function value is less than $1\mathrm {e}{-2}$  The hyper-parameters of neural network experiments are set as shown in table TABREF30  Since, word embedding of character-level and grapheme-level is random, their scores are near 
All models are evaluated using CoNLL-2003 evaluation scriptBIBREF25 to calculate entity-wise precision, recall and f1 score 
Discussion
In this paper we present that we can exploit the power of neural network to train the model to perform downstream NLP tasks like Named Entity Recognition even in Nepali language  We showed that the word vectors learned through fasttext skip gram model performs better than other word embedding because of its capability to represent sub-word and this is particularly important to capture morphological structure of words and sentences in highly inflectional like Nepali  This concept can come handy in other Devanagari languages as well because the written scripts have similar syntactical structure 
We also found out that stemming post-positions can help a lot in improving model performance because of inflectional characteristics of Nepali language  So when we separate out its inflections or morphemes, we can minimize the variations of same word which gives its root word a stronger word vector representations compared to its inflected versions 
We can clearly imply from tables TABREF23, TABREF24, and TABREF35 that we need more data to get better results because OurNepali dataset volume is almost ten times bigger compared to ILPRL dataset in terms of entities 
Conclusion and Future work
In this paper, we proposed a novel NER for Nepali language and achieved relative improvement of upto 10% and studies different factors effecting the performance of the NER for Nepali language 
We also present a neural architecture BiLSTM+CNN(grapheme-level) which turns out to be performing on par with BiLSTM+CNN(character-level) under the same configuration  We believe this will not only help Nepali language but also other languages falling under the umbrellas of Devanagari languages  Our model BiLSTM+CNN(grapheme-level) and BiLSTM+CNN(G)+POS outperforms all other model experimented in OurNepali and ILPRL dataset respectively 
Since this is the first named entity recognition research in Nepal language using neural network, there are many rooms for improvement  We believe initializing the grapheme-level embedding with fasttext embeddings might help boosting the performance, rather than randomly initializing it  In future, we plan to apply other latest techniques like BERT, ELMo and FLAIR to study its effect on low-resource language like Nepali  We also plan to improve the model using cross-lingual or multi-lingual parameter sharing techniques by jointly training with other Devanagari languages like Hindi and Bengali 
Finally, we would like to contribute our dataset to Nepali NLP community to move forward the research going on in language understanding domain  We believe there should be special committee to create and maintain such dataset for Nepali NLP and organize various competitions which would elevate the NLP research in Nepal 
Some of the future works are listed below:
Proper initialization of grapheme level embedding from fasttext embeddings 
Apply robust POS-tagger for Nepali dataset
Lemmatize the OurNepali dataset with robust and efficient lemmatizer
Improve Nepali language score with cross-lingual learning techniques
Create more dataset using Wikipedia/Wikidata framework
Acknowledgments
The authors of this paper would like to express sincere thanks to Bal Krishna Bal, Kathmandu University Professor for providing us the POS-tagged Nepali NER data 

ground_truth: BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800

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

Please answer the questions based on the text provided below. What is the size of the dataset?
Context 4:  We use categorical evaluations with majority agreement  We use only four emotional categories happy, sad, angry, and neutral to compare the performance of our model with other research using the same categories  The IEMOCAP dataset includes five sessions, and each session contains utterances from two speakers (one male and one female)  This data collection process resulted in 10 unique speakers  For consistent comparison with previous work, we merge the excitement dataset with the happiness dataset  The final dataset contains a total of 5531 utterances (1636 happy, 1084 sad, 1103 angry, 1708 neutral) 
Feature extraction
To extract speech information from audio signals, we use MFCC values, which are widely used in analyzing audio signals  The MFCC feature set contains a total of 39 features, which include 12 MFCC parameters (1-12) from the 26 Mel-frequency bands and log-energy parameters, 13 delta and 13 acceleration coefficients The frame size is set to 25 ms at a rate of 10 ms with the Hamming function  According to the length of each wave file, the sequential step of the MFCC features is varied  To extract additional information from the data, we also use prosodic features, which show effectiveness in affective computing  The prosodic features are composed of 35 features, which include the F0 frequency, the voicing probability, and the loudness contours  All of these MFCC and prosodic features are extracted from the data using the OpenSMILE toolkit BIBREF26  
Implementation details
Among the variants of the RNN function, we use GRUs as they yield comparable performance to that of the LSTM and include a smaller number of weight parameters BIBREF29   We use a max encoder step of 750 for the audio input, based on the implementation choices presented in BIBREF30 and 128 for the text input because it covers the maximum length of the transcripts  The vocabulary size of the dataset is 3,747, including the “_UNK_" token, which represents unknown words, and the “_PAD_" token, which is used to indicate padding information added while preparing mini-batch data  The number of hidden units and the number of layers in the RNN for each model (ARE, TRE, MDRE and MDREA) are selected based on extensive hyperparameter search experiments  The weights of the hidden units are initialized using orthogonal weights BIBREF31 ], and the text embedding layer is initialized from pretrained word-embedding vectors BIBREF32  
In preparing the textual dataset, we first use the released transcripts of the IEMOCAP dataset for simplicity  To investigate the practical performance, we then process all of the IEMOCAP audio data using an ASR system (the Google Cloud Speech API) and retrieve the transcripts  The performance of the Google ASR system is reflected by its word error rate (WER) of 5 53% 
Performance evaluation
As the dataset is not explicitly split beforehand into training, development, and testing sets, we perform 5-fold cross validation to determine the overall performance of the model  The data in each fold are split into training, development, and testing datasets (8:0 5:1 5, respectively)  After training the model, we measure the weighted average precision (WAP) over the 5-fold dataset  We train and evaluate the model 10 times per fold, and the model performance is assessed in terms of the mean score and standard deviation 
We examine the WAP values, which are shown in Table 1  First, our ARE model shows the baseline performance because we use minimal audio features, such as the MFCC and prosodic features with simple architectures  On the other hand, the TRE model shows higher performance gain compared to the ARE  From this result, we note that textual data are informative in emotion prediction tasks, and the recurrent encoder model is effective in understanding these types of sequential data  Second, the newly proposed model, MDRE, shows a substantial performance gain  It thus achieves the state-of-the-art performance with a WAP value of 0 718  This result shows that multimodal information is a key factor in affective computing  Lastly, the attention model, MDREA, also outperforms the best existing research results (WAP 0 690 to 0 688) BIBREF19   However, the MDREA model does not match the performance of the MDRE model, even though it utilizes a more complex architecture  We believe that this result arises because insufficient data are available to properly determine the complex model parameters in the MDREA model  Moreover, we presume that this model will show better performance when the audio signals are aligned with the textual sequence while applying the attention mechanism  We leave the implementation of this point as a future research direction 
To investigate the practical performance of the proposed models, we conduct further experiments with the ASR-processed transcript data (see “-ASR” models in Table )  The label accuracy of the processed transcripts is 5 53% WER  The TRE-ASR, MDRE-ASR and MDREA-ASR models reflect degraded performance compared to that of the TRE, MDRE and MDREA models  However, the performance of these models is still competitive; in particular, the MDRE-ASR model outperforms the previous best-performing model, 3CNN-LSTM10H (WAP 0 691 to 0 688) 
Error analysis
We analyze the predictions of the ARE, TRE, and MDRE models  Figure shows the confusion matrix of each model  The ARE model (Fig  ) incorrectly classifies most instances of happy as neutral (43 51%); thus, it shows reduced accuracy (35 15%) in predicting the the happy class  Overall, most of the emotion classes are frequently confused with the neutral class  This observation is in line with the findings of BIBREF30 , who noted that the neutral class is located in the center of the activation-valence space, complicating its discrimination from the other classes 
Interestingly, the TRE model (Fig  ) shows greater prediction gains in predicting the happy class when compared to the ARE model (35 15% to 75 73%)  This result seems plausible because the model can benefit from the differences among the distributions of words in happy and neutral expressions, which gives more emotional information to the model than that of the audio signal data  On the other hand, it is striking that the TRE model incorrectly predicts instances of the sad class as the happy class 16 20% of the time, even though these emotional states are opposites of one another 
The MDRE model (Fig  ) compensates for the weaknesses of the previous two models (ARE and TRE) and benefits from their strengths to a surprising degree  The values arranged along the diagonal axis show that all of the accuracies of the correctly predicted class have increased  Furthermore, the occurrence of the incorrect “sad-to-happy" cases in the TRE model is reduced from 16 20% to 9 15% 
Conclusions
In this paper, we propose a novel multimodal dual recurrent encoder model that simultaneously utilizes text data, as well as audio signals, to permit the better understanding of speech data  Our model encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model to predict the emotion class  Extensive experiments show that our proposed model outperforms other state-of-the-art methods in classifying the four emotion categories, and accuracies ranging from 68 8% to 71 8% are obtained when the model is applied to the IEMOCAP dataset  In particular, it resolves the issue in which predictions frequently incorrectly yield the neutral class, as occurs in previous models that focus on audio features 
In the future work, we aim to extend the modalities to audio, text and video inputs  Furthermore, we plan to investigate the application of the attention mechanism to data derived from multiple modalities  This approach seems likely to uncover enhanced learning schemes that will increase performance in both speech emotion recognition and other multimodal classification tasks 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (No 10073144) 

ground_truth: Dataset contains 3606 total sentences and 79087 total entities.

ILPRL contains 548 sentences, OurNepali contains 3606 sentences