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 NLP tasks do they consider?
Context 1: Introduction
We posses a wealth of prior knowledge about many natural language processing tasks  For example, in text categorization, we know that words such as NBA, player, and basketball are strong indicators of the sports category BIBREF0 , and words like terrible, boring, and messing indicate a negative polarity while words like perfect, exciting, and moving suggest a positive polarity in sentiment classification 
A key problem arisen here, is how to leverage such knowledge to guide the learning process, an interesting problem for both NLP and machine learning communities  Previous studies addressing the problem fall into several lines  First, to leverage prior knowledge to label data BIBREF1 , BIBREF2   Second, to encode prior knowledge with a prior on parameters, which can be commonly seen in many Bayesian approaches BIBREF3 , BIBREF4   Third, to formalise prior knowledge with additional variables and dependencies BIBREF5   Last, to use prior knowledge to control the distributions over latent output variables BIBREF6 , BIBREF7 , BIBREF8 , which makes the output variables easily interpretable 
However, a crucial problem, which has rarely been addressed, is the bias in the prior knowledge that we supply to the learning model  Would the model be robust or sensitive to the prior knowledge? Or, which kind of knowledge is appropriate for the task? Let's see an example: we may be a baseball fan but unfamiliar with hockey so that we can provide a few number of feature words of baseball, but much less of hockey for a baseball-hockey classification task  Such prior knowledge may mislead the model with heavy bias to baseball  If the model cannot handle this situation appropriately, the performance may be undesirable 
In this paper, we investigate into the problem in the framework of Generalized Expectation Criteria BIBREF7   The study aims to reveal the factors of reducing the sensibility of the prior knowledge and therefore to make the model more robust and practical  To this end, we introduce auxiliary regularization terms in which our prior knowledge is formalized as distribution over output variables  Recall the example just mentioned, though we do not have enough knowledge to provide features for class hockey, it is easy for us to provide some neutral words, namely words that are not strong indicators of any class, like player here  As one of the factors revealed in this paper, supplying neutral feature words can boost the performance remarkably, making the model more robust 
More attractively, we do not need manual annotation to label these neutral feature words in our proposed approach 
More specifically, we explore three regularization terms to address the problem: (1) a regularization term associated with neutral features; (2) the maximum entropy of class distribution regularization term; and (3) the KL divergence between reference and predicted class distribution  For the first manner, we simply use the most common features as neutral features and assume the neutral features are distributed uniformly over class labels  For the second and third one, we assume we have some knowledge about the class distribution which will be detailed soon later 
To summarize, the main contributions of this work are as follows:
The rest of the paper is structured as follows: In Section 2, we briefly describe the generalized expectation criteria and present the proposed regularization terms  In Section 3, we conduct extensive experiments to justify the proposed methods  We survey related work in Section 4, and summarize our work in Section 5 
Method
We address the robustness problem on top of GE-FL BIBREF0 , a GE method which leverages labeled features as prior knowledge  A labeled feature is a strong indicator of a specific class and is manually provided to the classifier  For example, words like amazing, exciting can be labeled features for class positive in sentiment classification 
Generalized Expectation Criteria
Generalized expectation (GE) criteria BIBREF7 provides us a natural way to directly constrain the model in the preferred direction  For example, when we know the proportion of each class of the dataset in a classification task, we can guide the model to predict out a pre-specified class distribution 
Formally, in a parameter estimation objective function, a GE term expresses preferences on the value of some constraint functions about the model's expectation  Given a constraint function $G({\rm x}, y)$ , a conditional model distribution $p_\theta (y|\rm x)$ , an empirical distribution $\tilde{p}({\rm x})$ over input samples and a score function $S$ , a GE term can be expressed as follows:
$$S(E_{\tilde{p}({\rm x})}[E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]])$$   (Eq  4)
Learning from Labeled Features
Druck et al  ge-fl proposed GE-FL to learn from labeled features using generalized expectation criteria  When given a set of labeled features $K$ , the reference distribution over classes of these features is denoted by $\hat{p}(y| x_k), k \in K$   GE-FL introduces the divergence between this reference distribution and the model predicted distribution $p_\theta (y | x_k)$ , as a term of the objective function:
$$\mathcal {O} = \sum _{k \in K} KL(\hat{p}(y|x_k) || p_\theta (y | x_k)) + \sum _{y,i} \frac{\theta _{yi}^2}{2 \sigma ^2}$$   (Eq  6)
where $\theta _{yi}$ is the model parameter which indicates the importance of word $i$ to class $y$   The predicted distribution $p_\theta (y | x_k)$ can be expressed as follows: $ p_\theta (y | x_k) = \frac{1}{C_k} \sum _{\rm x} p_\theta (y|{\rm x})I(x_k) $
in which $I(x_k)$ is 1 if feature $k$ occurs in instance ${\rm x}$ and 0 otherwise, $C_k = \sum _{\rm x} I(x_k)$ is the number of instances with a non-zero value of feature $k$ , and $p_\theta (y|{\rm x})$ takes a softmax form as follows: $ p_\theta (y|{\rm x}) = \frac{1}{Z(\rm x)}\exp (\sum _i \theta _{yi}x_i)  $
To solve the optimization problem, L-BFGS can be used for parameter estimation 
In the framework of GE, this term can be obtained by setting the constraint function $G({\rm x}, y) = \frac{1}{C_k} \vec{I} (y)I(x_k)$ , where $\vec{I}(y)$ is an indicator vector with 1 at the index corresponding to label $y$ and 0 elsewhere 
Regularization Terms
GE-FL reduces the heavy load of instance annotation and performs well when we provide prior knowledge with no bias  In our experiments, we observe that comparable numbers of labeled features for each class have to be supplied  But as mentioned before, it is often the case that we are not able to provide enough knowledge for some of the classes  For the baseball-hockey classification task, as shown before, GE-FL will predict most of the instances as baseball  In this section, we will show three terms to make the model more robust 
Neutral features are features that are not informative indicator of any classes, for instance, word player to the baseball-hockey classification task  Such features are usually frequent words across all categories  When we set the preference distribution of the neutral features to be uniform distributed, these neutral features will prevent the model from biasing to the class that has a dominate number of labeled features 
Formally, given a set of neutral features $K^{^{\prime }}$ , the uniform distribution is $\hat{p}_u(y|x_k) = \frac{1}{|C|}, k \in K^{^{\prime }}$ , where $|C|$ is the number of classes  The objective function with the new term becomes
$$\mathcal {O}_{NE} = \mathcal {O} + \sum _{k \in K^{^{\prime }}} KL(\hat{p}_u(y|x_k) || p_\theta (y | x_k)) $$   (Eq  9)
Note that we do not need manual annotation to provide neutral features  One simple way is to take the most common features as neutral features  Experimental results show that this strategy works successfully 
Another way to prevent the model from drifting from the desired direction is to constrain the predicted class distribution on unlabeled data  When lacking knowledge about the class distribution of the data, one feasible way is to take maximum entropy principle, as below:
$$\mathcal {O}_{ME} = \mathcal {O} + \lambda \sum _{y} p(y) \log p(y)$$   (Eq  11)
where $p(y)$ is the predicted class distribution, given by $ p(y) = \frac{1}{|X|} \sum _{\rm x} p_\theta (y | \rm x)  $ To control the influence of this term on the overall objective function, we can tune $\lambda $ according to the difference in the number of labeled features of each class  In this paper, we simply set $\lambda $ to be proportional to the total number of labeled features, say $\lambda = \beta |K|$  
This maximum entropy term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$   Therefore, $E_{p_\theta (y|{\rm x})}[G({\rm x}, y)]$ is just the model distribution $p_\theta (y|{\rm x})$ and its expectation with the empirical distribution $\tilde{p}(\rm x)$ is simply the average over input samples, namely $p(y)$   When $S$ takes the maximum entropy form, we can derive the objective function as above 
Sometimes, we have already had much knowledge about the corpus, and can estimate the class distribution roughly without labeling instances  Therefore, we introduce the KL divergence between the predicted and reference class distributions into the objective function 
Given the preference class distribution $\hat{p}(y)$ , we modify the objective function as follows:
$$\mathcal {O}_{KL} &= \mathcal {O} + \lambda KL(\hat{p}(y) || p(y))$$   (Eq  13)
Similarly, we set $\lambda = \beta |K|$  
This divergence term can be derived by setting the constraint function to $G({\rm x}, y) = \vec{I}(y)$ and setting the score function to $S(\hat{p}, p) = \sum _i \hat{p}_i \log \frac{\hat{p}_i}{p_i}$ , where $p$ and $\hat{p}$ are distributions  Note that this regularization term involves the reference class distribution which will be discussed later 
Experiments
In this section, we first justify the approach when there exists unbalance in the number of labeled features or in class distribution  Then, to test the influence of $\lambda $ , we conduct some experiments with the method which incorporates the KL divergence of class distribution  Last, we evaluate our approaches in 9 commonly used text classification datasets  We set $\lambda = 5|K|$ by default in all experiments unless there is explicit declaration  The baseline we choose here is GE-FL BIBREF0 , a method based on generalization expectation criteria 
Data Preparation
We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare  We use bag-of-words feature and remove stopwords in the preprocess stage  Though we have labels of all documents, we do not use them during the learning process, instead, we use the label of features 
The movie dataset, in which the task is to classify the movie reviews as positive or negtive, is used for testing the proposed approaches with unbalanced labeled features, unbalanced datasets or different $\lambda $ parameters  All unbalanced datasets are constructed based on the movie dataset by randomly removing documents of the positive class  For each experiment, we conduct 10-fold cross validation 
As described in BIBREF0 , there are two ways to obtain labeled features  The first way is to use information gain  We first calculate the mutual information of all features according to the labels of the documents and select the top 20 as labeled features for each class as a feature pool  Note that using information gain requires the document label, but this is only to simulate how we human provide prior knowledge to the model  The second way is to use LDA BIBREF9 to select features  We use the same selection process as BIBREF0 , where they first train a LDA on the dataset, and then select the most probable features of each topic (sorted by $P(w_i|t_j)$ , the probability of word $w_i$ given topic $t_j$ ) 
Similar to BIBREF10 , BIBREF0 , we estimate the reference distribution of the labeled features using a heuristic strategy  If there are $|C|$ classes in total, and $n$ classes are associated with a feature $k$ , the probability that feature $k$ is related with any one of the $n$ classes is $\frac{0 9}{n}$ and with any other class is $\frac{0 1}{|C| - n}$  
Neutral features are the most frequent words after removing stop words, and their reference distributions are uniformly distributed  We use the top 10 frequent words as neutral features in all experiments 
With Unbalanced Labeled Features
In this section, we evaluate our approach when there is unbalanced knowledge on the categories to be classified  The labeled features are obtained through information gain  Two settings are chosen:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
As shown in Figure 1 , Maximum entropy principle shows improvement only on the balanced case  An obvious reason is that maximum entropy only favors uniform distribution 
Incorporating Neutral features performs similarly to maximum entropy since we assume that neutral words are uniformly distributed  Its accuracy decreases slowly when the number of labeled features becomes larger ( $t>4$ ) (Figure 1 (a)), suggesting that the model gradually biases to the class with more labeled features, just like GE-FL 
Incorporating the KL divergence of class distribution performs much better than GE-FL on both balanced and unbalanced datasets  This shows that it is effective to control the unbalance in labeled features and in the dataset 
With Balanced Labeled Features
We also compare with the baseline when the labeled features are balanced  Similar to the experiment above, the labeled features are obtained by information gain  Two settings are experimented with:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for each class, and conduct comparisons on the original balanced movie dataset (positive:negtive=1:1) 
(b) Similar to (a), but the class distribution is unbalanced, by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 2   When the dataset is balanced (Figure 2 (a)), there is little difference between GE-FL and our methods  The reason is that the proposed regularization terms provide no additional knowledge to the model and there is no bias in the labeled features  On the unbalanced dataset (Figure 2 (b)), incorporating KL divergence is much better than GE-FL since we provide additional knowledge(the true class distribution), but maximum entropy and neutral features are much worse because forcing the model to approach the uniform distribution misleads it 
With Unbalanced Class Distributions
Our methods are also evaluated on datasets with different unbalanced class distributions  We manually construct several movie datasets with class distributions of 1:2, 1:3, 1:4 by randomly removing 50%, 67%, 75% positive documents  The original balanced movie dataset is used as a control group  We test with both balanced and unbalanced labeled features  For the balanced case, we randomly select 10 features from the feature pool for each class, and for the unbalanced case, we select 10 features for one class, and 1 feature for the other  Results are shown in Figure 3  
Figure 3 (a) shows that when the dataset and the labeled features are both balanced, there is little difference between our methods and GE-FL(also see Figure 2 (a))  But when the class distribution becomes more unbalanced, the difference becomes more remarkable  Performance of neutral features and maximum entropy decrease significantly but incorporating KL divergence increases remarkably  This suggests if we have more accurate knowledge about class distribution, KL divergence can guide the model to the right direction 
Figure 3 (b) shows that when the labeled features are unbalanced, our methods significantly outperforms GE-FL  Incorporating KL divergence is robust enough to control unbalance both in the dataset and in labeled features while the other three methods are not so competitive 
The Influence of λ\lambda 
We present the influence of $\lambda $ on the method that incorporates KL divergence in this section  Since we simply set $\lambda = \beta |K|$ , we just tune $\beta $ here  Note that when $\beta = 0$ , the newly introduced regularization term is disappeared, and thus the model is actually GE-FL  Again, we test the method with different $\lambda $ in two settings:
(a) We randomly select $t \in [1, 20]$ features from the feature pool for one class, and only one feature for the other class  The original balanced movie dataset is used (positive:negative=1:1) 
(b) Similar to (a), but the dataset is unbalanced, obtained by randomly removing 75% positive documents (positive:negative=1:4) 
Results are shown in Figure 4   As expected, $\lambda $ reflects how strong the regularization is  The model tends to be closer to our preferences with the increasing of $\lambda $ on both cases 
Using LDA Selected Features
We compare our methods with GE-FL on all the 9 datasets in this section  Instead of using features obtained by information gain, we use LDA to select labeled features  Unlike information gain, LDA does not employ any instance labels to find labeled features  In this setting, we can build classification models without any instance annotation, but just with labeled features 
Table 1 shows that our three methods significantly outperform GE-FL  Incorporating neutral features performs better than GE-FL on 7 of the 9 datasets, maximum entropy is better on 8 datasets, and KL divergence better on 7 datasets 
LDA selects out the most predictive features as labeled features without considering the balance among classes  GE-FL does not exert any control on such an issue, so the performance is severely suffered  Our methods introduce auxiliary regularization terms to control such a bias problem and thus promote the model significantly 
Related Work
There have been much work that incorporate prior knowledge into learning, and two related lines are surveyed here  One is to use prior knowledge to label unlabeled instances and then apply a standard learning algorithm  The other is to constrain the model directly with prior knowledge 
Liu et al text manually labeled features which are highly predictive to unsupervised clustering assignments and use them to label unlabeled data  Chang et al guiding proposed constraint driven learning  They first used constraints and the learned model to annotate unlabeled instances, and then updated the model with the newly labeled data  Daumé daume2008cross proposed a self training method in which several models are trained on the same dataset, and only unlabeled instances that satisfy the cross task knowledge constraints are used in the self training process 
MaCallum et al gec proposed generalized expectation(GE) criteria which formalised the knowledge as constraint terms about the expectation of the model into the objective function Graça et al pr proposed posterior regularization(PR) framework which projects the model's posterior onto a set of distributions that satisfy the auxiliary constraints  Druck et al ge-fl explored constraints of labeled features in the framework of GE by forcing the model's predicted feature distribution to approach the reference distribution  Andrzejewski et al andrzejewski2011framework proposed a framework in which general domain knowledge can be easily incorporated into LDA  Altendorf et al altendorf2012learning explored monotonicity constraints to improve the accuracy while learning from sparse data  Chen et al chen2013leveraging tried to learn comprehensible topic models by leveraging multi-domain knowledge 
Mann and McCallum simple,generalized incorporated not only labeled features but also other knowledge like class distribution into the objective function of GE-FL  But they discussed only from the semi-supervised perspective and did not investigate into the robustness problem, unlike what we addressed in this paper 
There are also some active learning methods trying to use prior knowledge  Raghavan et al feedback proposed to use feedback on instances and features interlacedly, and demonstrated that feedback on features boosts the model much  Druck et al active proposed an active learning method which solicits labels on features rather than on instances and then used GE-FL to train the model 
Conclusion and Discussions
This paper investigates into the problem of how to leverage prior knowledge robustly in learning models  We propose three regularization terms on top of generalized expectation criteria  As demonstrated by the experimental results, the performance can be considerably improved when taking into account these factors  Comparative results show that our proposed methods is more effective and works more robustly against baselines  To the best of our knowledge, this is the first work to address the robustness problem of leveraging knowledge, and may inspire other research 
We then present more detailed discussions about the three regularization methods  Incorporating neutral features is the simplest way of regularization, which doesn't require any modification of GE-FL but just finding out some common features  But as Figure 1 (a) shows, only using neutral features are not strong enough to handle extremely unbalanced labeled features 
The maximum entropy regularization term shows the strong ability of controlling unbalance 
This method doesn't need any extra knowledge, and is thus suitable when we know nothing about the corpus  But this method assumes that the categories are uniformly distributed, which may not be the case in practice, and it will have a degraded performance if the assumption is violated (see Figure 1 (b), Figure 2 (b), Figure 3 (a)) 
The KL divergence performs much better on unbalanced corpora than other methods  The reason is that KL divergence utilizes the reference class distribution and doesn't make any assumptions  The fact suggests that additional knowledge does benefit the model 
However, the KL divergence term requires providing the true class distribution  Sometimes, we may have the exact knowledge about the true distribution, but sometimes we may not  Fortunately, the model is insensitive to the true distribution and therefore a rough estimation of the true distribution is sufficient  In our experiments, when the true class distribution is 1:2, where the reference class distribution is set to 1:1 5/1:2/1:2 5, the accuracy is 0 755/0 756/0 760 respectively  This provides us the possibility to perform simple computing on the corpus to obtain the distribution in reality  Or, we can set the distribution roughly with domain expertise 
Context 2: Introduction
There has been significant progress on Named Entity Recognition (NER) in recent years using models based on machine learning algorithms BIBREF0 , BIBREF1 , BIBREF2   As with other Natural Language Processing (NLP) tasks, building NER systems typically requires a massive amount of labeled training data which are annotated by experts  In real applications, we often need to consider new types of entities in new domains where we do not have existing annotated data  For such new types of entities, however, it is very hard to find experts to annotate the data within short time limits and hiring experts is costly and non-scalable, both in terms of time and money 
In order to quickly obtain new training data, we can use crowdsourcing as one alternative way at lower cost in a short time  But as an exchange, crowd annotations from non-experts may be of lower quality than those from experts  It is one biggest challenge to build a powerful NER system on such a low quality annotated data  Although we can obtain high quality annotations for each input sentence by majority voting, it can be a waste of human labors to achieve such a goal, especially for some ambiguous sentences which may require a number of annotations to reach an agreement  Thus majority work directly build models on crowd annotations, trying to model the differences among annotators, for example, some of the annotators may be more trustful BIBREF3 , BIBREF4  
Here we focus mainly on the Chinese NER, which is more difficult than NER for other languages such as English for the lack of morphological variations such as capitalization and in particular the uncertainty in word segmentation  The Chinese NE taggers trained on news domain often perform poor in other domains  Although we can alleviate the problem by using character-level tagging to resolve the problem of poor word segmentation performances BIBREF5 , still there exists a large gap when the target domain changes, especially for the texts of social media  Thus, in order to get a good tagger for new domains and also for the conditions of new entity types, we require large amounts of labeled data  Therefore, crowdsourcing is a reasonable solution for these situations 
In this paper, we propose an approach to training a Chinese NER system on the crowd-annotated data  Our goal is to extract additional annotator independent features by adversarial training, alleviating the annotation noises of non-experts  The idea of adversarial training in neural networks has been used successfully in several NLP tasks, such as cross-lingual POS tagging BIBREF6 and cross-domain POS tagging BIBREF7   They use it to reduce the negative influences of the input divergences among different domains or languages, while we use adversarial training to reduce the negative influences brought by different crowd annotators  To our best knowledge, we are the first to apply adversarial training for crowd annotation learning 
In the learning framework, we perform adversarial training between the basic NER and an additional worker discriminator  We have a common Bi-LSTM for representing annotator-generic information and a private Bi-LSTM for representing annotator-specific information  We build another label Bi-LSTM by the crowd-annotated NE label sequence which reflects the mind of the crowd annotators who learn entity definitions by reading the annotation guidebook  The common and private Bi-LSTMs are used for NER, while the common and label Bi-LSTMs are used as inputs for the worker discriminator  The parameters of the common Bi-LSTM are learned by adversarial training, maximizing the worker discriminator loss and meanwhile minimizing the NER loss  Thus the resulting features of the common Bi-LSTM are worker invariant and NER sensitive 
For evaluation, we create two Chinese NER datasets in two domains: dialog and e-commerce  We require the crowd annotators to label the types of entities, including person, song, brand, product, and so on  Identifying these entities is useful for chatbot and e-commerce platforms BIBREF8   Then we conduct experiments on the newly created datasets to verify the effectiveness of the proposed adversarial neural network model  The results show that our system outperforms very strong baseline systems  In summary, we make the following contributions:
Related Work
Our work is related to three lines of research: Sequence labeling, Adversarial training, and Crowdsourcing 
Sequence labeling  NER is widely treated as a sequence labeling problem, by assigning a unique label over each sentential word BIBREF9   Early studies on sequence labeling often use the models of HMM, MEMM, and CRF BIBREF10 based on manually-crafted discrete features, which can suffer the feature sparsity problem and require heavy feature engineering  Recently, neural network models have been successfully applied to sequence labeling BIBREF1 , BIBREF11 , BIBREF2   Among these work, the model which uses Bi-LSTM for feature extraction and CRF for decoding has achieved state-of-the-art performances BIBREF11 , BIBREF2 , which is exploited as the baseline model in our work 
Adversarial Training  Adversarial Networks have achieved great success in computer vision such as image generation BIBREF12 , BIBREF13   In the NLP community, the method is mainly exploited under the settings of domain adaption BIBREF14 , BIBREF7 , cross-lingual BIBREF15 , BIBREF6 and multi-task learning BIBREF16 , BIBREF17   All these settings involve the feature divergences between the training and test examples, and aim to learn invariant features across the divergences by an additional adversarial discriminator, such as domain discriminator  Our work is similar to these work but is applies on crowdsourcing learning, aiming to find invariant features among different crowdsourcing workers 
Crowdsourcing  Most NLP tasks require a massive amount of labeled training data which are annotated by experts  However, hiring experts is costly and non-scalable, both in terms of time and money  Instead, crowdsourcing is another solution to obtain labeled data at a lower cost but with relative lower quality than those from experts  BIBREF18 snow2008cheap collected labeled results for several NLP tasks from Amazon Mechanical Turk and demonstrated that non-experts annotations were quite useful for training new systems  In recent years, a series of work have focused on how to use crowdsourcing data efficiently in tasks such as classification BIBREF19 , BIBREF20 , and compare quality of crowd and expert labels BIBREF21  
In sequence labeling tasks, BIBREF22 dredze2009sequence viewed this task as a multi-label problem while BIBREF3 rodrigues2014sequence took workers identities into account by assuming that each sentential word was tagged correctly by one of the crowdsourcing workers and proposed a CRF-based model with multiple annotators  BIBREF4 nguyen2017aggregating introduced a crowd representation in which the crowd vectors were added into the LSTM-CRF model at train time, but ignored them at test time  In this paper, we apply adversarial training on crowd annotations on Chinese NER in new domains, and achieve better performances than previous studies on crowdsourcing learning 
Baseline: LSTM-CRF
We use a neural CRF model as the baseline system BIBREF9 , treating NER as a sequence labeling problem over Chinese characters, which has achieved state-of-the-art performances BIBREF5   To this end, we explore the BIEO schema to convert NER into sequence labeling, following BIBREF2 lample-EtAl:2016:N16-1, where sentential character is assigned with one unique tag  Concretely, we tag the non-entity character by label “O”, the beginning character of an entity by “B-XX”, the ending character of an entity by “E-XX” and the other character of an entity by “I-XX”, where “XX” denotes the entity type 
We build high-level neural features from the input character sequence by a bi-directional LSTM BIBREF2   The resulting features are combined and then are fed into an output CRF layer for decoding  In summary, the baseline model has three main components  First, we make vector representations for sentential characters $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ , transforming the discrete inputs into low-dimensional neural inputs  Second, feature extraction is performed to obtain high-level features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ , by using a bi-directional LSTM (Bi-LSTM) structure together with a linear transformation over $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$   Third, we apply a CRF tagging module over $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ , obtaining the final output NE labels  The overall framework of the baseline model is shown by the right part of Figure 1  
Vector Representation of Characters
To represent Chinese characters, we simply exploit a neural embedding layer to map discrete characters into the low-dimensional vector representations  The goal is achieved by a looking-up table $\mathbf {E}^W$ , which is a model parameter and will be fine-tuned during training  The looking-up table can be initialized either by random or by using a pretrained embeddings from large scale raw corpus  For a given Chinese character sequence $c_1c_2\cdots c_n$ , we obtain the vector representation of each sentential character by: $ \mathbf {x}_t = \text{look-up}(c_t, \mathbf {E}^W), \text{~~~} t \in [1, n]$  
Feature Extraction
Based on the vector sequence $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ , we extract higher-level features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ by using a bidirectional LSTM module and a simple feed-forward neural layer, which are then used for CRF tagging at the next step 
LSTM is a type of recurrent neural network (RNN), which is designed for solving the exploding and diminishing gradients of basic RNNs BIBREF23   It has been widely used in a number of NLP tasks, including POS-tagging BIBREF11 , BIBREF24 , parsing BIBREF25 and machine translation BIBREF26 , because of its strong capabilities of modeling natural language sentences 
By traversing $\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n$ by order and reversely, we obtain the output features $\mathbf {h}_1^{\text{private}}\mathbf {h}_2^{\text{private}}\cdots \mathbf {h}_n^{\text{private}}$ of the bi-LSTM, where $\mathbf {h}_t^{\text{private}} = \overrightarrow{\mathbf {h}}_t \oplus \overleftarrow{\mathbf {h}}_t $   Here we refer this Bi-LSTM as private in order to differentiate it with the common Bi-LSTM over the same character inputs which will be introduced in the next section 
Further we make an integration of the output vectors of bi-directional LSTM by a linear feed-forward neural layer, resulting in the features $\mathbf {h}_1^{\text{ner}}\mathbf {h}_2^{\text{ner}}\cdots \mathbf {h}_n^{\text{ner}}$ by equation:
$$\mathbf {h}_t^{\text{ner}} = \mathbf {W} \mathbf {h}_t^{\text{private}} + \mathbf {b},$$   (Eq  6)
where $\mathbf {W}$ and $\mathbf {b}$ are both model parameters 
CRF Tagging
Finally we feed the resulting features $\mathbf {h}_t^{\text{ner}}, t\in [1, n]$ into a CRF layer directly for NER decoding  CRF tagging is one globally normalized model, aiming to find the best output sequence considering the dependencies between successive labels  In the sequence labeling setting for NER, the output label of one position has a strong dependency on the label of the previous position  For example, the label before “I-XX” must be either “B-XX” or “I-XX”, where “XX” should be exactly the same 
CRF involves two parts for prediction  First we should compute the scores for each label based $\mathbf {h}_t^{\text{ner}}$ , resulting in $\mathbf {o}_t^{\text{ner}}$ , whose dimension is the number of output labels  The other part is a transition matrix $\mathbf {T}$ which defines the scores of two successive labels  $\mathbf {T}$ is also a model parameter  Based on $\mathbf {o}_t^{\text{ner}}$ and $\mathbf {T}$ , we use the Viterbi algorithm to find the best-scoring label sequence 
We can formalize the CRF tagging process as follows:
$$\begin{split} & \mathbf {o}_t^{\text{ner}} = \mathbf {W}^{\text{ner}} \mathbf {h}_t^{\text{ner}}, \text{~~~~} t \in [1,n] \\ & \text{score}(\mathbf {X}, \mathbf {y}) = \sum _{t = 1}^{n}(\mathbf {o}_{t,y_t} + T_{y_{t-1},y_t}) \\ & \mathbf {y}^{\text{ner}} = \mathop {arg~max}_{\mathbf {y}}\big (\text{score}(\mathbf {X}, \mathbf {y}))\big ), \\ \end{split}$$   (Eq  8)
where $\text{score}(\cdot )$ is the scoring function for a given output label sequence $\mathbf {y} = y_1y_2 \cdots y_n$ based on input $\mathbf {X}$ , $\mathbf {y}^{\text{ner}}$ is the resulting label sequence, $\mathbf {W}^{\text{ner}}$ is a model parameter 
Training
To train model parameters, we exploit a negative log-likelihood objective as the loss function  We apply softmax over all candidate output label sequences, thus the probability of the crowd-annotated label sequence is computed by:
$$p(\mathbf {\bar{y}}|\mathbf {X}) = \frac{\exp \big (\text{score}(\mathbf {X}, \mathbf {\bar{y}})\big )}{\sum _{\mathbf {y} \in \mathbf {Y}_{\mathbf {X}}} \exp \big (\text{score}(\mathbf {X}, \mathbf {y})\big )},$$   (Eq  10)
where $\mathbf {\bar{y}}$ is the crowd-annotated label sequences and $\mathbf {Y}_{\mathbf {X}}$ is all candidate label sequence of input $\mathbf {X}$  
Based on the above formula, the loss function of our baseline model is:
$$\text{loss}(\Theta , \mathbf {X}, \mathbf {\bar{y}}) = -\log p(\mathbf {\bar{y}}|\mathbf {X}),$$   (Eq  11)
where $\Theta $ is the set of all model parameters  We use standard back-propagation method to minimize the loss function of the baseline CRF model 
Worker Adversarial
Adversarial learning has been an effective mechanism to resolve the problem of the input features between the training and test examples having large divergences BIBREF27 , BIBREF13   It has been successfully applied on domain adaption BIBREF7 , cross-lingual learning BIBREF15 and multi-task learning BIBREF17   All settings involve feature shifting between the training and testing 
In this paper, our setting is different  We are using the annotations from non-experts, which are noise and can influence the final performances if they are not properly processed  Directly learning based on the resulting corpus may adapt the neural feature extraction into the biased annotations  In this work, we assume that individual workers have their own guidelines in mind after short training  For example, a perfect worker can annotate highly consistently with an expert, while common crowdsourcing workers may be confused and have different understandings on certain contexts  Based on the assumption, we make an adaption for the original adversarial neural network to our setting 
Our adaption is very simple  Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs, for example, the domain category in the domain adaption setting, while we add a discriminator to classify the annotation workers  Solely the features from the input sentence is not enough for worker classification  The annotation result of the worker is also required  Thus the inputs of our discriminator are different  Here we exploit both the source sentences and the crowd-annotated NE labels as basic inputs for the worker discrimination 
In the following, we describe the proposed adversarial learning module, including both the submodels and the training method  As shown by the left part of Figure 1 , the submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction 
Common Bi-LSTM over Characters
To build the adversarial part, first we create a new bi-directional LSTM, named by the common Bi-LSTM:
$$\mathbf {h}_1^{\text{\tiny common}} \mathbf {h}_2^{\text{\tiny common}} \cdots \mathbf {h}_n^{\text{\tiny common}} = \text{Bi-LSTM}(\mathbf {x}_1\mathbf {x}_2\cdots \mathbf {x}_n) $$   (Eq  13)
As shown in Figure 1 , this Bi-LSTM is constructed over the same input character representations of the private Bi-LSTM, in order to extract worker independent features 
The resulting features of the common Bi-LSTM are used for both NER and the worker discriminator, different with the features of private Bi-LSTM which are used for NER only  As shown in Figure 1 , we concatenate the outputs of the common and private Bi-LSTMs together, and then feed the results into the feed-forward combination layer of the NER part  Thus Formula 6 can be rewritten as:
$$\mathbf {h}_t^{\text{ner}} = \mathbf {W} (\mathbf {h}_t^{\text{common}} \oplus \mathbf {h}_t^{\text{private}}) + \mathbf {b},$$   (Eq  14)
where $\mathbf {W}$ is wider than the original combination because the newly-added $\mathbf {h}_t^{\text{common}}$  
Noticeably, although the resulting common features are used for the worker discriminator, they actually have no capability to distinguish the workers  Because this part is exploited to maximize the loss of the worker discriminator, it will be interpreted in the later training subsection  These features are invariant among different workers, thus they can have less noises for NER  This is the goal of adversarial learning, and we hope the NER being able to find useful features from these worker independent features 
Additional Bi-LSTM over Annotated NER Labels
In order to incorporate the annotated NE labels to predict the exact worker, we build another bi-directional LSTM (named by label Bi-LSTM) based on the crowd-annotated NE label sequence  This Bi-LSTM is used for worker discriminator only  During the decoding of the testing phase, we will never have this Bi-LSTM, because the worker discriminator is no longer required 
Assuming the crowd-annotated NE label sequence annotated by one worker is $\mathbf {\bar{y}} = \bar{y}_1\bar{y}_2 \cdots \bar{y}_n$ , we exploit a looking-up table $\mathbf {E}^{L}$ to obtain the corresponding sequence of their vector representations $\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n$ , similar to the method that maps characters into their neural representations  Concretely, for one NE label $\bar{y}_t$ ( $t \in [1, n]$ ), we obtain its neural vector by: $\mathbf {x^{\prime }}_t = \text{look-up}(\bar{y}_t, \mathbf {E}^L)$  
Next step we apply bi-directional LSTM over the sequence $\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n$ , which can be formalized as:
$$\mathbf {h}_1^{\text{label}} \mathbf {h}_2^{\text{label}} \cdots \mathbf {h}_n^{\text{label}} = \text{Bi-LSTM}(\mathbf {x^{\prime }}_1\mathbf {x^{\prime }}_2\cdots \mathbf {x^{\prime }}_n) $$   (Eq  16)
The resulting feature sequence is concatenated with the outputs of the common Bi-LSTM, and further be used for worker classification 
CNN
Following, we add a convolutional neural network (CNN) module based on the concatenated outputs of the common Bi-LSTM and the label Bi-LSTM, to produce the final features for worker discriminator  A convolutional operator with window size 5 is used, and then max pooling strategy is applied over the convolution sequence to obtain the final fixed-dimensional feature vector  The whole process can be described by the following equations:
$$\begin{split} &\mathbf {h}_t^{\text{worker}} = \mathbf {h}_t^{\text{common}} \oplus \mathbf {h}_t^{\text{label}} \\ &\mathbf {\tilde{h}}_t^{\text{worker}} = \tanh (\mathbf {W}^{\text{cnn}}[\mathbf {h}_{t-2}^{\text{worker}}, \mathbf {h}_{t-1}^{\text{worker}}, \cdots , \mathbf {h}_{t+2}^{\text{worker}}]) \\ &\mathbf {h}^{\text{worker}} = \text{max-pooling}(\mathbf {\tilde{h}}_1^{\text{worker}}\mathbf {\tilde{h}}_2^{\text{worker}} \cdots \mathbf {\tilde{h}}_n^{\text{worker}}) \\ \end{split}$$   (Eq  18)
where $t \in [1,n]$ and $\mathbf {W}^{\text{cnn}}$ is one model parameter  We exploit zero vector to paddle the out-of-index vectors 
Output and Prediction
After obtaining the final feature vector for the worker discriminator, we use it to compute the output vector, which scores all the annotation workers  The score function is defined by:
$$\mathbf {o}^{\text{worker}} = \mathbf {W}^{\text{worker}} \mathbf {h}^{\text{worker}},$$   (Eq  20)
where $\mathbf {W}^{\text{worker}}$ is one model parameter and the output dimension equals the number of total non-expert annotators  The prediction is to find the worker which is responsible for this annotation 
Adversarial Training
The training objective with adversarial neural network is different from the baseline model, as it includes the extra worker discriminator  Thus the new objective includes two parts, one being the negative log-likelihood from NER which is the same as the baseline, and the other being the negative the log-likelihood from the worker discriminator 
In order to obtain the negative log-likelihood of the worker discriminator, we use softmax to compute the probability of the actual worker $\bar{z}$ as well, which is defined by:
$$p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}) = \frac{\exp (\mathbf {o}^{\text{worker}}_{\bar{z}})}{\sum _{z} \exp (\mathbf {o}^{\text{worker}}_z)},$$   (Eq  22)
where $z$ should enumerate all workers 
Based on the above definition of probability, our new objective is defined as follows:
$$\begin{split} \text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) &= \text{loss}(\Theta , \mathbf {X}, \mathbf {\bar{y}}) - \text{loss}(\Theta , \Theta ^{\prime }, \mathbf {X}) \\ \text{~~~~~~} &= -\log p(\mathbf {\bar{y}}|\mathbf {X}) + \log p(\bar{z}|\mathbf {X}, \mathbf {\bar{y}}), \end{split}$$   (Eq  23)
where $\Theta $ is the set of all model parameters related to NER, and $\Theta ^{\prime }$ is the set of the remaining parameters which are only related to the worker discriminator, $\mathbf {X}$ , $\mathbf {\bar{y}}$ and $\bar{z}$ are the input sentence, the crowd-annotated NE labels and the corresponding annotator for this annotation, respectively  It is worth noting that the parameters of the common Bi-LSTM are included in the set of $\Theta $ by definition 
In particular, our goal is not to simply minimize the new objective  Actually, we aim for a saddle point, finding the parameters $\Theta $ and $\Theta ^{\prime }$ satisfying the following conditions:
$$\begin{split} \hat{\Theta } &= \mathop {arg~min}_{\Theta }\text{R}(\Theta , \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \hat{\Theta }^{\prime } &= \mathop {arg~max}_{\Theta ^{\prime }}\text{R}(\hat{\Theta }, \Theta ^{\prime }, \mathbf {X}, \mathbf {\bar{y}}, \bar{z}) \\ \end{split}$$   (Eq  24)
where the first equation aims to find one $\Theta $ that minimizes our new objective $\text{R}(\cdot )$ , and the second equation aims to find one $\Theta ^{\prime }$ maximizing the same objective 
Intuitively, the first equation of Formula 24 tries to minimize the NER loss, but at the same time maximize the worker discriminator loss by the shared parameters of the common Bi-LSTM  Thus the resulting features of common Bi-LSTM actually attempt to hurt the worker discriminator, which makes these features worker independent since they are unable to distinguish different workers  The second equation tries to minimize the worker discriminator loss by its own parameter $\Theta ^{\prime }$  
We use the standard back-propagation method to train the model parameters, the same as the baseline model  In order to incorporate the term of the argmax part of Formula 24 , we follow the previous work of adversarial training BIBREF13 , BIBREF15 , BIBREF17 , by introducing a gradient reverse layer between the common Bi-LSTM and the CNN module, whose forward does nothing but the backward simply negates the gradients 
Data Sets
With the purpose of obtaining evaluation datasets from crowd annotators, we collect the sentences from two domains: Dialog and E-commerce domain  We hire undergraduate students to annotate the sentences  They are required to identify the predefined types of entities in the sentences  Together with the guideline document, the annotators are educated some tips in fifteen minutes and also provided with 20 exemplifying sentences 
Labeled Data: DL-PS  In Dialog domain (DL), we collect raw sentences from a chatbot application  And then we randomly select 20K sentences as our pool and hire 43 students to annotate the sentences  We ask the annotators to label two types of entities: Person-Name and Song-Name  The annotators label the sentences independently  In particular, each sentence is assigned to three annotators for this data  Although the setting can be wasteful of labor, we can use the resulting dataset to test several well-known baselines such as majority voting 
After annotation, we remove some illegal sentences reported by the annotators  Finally, we have 16,948 sentences annotated by the students  Table 1 shows the information of annotated data  The average Kappa value among the annotators is 0 6033, indicating that the crowd annotators have moderate agreement on identifying entities on this data 
In order to evaluate the system performances, we create a set of corpus with gold annotations  Concretely, we randomly select 1,000 sentences from the final dataset and let two experts generate the gold annotations  Among them, we use 300 sentences as the development set and the remaining 700 as the test set  The rest sentences with only student annotations are used as the training set 
Labeled data: EC-MT and EC-UQ  In E-commerce domain (EC), we collect raw sentences from two types of texts: one is titles of merchandise entries (EC-MT) and another is user queries (EC-UQ)  The annotators label five types of entities: Brand, Product, Model, Material, and Specification  These five types of entities are very important for E-commerce platform, for example building knowledge graph of merchandises  Five students participate the annotations for this domain since the number of sentences is small  We use the similar strategy as DL-PS to annotate the sentences, except that only two annotators are assigned for each sentence, because we aim to test the system performances under very small duplicated annotations 
Finally, we obtain 2,337 sentences for EC-MT and 2,300 for EC-UQ  Table 1 shows the information of annotated results  Similarly, we produce the development and test datasets for system evaluation, by randomly selecting 400 sentences and letting two experts to generate the groundtruth annotations  Among them, we use 100 sentences as the development set and the remaining 300 as the test set  The rest sentences with only crowdsourcing annotations are used as the training set 
Unlabeled data  The vector representations of characters are basic inputs of our baseline and proposed models, which are obtained by the looking-up table $\mathbf {E}^W$   As introduced before, we can use pretrained embeddings from large-scale raw corpus to initialize the table  In order to pretrain the character embeddings, we use one large-scale unlabeled data from the user-generated content in Internet  Totally, we obtain a number of 5M sentences  Finally, we use the tool word2vec to pretrain the character embeddings based on the unlabeled dataset in our experiments 
Settings
For evaluation, we use the entity-level metrics of Precision (P), Recall (R), and their F1 value in our experiments, treating one tagged entity as correct only when it matches the gold entity exactly 
There are several hyper-parameters in the baseline LSTM-CRF and our final models  We set them empirically by the development performances  Concretely, we set the dimension size of the character embeddings by 100, the dimension size of the NE label embeddings by 50, and the dimension sizes of all the other hidden features by 200 
We exploit online training with a mini-batch size 128 to learn model parameters  The max-epoch iteration is set by 200, and the best-epoch model is chosen according to the development performances  We use RMSprop BIBREF28 with a learning rate $10^{-3}$ to update model parameters, and use $l_2$ -regularization by a parameter $10^{-5}$   We adopt the dropout technique to avoid overfitting by a drop value of $0 2$  
Comparison Systems
The proposed approach (henceforward referred to as “ALCrowd”) is compared with the following systems:
CRF: We use the Crfsuite tool to train a model on the crowdsourcing labeled data  As for the feature settings, we use the supervised version of BIBREF0 zhao2008unsupervised 
CRF-VT: We use the same settings of the CRF system, except that the training data is the voted version, whose groundtruths are produced by majority voting at the character level for each annotated sentence 
CRF-MA: The CRF model proposed by BIBREF3 rodrigues2014sequence, which uses a prior distributation to model multiple crowdsourcing annotators  We use the source code provided by the authors 
LSTM-CRF: Our baseline system trained on the crowdsourcing labeled data 
LSTM-CRF-VT: Our baseline system trained on the voted corpus, which is the same as CRF-VT 
LSTM-Crowd: The LSTM-CRF model with crowd annotation learning proposed by BIBREF4 nguyen2017aggregating  We use the source code provided by the authors 
The first three systems are based on the CRF model using traditional handcrafted features, and the last three systems are based on the neural LSTM-CRF model  Among them, CRF-MA, LSTM-Crowd and our system with adversarial learning (ALCrowd) are based on crowd annotation learning that directly trains the model on the crowd-annotations  Five systems, including CRF, CRF-MA, LSTM-CRF, LSTM-Crowd, and ALCrowd, are trained on the original version of labeled data, while CRF-VT and LSTM-CRF-VT are trained on the voted version  Since CRF-VT, CRF-MA and LSTM-CRF-VT all require ground-truth answers for each training sentence, which are difficult to be produced with only two annotations, we do not apply the three models on the two EC datasets 
Main Results
In this section, we show the model performances of our proposed crowdsourcing learning system (ALCrowd), and meanwhile compare it with the other systems mentioned above  Table 2 shows the experimental results on the DL-PS datasets and Table 3 shows the experiment results on the EC-MT and EC-UQ datasets, respectively 
The results of CRF and LSTM-CRF mean that the crowd annotation is an alternative solution with low cost for labeling data that could be used for training a NER system even there are some inconsistencies  Compared with CRF, LSTM-CRF achieves much better performances on all the three data, showing +6 12 F1 improvement on DL-PS, +4 51 on EC-MT, and +9 19 on EC-UQ  This indicates that LSTM-CRF is a very strong baseline system, demonstrating the effectiveness of neural network 
Interestingly, when compared with CRF and LSTM-CRF, CRF-VT and LSTM-CRF-VT trained on the voted version perform worse in the DL-PS dataset  This trend is also mentioned in BIBREF4 nguyen2017aggregating  This fact shows that the majority voting method might be unsuitable for our task  There are two possible reasons accounting for the observation  On the one hand, simple character-level voting based on three annotations for each sentence may be still not enough  In the DL-PS dataset, even with only two predefined entity types, one character can have nine NE labels  Thus the majority-voting may be incapable of handling some cases  While the cost by adding more annotations for each sentence would be greatly increased  On the other hand, the lost information produced by majority-voting may be important, at least the ambiguous annotations denote that the input sentence is difficult for NER  The normal CRF and LSTM-CRF models without discard any annotations can differentiate these difficult contexts through learning 
Three crowd-annotation learning systems provide better performances than their counterpart systems, (CRF-MA VS CRF) and (LSTM-Crowd/ALCrowd VS LSTM-CRF)  Compared with the strong baseline LSTM-CRF, ALCrowd shows its advantage with +1 08 F1 improvements on DL-PS, +1 24 on EC-MT, and +2 38 on EC-UQ, respectively  This indicates that adding the crowd-annotation learning is quite useful for building NER systems  In addition, ALCrowd also outperforms LSTM-Crowd on all the datasets consistently, demonstrating the high effectiveness of ALCrowd in extracting worker independent features  Among all the systems, ALCrowd performs the best, and significantly better than all the other models (the p-value is below $10^{-5}$ by using t-test)  The results indicate that with the help of adversarial training, our system can learn a better feature representation from crowd annotation 
Discussion
Impact of Character Embeddings  First, we investigate the effect of the pretrained character embeddings in our proposed crowdsourcing learning model  The comparison results are shown in Figure 2 , where Random refers to the random initialized character embeddings, and Pretrained refers to the embeddings pretrained on the unlabeled data  According to the results, we find that our model with the pretrained embeddings significantly outperforms that using the random embeddings, demonstrating that the pretrained embeddings successfully provide useful information 
Case Studies  Second, we present several case studies in order to study the differences between our baseline and the worker adversarial models  We conduct a closed test on the training set, the results of which can be regarded as modifications of the training corpus, since there exist inconsistent annotations for each training sentence among the different workers  Figure 3 shows the two examples from the DL-PS dataset, which compares the outputs of the baseline and our final models, as well as the majority-voting strategy 
In the first case, none of the annotations get the correct NER result, but our proposed model can capture it  The result of LSTM-CRF is the same as majority-voting  In the second example, the output of majority-voting is the worst, which can account for the reason why the same model trained on the voted corpus performs so badly, as shown in Table 2   The model of LSTM-CRF fails to recognize the named entity “Xiexie” because of not trusting the second annotation, treating it as one noise annotation  Our proposed model is able to recognize it, because of its ability of extracting worker independent features 
Conclusions
In this paper, we presented an approach to performing crowd annotation learning based on the idea of adversarial training for Chinese Named Entity Recognition (NER)  In our approach, we use a common and private Bi-LSTMs for representing annotator-generic and -specific information, and learn a label Bi-LSTM from the crowd-annotated NE label sequences  Finally, the proposed approach adopts a LSTM-CRF model to perform tagging  In our experiments, we create two data sets for Chinese NER tasks in the dialog and e-commerce domains  The experimental results show that the proposed approach outperforms strong baseline systems 
 Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No  61572338, 61525205, and 61602160)  This work is also partially supported by the joint research project of Alibaba and Soochow University  Wenliang is also partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization 
Context 3: Introduction
Natural languages evolve and words have always been subject to semantic change over time BIBREF1  With the rise of large digitized text resources recent NLP technologies have made it possible to capture such change with vector space models BIBREF2, BIBREF3, BIBREF4, BIBREF5, topic models BIBREF6, BIBREF7, BIBREF8, and sense clustering models BIBREF9  However, many approaches for detecting LSC differ profoundly from each other and therefore drawing comparisons between them can be challenging BIBREF10  Not only do architectures for detecting LSC vary, their performance is also often evaluated without access to evaluation data or too sparse data sets  In cases where evaluation data is available, oftentimes LSCD systems are not evaluated on the same data set which hinders the research community to draw comparisons 
For this reason we report the results of the first shared task on unsupervised lexical semantic change detection in German that is based on an annotated data set to guarantee objective reasoning throughout different approaches  The task was organized as part of the seminar 'Lexical Semantic Change Detection' at the IMS Stuttgart in the summer term of 2019 
Task
The goal of the shared task was to create an architecture to detect semantic change and to rank words according to their degree of change between two different time periods  Given two corpora Ca and Cb, the target words had to be ranked according to their degree of lexical semantic change between Ca and Cb as annotated by human judges  A competition was set up on Codalab and teams mostly consisting of 2 people were formed to take part in the task  There was one group consisting of 3 team members and two individuals who entered the task on their own  In total there were 12 LSCD systems participating in the shared task 
The shared task was divided into three phases, i e , development, testing and analysis phase  In the development phase each team implemented a first version of their model based on a trial data set and submitted it subsequently  In the testing phase the testing data was made public and participants applied their models to the test data with a restriction of possible result uploads to 30  The leaderboard was public at all times  Eventually, the analysis phase was entered and the models of the testing phase were evaluated in terms of the predictions they made and parameters could be tuned further  The models and results will be discussed in detail in sections 7 and 8 
Corpora
The task, as framed above, requires to detect the semantic change between two corpora  The two corpora used in the shared task correspond to the diachronic corpus pair from BIBREF0: DTA18 and DTA19  They consist of subparts of DTA corpus BIBREF11 which is a freely available lemmatized, POS-tagged and spelling-normalized diachronic corpus of German containing texts from the 16th to the 20th century  DTA18 contains 26 million sentences published between 1750-1799 and DTA19 40 million between 1850-1899  The corpus version used in the task has the following format: "year [tab] lemma1 lemma2 lemma3    " 
Evaluation
The Diachronic Usage Relatedness (DURel) gold standard data set includes 22 target words and their varying degrees of semantic change BIBREF12  For each of these target words a random sample of use pairs from the DTA corpus was retrieved and annotated  The annotators were required to rate the pairs according to their semantic relatedness on a scale from 1 to 4 (unrelated - identical meanings) for two time periods  The average Spearman's $\rho $ between the five annotators was 0 66 for 1,320 use paris

ground_truth: text classification for themes including sentiment, web-page, science, medical and healthcare

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 accuracy does the proposed system achieve?
Context 3:  The resulting word ranking of the DURel data set is determined by the mean usage relatedness across two time periods and is used as the benchmark to compare the models’ performances in the shared task 
Evaluation ::: Metric
The output of a system with the target words in the predicted order is compared to the gold ranking of the DURel data set  As the metric to assess how well the model's output fits the gold ranking Spearman's $\rho $ was used  The higher Spearman's rank-order correlation the better the system's performance 
Evaluation ::: Baselines
Models were compared to two baselines for the shared task:
log-transformed normalized frequency difference (FD)
count vectors with column intersection and cosine distance (CNT + CI + CD)
The window size for CNT + CI + CD was 10  Find more information on these models in BIBREF0 
Participating Systems
Participants mostly rely on the models compared in BIBREF0 and apply modifications to improve them  In particular, most teams make use of skip-gram with negative sampling (SGNS) based on BIBREF13 to learn the semantic spaces of the two time periods and orthogonal procrustes (OP) to align these vector spaces, similar to the approach by BIBREF14  Different meaning representations such as sense clusters are used as well  As measure to detect the degree of LSC all teams except one choose cosine distance (CD)  This team uses Jensen-Shannon distance (JSD) instead, which computes the distance between probability distributions BIBREF15 
The models of each team will be briefly introduced in this section 
Participating Systems ::: sorensbn
Team sorensbn makes use of SGNS + OP + CD to detect LSC  They use similar hyperparameters as in BIBREF0 to tune the SGNS model  They use an open-sourced noise-aware implementation to improve the OP alignment BIBREF16 
Participating Systems ::: tidoe
Team tidoe builds on SGNS + OP + CD, but they add a transformation step to receive binarized representations of matrices BIBREF17  This step is taken to counter the bias that can occur in vector-space models based on frequencies BIBREF18 
Participating Systems ::: in vain
The team applies a model based on SGNS with vector initialization alignment and cosine distance (SGNS + VI + CD)  Vector initialization is an alignment strategy where the vector space learning model for $t_2$ is initialized with the vectors from $t_1$ BIBREF19  Since SGNS + VI + OP does not perform as well as other models in BIBREF0, they alter the vector initialization process by initializing on the complete model instead of only the word matrix of $t_1$ to receive improved results 
Participating Systems ::: Evilly
In line with previous approaches, team Evilly builds upon SGNS + OP + CD  They alter the OP step by using only high-frequency words for alignment 
Participating Systems ::: DAF
Team DAF uses an architecture based on learning vectors with fastText, alignment with unsupervised and supervised variations of OP, and CD, using the MUSE package BIBREF20, BIBREF21  For the supervised alignment stop words are used  The underlying assumption is that stop words serve as functional units of language and their usage should be consistent over time 
Participating Systems ::: SnakesOnAPlane
The team learns vector spaces with count vectors, positive pointwise mutual information (PPMI), SGNS and uses column intersection (CI) and OP as alignment techniques where applicable  Then they compare two distance measures (CD and JSD) for the different models CNT + CI, PPMI + CI and SGNS + OP to identify which measure performs better for these models  They also experiment with different ways to remove negative values from SGNS vectors, which is needed for JSD 
Participating Systems ::: TeamKulkarni15
TeamKulkarni15 uses SGNS + OP + CD with the modification of local alignment with k nearest neighbors, since other models often use global alignment that can be prone to noise BIBREF22 
Participating Systems ::: Bashmaistori
They use word injection (WI) alignment on PPMI vectors with CD  This approach avoids the complex alignment procedure for embeddings and is applicable to embeddings and count-based methods  They compare two implementations of word injection BIBREF23, BIBREF0 as these showed different results on different data sets 
Participating Systems ::: giki
Team giki uses PPMI + CI + CD to detect LSC

ground_truth: F1 scores of 85.99 on the DL-PS data,  75.15 on the EC-MT data and 71.53 on the EC-UQ data 

F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)

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 corpus used for the task?
Context 3:  They state that a word sense is determined by its context, but relevant context words can also be found outside a predefined window  Therefore, they use tf-idf to select relevant context BIBREF24 
Participating Systems ::: Edu-Phil
Similar to team DAF they also use fastText + OP + CD  Their hypothesis is that fastText may increase the performance for less frequent words in the corpus since generating word embeddings in fasttext is based on character n-grams 
Participating Systems ::: orangefoxes
They use the model by BIBREF5 which is based on SGNS, but avoids alignment by treating time as a vector that may be combined with word vectors to get time-specific word vectors 
Participating Systems ::: Loud Whisper
Loud Whisper base their approach on BIBREF9 which is a graph-based sense clustering model  They process the data set to receive bigrams, create a co-occurence graph representation and after clustering assess the type of change per word by comparing the results against an intersection table  Their motivation is not only to use a graph-based approach, but to extend the approach by enabling change detection for all parts of speech as opposed to the original model 
Results and Discussion
Table TABREF8 shows the results of the shared task  All teams receive better results than baseline 1 (FD), of which a total of 8 teams outperform baseline 2 (CNT + CI + CD)  The 4 top scores with $\rho $ $>$ 0 7 are either modified versions of SGNS + OP + CD or use SGNS + VI + CD  The following 4 scores in the range of 0 5 $<$ $\rho $ $<$ 0 6 are generated by the models fastText + OP + CD, SGNS + OP + CD/JSD, and PPMI + WI + CD 
Contrary to the results by BIBREF0 the modified version of vector initialization shows high performance similar to OP alignment, as previously reported by BIBREF14  Some modifications to the SGNS + OP + CD approach are able to yield better results than others, e g  noise-aware alignment and binarized matrices as compared to frequency-driven OP alignment or local alignment with KNN  Team SnakesOnAPlane compare two distance measures and their results show that JSD ($\rho $ $=$  561) performs minimally worse than CD ($\rho $ $=$  565) as the semantic change measure for their model 
The overall best-performing model is Skip-Gram with orthogonal alignment and cosine distance (SGNS + OP + CD) with similar hyperparameters as in the model architecture described previously BIBREF0  Said architecture was used as the basis for the two best performing models  Team tidoe reports that binarizing matrices leads to a generally worse performance ($\rho $ $=$  811) compared to the unmodified version of SGNS + OP + CD ($\rho $ $=$ 0 9)  The noise aware alignment approach applied by team sorensbn obtains a higher score ($\rho $ $=$  854) compared to the result reported by tidoe, but is unable to exceed the performance of the unmodified SNGS + OP + CD for the same set of hyperparameters (window size = 10, negative sampling = 1; subsampling = None)  Of the 8 scores above the second baseline, 5 use an architecture that builds upon SGNS + OP + CD  Whereas in the lower score segment $\rho $ $<$ 0 5 none of the models use SGNS + OP + CD  These findings are in line with the results reported by BIBREF0, however the overall best results are lower in this shared task, which is expected from the smaller number of parameter combinations explored  Additionally, in the shared task the objective was to report the best score and not to calculate the mean which makes it more difficult to compare the robustness of the models presented here 

ground_truth: DTA18, DTA19

Diachronic Usage Relatedness (DURel) gold standard data set