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 are strong baselines model is compared to?
Context 1: Introduction
Chinese word segmentation (CWS) is a task for Chinese natural language process to delimit word boundary  CWS is a basic and essential task for Chinese which is written without explicit word delimiters and different from alphabetical languages like English  BIBREF0 treats Chinese word segmentation (CWS) as a sequence labeling task with character position tags, which is followed by BIBREF1, BIBREF2, BIBREF3  Traditional CWS models depend on the design of features heavily which effects the performance of model  To minimize the effort in feature engineering, some CWS models BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11 are developed following neural network architecture for sequence labeling tasks BIBREF12  Neural CWS models perform strong ability of feature representation, employing unigram and bigram character embedding as input and approach good performance 
The CWS task is often modeled as one graph model based on a scoring model that means it is composed of two parts, one part is an encoder which is used to generate the representation of characters from the input sequence, the other part is a decoder which performs segmentation according to the encoder scoring  Table TABREF1 summarizes typical CWS models according to their decoding ways for both traditional and neural models  Markov models such as BIBREF13 and BIBREF4 depend on the maximum entropy model or maximum entropy Markov model both with a Viterbi decoder  Besides, conditional random field (CRF) or Semi-CRF for sequence labeling has been used for both traditional and neural models though with different representations BIBREF2, BIBREF15, BIBREF10, BIBREF17, BIBREF18  Generally speaking, the major difference between traditional and neural network models is about the way to represent input sentences 
Recent works about neural CWS which focus on benchmark dataset, namely SIGHAN Bakeoff BIBREF21, may be put into the following three categories roughly 
Encoder  Practice in various natural language processing tasks has been shown that effective representation is essential to the performance improvement  Thus for better CWS, it is crucial to encode the input character, word or sentence into effective representation  Table TABREF2 summarizes regular feature sets for typical CWS models including ours as well  The building blocks that encoders use include recurrent neural network (RNN) and convolutional neural network (CNN), and long-term memory network (LSTM) 
Graph model  As CWS is a kind of structure learning task, the graph model determines which type of decoder should be adopted for segmentation, also it may limit the capability of defining feature, as shown in Table 2, not all graph models can support the word features  Thus recent work focused on finding more general or flexible graph model to make model learn the representation of segmentation more effective as BIBREF9, BIBREF11 
External data and pre-trained embedding  Whereas both encoder and graph model are about exploring a way to get better performance only by improving the model strength itself  Using external resource such as pre-trained embeddings or language representation is an alternative for the same purpose BIBREF22, BIBREF23  SIGHAN Bakeoff defines two types of evaluation settings, closed test limits all the data for learning should not be beyond the given training set, while open test does not take this limitation BIBREF21  In this work, we will focus on the closed test setting by finding a better model design for further CWS performance improvement 
Shown in Table TABREF1, different decoders have particular decoding algorithms to match the respective CWS models  Markov models and CRF-based models often use Viterbi decoders with polynomial time complexity  In general graph model, search space may be too large for model to search  Thus it forces graph models to use an approximate beam search strategy  Beam search algorithm has a kind low-order polynomial time complexity  Especially, when beam width $b$=1, the beam search algorithm will reduce to greedy algorithm with a better time complexity $O(Mn)$ against the general beam search time complexity $O(Mnb^2)$, where $n$ is the number of units in one sentences, $M$ is a constant representing the model complexity  Greedy decoding algorithm can bring the fastest speed of decoding while it is not easy to guarantee the precision of decoding when the encoder is not strong enough 
In this paper, we focus on more effective encoder design which is capable of offering fast and accurate Chinese word segmentation with only unigram feature and greedy decoding  Our proposed encoder will only consist of attention mechanisms as building blocks but nothing else  Motivated by the Transformer BIBREF24 and its strength of capturing long-range dependencies of input sentences, we use a self-attention network to generate the representation of input which makes the model encode sentences at once without feeding input iteratively  Considering the weakness of the Transformer to model relative and absolute position information directly BIBREF25 and the importance of localness information, position information and directional information for CWS, we further improve the architecture of standard multi-head self-attention of the Transformer with a directional Gaussian mask and get a variant called Gaussian-masked directional multi-head attention  Based on the newly improved attention mechanism, we expand the encoder of the Transformer to capture different directional information  With our powerful encoder, our model uses only simple unigram features to generate representation of sentences 
For decoder which directly performs the segmentation, we use the bi-affinal attention scorer, which has been used in dependency parsing BIBREF26 and semantic role labeling BIBREF27, to implement greedy decoding on finding the boundaries of words  In our proposed model, greedy decoding ensures a fast segmentation while powerful encoder design ensures a good enough segmentation performance even working with greedy decoder together  Our model will be strictly evaluated on benchmark datasets from SIGHAN Bakeoff shared task on CWS in terms of closed test setting, and the experimental results show that our proposed model achieves new state-of-the-art 
The technical contributions of this paper can be summarized as follows 
We propose a CWS model with only attention structure  The encoder and decoder are both based on attention structure 
With a powerful enough encoder, we for the first time show that unigram (character) featues can help yield strong performance instead of diverse $n$-gram (character and word) features in most of previous work 
To capture the representation of localness information and directional information, we propose a variant of directional multi-head self-attention to further enhance the state-of-the-art Transformer encoder 
Models
The CWS task is often modelled as one graph model based on an encoder-based scoring model  The model for CWS task is composed of an encoder to represent the input and a decoder based on the encoder to perform actual segmentation  Figure FIGREF6 is the architecture of our model  The model feeds sentence into encoder  Embedding captures the vector $e=(e_1,   ,e_n)$ of the input character sequences of $c=(c_1,   ,c_n)$  The encoder maps vector sequences of $ {e}=(e_1,  ,e_n)$ to two sequences of vector which are $ {v^b}=(v_1^b,   ,v_n^b)$ and ${v^f}=(v_1^f,   v_n^f)$ as the representation of sentences  With $v^b$ and $v^f$, the bi-affinal scorer calculates the probability of each segmentation gaps and predicts the word boundaries of input  Similar as the Transformer, the encoder is an attention network with stacked self-attention and point-wise, fully connected layers while our encoder includes three independent directional encoders 
Models ::: Encoder Stacks
In the Transformer, the encoder is composed of a stack of N identical layers and each layer has one multi-head self-attention layer and one position-wise fully connected feed-forward layer  One residual connection is around two sub-layers and followed by layer normalization BIBREF24  This architecture provides the Transformer a good ability to generate representation of sentence 
With the variant of multi-head self-attention, we design a Gaussian-masked directional encoder to capture representation of different directions to improve the ability of capturing the localness information and position information for the importance of adjacent characters  One unidirectional encoder can capture information of one particular direction 
For CWS tasks, one gap of characters, which is from a word boundary, can divide one sequence into two parts, one part in front of the gap and one part in the rear of it  The forward encoder and backward encoder are used to capture information of two directions which correspond to two parts divided by the gap 
One central encoder is paralleled with forward and backward encoders to capture the information of entire sentences  The central encoder is a special directional encoder for forward and backward information of sentences  The central encoder can fuse the information and enable the encoder to capture the global information 
The encoder outputs one forward information and one backward information of each positions  The representation of sentence generated by center encoder will be added to these information directly:
where $v^{b}=(v^b_1,   ,v^b_n)$ is the backward information, $v^{f}=(v^f_1,   ,v^f_n)$ is the forward information, $r^{b}=(r^b_1,   ,r^b_n)$ is the output of backward encoder, $r^{c}=(r^c_1,   ,r^c_n)$ is the output of center encoder and $r^{f}=(r^f_1,   ,r^f_n)$ is the output of forward encoder 
Models ::: Gaussian-Masked Directional Multi-Head Attention
Similar as scaled dot-product attention BIBREF24, Gaussian-masked directional attention can be described as a function to map queries and key-value pairs to the representation of input  Here queries, keys and values are all vectors  Standard scaled dot-product attention is calculated by dotting query $Q$ with all keys $K$, dividing each values by $\sqrt{d_k}$, where $\sqrt{d_k}$ is the dimension of keys, and apply a softmax function to generate the weights in the attention:
Different from scaled dot-product attention, Gaussian-masked directional attention expects to pay attention to the adjacent characters of each positions and cast the localness relationship between characters as a fix Gaussian weight for attention  We assume that the Gaussian weight only relys on the distance between characters 
Firstly we introduce the Gaussian weight matrix $G$ which presents the localness relationship between each two characters:
where $g_{ij}$ is the Gaussian weight between character $i$ and $j$, $dis_{ij}$ is the distance between character $i$ and $j$, $\Phi (x)$ is the cumulative distribution function of Gaussian, $\sigma $ is the standard deviation of Gaussian function and it is a hyperparameter in our method  Equation (DISPLAY_FORM13) can ensure the Gaussian weight equals 1 when $dis_{ij}$ is 0  The larger distance between charactersis, the smaller the weight is, which makes one character can affect its adjacent characters more compared with other characters 
To combine the Gaussian weight to the self-attention, we produce the Hadamard product of Gaussian weight matrix $G$ and the score matrix produced by $Q{K^{T}}$
where $AG$ is the Gaussian-masked attention  It ensures that the relationship between two characters with long distances is weaker than adjacent characters 
The scaled dot-product attention models the relationship between two characters without regard to their distances in one sequence  For CWS task, the weight between adjacent characters should be more important while it is hard for self-attention to achieve the effect explicitly because the self-attention cannot get the order of sentences directly  The Gaussian-masked attention adjusts the weight between characters and their adjacent character to a larger value which stands for the effect of adjacent characters 
For forward and backward encoder, the self-attention sublayer needs to use a triangular matrix mask to let the self-attention focus on different weights:
where $pos_i$ is the position of character $c_i$  The triangular matrix for forward and backward encode are:
$\left[ \begin{matrix} 1 & 0 & 0 & \cdots &0\\ 1 & 1 & 0 & \cdots &0\\ 1 & 1 & 1 & \cdots &0\\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 1 & 1 & 1 & \cdots & 1\\ \end{matrix} \right]$ $\left[ \begin{matrix} 1 & 1 & 1 & \cdots &1 \\ 0 & 1 & 1 & \cdots &1 \\ 0 & 0& 1 & \cdots &1 \\ \vdots &\vdots &\vdots &\ddots &\vdots \\ 0 & 0 & 0 & \cdots & 1\\ \end{matrix}\right]$
Similar as BIBREF24, we use multi-head attention to capture information from different dimension positions as Figure FIGREF16 and get Gaussian-masked directional multi-head attention  With multi-head attention architecture, the representation of input can be captured by
where $MH$ is the Gaussian-masked multi-head attention, ${W_i^q, W_i^k,W_i^v} \in \mathbb {R}^{d_k \times d_h}$ is the parameter matrices to generate heads, $d_k$ is the dimension of model and $d_h$ is the dimension of one head 
Models ::: Bi-affinal Attention Scorer
Regarding word boundaries as gaps between any adjacent words converts the character labeling task to the gap labeling task  Different from character labeling task, gap labeling task requires information of two adjacent characters  The relationship between adjacent characters can be represented as the type of gap  The characteristic of word boundaries makes bi-affine attention an appropriate scorer for CWS task 
Bi-affinal attention scorer is the component that we use to label the gap  Bi-affinal attention is developed from bilinear attention which has been used in dependency parsing BIBREF26 and SRL BIBREF27  The distribution of labels in a labeling task is often uneven which makes the output layer often include a fixed bias term for the prior probability of different labels BIBREF27  Bi-affine attention uses bias terms to alleviate the burden of the fixed bias term and get the prior probability which makes it different from bilinear attention  The distribution of the gap is uneven that is similar as other labeling task which fits bi-affine 
Bi-affinal attention scorer labels the target depending on information of independent unit and the joint information of two units  In bi-affinal attention, the score $s_{ij}$ of characters $c_i$ and $c_j$ $(i < j)$ is calculated by:
where $v_i^f$ is the forward information of $c_i$ and $v_i^b$ is the backward information of $c_j$  In Equation (DISPLAY_FORM21), $W$, $U$ and $b$ are all parameters that can be updated in training  $W$ is a matrix with shape $(d_i \times N\times d_j)$ and $U$ is a $(N\times (d_i + d_j))$ matrix where $d_i$ is the dimension of vector $v_i^f$ and $N$ is the number of labels 
In our model, the biaffine scorer uses the forward information of character in front of the gap and the backward information of the character behind the gap to distinguish the position of characters  Figure FIGREF22 is an example of labeling gap  The method of using biaffine scorer ensures that the boundaries of words can be determined by adjacent characters with different directional information  The score vector of the gap is formed by the probability of being a boundary of word  Further, the model generates all boundaries using activation function in a greedy decoding way 
Experiments ::: Experimental Settings ::: Data
We train and evaluate our model on datasets from SIGHAN Bakeoff 2005 BIBREF21 which has four datasets, PKU, MSR, AS and CITYU  Table TABREF23 shows the statistics of train data  We use F-score to evaluate CWS models  To train model with pre-trained embeddings in AS and CITYU, we use OpenCC to transfer data from traditional Chinese to simplified Chinese 
Experiments ::: Experimental Settings ::: Pre-trained Embedding
We only use unigram feature so we only trained character embeddings  Our pre-trained embedding are pre-trained on Chinese Wikipedia corpus by word2vec BIBREF29 toolkit  The corpus used for pre-trained embedding is all transferred to simplified Chinese and not segmented  On closed test, we use embeddings initialized randomly 
Experiments ::: Experimental Settings ::: Hyperparameters
For different datasets, we use two kinds of hyperparameters which are presented in Table TABREF24  We use hyperparameters in Table TABREF24 for small corpora (PKU and CITYU) and normal corpora (MSR and AS)  We set the standard deviation of Gaussian function in Equation (DISPLAY_FORM13) to 2  Each training batch contains sentences with at most 4096 tokens 
Experiments ::: Experimental Settings ::: Optimizer
To train our model, we use the Adam BIBREF30 optimizer with $\beta _1=0 9$, $\beta _2=0 98$ and $\epsilon =10^{-9}$  The learning rate schedule is the same as BIBREF24:
where $d$ is the dimension of embeddings, $step$ is the step number of training and $warmup_step$ is the step number of warmup  When the number of steps is smaller than the step of warmup, the learning rate increases linearly and then decreases 
Experiments ::: Hardware and Implements
We trained our models on a single CPU (Intel i7-5960X) with an nVidia 1080 Ti GPU  We implement our model in Python with Pytorch 1 0 
Experiments ::: Results
Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting  Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in PKU and CITYU  Note that all the other models for this comparison adopt various $n$-gram features while only our model takes unigram ones 
With unsupervised segmentation features introduced by BIBREF20, our model gets a higher result  Specially, the results in MSR and AS achieve new state-of-the-art and approaching previous state-of-the-art in CITYU and PKU  The unsupervised segmentation features are derived from the given training dataset, thus using them does not violate the rule of closed test of SIGHAN Bakeoff 
Table TABREF36 compares our model and recent neural models in terms of open test setting in which any external resources, especially pre-trained embeddings or language models can be used  In MSR and AS, our model gets a comparable result while our results in CITYU and PKU are not remarkable 
However, it is well known that it is always hard to compare models when using open test setting, especially with pre-trained embedding  Not all models may use the same method and data to pre-train  Though pre-trained embedding or language model can improve the performance, the performance improvement itself may be from multiple sources  It often that there is a success of pre-trained embedding to improve the performance, while it cannot prove that the model is better 
Compared with other LSTM models, our model performs better in AS and MSR than in CITYU and PKU  Considering the scale of different corpora, we believe that the size of corpus affects our model and the larger size is, the better model performs  For small corpus, the model tends to be overfitting 
Tables TABREF25 and TABREF26 also show the decoding time in different datasets  Our model finishes the segmentation with the least decoding time in all four datasets, thanks to the architecture of model which only takes attention mechanism as basic block 
Related Work ::: Chinese Word Segmentation
CWS is a task for Chinese natural language process to delimit word boundary  BIBREF0 for the first time formulize CWS as a sequence labeling task  BIBREF3 show that different character tag sets can make essential impact for CWS  BIBREF2 use CRFs as a model for CWS, achieving new state-of-the-art  Works of statistical CWS has built the basis for neural CWS 
Neural word segmentation has been widely used to minimize the efforts in feature engineering which was important in statistical CWS  BIBREF4 introduce the neural model with sliding-window based sequence labeling  BIBREF6 propose a gated recursive neural network (GRNN) for CWS to incorporate complicated combination of contextual character and n-gram features  BIBREF7 use LSTM to learn long distance information  BIBREF9 propose a neural framework that eliminates context windows and utilize complete segmentation history  BIBREF33 explore a joint model that performs segmentation, POS-Tagging and chunking simultaneously  BIBREF34 propose a feature-enriched neural model for joint CWS and part-of-speech tagging  BIBREF35 present a joint model to enhance the segmentation of Chinese microtext by performing CWS and informal word detection simultaneously  BIBREF17 propose a character-based convolutional neural model to capture $n$-gram features automatically and an effective approach to incorporate word embeddings  BIBREF11 improve the model in BIBREF9 and propose a greedy neural word segmenter with balanced word and character embedding inputs  BIBREF23 propose a novel neural network model to incorporate unlabeled and partially-labeled data  BIBREF36 propose two methods that extend the Bi-LSTM to perform incorporating dictionaries into neural networks for CWS  BIBREF37 propose Switch-LSTMs to segment words and provided a more flexible solution for multi-criteria CWS which is easy to transfer the learned knowledge to new criteria 
Related Work ::: Transformer
Transformer BIBREF24 is an attention-based neural machine translation model  The Transformer is one kind of self-attention networks (SANs) which is proposed in BIBREF38  Encoder of the Transformer consists of one self-attention layer and a position-wise feed-forward layer  Decoder of the Transformer contains one self-attention layer, one encoder-decoder attention layer and one position-wise feed-forward layer  The Transformer uses residual connections around the sublayers and then followed by a layer normalization layer 
Scaled dot-product attention is the key component in the Transformer  The input of attention contains queries, keys, and values of input sequences  The attention is generated using queries and keys like Equation (DISPLAY_FORM11)  Structure of scaled dot-product attention allows the self-attention layer generate the representation of sentences at once and contain the information of the sentence which is different from RNN that process characters of sentences one by one  Standard self-attention is similar as Gaussian-masked direction attention while it does not have directional mask and gaussian mask  BIBREF24 also propose multi-head attention which is better to generate representation of sentence by dividing queries, keys and values to different heads and get information from different subspaces 
Conclusion
In this paper, we propose an attention mechanism only based Chinese word segmentation model  Our model uses self-attention from the Transformer encoder to take sequence input and bi-affine attention scorer to predict the label of gaps  To improve the ability of capturing the localness and directional information of self-attention based encoder, we propose a variant of self-attention called Gaussian-masked directional multi-head attention to replace the standard self-attention  We also extend the Transformer encoder to capture directional features  Our model uses only unigram features instead of multiple $n$-gram features in previous work  Our model is evaluated on standard benchmark dataset, SIGHAN Bakeoff 2005, which shows not only our model performs segmentation faster than any previous models but also gives new higher or comparable segmentation performance against previous state-of-the-art models 
Context 2: Introduction
In practice, it is often difficult and costly to annotate sufficient training data for diverse application domains on-the-fly  We may have sufficient labeled data in an existing domain (called the source domain), but very few or no labeled data in a new domain (called the target domain)  This issue has motivated research on cross-domain sentiment classification, where knowledge in the source domain is transferred to the target domain in order to alleviate the required labeling effort 
One key challenge of domain adaptation is that data in the source and target domains are drawn from different distributions  Thus, adaptation performance will decline with an increase in distribution difference  Specifically, in sentiment analysis, reviews of different products have different vocabulary  For instance, restaurants reviews would contain opinion words such as “tender”, “tasty”, or “undercooked” and movie reviews would contain “thrilling”, “horrific”, or “hilarious”  The intersection between these two sets of opinion words could be small which makes domain adaptation difficult 
Several techniques have been proposed for addressing the problem of domain shifting  The aim is to bridge the source and target domains by learning domain-invariant feature representations so that a classifier trained on a source domain can be adapted to another target domain  In cross-domain sentiment classification, many works BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 utilize a key intuition that domain-specific features could be aligned with the help of domain-invariant features (pivot features)  For instance, “hilarious” and “tasty” could be aligned as both of them are relevant to “good” 
Despite their promising results, these works share two major limitations  First, they highly depend on the heuristic selection of pivot features, which may be sensitive to different applications  Thus the learned new representations may not effectively reduce the domain difference  Furthermore, these works only utilize the unlabeled target data for representation learning while the sentiment classifier was solely trained on the source domain  There have not been many studies on exploiting unlabeled target data for refining the classifier, even though it may contain beneficial information  How to effectively leverage unlabeled target data still remains an important challenge for domain adaptation 
In this work, we argue that the information from unlabeled target data is beneficial for domain adaptation and we propose a novel Domain Adaptive Semi-supervised learning framework (DAS) to better exploit it  Our main intuition is to treat the problem as a semi-supervised learning task by considering target instances as unlabeled data, assuming the domain distance can be effectively reduced through domain-invariant representation learning  Specifically, the proposed approach jointly performs feature adaptation and semi-supervised learning in a multi-task learning setting  For feature adaptation, it explicitly minimizes the distance between the encoded representations of the two domains  On this basis, two semi-supervised regularizations – entropy minimization and self-ensemble bootstrapping – are jointly employed to exploit unlabeled target data for classifier refinement 
We evaluate our method rigorously under multiple experimental settings by taking label distribution and corpus size into consideration  The results show that our model is able to obtain significant improvements over strong baselines  We also demonstrate through a series of analysis that the proposed method benefits greatly from incorporating unlabeled target data via semi-supervised learning, which is consistent with our motivation  Our datasets and source code can be obtained from https://github com/ruidan/DAS 
Related Work
Domain Adaptation: The majority of feature adaptation methods for sentiment analysis rely on a key intuition that even though certain opinion words are completely distinct for each domain, they can be aligned if they have high correlation with some domain-invariant opinion words (pivot words) such as “excellent” or “terrible”  Blitzer et al  ( BIBREF0 ) proposed a method based on structural correspondence learning (SCL), which uses pivot feature prediction to induce a projected feature space that works well for both the source and the target domains  The pivot words are selected in a way to cover common domain-invariant opinion words  Subsequent research aims to better align the domain-specific words BIBREF1 , BIBREF5 , BIBREF3 such that the domain discrepancy could be reduced  More recently, Yu and Jiang ( BIBREF4 ) borrow the idea of pivot feature prediction from SCL and extend it to a neural network-based solution with auxiliary tasks  In their experiment, substantial improvement over SCL has been observed due to the use of real-valued word embeddings  Unsupervised representation learning with deep neural networks (DNN) such as denoising autoencoders has also been explored for feature adaptation BIBREF6 , BIBREF7 , BIBREF8   It has been shown that DNNs could learn transferable representations that disentangle the underlying factors of variation behind data samples 
Although the aforementioned methods aim to reduce the domain discrepancy, they do not explicitly minimize the distance between distributions, and some of them highly rely on the selection of pivot features  In our method, we formally construct an objective for this purpose  Similar ideas have been explored in many computer vision problems, where the representations of the underlying domains are encouraged to be similar through explicit objectives BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 such as maximum mean discrepancy (MMD) BIBREF14   In NLP tasks, Li et al  ( BIBREF15 ) and Chen et al  ( BIBREF16 ) both proposed using adversarial training framework for reducing domain difference  In their model, a sub-network is added as a domain discriminator while deep features are learned to confuse the discriminator  The feature adaptation component in our model shares similar intuition with MMD and adversary training  We will show a detailed comparison with them in our experiments 
Semi-supervised Learning: We attempt to treat domain adaptation as a semi-supervised learning task by considering the target instances as unlabeled data  Some efforts have been initiated on transfer learning from unlabeled data BIBREF17 , BIBREF18 , BIBREF19   In our model, we reduce the domain discrepancy by feature adaptation, and thereafter adopt semi-supervised learning techniques to learn from unlabeled data  Primarily motivated by BIBREF20 and BIBREF21 , we employed entropy minimization and self-ensemble bootstrapping as regularizations to incorporate unlabeled data  Our experimental results show that both methods are effective when jointly trained with the feature adaptation objective, which confirms to our motivation 
Notations and Model Overview
We conduct most of our experiments under an unsupervised domain adaptation setting, where we have no labeled data from the target domain  Consider two sets INLINEFORM0 and INLINEFORM1   INLINEFORM2 is from the source domain with INLINEFORM3 labeled examples, where INLINEFORM4 is a one-hot vector representation of sentiment label and INLINEFORM5 denotes the number of classes  INLINEFORM6 is from the target domain with INLINEFORM7 unlabeled examples  INLINEFORM8 denotes the total number of training documents including both labeled and unlabeled  We aim to learn a sentiment classifier from INLINEFORM13 and INLINEFORM14 such that the classifier would work well on the target domain  We also present some results under a setting where we assume that a small number of labeled target examples are available (see Figure FIGREF27 ) 
For the proposed model, we denote INLINEFORM0 parameterized by INLINEFORM1 as a neural-based feature encoder that maps documents from both domains to a shared feature space, and INLINEFORM2 parameterized by INLINEFORM3 as a fully connected layer with softmax activation serving as the sentiment classifier  We aim to learn feature representations that are domain-invariant and at the same time discriminative on both domains, thus we simultaneously consider three factors in our objective: (1) minimize the classification error on the labeled source examples; (2) minimize the domain discrepancy; and (3) leverage unlabeled data via semi-supervised learning 
Suppose we already have the encoded features of documents INLINEFORM0 (see Section SECREF10 ), the objective function for purpose (1) is thus the cross entropy loss on the labeled source examples DISPLAYFORM0
where INLINEFORM0 denotes the predicted label distribution  In the following subsections, we will explain how to perform feature adaptation and domain adaptive semi-supervised learning in details for purpose (2) and (3) respectively 
Feature Adaptation
Unlike prior works BIBREF0 , BIBREF4 , our method does not attempt to align domain-specific words through pivot words  In our preliminary experiments, we found that word embeddings pre-trained on a large corpus are able to adequately capture this information  As we will later show in our experiments, even without adaptation, a naive neural network classifier with pre-trained word embeddings can already achieve reasonably good results 
We attempt to explicitly minimize the distance between the source and target feature representations ( INLINEFORM0 and INLINEFORM1 )  A few methods from literature can be applied such as Maximum Mean Discrepancy (MMD) BIBREF14 or adversary training BIBREF15 , BIBREF16   The main idea of MMD is to estimate the distance between two distributions as the distance between sample means of the projected embeddings in Hilbert space  MMD is implicitly computed through a characteristic kernel, which is used to ensure that the sample mean is injective, leading to the MMD being zero if and only if the distributions are identical  In our implementation, we skip the mapping procedure induced by a characteristic kernel for simplifying the computation and learning  We simply estimate the distribution distance as the distance between the sample means in the current embedding space  Although this approximation cannot preserve all statistical features of the underlying distributions, we find it performs comparably to MMD on our problem  The following equations formally describe the feature adaptation loss INLINEFORM2 : DISPLAYFORM0
INLINEFORM0 normalization is applied on the mean representations INLINEFORM1 and INLINEFORM2 , rescaling the vectors such that all entries sum to 1  We adopt a symmetric version of KL divergence BIBREF12 as the distance function  Given two distribution vectors INLINEFORM3 , INLINEFORM4  
Domain Adaptive Semi-supervised Learning (DAS)
We attempt to exploit the information in target data through semi-supervised learning objectives, which are jointly trained with INLINEFORM0 and INLINEFORM1   Normally, to incorporate target data, we can minimize the cross entropy loss between the true label distributions INLINEFORM2 and the predicted label distributions INLINEFORM3 over target samples  The challenge here is that INLINEFORM4 is unknown, and thus we attempt to estimate it via semi-supervised learning  We use entropy minimization and bootstrapping for this purpose  We will later show in our experiments that both methods are effective, and jointly employing them overall yields the best results 
Entropy Minimization: In this method, INLINEFORM0 is estimated as the predicted label distribution INLINEFORM1 , which is a function of INLINEFORM2 and INLINEFORM3   The loss can thus be written as DISPLAYFORM0
Assume the domain discrepancy can be effectively reduced through feature adaptation, by minimizing the entropy penalty, training of the classifier is influenced by the unlabeled target data and will generally maximize the margins between the target examples and the decision boundaries, increasing the prediction confidence on the target domain 
Self-ensemble Bootstrapping: Another way to estimate INLINEFORM0 corresponds to bootstrapping  The idea is to estimate the unknown labels as the predictions of the model learned from the previous round of training  Bootstrapping has been explored for domain adaptation in previous works BIBREF18 , BIBREF19   However, in their methods, domain discrepancy was not explicitly minimized via feature adaptation  Applying bootstrapping or other semi-supervised learning techniques in this case may worsen the results as the classifier can perform quite bad on the target data 
[t] Pseudocode for training DAS INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 INLINEFORM4 = ensembling momentum, INLINEFORM5 INLINEFORM6 = weight ramp-up function INLINEFORM7 INLINEFORM8 INLINEFORM9 each minibatch INLINEFORM10 , INLINEFORM11 , INLINEFORM12 in
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 compute loss INLINEFORM3 on INLINEFORM4 compute loss INLINEFORM5 on INLINEFORM6 compute loss INLINEFORM7 on INLINEFORM8 compute loss INLINEFORM9 on INLINEFORM10 INLINEFORM11
update network parameters INLINEFORM0 , for INLINEFORM1 INLINEFORM2 INLINEFORM3
Inspired by the ensembling method proposed in BIBREF21 , we estimate INLINEFORM0 by forming ensemble predictions of labels during training, using the outputs on different training epochs  The loss is formulated as follows: DISPLAYFORM0
where INLINEFORM0 denotes the estimated labels computed on the ensemble predictions from different epochs  The loss is applied on all documents  It serves for bootstrapping on the unlabeled target data, and it also serves as a regularization that encourages the network predictions to be consistent in different training epochs  INLINEFORM1 is jointly trained with INLINEFORM2 , INLINEFORM3 , and INLINEFORM4   Algorithm SECREF6 illustrates the overall training process of the proposed domain adaptive semi-supervised learning (DAS) framework 
In Algorithm SECREF6 , INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are weights to balance the effects of INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 respectively  INLINEFORM6 and INLINEFORM7 are constant hyper-parameters  We set INLINEFORM8 as a Gaussian curve to ramp up the weight from 0 to INLINEFORM9   This is to ensure the ramp-up of the bootstrapping loss component is slow enough in the beginning of the training  After each training epoch, we compute INLINEFORM10 which denotes the predictions made by the network in current epoch, and then the ensemble prediction INLINEFORM11 is updated as a weighted average of the outputs from previous epochs and the current epoch, with recent epochs having larger weight  For generating estimated labels INLINEFORM12 , INLINEFORM13 is converted to a one-hot vector where the entry with the maximum value is set to one and other entries are set to zeros  The self-ensemble bootstrapping is a generalized version of bootstrappings that only use the outputs from the previous round of training BIBREF18 , BIBREF19   The ensemble prediction is likely to be closer to the correct, unknown labels of the target data 
CNN Encoder Implementation
We have left the feature encoder INLINEFORM0 unspecified, for which, a few options can be considered  In our implementation, we adopt a one-layer CNN structure from previous works BIBREF22 , BIBREF4 , as it has been demonstrated to work well for sentiment classification tasks  Given a review document INLINEFORM1 consisting of INLINEFORM2 words, we begin by associating each word with a continuous word embedding BIBREF23 INLINEFORM3 from an embedding matrix INLINEFORM4 , where INLINEFORM5 is the vocabulary size and INLINEFORM6 is the embedding dimension  INLINEFORM7 is jointly updated with other network parameters during training  Given a window of dense word embeddings INLINEFORM8 , the convolution layer first concatenates these vectors to form a vector INLINEFORM9 of length INLINEFORM10 and then the output vector is computed by Equation ( EQREF11 ): DISPLAYFORM0
INLINEFORM0 , INLINEFORM1 is the parameter set of the encoder INLINEFORM2 and is shared across all windows of the sequence  INLINEFORM3 is an element-wise non-linear activation function  The convolution operation can capture local contextual dependencies of the input sequence and the extracted feature vectors are similar to INLINEFORM4 -grams  After the convolution operation is applied to the whole sequence, we obtain a list of hidden vectors INLINEFORM5   A max-over-time pooling layer is applied to obtain the final vector representation INLINEFORM6 of the input document 
Datasets and Experimental Settings
Existing benchmark datasets such as the Amazon benchmark BIBREF0 typically remove reviews with neutral labels in both domains  This is problematic as the label information of the target domain is not accessible in an unsupervised domain adaptation setting  Furthermore, removing neutral instances may bias the dataset favorably for max-margin-based algorithms like ours, since the resulting dataset has all uncertain labels removed, leaving only high confidence examples  Therefore, we construct new datasets by ourselves  The results on the original Amazon benchmark is qualitatively similar, and we present them in Appendix SECREF6 for completeness since most of previous works reported results on it 
Small-scale datasets: Our new dataset was derived from the large-scale Amazon datasets released by McAuley et al  ( BIBREF24 )  It contains four domains: Book (BK), Electronics (E), Beauty (BT), and Music (M)  Each domain contains two datasets  Set 1 contains 6000 instances with exactly balanced class labels, and set 2 contains 6000 instances that are randomly sampled from the large dataset, preserving the original label distribution, which we believe better reflects the label distribution in real life  The examples in these two sets do not overlap  Detailed statistics of the generated datasets are given in Table TABREF9  
In all our experiments on the small-scale datasets, we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain  Since we cannot control the label distribution of unlabeled data during training, we consider two different settings:
Setting (1): Only set 1 of the target domain is used as the unlabeled set  This tells us how the method performs in a condition when the target domain has a close-to-balanced label distribution  As we also evaluate on set 1 of the target domain, this is also considered as a transductive setting 
Setting (2): Set 2 from both the source and target domains are used as unlabeled sets

ground_truth: Baseline models are:
- Chen et al., 2015a
- Chen et al., 2015b
- Liu et al., 2016
- Cai and Zhao, 2016
- Cai et al., 2017
- Zhou et al., 2017
- Ma et al., 2018
- Wang et al., 2019

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 are the source and target domains?
Context 2:  Since set 2 is directly sampled from millions of reviews, it better reflects real-life sentiment distribution 
Large-scale datasets: We further conduct experiments on four much larger datasets: IMDB (I), Yelp2014 (Y), Cell Phone (C), and Baby (B)  IMDB and Yelp2014 were previously used in BIBREF25 , BIBREF26   Cell phone and Baby are from the large-scale Amazon dataset BIBREF24 , BIBREF27   Detailed statistics are summarized in Table TABREF9   We keep all reviews in the original datasets and consider a transductive setting where all target examples are used for both training (without label information) and evaluation  We perform sampling to balance the classes of labeled source data in each minibatch INLINEFORM3 during training 
Selection of Development Set
Ideally, the development set should be drawn from the same distribution as the test set  However, under the unsupervised domain adaptation setting, we do not have any labeled target data at training phase which could be used as development set  In all of our experiments, for each pair of domains, we instead sample 1000 examples from the training set of the source domain as development set  We train the network for a fixed number of epochs, and the model with the minimum classification error on this development set is saved for evaluation  This approach works well on most of the problems since the target domain is supposed to behave like the source domain if the domain difference is effectively reduced 
Another problem is how to select the values for hyper-parameters  If we tune INLINEFORM0 and INLINEFORM1 directly on the development set from the source domain, most likely both of them will be set to 0, as unlabeled target data is not helpful for improving in-domain accuracy of the source domain  Other neural network models also have the same problem for hyper-parameter tuning  Therefore, our strategy is to use the development set from the target domain to optimize INLINEFORM2 and INLINEFORM3 for one problem (e g , we only do this on E INLINEFORM4 BK), and fix their values on the other problems  This setting assumes that we have at least two labeled domains such that we can optimize the hyper-parameters, and then we fix them for other new unlabeled domains to transfer to 
Training Details and Hyper-parameters
We initialize word embeddings using the 300-dimension GloVe vectors supplied by Pennington et al , ( BIBREF28 ), which were trained on 840 billion tokens from the Common Crawl  For each pair of domains, the vocabulary consists of the top 10000 most frequent words  For words in the vocabulary but not present in the pre-trained embeddings, we randomly initialize them 
We set hyper-parameters of the CNN encoder following previous works BIBREF22 , BIBREF4 without specific tuning on our datasets  The window size is set to 3 and the size of the hidden layer is set to 300  The nonlinear activation function is Relu  For regularization, we also follow their settings and employ dropout with probability set to 0 5 on INLINEFORM0 before feeding it to the output layer INLINEFORM1 , and constrain the INLINEFORM2 -norm of the weight vector INLINEFORM3 , setting its max norm to 3 
On the small-scale datasets and the Aamzon benchmark, INLINEFORM0 and INLINEFORM1 are set to 200 and 1, respectively, tuned on the development set of task E INLINEFORM2 BK under setting 1  On the large-scale datasets, INLINEFORM3 and INLINEFORM4 are set to 500 and 0 2, respectively, tuned on I INLINEFORM5 Y  We use a Gaussian curve INLINEFORM6 to ramp up the weight of the bootstrapping loss INLINEFORM7 from 0 to INLINEFORM8 , where INLINEFORM9 denotes the maximum number of training epochs  We train 30 epochs for all experiments  We set INLINEFORM10 to 3 and INLINEFORM11 to 0 5 for all experiments 
The batch size is set to 50 on the small-scale datasets and the Amazon benchmark  We increase the batch size to 250 on the large-scale datasets to reduce the number of iterations  RMSProp optimizer with learning rate set to 0 0005 is used for all experiments 
Models for Comparison
We compare with the following baselines:
(1) Naive: A non-domain-adaptive baseline with bag-of-words representations and SVM classifier trained on the source domain 
(2) mSDA BIBREF7 : This is the state-of-the-art method based on discrete input features  Top 1000 bag-of-words features are kept as pivot features  We set the number of stacked layers to 3 and the corruption probability to 0 5 
(3) NaiveNN: This is a non-domain-adaptive CNN trained on source domain, which is a variant of our model by setting INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 to zeros 
(4) AuxNN BIBREF4 : This is a neural model that exploits auxiliary tasks, which has achieved state-of-the-art results on cross-domain sentiment classification  The sentence encoder used in this model is the same as ours 
(5) ADAN BIBREF16 : This method exploits adversarial training to reduce representation difference between domains  The original paper uses a simple feedforward network as encoder  For fair comparison, we replace it with our CNN-based encoder  We train 5 iterations on the discriminator per iteration on the encoder and sentiment classifier as suggested in their paper 
(6) MMD: MMD has been widely used for minimizing domain discrepancy on images  In those works BIBREF9 , BIBREF13 , variants of deep CNNs are used for encoding images and the MMDs of multiple layers are jointly minimized  In NLP, adding more layers of CNNs may not be very helpful and thus those models from image-related tasks can not be directly applied to our problem  To compare with MMD-based method, we train a model that jointly minimize the classification loss INLINEFORM0 on the source domain and MMD between INLINEFORM1 and INLINEFORM2   For computing MMD, we use a Gaussian RBF which is a common choice for characteristic kernel 
In addition to the above baselines, we also show results of different variants of our model  DAS as shown in Algorithm SECREF6 denotes our full model  DAS-EM denotes the model with only entropy minimization for semi-supervised learning (set INLINEFORM0 )  DAS-SE denotes the model with only self-ensemble bootstrapping for semi-supervised learning (set INLINEFORM1 )  FANN (feature-adaptation neural network) denotes the model without semi-supervised learning performed (set both INLINEFORM2 and INLINEFORM3 to zeros) 
Main Results
Figure FIGREF17 shows the comparison of adaptation results (see Appendix SECREF7 for the exact numerical numbers)  We report classification accuracy on the small-scale dataset  For the large-scale dataset, macro-F1 is instead used since the label distribution in the test set is extremely unbalanced  Key observations are summarized as follows  (1) Both DAS-EM and DAS-SE perform better in most cases compared with ADAN, MDD, and FANN, in which only feature adaptation is performed  This demonstrates the effectiveness of the proposed domain adaptive semi-supervised learning framework  DAS-EM is more effective than DAS-SE in most cases, and the full model DAS with both techniques jointly employed overall has the best performance  (2) When comparing the two settings on the small-scale dataset, all domain-adaptive methods generally perform better under setting 1  In setting 1, the target examples are balanced in classes, which can provide more diverse opinion-related features  However, when considering unsupervised domain adaptation, we should not presume the label distribution of the unlabeled data  Thus, it is necessary to conduct experiments using datasets that reflect real-life sentiment distribution as what we did on setting2 and the large-scale dataset  Unfortunately, this is ignored by most of previous works  (3) Word-embeddings are very helpful, as we can see even NaiveNN can substantially outperform mSDA on most tasks 
To see the effect of semi-supervised learning alone, we also conduct experiments by setting INLINEFORM0 to eliminate the effect of feature adaptation  Both entropy minimization and bootstrapping perform very badly in this setting  Entropy minimization gives almost random predictions with accuracy below 0 4, and the results of bootstrapping are also much lower compared to NaiveNN  This suggests that the feature adaptation component is essential  Without it, the learned target representations are less meaningful and discriminative  Applying semi-supervised learning in this case is likely to worsen the results 
Further Analysis
In Figure FIGREF23 , we show the change of accuracy with respect to the percentage of unlabeled data used for training on three particular problems under setting 1  The value at INLINEFORM0 denotes the accuracies of NaiveNN which does not utilize any target data  For DAS, we observe a nonlinear increasing trend where the accuracy quickly improves at the beginning, and then gradually stabilizes  For other methods, this trend is less obvious, and adding more unlabeled data sometimes even worsen the results  This finding again suggests that the proposed approach can better exploit the information from unlabeled data 
We also conduct experiments under a setting with a small number of labeled target examples available  Figure FIGREF27 shows the change of accuracy with respect to the number of labeled target examples added for training  We can observe that DAS is still more effective under this setting, while the performance differences to other methods gradually decrease with the increasing number of labeled target examples 
CNN Filter Analysis
In this subsection, we aim to better understand DAS by analyzing sentiment-related CNN filters  To do that, 1) we first select a list of the most related CNN filters for predicting each sentiment label (positive, negative neutral)  Those filters can be identified according to the learned weights INLINEFORM0 of the output layer INLINEFORM1   Higher weight indicates stronger relatedness  2) Recall that in our implementation, each CNN filter has a window size of 3 with Relu activation  We can thus represent each selected filter as a ranked list of trigrams with highest activation values 
We analyze the CNN filters learned by NaiveNN, FANN and DAS respectively on task E INLINEFORM0 BT under setting 1  We focus on E INLINEFORM1 BT for study because electronics and beauty are very different domains and each of them has a diverse set of domain-specific sentiment expressions  For each method, we identify the top 10 most related filters for each sentiment label, and extract the top trigrams of each selected filter on both source and target domains  Since labeled source examples are used for training, we find the filters learned by the three methods capture similar expressions on the source domain, containing both domain-invariant and domain-specific trigrams  On the target domain, DAS captures more target-specific expressions compared to the other two methods  Due to space limitation, we only present a small subset of positive-sentiment-related filters in Table TABREF34   The complete results are provided in Appendix SECREF8   From Table TABREF34 , we can observe that the filters learned by NaiveNN are almost unable to capture target-specific sentiment expressions, while FANN is able to capture limited target-specific words such as “clean” and “scent”  The filters learned by DAS are more domain-adaptive, capturing diverse sentiment expressions in the target domain 
Conclusion
In this work, we propose DAS, a novel framework that jointly performs feature adaptation and semi-supervised learning  We have demonstrated through multiple experiments that DAS can better leverage unlabeled data, and achieve substantial improvements over baseline methods  We have also shown that feature adaptation is an essential component, without which, semi-supervised learning is not able to function properly  The proposed framework could be potentially adapted to other domain adaptation tasks, which is the focus of our future studies 
Results on Amazon Benchmark
Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al  ( BIBREF0 )  The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K)  Following their experimental settings, we consider the binary classification task to predict whether a review is positive or negative on the target domain  Each domain consists of 1000 positive and 1000 negative reviews respectively  We also allow 4000 unlabeled reviews to be used for both the source and the target domains, of which the positive and negative reviews are balanced as well, following the settings in previous works  We construct 12 cross-domain sentiment classification tasks and split the labeled data in each domain into a training set of 1600 reviews and a test set of 400 reviews  The classifier is trained on the training set of the source domain and is evaluated on the test set of the target domain  The comparison results are shown in Table TABREF37  
Numerical Results of Figure 
Due to space limitation, we only show results in figures in the paper  All numerical numbers used for plotting Figure FIGREF17 are presented in Table TABREF38   We can observe that DAS-EM, DAS-SE, and DAS all achieve substantial improvements over baseline methods under different settings 
CNN Filter Analysis Full Results
As mentioned in Section SECREF36 , we conduct CNN filter analysis on NaiveNN, FANN, and DAS  For each method, we identify the top 10 most related filters for positive, negative, neutral sentiment labels respectively, and then represent each selected filter as a ranked list of trigrams with the highest activation values on it  Table TABREF39 , TABREF40 , TABREF41 in the following pages illustrate the trigrams from the target domain (beauty) captured by the selected filters learned on E INLINEFORM0 BT for each method 
We can observe that compared to NaiveNN and FANN, DAS is able to capture a more diverse set of relevant sentiment expressions on the target domain for each sentiment label  This observation is consistent with our motivation  Since NaiveNN, FANN and other baseline methods solely train the sentiment classifier on the source domain, the learned encoder is not able to produce discriminative features on the target domain  DAS addresses this problem by refining the classifier on the target domain with semi-supervised learning, and the overall objective forces the encoder to learn feature representations that are not only domain-invariant but also discriminative on both domains 
Context 3: Introduction
The irony is a kind of figurative language, which is widely used on social media BIBREF0   The irony is defined as a clash between the intended meaning of a sentence and its literal meaning BIBREF1   As an important aspect of language, irony plays an essential role in sentiment analysis BIBREF2 , BIBREF0 and opinion mining BIBREF3 , BIBREF4  
Although some previous studies focus on irony detection, little attention is paid to irony generation  As ironies can strengthen sentiments and express stronger emotions, we mainly focus on generating ironic sentences  Given a non-ironic sentence, we implement a neural network to transfer it to an ironic sentence and constrain the sentiment polarity of the two sentences to be the same  For example, the input is “I hate it when my plans get ruined" which is negative in sentiment polarity and the output should be ironic and negative in sentiment as well, such as “I like it when my plans get ruined"  The speaker uses “like" to be ironic and express his or her negative sentiment  At the same time, our model can preserve contents which are irrelevant to sentiment polarity and irony  According to the categories mentioned in BIBREF5 , irony can be classified into 3 classes: verbal irony by means of a polarity contrast, the sentences containing expression whose polarity is inverted between the intended and the literal evaluation; other types of verbal irony, the sentences that show no polarity contrast between the literal and intended meaning but are still ironic; and situational irony, the sentences that describe situations that fail to meet some expectations  As ironies in the latter two categories are obscure and hard to understand, we decide to only focus on ironies in the first category in this work  For example, our work can be specifically described as: given a sentence “I hate to be ignored", we train our model to generate an ironic sentence such as “I love to be ignored"  Although there is “love" in the generated sentence, the speaker still expresses his or her negative sentiment by irony  We also make some explorations in the transformation from ironic sentences to non-ironic sentences at the end of our work  Because of the lack of previous work and baselines on irony generation, we implement our model based on style transfer  Our work will not only provide the first large-scale irony dataset but also make our model as a benchmark for the irony generation 
Recently, unsupervised style transfer becomes a very popular topic  Many state-of-the-art studies try to solve the task with sequence-to-sequence (seq2seq) framework  There are three main ways to build up models  The first is to learn a latent style-independent content representation and generate sentences with the content representation and another style BIBREF6 , BIBREF7   The second is to directly transfer sentences from one style to another under the control of classifiers and reinforcement learning BIBREF8   The third is to remove style attribute words from the input sentence and combine the remaining content with new style attribute words BIBREF9 , BIBREF10   The first method usually obtains better performances via adversarial training with discriminators  The style-independent content representation, nevertheless, is not easily obtained BIBREF11 , which results in poor performances  The second method is suitable for complex styles which are difficult to model and describe  The model can learn the deep semantic features by itself but sometimes the model is sensitive to parameters and hard to train  The third method succeeds to preserve content but cannot work for some complex styles such as democratic and republican  Sentences with those styles usually do not have specific style attribute words  Unfortunately, due to the lack of large irony dataset and difficulties of modeling ironies, there has been little work trying to generate ironies based on seq2seq framework as far as we know  Inspired by methods for style transfer, we decide to implement a specifically designed model based on unsupervised style transfer to explore irony generation 
In this paper, in order to address the lack of irony data, we first crawl over 2M tweets from twitter to build a dataset with 262,755 ironic and 112,330 non-ironic tweets  Then, due to the lack of parallel data, we propose a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way  As ironic style is hard to model and describe, we implement our model with the control of classifiers and reinforcement learning  Different from other studies in style transfer, the transformation from non-ironic to ironic sentences has to preserve sentiment polarity as mentioned above  Therefore, we not only design an irony reward to control the irony accuracy and implement denoising auto-encoder and back-translation to control content preservation but also design a sentiment reward to control sentiment preservation 
Experimental results demonstrate that our model achieves a high irony accuracy with well-preserved sentiment and content  The contributions of our work are as follows:
Related Work
Style Transfer: As irony is a complicated style and hard to model with some specific style attribute words, we mainly focus on studies without editing style attribute words 
Some studies are trying to disentangle style representation from content representation  In BIBREF12 , authors leverage adversarial networks to learn separate content representations and style representations  In BIBREF13 and BIBREF6 , researchers combine variational auto-encoders (VAEs) with style discriminators 
However, some recent studies BIBREF11 reveal that the disentanglement of content and style representations may not be achieved in practice  Therefore, some other research studies BIBREF9 , BIBREF10 strive to separate content and style by removing stylistic words  Nonetheless, many non-ironic sentences do not have specific stylistic words and as a result, we find it difficult to transfer non-ironic sentences to ironic sentences through this way in practice 
Besides, some other research studies do not disentangle style from content but directly learn representations of sentences  In BIBREF8 , authors propose a dual reinforcement learning framework without separating content and style representations  In BIBREF7 , researchers utilize a machine translation model to learn a sentence representation preserving the meaning of the sentence but reducing stylistic properties  In this method, the quality of generated sentences relies on the performance of classifiers to a large extent  Meanwhile, such models are usually sensitive to parameters and difficult to train  In contrast, we combine a pre-training process with reinforcement learning to build up a stable language model and design special rewards for our task 
Irony Detection: With the development of social media, irony detection becomes a more important task  Methods for irony detection can be mainly divided into two categories: methods based on feature engineering and methods based on neural networks 
As for methods based on feature engineering, In BIBREF1 , authors investigate pragmatic phenomena and various irony markers  In BIBREF14 , researchers leverage a combination of sentiment, distributional semantic and text surface features  Those models rely on hand-crafted features and are hard to implement 
When it comes to methods based on neural networks, long short-term memory (LSTM) BIBREF15 network is widely used and is very efficient for irony detection  In BIBREF16 , a tweet is divided into two segments and a subtract layer is implemented to calculate the difference between two segments in order to determine whether the tweet is ironic  In BIBREF17 , authors utilize a recurrent neural network with Bi-LSTM and self-attention without hand-crafted features  In BIBREF18 , researchers propose a system based on a densely connected LSTM network 
Our Dataset
In this section, we describe how we build our dataset with tweets  First, we crawl over 2M tweets from twitter using GetOldTweets-python  We crawl English tweets from 04/09/2012 to /12/18/2018  We first remove all re-tweets and use langdetect to remove all non-English sentences  Then, we remove hashtags attached at the end of the tweets because they are usually not parts of sentences and will confuse our language model  After that, we utilize Ekphrasis to process tweets  We remove URLs and restore remaining hashtags, elongated words, repeated words, and all-capitalized words  To simplify our dataset, We replace all “ INLINEFORM0 money INLINEFORM1 " and “ INLINEFORM2 time INLINEFORM3 " tokens with “ INLINEFORM4 number INLINEFORM5 " token when using Ekphrasis  And we delete sentences whose lengths are less than 10 or greater than 40  In order to restore abbreviations, we download an abbreviation dictionary from webopedia and restore abbreviations to normal words or phrases according to the dictionary  Finally, we remove sentences which have more than two rare words (appearing less than three times) in order to constrain the size of vocabulary  Finally, we get 662,530 sentences after pre-processing 
As neural networks are proved effective in irony detection, we decide to implement a neural classifier in order to classify the sentences into ironic and non-ironic sentences  However, the only high-quality irony dataset we can obtain is the dataset of Semeval-2018 Task 3 and the dataset is pretty small, which will cause overfitting to complex models  Therefore, we just implement a simple one-layer RNN with LSTM cell to classify pre-processed sentences into ironic sentences and non-ironic sentences because LSTM networks are widely used in irony detection  We train the model with the dataset of Semeval-2018 Task 3  After classification, we get 262,755 ironic sentences and 399,775 non-ironic sentences  According to our observation, not all non-ironic sentences are suitable to be transferred into ironic sentences  For example, “just hanging out   watching   is it monday yet" is hard to transfer because it does not have an explicit sentiment polarity  So we remove all interrogative sentences from the non-ironic sentences and only obtain the sentences which have words expressing strong sentiments  We evaluate the sentiment polarity of each word with TextBlob and we view those words with sentiment scores greater than 0 5 or less than -0 5 as words expressing strong sentiments  Finally, we build our irony dataset with 262,755 ironic sentences and 102,330 non-ironic sentences 
[t] Irony Generation Algorithm
INLINEFORM0 pre-train with auto-encoder Pre-train INLINEFORM1 , INLINEFORM2 with INLINEFORM3 using MLE based on Eq  EQREF16 Pre-train INLINEFORM4 , INLINEFORM5 with INLINEFORM6 using MLE based on Eq  EQREF17 INLINEFORM7 pre-train with back-translation Pre-train INLINEFORM8 , INLINEFORM9 , INLINEFORM10 , INLINEFORM11 with INLINEFORM12 using MLE based on Eq  EQREF19 Pre-train INLINEFORM13 , INLINEFORM14 , INLINEFORM15 , INLINEFORM16 with INLINEFORM17 using MLE based on Eq  EQREF20
INLINEFORM0 train with RL each epoch e = 1, 2,    , INLINEFORM1 INLINEFORM2 train non-irony2irony with RL INLINEFORM3 in N INLINEFORM4 update INLINEFORM5 , INLINEFORM6 , using INLINEFORM7 based on Eq  EQREF29 INLINEFORM8 back-translation INLINEFORM9 INLINEFORM10 INLINEFORM11 update INLINEFORM12 , INLINEFORM13 , INLINEFORM14 , INLINEFORM15 using MLE based on Eq  EQREF19 INLINEFORM16 train irony2non-irony with RL INLINEFORM17 in I INLINEFORM18 update INLINEFORM19 , INLINEFORM20 , using INLINEFORM21 similar to Eq  EQREF29 INLINEFORM22 back-translation INLINEFORM23 INLINEFORM24 INLINEFORM25 update INLINEFORM26 , INLINEFORM27 , INLINEFORM28 , INLINEFORM29 using MLE based on Eq  EQREF20
Our Method
Given two non-parallel corpora: non-ironic corpus N={ INLINEFORM0 , INLINEFORM1 ,    , INLINEFORM2 } and ironic corpus I={ INLINEFORM3 , INLINEFORM4 ,    , INLINEFORM5 }, the goal of our irony generation model is to generate an ironic sentence from a non-ironic sentence while preserving the content and sentiment polarity of the source input sentence  We implement an encoder-decoder framework where two encoders are utilized to encode ironic sentences and non-ironic sentences respectively and two decoders are utilized to decode ironic sentences and non-ironic sentences from latent representations respectively  In order to enforce a shared latent space, we share two layers on both the encoder side and the decoder side  Our model architecture is illustrated in Figure FIGREF13   We denote irony encoder as INLINEFORM6 , irony decoder as INLINEFORM7 and non-irony encoder as INLINEFORM8 , non-irony decoder as INLINEFORM9   Their parameters are INLINEFORM10 , INLINEFORM11 , INLINEFORM12 and INLINEFORM13  
Our irony generation algorithm is shown in Algorithm SECREF3   We first pre-train our model using denoising auto-encoder and back-translation to build up language models for both styles (section SECREF14 )  Then we implement reinforcement learning to train the model to transfer sentences from one style to another (section SECREF21 )  Meanwhile, to achieve content preservation, we utilize back-translation for one time in every INLINEFORM0 time steps 
Pretraining
In order to build up our language model and preserve the content, we apply the auto-encoder model  To prevent the model from simply copying the input sentence, we randomly add some noises in the input sentence  Specifically, for every word in the input sentence, there is 10% chance that we delete it, 10 % chance that we duplicate it, 10% chance that we swap it with the next word, or it remains unchanged  We first encode the input sentence INLINEFORM0 or INLINEFORM1 with respective encoder INLINEFORM2 or INLINEFORM3 to obtain its latent representation INLINEFORM4 or INLINEFORM5 and reconstruct the input sentence with the latent representation and respective decoder  So we can get the reconstruction loss for auto-encoder INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1
In addition to denoising auto-encoder, we implement back-translation BIBREF19 to generate a pseudo-parallel corpus  Suppose our model takes non-ironic sentence INLINEFORM0 as input  We first encode INLINEFORM1 with INLINEFORM2 to obtain its latent representation INLINEFORM3 and decode the latent representation with INLINEFORM4 to get a transferred sentence INLINEFORM5   Then we encode INLINEFORM6 with INLINEFORM7 and decode its latent representation with INLINEFORM8 to reconstruct the original input sentence INLINEFORM9   Therefore, our reconstruction loss for back-translation INLINEFORM10 : DISPLAYFORM0
And if our model takes ironic sentence INLINEFORM0 as input, we can get the reconstruction loss for back-translation as: DISPLAYFORM0
Reinforcement Learning
Since the gold transferred result of input is unavailable, we cannot evaluate the quality of the generated sentence directly  Therefore, we implement reinforcement learning and elaborately design two rewards to describe the irony accuracy and sentiment preservation, respectively 
A pre-trained binary irony classifier based on CNN BIBREF20 is used to evaluate how ironic a sentence is  We denote the parameter of the classifier as INLINEFORM0 and it is fixed during the training process 
In order to facilitate the transformation, we design the irony reward as the difference between the irony score of the input sentence and that of the output sentence  Formally, when we input a non-ironic sentence INLINEFORM0 and transfer it to an ironic sentence INLINEFORM1 , our irony reward is defined as: DISPLAYFORM0
where INLINEFORM0 denotes ironic style and INLINEFORM1 is the probability of that a sentence INLINEFORM2 is ironic 
To preserve the sentiment polarity of the input sentence, we also need to use classifiers to evaluate the sentiment polarity of the sentences  However, the sentiment analysis of ironic sentences and non-ironic sentences are different  In the case of figurative languages such as irony, sarcasm or metaphor, the sentiment polarity of the literal meaning may differ significantly from that of the intended figurative meaning BIBREF0   As we aim to train our model to transfer sentences from non-ironic to ironic, using only one classifier is not enough  As a result, we implement two pre-trained sentiment classifiers for non-ironic sentences and ironic sentences respectively  We denote the parameter of the sentiment classifier for ironic sentences as INLINEFORM0 and that of the sentiment classifier for non-ironic sentences as INLINEFORM1  
A challenge, when we implement two classifiers to evaluate the sentiment polarity, is that the two classifiers trained with different datasets may have different distributions of scores  That means we cannot directly calculate the sentiment reward with scores applied by two classifiers  To alleviate this problem and standardize the prediction results of two classifiers, we set a threshold for each classifier and subtract the respective threshold from scores applied by the classifier to obtain the comparative sentiment polarity score  We get the optimal threshold by maximizing the ability of the classifier according to the distribution of our training data 
We denote the threshold of ironic sentiment classifier as INLINEFORM0 and the threshold of non-ironic sentiment classifier as INLINEFORM1   The standardized sentiment score is defined as INLINEFORM2 and INLINEFORM3 where INLINEFORM4 denotes the positive sentiment polarity and INLINEFORM5 is the probability of that a sentence is positive in sentiment polarity 
As mentioned above, the input sentence and the generated sentence should express the same sentiment  For example, if we input a non-ironic sentence “I hate to be ignored" which is negative in sentiment polarity, the generated ironic sentence should be also negative, such as “I love to be ignored"  To achieve sentiment preservation, we design the sentiment reward as that one minus the absolute value of the difference between the standardized sentiment score of the input sentence and that of the generated sentence  Formally, when we input a non-ironic sentence INLINEFORM0 and transfer it to an ironic sentence INLINEFORM1 , our sentiment reward is defined as: DISPLAYFORM0
To encourage our model to focus on both the irony accuracy and the sentiment preservation, we apply the harmonic mean of irony reward and sentiment reward: DISPLAYFORM0
Policy Gradient
The policy gradient algorithm BIBREF21 is a simple but widely-used algorithm in reinforcement learning  It is used to maximize the expected reward INLINEFORM0   The objective function to minimize is defined as: DISPLAYFORM0
where INLINEFORM0 , INLINEFORM1 is the reward of INLINEFORM2 and INLINEFORM3 is the input size 
Training Details
INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 in our model are Transformers BIBREF22 with 4 layers and 2 shared layers  The word embeddings of 128 dimensions are learned during the training process  Our maximum sentence length is set as 40  The optimizer is Adam BIBREF23 and the learning rate is INLINEFORM4   The batch size is 32 and harmonic weight INLINEFORM5 in Eq 9 is 0 5  We set the interval INLINEFORM6 as 200  The model is pre-trained for 6 epochs and trained for 15 epochs for reinforcement learning 
Irony Classifier: We implement a CNN classifier trained with our irony dataset  All the CNN classifiers we utilize in this paper use the same parameters as BIBREF20  
Sentiment Classifier for Irony: We first implement a one-layer LSTM network to classify ironic sentences in our dataset into positive and negative ironies  The LSTM network is trained with the dataset of Semeval 2015 Task 11 BIBREF0 which is used for the sentiment analysis of figurative language in twitter  Then, we use the positive ironies and negative ironies to train the CNN sentiment classifier for irony 
Sentiment Classifier for Non-irony: Similar to the training process of the sentiment classifier for irony, we first implement a one-layer LSTM network trained with the dataset for the sentiment analysis of common twitters to classify the non-ironies into positive and negative non-ironies  Then we use the positive and negative non-ironies to train the sentiment classifier for non-irony 
Baselines
We compare our model with the following state-of-art generative models:
BackTrans BIBREF7 : In BIBREF7 , authors propose a model using machine translation in order to preserve the meaning of the sentence while reducing stylistic properties 
Unpaired BIBREF10 : In BIBREF10 , researchers implement a method to remove emotional words and add desired sentiment controlled by reinforcement learning 
CrossAlign BIBREF6 : In BIBREF6 , authors leverage refined alignment of latent representations to perform style transfer and a cross-aligned auto-encoder is implemented 
CPTG BIBREF24 : An interpolated reconstruction loss is introduced in BIBREF24 and a discriminator is implemented to control attributes in this work 
DualRL BIBREF8 : In BIBREF8 , researchers use two reinforcement rewards simultaneously to control style accuracy and content preservation 
Evaluation Metrics
In order to evaluate sentiment preservation, we use the absolute value of the difference between the standardized sentiment score of the input sentence and that of the generated sentence  We call the value as sentiment delta (senti delta)  Besides, we report the sentiment accuracy (Senti ACC) which measures whether the output sentence has the same sentiment polarity as the input sentence based on our standardized sentiment classifiers  The BLEU score BIBREF25 between the input sentences and the output sentences is calculated to evaluate the content preservation performance  In order to evaluate the overall performance of different models, we also report the geometric mean (G2) and harmonic mean (H2) of the sentiment accuracy and the BLEU score  As for the irony accuracy, we only report it in human evaluation results because it is more accurate for the human to evaluate the quality of irony as it is very complicated 
We first sample 50 non-ironic input sentences and their corresponding output sentences of different models  Then, we ask four annotators who are proficient in English to evaluate the qualities of the generated sentences of different models  They are required to rank the output sentences of our model and baselines from the best to the worst in terms of irony accuracy (Irony), Sentiment preservation (Senti) and content preservation (Content)  The best output is ranked with 1 and the worst output is ranked with 6  That means that the smaller our human evaluation value is, the better the corresponding model is 
Results and Discussions
Table TABREF35 shows the automatic evaluation results of the models in the transformation from non-ironic sentences to ironic sentences  From the results, our model obtains the best result in sentiment delta  The DualRL model achieves the highest result in other metrics, but most of its outputs are the almost same as the input sentences  So it is reasonable that DualRL system outperforms ours in these metrics but it actually does not transfer the non-ironic sentences to ironic sentences at all  From this perspective, we cannot view DualRL as an effective model for irony generation  In contrast, our model gets results close to those of DualRL and obtains a balance between irony accuracy, sentiment preservation, and content preservation if we also consider the irony accuracy discussed below 
And from human evaluation results shown in Table TABREF36 , our model gets the best average rank in irony accuracy  And as mentioned above, the DualRL model usually does not change the input sentence and outputs the same sentence  Therefore, it is reasonable that it obtains the best rank in sentiment and content preservation and ours is the second  However, it still demonstrates that our model, instead of changing nothing, transfers the style of the input sentence with content and sentiment preservation at the same time 
Case Study
In the section, we present some example outputs of different models  Table TABREF37 shows the results of the transformation from non-ironic sentences to ironic sentences  We can observe that: (1) The BackTrans system, the Unpaired system, the CrossAlign system and the CPTG system tends to generate sentences which are towards irony but do not preserve content  (2) The DualRL system preserves content and sentiment very well but even does not change the input sentence  (3) Our model considers both aspects and achieves a better balance among irony accuracy, sentiment and content preservation 
Error Analysis
Although our model outperforms other style transfer baselines according to automatic and human evaluation results, there are still some failure cases because irony generation is still a very challenging task  We would like to share the issues we meet during our experiments and our solutions to some of them in this section 
No Change: As mentioned above, many style transfer models, such as DualRL, tend to make few changes to the input sentence and output the same sentence  Actually, this is a common issue for unsupervised style transfer systems and we also meet it during our experiments  The main reason for the issue is that rewards for content preservation are too prominent and rewards for style accuracy cannot work well  In contrast, in order to guarantee the readability and fluency of the output sentence, we also cannot emphasize too much on rewards for style accuracy because it may cause some other issues such as word repetition mentioned below  A method to solve the problem is tuning hyperparameters and this is also the method we implement in this work  As for content preservation, maybe MLE methods such as back-translation are not enough because they tend to force models to generate specific words  In the future, we should further design some more suitable methods to control content preservation for models without disentangling style and content representations, such as DualRL and ours 
Word Repetition: During our experiments, we observe that some of the outputs prefer to repeat the same word as shown in Table TABREF38   This is because reinforcement learning rewards encourage the model to generate words which can get high scores from classifiers and even back-translation cannot stop it  Our solution is that we can lower the probability of decoding a word in decoders if the word has been generated in the previous time steps during testing  We also try to implement this method during training time but obtain worse performances because it may limit the effects of training  Some previous studies utilize language models to control the fluency of the output sentence and we also try this method  Nonetheless, pre-training a language model with tweets and using it to generate rewards is difficult because tweets are more casual and have more noise  Rewards from that kind of language model are usually not accurate and may confuse the model  In the future, we should come up with better methods to model language fluency with the consideration of irony accuracy, sentiment and content preservation, especially for tweets 
Improper Words: As ironic style is hard for our model to learn, it may generate some improper words which make the sentence strange  As the example shown in the Table TABREF38 , the sentiment word in the input sentence is “wonderful" and the model should change it into a negative word such as “sad" to make the output sentence ironic  However, the model changes “friday" and “fifa" which are not related to ironic styles  We have not found a very effective method to address this issue and maybe we should further explore stronger models to learn ironic styles better 
Additional Experiments
In this section, we describe some additional experiments on the transformation from ironic sentences to non-ironic sentences  Sometimes ironies are hard to understand and may cause misunderstanding, for which our task also explores the transformation from ironic sentences to non-ironic sentences 
As shown in Table TABREF46 , we also conduct automatic evaluations and the conclusions are similar to those of the transformation from non-ironic sentences to ironic sentences  As for human evaluation results in Table TABREF47 , our model still can achieve the second-best results in sentiment and content preservation  Nevertheless, DualRL system and ours get poor performances in irony accuracy  The reason may be that the other four baselines tend to generate common and even not fluent sentences which are irrelevant to the input sentences and are hard to be identified as ironies  So annotators usually mark these output sentences as non-ironic sentences, which causes these models to obtain better performances than DualRL and ours but much poorer results in sentiment and content preservation  Some examples are shown in Table TABREF52  
Conclusion and Future Work
In this paper, we first systematically define irony generation based on style transfer  Because of the lack of irony data, we make use of twitter and build a large-scale dataset  In order to control irony accuracy, sentiment preservation and content preservation at the same time, we also design a combination of rewards for reinforcement learning and incorporate reinforcement learning with a pre-training process  Experimental results demonstrate that our model outperforms other generative models and our rewards are effective  Although our model design is effective, there are still many errors and we systematically analyze them  In the future, we are interested in exploring these directions and our work may extend to other kinds of ironies which are more difficult to model 

ground_truth: Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen

we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics (E), Beauty (BT), and Music (M)

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 experiments are conducted?
Context 4:  If present, the word is considered as the ‘LAT’; else, the Noun in close proximity to the question word and following it is returned as the ‘LAT’ 
For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself  For the word ‘How', e g  in How many selenoproteins are encoded in the human genome?, we look at the adjective and if we find one, we take it to be the LAT, otherwise the word 'How' is considered as the ‘LAT’ 
Perhaps because of using only very simple rules, the accuracy for ‘LAT’ derivation is 75%; that is, in the remaining 25% of the cases the LAT word is identified incorrectly  Worth noting is that the overall performance the system that used LATs was slightly inferior to the system without LATs, but the types of errors changed  The training used BioBERT with the LAT feature as part of the input string  The errors it introduces usually involve finding the wrong element of the correct type e g  wrong enzyme when two similar enzymes are described in the text, or 'neuron' when asked about a type of cell with a certain function, when the answer calls for a different cell category, adipocytes, and both are mentioned in the text  We feel with more data and additional tuning or perhaps using an ensemble model, we might be able to keep the correct answers, and improve the results on the confusing examples like the one mentioned above  Therefore if we improve our ‘LAT’ derivation logic, or have larger datasets, then perhaps the neural network techniques they will yield better results 
Our Systems and Their Performance on Factoid Questions ::: Impact of Training using BioAsq data (slightly negative)
Training on BioAsq data in our entry in Batch 1 and Batch 2 under the name QA1 showed it might lead to overfitting  This happened both with (Batch 2) and without (Batch 1) hyperparameters tuning: abysmal 18% MRR in Batch 1, and slighly better one, 40% in Batch 2 (although in Batch 2 it was overall the second best result in MRR but 16% lower than the highest score) 
In Batch 3 (only), our UNCC_QA3 system was fine tuned on BioAsq and SQuAD 2 0 BIBREF7, and for data preprocessing Context paragraph is generated from relevant snippets provided in the test data  This system underperformed, by about 2% in MRR, our other entry UNCC_QA1, which was also an overall category winner for this batch  The latter was also trained on SQuAD, but not on BioAsq  We suspect that the reason could be the simplistic nature of the find() function described in Section 3 1  So, this could be an area where a better algorithm for finding the best occurrence of an entity could improve performance 
Our Systems and Their Performance on Factoid Questions ::: Impact of Using Context from URLs (negative)
In some experiments, for context in testing, we used documents for which URL pointers are provided in BioAsq  However, our system UNCC_QA3 underperformed our other system tested only on the provided snippets 
In Batch 5 the underperformance was about 6% of MRR, compared to our best system UNCC_QA1, and by 9% to the top performer 
Performance on Yes/No and List questions
Our work focused on Factoid questions  But we also have done experiments on List-type and Yes/No questions 
Performance on Yes/No and List questions ::: Entailment improves Yes/No accuracy
We started by answering always YES (in batch 2 and 3) to get the baseline performance  For batch 4 we used entailment  Our algorithm was very simple: Given a question we iterate through the candidate sentences and try to find any candidate sentence is contradicting the question (with confidence over 50%), if so 'No' is returned as answer, else 'Yes' is returned  In batch 4 this strategy produced better than the BioAsq baseline performance, and compared to our other systems, the use of entailment increased the performance by about 13% (macro F1 score)  We used 'AllenNlp' BIBREF13 entailment library to find entailment of the candidate sentences with question 
Performance on Yes/No and List questions ::: For List-type the URLs have negative impact
Overall, we followed the similar strategy that's been followed for Factoid Question Answering task  We started our experiment with batch 2, where we submitted 20 best answers (with context from snippets)  Starting with batch 3, we performed post processing: once models generate answer predictions (n-best predictions), we do post-processing on the predicted answers  In test batch 4, our system (called FACTOIDS) achieved highest recall score of ‘0 7033’ but low precision of 0 1119, leaving open the question of how could we have better balanced the two measures 
In the post-processing phase, we take the top ‘20’ (batch 3) and top 5 (batch 4 and 5), predicted answers, tokenize them using common separators: 'comma' , 'and', 'also', 'as well as'  Tokens with characters count more than ‘100’ are eliminated and rest of the tokens are added to the list of possible answers  BioASQ evaluation mechanism does not consider snippets with more than ‘100’ characters as a valid answer  Considering lengthy snippets in to the list of answers would reduce the mean precision score  As a final step, duplicate snippets in the answer pool are removed  For example, consider these top 3 answers predicted by the system (before post-processing):
{
"text": "dendritic cells",
"probability": 0 7554540733426441,
"start_logit": 8 466046333312988,
"end_logit": 9 536355018615723
},
{
"text": "neutrophils, macrophages and
distinct subtypes of dendritic cells",
"probability": 0 13806867348304214,
"start_logit": 6 766478538513184,
"end_logit": 9 536355018615723
},
{
"text": "macrophages and distinct subtypes of dendritic",
"probability": 0 013973475271178242,
"start_logit": 6 766478538513184,
"end_logit": 7 24576473236084
},
After execution of post-processing heuristics, the list of answers returned is as follows:
["dendritic cells"],
["neutrophils"],
["macrophages"],
["distinct subtypes of dendritic cells"]
Summary of our results
The tables below summarize all our results  They show that the performance of our systems was mixed  The simple architectures and algorithm we used worked very well only in Batch 3  However, we feel we can built a better system based on this experience  In particular we observed both the value of contextual embeddings and of feature engineering (LAT), however we failed to combine them properly 
Summary of our results ::: Factoid questions ::: Systems used in Batch 5 experiments
System description for ‘UNCC_QA1’: The system was finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘QA1’ : ‘LAT’ feature was added and finetuned with SQuAD 2 0  For data preprocessing Context / paragraph was generated from relevant snippets provided in the test data 
System description for ‘UNCC_QA3’ : Fine tuning process is same as it is done for the system ‘UNCC_QA1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated from the relevant documents for which URLS are included in the test data 
Summary of our results ::: List Questions
For List-type questions, although post processing helped in the later batches, we never managed to obtain competitive precision, although our recall was good 
Summary of our results ::: Yes/No questions
The only thing worth remembering from our performance is that using entailment can have a measurable impact (at least with respect to a weak baseline)  The results (weak) are in Table 3 
Discussion, Future Experiments, and Conclusions ::: Summary:
In contrast to 2018, when we submitted BIBREF2 to BioASQ a system based on extractive summarization (and scored very high in the ideal answer category), this year we mainly targeted factoid question answering task and focused on experimenting with BioBERT  After these experiments we see the promise of BioBERT in QA tasks, but we also see its limitations  The latter we tried to address with mixed results using feature engineering  Overall these experiments allowed us to secure a best and a second best score in different test batches  Along with Factoid-type question, we also tried ‘Yes/No’ and ‘List’-type questions, and did reasonably well with our very simple approach 
For Yes/No the moral worth remembering is that reasoning has a potential to influence results, as evidenced by our adding the AllenNLP entailment BIBREF13 system increased its performance 
All our data and software is available at Github, in the previously referenced URL (end of Section 2) 
Discussion, Future Experiments, and Conclusions ::: Future experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding this word piece embedding directly to the BioBERT (as we did in our above experiments) would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 
We also see potential for incorporating domain specific inference into the task e g  using the MedNLI dataset BIBREF14  For all types of experiments it might be worth exploring clinical BERT embeddings BIBREF15, explicitly incorporating domain knowledge (e g  BIBREF16) and possibly deeper discourse representations (e g  BIBREF17) 
APPENDIX
In this appendix we provide additional details about the implementations 
APPENDIX ::: Systems and their descriptions:
We used several variants of our systems when experimenting with the BioASQ problems  In retrospect, it would be much easier to understand the changes if we adopted some mnemonic conventions in naming the systems  So, we apologize for the names that do not reflect the modifications, and necessitate this list 
APPENDIX ::: Systems and their descriptions: ::: Factoid Type Question Answering:
We preprocessed the test data to convert test data to BioBERT format, We generated Context/paragraph by either aggregating relevant snippets provided or by aggregating documents for which URLS are provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: System description for QA1:
We generated Context/paragraph by aggregating relevant snippets available in the test data and mapped it against the question text and question id  We ignored the content present in the documents (document URLS were provided in the original test data)  The model is finetuned with BioASQ data 
data preprocessing is done in the same way as it is done for test batch-1  Model fine tuned on BioASQ data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA_1:
System is finetuned on the SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
‘LAT’/ Focus word feature added and fine tuned with SQuAD 2 0 [reference]  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA3:
System is finetuned on the SQuAD 2 0 [reference] and BioASQ dataset[] For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
Fine tuning process is same as it is done for the system ‘UNCC_QA_1’ in test batch-5  Difference is during data preprocessing, Context/paragraph is generated form from the relevant documents for which URLS are included in the test data 
APPENDIX ::: Systems and their descriptions: ::: System description for UNCC_QA2:
Fine tuning process is same as for ‘UNCC_QA_1 ’  Difference is Context/paragraph is generated form from the relevant documents for which URLS are included in the test data  System ‘UNCC_QA_1’ got the highest ‘MRR’ score in the 3rd test batch set 
APPENDIX ::: Systems and their descriptions: ::: System description for FACTOIDS:
The System is finetuned on the SQuAD 2 0  For data preprocessing Context / paragraph is generated from relevant snippets provided in the test data 
APPENDIX ::: Systems and their descriptions: ::: List Type Questions:
We attempted List type questions starting from test batch ‘2’  Used similar approach that's been followed for Factoid Question answering task  For all the test batch sets, in the data pre processing phase Context/ paragraph is generated either by aggregating relevant snippets or by aggregating documents(URLS) provided in the BioASQ test data 
For test batch-2, model (System: QA1) is finetuned on BioASQ data and submitted top ‘20’ answers predicted by the model as the list of answers  system ‘QA1’ achieved low F-Measure score:‘0 0786’ in the second test batch  In the further test batches for List type questions, we finetuned the model on Squad data set [reference], implemented post processing techniques (refer section 5 2) and achieved a better F-measure score: ‘0 2862’ in the final test batch set 
In test batch-3 (Systems : ‘QA1’/’’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’) top 20 answers returned by the model is sent for post processing and in test batch 4 and 5 only top 5 answers are sent for post processing  System UNCC_QA2(in batch 3) for List type question answering, Context is generated from documents for which URLS are provided in the BioASQ test data  for the rest of the systems (in test batch-3) for List Type question answering task snippets present in the BioaSQ test data are used to generate context 
In test batch-4 (System : ‘FACTOIDS’/’UNCC_QA_1’/’UNCC_QA3’’) top 5 answers returned by the model is sent for post processing  In case of system ‘FACTOIDS’ snippets in the test data were used to generate context  for systems ’UNCC_QA_1’ and ’UNCC_QA3’ context is generated from the documents for which URLS are provided in the BioASQ test data 
In test batch-5 ( Systems: ‘QA1’/’UNCC_QA_1’/’UNCC_QA3’/’UNCC_QA2’ ) our approach is the same as that of test batch-4 where top 5 answers returned by the model is sent for post processing  for all the systems (in test batch-5) context is generated from the snippets provided in the BioASQ test data 
APPENDIX ::: Systems and their descriptions: ::: Yes/No Type Questions:
For the first 3 test batches, We have submitted answer ‘Yes’ to all the questions  Later, we employed ‘Sentence Entailment’ techniques(refer section 6 0) for the fourth and fifth test batch sets  Our Systems with ‘Sentence Entailment’ approach (for ‘Yes’/ ‘No’ question answering): ‘UNCC_QA_1’(test batch-4), UNCC_QA3(test batch-5) 
APPENDIX ::: Additional details for Yes/No Type Questions
We used Textual Entailment in Batch 4 and 5 for ‘Yes’/‘No’ question type  The algorithm was very simple: Given a question we iterate through the candidate sentences, and look for any candidate sentences contradicting the question  If we find one 'No' is returned as answer, else 'Yes' is returned  (The confidence for contradiction was set at 50%) We used AllenNLP BIBREF13 entailment library to find entailment of the candidate sentences with question 
Flow Chart for Yes/No Question answer processing is shown in Fig FIGREF51
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions
There are different question types, and we distinguished them based on the question words: ‘Which’, ‘What’, ‘When’, ‘How’ etc  Each type of question is being handled differently and there are commonalities among the rules written for different question types  How are question words identified? question words have parts of speech(POS): 'WDT', 'WRB', 'WP' 
Assumptions:
1) Lexical answer type (‘LAT’) or focus word is of type Noun and follows the question word 
2) The LAT word is a Subject  (This clearly not always true, but we used a very simple method)  Note: ‘StanfordNLP’ dependency parsing tag for identifying subject is 'nsubj' or 'nsubjpass' 
3) When a question has multiple words that are of type Subject (and Noun), a word that is in proximity to the question word is considered as ‘LAT’ 
4) For questions with question words: ‘When’, ‘Who’, ‘Why’, the ’LAT’ is a question word itself that is, ‘When’, ‘Who’, ‘Why’ respectively 
Rules and logic flow to traverse a question: The three cases below describe the logic flow of finding LATs  The figures show the grammatical structures used for this purpose 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-1:
Question with question word ‘How’ 
For questions with question word 'How', the adjective that follows the question word is considered as ‘LAT’ (need not follow immediately)  If an adjective is absent, word 'How' is considered as ‘LAT’  When there are multiple words that are adjectives, a word in close proximity to the question word and follows it is returned as ‘LAT’  Note: The part of speech tag to identify adjectives is 'JJ'  For Other possible question words like ‘whose’  ‘LAT’/Focus word is question words itself 
Example Question: How many selenoproteins are encoded in the human genome?
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-2:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; a 'Noun' immediately following the question word 
Example Question: Which enzyme is targeted by Evolocumab?
Here, Focus word/LAT is ‘enzyme’ which is both Noun and Subject and immediately follows the question word 
When the word immediately following the question word is a noun, the window size is set to ‘3’  This size ‘3’ means that we iterate through the next ‘3’ words (if present) to check if any of the word is both 'Noun' and 'Subject', If so, the word is considered as ‘LAT’/Focus Word  Else the word that is present very next to the question word is considered as ‘LAT’ 
APPENDIX ::: Assumptions, rules and logic flow for deriving Lexical Answer Types from questions ::: Case-3:
Questions with question words ‘Which’ , ‘What’ and all other possible question words; word immediately following the question word is not a 'Noun' 
Example Question: What is the function of the protein Magt1?
Here, Focus word/LAT is ‘function ’ which is both Noun and Subject and does not immediately follow the question word 
When the very next word following the question word is not a Noun, window size is set to ‘5’  Window size ‘5’ corresponds that we iterate through the next ‘5’ words (if present) and search for the word that is both Noun and Subject  If present, the word is considered as ‘LAT’  Else, the 'Noun' close proximity to the question word and follows it is returned as ‘LAT’ 
Ad we mentioned earlier, the accuracy for ‘LAT’ derivation is 75 percent  But clearly the simple logic described above can be improved, as shown in BIBREF9, BIBREF10  Whether this in turn produces improvements in this particular task is an open question 
APPENDIX ::: Proposing Future Experiments
In the current model, we have a shallow neural network with a softmax layer for predicting answer span  Shallow networks however are not good at generalizations  In our future experiments we would like to create dense question answering neural network with a softmax layer for predicting answer span  The main idea is to get contextual word embedding for the words present in the question and paragraph (Context) and feed the contextual word embeddings retrieved from the last layer of BioBERT to the dense question answering network  The mentioned dense layered question answering Neural network need to be tuned for finding right hyper parameters  An example of such architecture is shown in Fig FIGREF30 
In another experiment we would like to only feed contextual word embeddings for Focus word/ ‘LAT’, paragraph/ Context as input to the question answering neural network  In this experiment we would neglect all embeddings for the question text except that of Focus word/ ‘LAT’  Our assumption and idea for considering focus word and neglecting remaining words in the question is that during training phase it would make more precise for the model to identify the focus of the question and map answers against the question’s focus  To validate our assumption, we would like to take sample question answering data and find the cosine distance between contextual embedding of Focus word and that of the actual answer and verify if the cosine distance is comparatively low in most of the cases 
In one more experiment, we would like to add a better version of ‘LAT’ contextual word embedding as a feature, along with the actual contextual word embeddings for question text, and Context and feed them as input to the dense question answering neural network  By this experiment, we would like to find if ‘LAT’ feature is improving overall answer prediction accuracy  Adding ‘LAT’ feature this way instead of feeding Focus word’s word piece embedding directly (as we did in our above experiments) to the BioBERT would not downgrade the quality of contextual word embeddings generated form ‘BioBERT'  Quality contextual word embeddings would lead to efficient transfer learning and chances are that it would improve the model's answer prediction accuracy 

ground_truth: Irony Classifier, Sentiment Classifier for Irony, Sentiment Classifier for Non-irony, transformation from ironic sentences to non-ironic sentences