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. Where does the ancient Chinese dataset come from?
Context 1: Introduction
Ancient Chinese is the writing language in ancient China  It is a treasure of Chinese culture which brings together the wisdom and ideas of the Chinese nation and chronicles the ancient cultural heritage of China  Learning ancient Chinese not only helps people to understand and inherit the wisdom of the ancients, but also promotes people to absorb and develop Chinese culture 
However, it is difficult for modern people to read ancient Chinese  Firstly, compared with modern Chinese, ancient Chinese is more concise and shorter  The grammatical order of modern Chinese is also quite different from that of ancient Chinese  Secondly, most modern Chinese words are double syllables, while the most of the ancient Chinese words are monosyllabic  Thirdly, there is more than one polysemous phenomenon in ancient Chinese  In addition, manual translation has a high cost  Therefore, it is meaningful and useful to study the automatic translation from ancient Chinese to modern Chinese  Through ancient-modern Chinese translation, the wisdom, talent and accumulated experience of the predecessors can be passed on to more people 
Neural machine translation (NMT) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 has achieved remarkable performance on many bilingual translation tasks  It is an end-to-end learning approach for machine translation, with the potential to show great advantages over the statistic machine translation (SMT) systems  However, NMT approach has not been widely applied to the ancient-modern Chinese translation task  One of the main reasons is the limited high-quality parallel data resource 
The most popular method of acquiring translation examples is bilingual text alignment BIBREF5   This kind of method can be classified into two types: lexical-based and statistical-based  The lexical-based approaches BIBREF6 , BIBREF7 focus on lexical information, which utilize the bilingual dictionary BIBREF8 , BIBREF9 or lexical features  Meanwhile, the statistical-based approaches BIBREF10 , BIBREF11 rely on statistical information, such as sentence length ratio in two languages and align mode probability 
However, these methods are designed for other bilingual language pairs that are written in different language characters (e g  English-French, Chinese-Japanese)  The ancient-modern Chinese has some characteristics that are quite different from other language pairs  For example, ancient and modern Chinese are both written in Chinese characters, but ancient Chinese is highly concise and its syntactical structure is different from modern Chinese  The traditional methods do not take these characteristics into account  In this paper, we propose an effective ancient-modern Chinese text alignment method at the level of clause based on the characteristics of these two languages  The proposed method combines both lexical-based information and statistical-based information, which achieves 94 2 F1-score on Test set  Recently, a simple longest common subsequence based approach for ancient-modern Chinese sentence alignment is proposed in BIBREF12   Our experiments showed that our proposed alignment approach performs much better than their method 
We apply the proposed method to create a large translation parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  Furthermore, we test SMT models and various NMT models on the created dataset and provide a strong baseline for this task 
Overview
There are four steps to build the ancient-modern Chinese translation dataset: (i) The parallel corpus crawling and cleaning  (ii) The paragraph alignment  (iii) The clause alignment based on aligned paragraphs  (iv) Augmenting data by merging aligned adjacent clauses  The most critical step is the third step 
Clause Alignment
In the clause alignment step, we combine both statistical-based and lexical-based information to measure the score for each possible clause alignment between ancient and modern Chinese strings  The dynamic programming is employed to further find overall optimal alignment paragraph by paragraph  According to the characteristics of the ancient and modern Chinese languages, we consider the following factors to measure the alignment score INLINEFORM0 between a bilingual clause pair:
Lexical Matching  The lexical matching score is used to calculate the matching coverage of the ancient clause INLINEFORM0   It contains two parts: exact matching and dictionary matching  An ancient Chinese character usually corresponds to one or more modern Chinese words  In the first part, we carry out Chinese Word segmentation to the modern Chinese clause INLINEFORM1   Then we match the ancient characters and modern words in the order from left to right  In further matching, the words that have been matched will be deleted from the original clauses 
However, some ancient characters do not appear in its corresponding modern Chinese words  An ancient Chinese dictionary is employed to address this issue  We preprocess the ancient Chinese dictionary and remove the stop words  In this dictionary matching step, we retrieve the dictionary definition of each unmatched ancient character and use it to match the remaining modern Chinese words  To reduce the impact of universal word matching, we use Inverse Document Frequency (IDF) to weight the matching words  The lexical matching score is calculated as: DISPLAYFORM0
The above equation is used to calculate the matching coverage of the ancient clause INLINEFORM0   The first term of equation ( EQREF8 ) represents exact matching score  INLINEFORM1 denotes the length of INLINEFORM2 , INLINEFORM3 denotes each ancient character in INLINEFORM4 , and the indicator function INLINEFORM5 indicates whether the character INLINEFORM6 can match the words in the clause INLINEFORM7   The second term is dictionary matching score  Here INLINEFORM8 and INLINEFORM9 represent the remaining unmatched strings of INLINEFORM10 and INLINEFORM11 , respectively  INLINEFORM12 denotes the INLINEFORM13 -th character in the dictionary definition of the INLINEFORM14 and its IDF score is denoted as INLINEFORM15   The INLINEFORM16 is a predefined parameter which is used to normalize the IDF score  We tuned the value of this parameter on the Dev set 
Statistical Information  Similar to BIBREF11 and BIBREF6 , the statistical information contains alignment mode and length information  There are many alignment modes between ancient and modern Chinese languages  If one ancient Chinese clause aligns two adjacent modern Chinese clauses, we call this alignment as 1-2 alignment mode  We show some examples of different alignment modes in Figure FIGREF9   In this paper, we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes which account for INLINEFORM0 of the Dev set  We estimate the probability Pr INLINEFORM1 n-m INLINEFORM2 of each alignment mode n-m on the Dev set  To utilize length information, we make an investigation on length correlation between these two languages  Based on the assumption of BIBREF11 that each character in one language gives rise to a random number of characters in the other language and those random variables INLINEFORM3 are independent and identically distributed with a normal distribution, we estimate the mean INLINEFORM4 and standard deviation INLINEFORM5 from the paragraph aligned parallel corpus  Given a clause pair INLINEFORM6 , the statistical information score can be calculated by: DISPLAYFORM0
where INLINEFORM0 denotes the normal distribution probability density function 
Edit Distance  Because ancient and modern Chinese are both written in Chinese characters, we also consider using the edit distance  It is a way of quantifying the dissimilarity between two strings by counting the minimum number of operations (insertion, deletion, and substitution) required to transform one string into the other  Here we define the edit distance score as: DISPLAYFORM0
Dynamic Programming  The overall alignment score for each possible clause alignment is as follows: DISPLAYFORM0
Here INLINEFORM0 and INLINEFORM1 are pre-defined interpolation factors  We use dynamic programming to find the overall optimal alignment paragraph by paragraph  Let INLINEFORM2 be total alignment scores of aligning the first to INLINEFORM3 -th ancient Chinese clauses with the first to to INLINEFORM4 -th modern Chinese clauses, and the recurrence then can be described as follows: DISPLAYFORM0
Where INLINEFORM0 denotes concatenate clause INLINEFORM1 to clause INLINEFORM2   As we discussed above, here we only consider 1-0, 0-1, 1-1, 1-2, 2-1 and 2-2 alignment modes 
Ancient-Modern Chinese Dataset
Data Collection  To build the large ancient-modern Chinese dataset, we collected 1 7K bilingual ancient-modern Chinese articles from the internet  More specifically, a large part of the ancient Chinese data we used come from ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era  They used plain and accurate words to express what happened at that time, and thus ensure the generality of the translated materials 
Paragraph Alignment  To further ensure the quality of the new dataset, the work of paragraph alignment is manually completed  After data cleaning and manual paragraph alignment, we obtained 35K aligned bilingual paragraphs 
Clause Alignment  We applied our clause alignment algorithm on the 35K aligned bilingual paragraphs and obtained 517K aligned bilingual clauses  The reason we use clause alignment algorithm instead of sentence alignment is because we can construct more aligned sentences more flexibly and conveniently  To be specific, we can get multiple additional sentence level bilingual pairs by “data augmentation” 
Data Augmentation  We augmented the data in the following way: Given an aligned clause pair, we merged its adjacent clause pairs as a new sample pair  For example, suppose we have three adjacent clause level bilingual pairs: ( INLINEFORM0 , INLINEFORM1 ), ( INLINEFORM2 , INLINEFORM3 ), and ( INLINEFORM4 , INLINEFORM5 )  We can get some additional sentence level bilingual pairs, such as: ( INLINEFORM6 , INLINEFORM7 ) and ( INLINEFORM8 , INLINEFORM9 )  Here INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 are adjacent clauses in the original paragraph, and INLINEFORM13 denotes concatenate clause INLINEFORM14 to clause INLINEFORM15   The advantage of using this data augmentation method is that compared with only using ( INLINEFORM16 , INLINEFORM17 ) as the training data, we can also use ( INLINEFORM18 , INLINEFORM19 ) and ( INLINEFORM20 , INLINEFORM21 ) as the training data, which can provide richer supervision information for the model and make the model learn the align information between the source language and the target language better  After the data augmentation, we filtered the sentences which are longer than 50 or contain more than four clause pairs 
Dataset Creation  Finally, we split the dataset into three sets: training (Train), development (Dev) and testing (Test)  Note that the unaugmented dataset contains 517K aligned bilingual clause pairs from 35K aligned bilingual paragraphs  To keep all the sentences in different sets come from different articles, we split the 35K aligned bilingual paragraphs into Train, Dev and Test sets following these ratios respectively: 80%, 10%, 10%  Before data augmentation, the unaugmented Train set contains INLINEFORM0 aligned bilingual clause pairs from 28K aligned bilingual paragraphs  Then we augmented the Train, Dev and Test sets respectively  Note that the augmented Train, Dev and Test sets also contain the unaugmented data  The statistical information of the three data sets is shown in Table TABREF17   We show some examples of data in Figure FIGREF14  
RNN-based NMT model
We first briefly introduce the RNN based Neural Machine Translation (RNN-based NMT) model  The RNN-based NMT with attention mechanism BIBREF0 has achieved remarkable performance on many translation tasks  It consists of encoder and decoder part 
We firstly introduce the encoder part  The input word sequence of source language are individually mapped into a INLINEFORM0 -dimensional vector space INLINEFORM1   Then a bi-directional RNN BIBREF15 with GRU BIBREF16 or LSTM BIBREF17 cell converts these vectors into a sequences of hidden states INLINEFORM2  
For the decoder part, another RNN is used to generate target sequence INLINEFORM0   The attention mechanism BIBREF0 , BIBREF18 is employed to allow the decoder to refer back to the hidden state sequence and focus on a particular segment  The INLINEFORM1 -th hidden state INLINEFORM2 of decoder part is calculated as: DISPLAYFORM0
Here g INLINEFORM0 is a linear combination of attended context vector c INLINEFORM1 and INLINEFORM2 is the word embedding of (i-1)-th target word: DISPLAYFORM0
The attended context vector c INLINEFORM0 is computed as a weighted sum of the hidden states of the encoder: DISPLAYFORM0
The probability distribution vector of the next word INLINEFORM0 is generated according to the following: DISPLAYFORM0
We take this model as the basic RNN-based NMT model in the following experiments 
Transformer-NMT
Recently, the Transformer model BIBREF4 has made remarkable progress in machine translation  This model contains a multi-head self-attention encoder and a multi-head self-attention decoder 
As proposed by BIBREF4 , an attention function maps a query and a set of key-value pairs to an output, where the queries INLINEFORM0 , keys INLINEFORM1 , and values INLINEFORM2 are all vectors  The input consists of queries and keys of dimension INLINEFORM3 , and values of dimension INLINEFORM4   The attention function is given by: DISPLAYFORM0
Multi-head attention mechanism projects queries, keys and values to INLINEFORM0 different representation subspaces and calculates corresponding attention  The attention function outputs are concatenated and projected again before giving the final output  Multi-head attention allows the model to attend to multiple features at different positions 
The encoder is composed of a stack of INLINEFORM0 identical layers  Each layer has two sub-layers: multi-head self-attention mechanism and position-wise fully connected feed-forward network  Similarly, the decoder is also composed of a stack of INLINEFORM1 identical layers  In addition to the two sub-layers in each encoder layer, the decoder contains a third sub-layer which performs multi-head attention over the output of the encoder stack (see more details in BIBREF4 ) 
Experiments
Our experiments revolve around the following questions: Q1: As we consider three factors for clause alignment, do all these factors help? How does our method compare with previous methods? Q2: How does the NMT and SMT models perform on this new dataset we build?
Clause Alignment Results (Q1)
In order to evaluate our clause alignment algorithm, we manually aligned bilingual clauses from 37 bilingual ancient-modern Chinese articles, and finally got 4K aligned bilingual clauses as the Test set and 2K clauses as the Dev set 
Metrics  We used F1-score and precision score as the evaluation metrics  Suppose that we get INLINEFORM0 bilingual clause pairs after running the algorithm on the Test set, and there are INLINEFORM1 bilingual clause pairs of these INLINEFORM2 pairs are in the ground truth of the Test set, the precision score is defined as INLINEFORM3 (the algorithm gives INLINEFORM4 outputs, INLINEFORM5 of which are correct)  And suppose that the ground truth of the Test set contains INLINEFORM6 bilingual clause pairs, the recall score is INLINEFORM7 (there are INLINEFORM8 ground truth samples, INLINEFORM9 of which are output by the algorithm), then the F1-score is INLINEFORM10  
Baselines  Since the related work BIBREF10 , BIBREF11 can be seen as the ablation cases of our method (only statistical score INLINEFORM0 with dynamic programming), we compared the full proposed method with its variants on the Test set for ablation study  In addition, we also compared our method with the longest common subsequence (LCS) based approach proposed by BIBREF12   To the best of our knowledge, BIBREF12 is the latest related work which are designed for Ancient-Modern Chinese alignment 
Hyper-parameters  For the proposed method, we estimated INLINEFORM0 and INLINEFORM1 on all aligned paragraphs  The probability Pr INLINEFORM2 n-m INLINEFORM3 of each alignment mode n-m was estimated on the Dev set  For the hyper-parameters INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , the grid search was applied to tune them on the Dev set  In order to show the effect of hyper-parameters INLINEFORM7 , INLINEFORM8 , and INLINEFORM9 , we reported the results of various hyper-parameters on the Dev set in Table TABREF26   Based on the results of grid search on the Dev set, we set INLINEFORM10 , INLINEFORM11 , and INLINEFORM12 in the following experiment  The Jieba Chinese text segmentation is employed for modern Chinese word segmentation 
Results  The results on the Test set are shown in Table TABREF28 , the abbreviation w/o means removing a particular part from the setting  From the results, we can see that the lexical matching score is the most important among these three factors, and statistical information score is more important than edit distance score  Moreover, the dictionary term in lexical matching score significantly improves the performance  From these results, we obtain the best setting that involves all these three factors  We used this setting for dataset creation  Furthermore, the proposed method performs much better than LCS BIBREF12  
Translation Results (Q2)
In this experiment, we analyzed and compared the performance of the SMT and various NMT models on our built dataset  To verify the effectiveness of our data augmented method  We trained the NMT and SMT models on both unaugmented dataset (including 0 46M training pairs) and augmented dataset, and test all the models on the same Test set which is augmented  The models to be tested and their configurations are as follows:
SMT  The state-of-art Moses toolkit BIBREF19 was used to train SMT model  We used KenLM BIBREF20 to train a 5-gram language model, and the GIZA++ toolkit to align the data 
RNN-based NMT  The basic RNN-based NMT model is based on BIBREF0 which is introduced above  Both the encoder and decoder used 2-layer RNN with 1024 LSTM cells, and the encoder is a bi-directional RNN  The batch size, threshold of element-wise gradient clipping and initial learning rate of Adam optimizer BIBREF21 were set to 128, 5 0 and 0 001  When trained the model on augmented dataset, we used 4-layer RNN  Several techniques were investigated to train the model, including layer-normalization BIBREF22 , RNN-dropout BIBREF23 , and learning rate decay BIBREF1   The hyper-parameters were chosen empirically and adjusted in the Dev set  Furthermore, we tested the basic NMT model with several techniques, such as target language reversal BIBREF24 (reversing the order of the words in all target sentences, but not source sentences), residual connection BIBREF25 and pre-trained word2vec BIBREF26   For word embedding pre-training, we collected an external ancient corpus which contains INLINEFORM0 134M tokens 
Transformer-NMT  We also trained the Transformer model BIBREF4 which is a strong baseline of NMT on both augmented and unaugmented parallel corpus  The training configuration of the Transformer model is shown in Table TABREF32   The hyper-parameters are set based on the settings in the paper BIBREF4 and the sizes of our training sets 
For the evaluation, we used the average of 1 to 4 gram BLEUs multiplied by a brevity penalty BIBREF27 which computed by multi-bleu perl in Moses as metrics  The results are reported in Table TABREF34   For RNN-based NMT, we can see that target language reversal, residual connection, and word2vec can further improve the performance of the basic RNN-based NMT model  However, we find that word2vec and reversal tricks seem no obvious improvement when trained the RNN-based NMT and Transformer models on augmented parallel corpus  For SMT, it performs better than NMT models when they were trained on the unaugmented dataset  Nevertheless, when trained on the augmented dataset, both the RNN-based NMT model and Transformer based NMT model outperform the SMT model  In addition, as with other translation tasks BIBREF4 , the Transformer also performs better than RNN-based NMT 
Because the Test set contains both augmented and unaugmented data, it is not surprising that the RNN-based NMT model and Transformer based NMT model trained on unaugmented data would perform poorly  In order to further verify the effect of data augmentation, we report the test results of the models on only unaugmented test data (including 48K test pairs) in Table TABREF35   From the results, it can be seen that the data augmentation can still improve the models 
Analysis
The generated samples of various models are shown in Figure FIGREF36   Besides BLEU scores, we analyze these examples from a human perspective and draw some conclusions  At the same time, we design different metrics and evaluate on the whole Test set to support our conclusions as follows:
On the one hand, we further compare the translation results from the perspective of people  We find that although the original meaning can be basically translated by SMT, its translation results are less smooth when compared with the other two NMT models (RNN-based NMT and Transformer)  For example, the translations of SMT are usually lack of auxiliary words, conjunctions and function words, which is not consistent with human translation habits  To further confirm this conclusion, the average length of the translation results of the three models are measured (RNN-based NMT:17 12, SMT:15 50, Transformer:16 78, Reference:16 47)  We can see that the average length of the SMT outputs is shortest, and the length gaps between the SMT outputs and the references are largest  Meanwhile, the average length of the sentences translated by Transformer is closest to the average length of references  These results indirectly verify our point of view, and show that the NMT models perform better than SMT in this task 
On the other hand, there still exists some problems to be solved  We observe that translating proper nouns and personal pronouns (such as names, place names and ancient-specific appellations) is very difficult for all of these models  For instance, the ancient Chinese appellation `Zhen' should be translated into `Wo' in modern Chinese  Unfortunately, we calculate the accurate rate of some special words (such as `Zhen',`Chen' and `Gua'), and find that this rate is very low (the accurate rate of translating `Zhen' are: RNN-based NMT:0 14, SMT:0 16, Transformer:0 05)  We will focus on this issue in the future 
Conclusion and Future Work
We propose an effective ancient-modern Chinese clause alignment method which achieves 94 2 F1-score on Test set  Based on it, we build a large scale parallel corpus which contains INLINEFORM0 1 24M bilingual sentence pairs  To our best knowledge, this is the first large high-quality ancient-modern Chinese dataset  In addition, we test the performance of the SMT and various NMT models on our built dataset and provide a strong NMT baseline for this task which achieves 27 16 BLEU score (4-gram)  We further analyze the performance of the SMT and various NMT models and summarize some specific problems that machine translation models will encounter when translating ancient Chinese 
For the future work, firstly, we are going to expand the dataset using the proposed method continually  Secondly, we will focus on solving the problem of proper noun translation and improve the translation system according to the features of ancient Chinese translation  Finally, we plan to introduce some techniques of statistical translation into neural machine translation to improve the performance 
This work is supported by National Natural Science Fund for Distinguished Young Scholar (Grant No  61625204) and partially supported by the State Key Program of National Science Foundation of China (Grant Nos  61836006 and 61432014) 
Context 2: Introduction
The concept of message passing over graphs has been around for many years BIBREF0, BIBREF1, as well as that of graph neural networks (GNNs) BIBREF2, BIBREF3  However, GNNs have only recently started to be closely investigated, following the advent of deep learning  Some notable examples include BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12  These approaches are known as spectral  Their similarity with message passing (MP) was observed by BIBREF9 and formalized by BIBREF13 and BIBREF14 
The MP framework is based on the core idea of recursive neighborhood aggregation  That is, at every iteration, the representation of each vertex is updated based on messages received from its neighbors  All spectral GNNs can be described in terms of the MP framework 
GNNs have been applied with great success to bioinformatics and social network data, for node classification, link prediction, and graph classification  However, a few studies only have focused on the application of the MP framework to representation learning on text  This paper proposes one such application  More precisely, we represent documents as word co-occurrence networks, and develop an expressive MP GNN tailored to document understanding, the Message Passing Attention network for Document understanding (MPAD)  We also propose several hierarchical variants of MPAD  Evaluation on 10 document classification datasets show that our architectures learn representations that are competitive with the state-of-the-art  Furthermore, ablation experiments shed light on the impact of various architectural choices 
In what follows, we first provide some background about the MP framework (in sec  SECREF2), thoroughly describe and explain MPAD (sec  SECREF3), present our experimental framework (sec  SECREF4), report and interpret our results (sec  SECREF5), and provide a review of the relevant literature (sec  SECREF6) 
Message Passing Neural Networks
BIBREF13 proposed a MP framework under which many of the recently introduced GNNs can be reformulated  MP consists in an aggregation phase followed by a combination phase BIBREF14  More precisely, let $G(V,E)$ be a graph, and let us consider $v \in V$  At time $t+1$, a message vector $\mathbf {m}_v^{t+1}$ is computed from the representations of the neighbors $\mathcal {N}(v)$ of $v$:
The new representation $\mathbf {h}^{t+1}_v$ of $v$ is then computed by combining its current feature vector $\mathbf {h}^{t}_v$ with the message vector $\mathbf {m}_v^{t+1}$:
Messages are passed for $T$ time steps  Each step is implemented by a different layer of the MP network  Hence, iterations correspond to network depth  The final feature vector $\mathbf {h}_v^T$ of $v$ is based on messages propagated from all the nodes in the subtree of height $T$ rooted at $v$  It captures both the topology of the neighborhood of $v$ and the distribution of the vertex representations in it 
If a graph-level feature vector is needed, e g , for classification or regression, a READOUT pooling function, that must be invariant to permutations, is applied:
Next, we present the MP network we developed for document understanding 
Message Passing Attention network for Document understanding (MPAD) ::: Word co-occurrence networks
We represent a document as a statistical word co-occurrence network BIBREF18, BIBREF19 with a sliding window of size 2 overspanning sentences  Let us denote that graph $G(V,E)$  Each unique word in the preprocessed document is represented by a node in $G$, and an edge is added between two nodes if they are found together in at least one instantiation of the window  $G$ is directed and weighted: edge directions and weights respectively capture text flow and co-occurrence counts 
$G$ is a compact representation of its document  In $G$, immediate neighbors are consecutive words in the same sentence  That is, paths of length 2 correspond to bigrams  Paths of length more than 2 can correspond either to traditional $n$-grams or to relaxed $n$-grams, that is, words that never appear in the same sentence but co-occur with the same word(s)  Such nodes are linked through common neighbors 
Master node  Inspired by BIBREF3, our $G$ also includes a special document node, linked to all other nodes via unit weight bi-directional edges  In what follows, let us denote by $n$ the number of nodes in $G$, including the master node 
Message Passing Attention network for Document understanding (MPAD) ::: Message passing
We formulate our AGGREGATE function as:
where $\mathbf {H}^t \in \mathbb {R}^{n \times d}$ contains node features ($d$ is a hyperparameter), and $\mathbf {A} \in \mathbb {R}^{n \times n}$ is the adjacency matrix of $G$  Since $G$ is directed, $\mathbf {A}$ is asymmetric  Also, $\mathbf {A}$ has zero diagonal as we choose not to consider the feature of the node itself, only that of its incoming neighbors, when updating its representation  Since $G$ is weighted, the $i^{th}$ row of $A$ contains the weights of the edges incoming on node $v_i$  $\mathbf {D} \in \mathbb {R}^{n \times n}$ is the diagonal in-degree matrix of $G$  MLP denotes a multi-layer perceptron, and $\mathbf {M}^{t+1} \in \mathbb {R}^{n \times d}$ is the message matrix 
The use of a MLP was motivated by the observation that for graph classification, MP neural nets with 1-layer perceptrons are inferior to their MLP counterparts BIBREF14  Indeed, 1-layer perceptrons are not universal approximators of multiset functions  Note that like in BIBREF14, we use a different MLP at each layer 
Renormalization  The rows of $\mathbf {D}^{-1}\mathbf {A}$ sum to 1  This is equivalent to the renormalization trick of BIBREF9, but using only the in-degrees  That is, instead of computing a weighted sum of the incoming neighbors' feature vectors, we compute a weighted average of them  The coefficients are proportional to the strength of co-occurrence between words  One should note that by averaging, we lose the ability to distinguish between different neighborhood structures in some special cases, that is, we lose injectivity  Such cases include neighborhoods in which all nodes have the same representations, and neighborhoods of different sizes containing various representations in equal proportions BIBREF14  As suggested by the results of an ablation experiment, averaging is better than summing in our application (see subsection SECREF30)  Note that instead of simply summing/averaging, we also tried using GAT-like attention BIBREF11 in early experiments, without obtaining better results 
As far as our COMBINE function, we use the Gated Recurrent Unit BIBREF20, BIBREF21:
Omitting biases for readability, we have:
where the $\mathbf {W}$ and $\mathbf {U}$ matrices are trainable weight matrices not shared across time steps, $\sigma (\mathbf {x}) = 1/(1+\exp (-\mathbf {x}))$ is the sigmoid function, and $\mathbf {R}$ and $\mathbf {Z}$ are the parameters of the reset and update gates  The reset gate controls the amount of information from the previous time step (in $\mathbf {H}^t$) that should propagate to the candidate representations, $\tilde{\mathbf {H}}^{t+1}$  The new representations $\mathbf {H}^{t+1}$ are finally obtained by linearly interpolating between the previous and the candidate ones, using the coefficients returned by the update gate 
Interpretation  Updating node representations through a GRU should in principle allow nodes to encode a combination of local and global signals (low and high values of $t$, resp ), by allowing them to remember about past iterations  In addition, we also explicitly consider node representations at all iterations when reading out (see Eq  DISPLAY_FORM18) 
Message Passing Attention network for Document understanding (MPAD) ::: Readout
After passing messages and performing updates for $T$ iterations, we obtain a matrix $\mathbf {H}^T \in \mathbb {R}^{n \times d}$ containing the final vertex representations  Let $\hat{G}$ be graph $G$ without the special document node, and matrix $\mathbf {\hat{H}}^T \in \mathbb {R}^{(n-1) \times d}$ be the corresponding representation matrix (i e , $\mathbf {H}^T$ without the row of the document node) 
We use as our READOUT function the concatenation of self-attention applied to $\mathbf {\hat{H}}^T$ with the final document node representation  More precisely, we apply a global self-attention mechanism BIBREF22 to the rows of $\mathbf {\hat{H}}^T$  As shown in Eq  DISPLAY_FORM17, $\mathbf {\hat{H}}^T$ is first passed to a dense layer parameterized by matrix $\mathbf {W}_A^T \in \mathbb {R}^{d \times d}$  An alignment vector $\mathbf {a}$ is then derived by comparing, via dot products, the rows of the output of the dense layer $\mathbf {Y}^T \in \mathbb {R}^{(n-1) \times d}$ with a trainable vector $\mathbf {v}^T \in \mathbb {R}^d$ (initialized randomly) and normalizing with a softmax  The normalized alignment coefficients are finally used to compute the attentional vector $\mathbf {u}^T \in \mathbb {R}^d$ as a weighted sum of the final representations $\mathbf {\hat{H}}^T$ 
Note that we tried with multiple context vectors, i e , with a matrix $\mathbf {V}^T$ instead of a vector $\mathbf {v}^T$, like in BIBREF22, but results were not convincing, even when adding a regularization term to the loss to favor diversity among the rows of $\mathbf {V}^T$ 
Master node skip connection  $\mathbf {h}_G^T \in \mathbb {R}^{2d}$ is obtained by concatenating $\mathbf {u}^T$ and the final master node representation  That is, the master node vector bypasses the attention mechanism  This is equivalent to a skip or shortcut connection BIBREF23  The reason behind this choice is that we expect the special document node to learn a high-level summary about the document, such as its size, vocabulary, etc  (more details are given in subsection SECREF30)  Therefore, by making the master node bypass the attention layer, we directly inject global information about the document into its final representation 
Multi-readout  BIBREF14, inspired by Jumping Knowledge Networks BIBREF12, recommend to not only use the final representations when performing readout, but also that of the earlier steps  Indeed, as one iterates, node features capture more and more global information  However, retaining more local, intermediary information might be useful too  Thus, instead of applying the readout function only to $t=T$, we apply it to all time steps and concatenate the results, finally obtaining $\mathbf {h}_G \in \mathbb {R}^{T \times 2d}$ :
In effect, with this modification, we take into account features based on information aggregated from subtrees of different heights (from 1 to $T$), corresponding to local and global features 
Message Passing Attention network for Document understanding (MPAD) ::: Hierarchical variants of MPAD
Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words $\rightarrow $ bigrams $\rightarrow $ compositions of bigrams, etc )  However, it might be beneficial to explicitly capture document hierarchy  Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation BIBREF24, BIBREF25, sentiment and topic classification BIBREF26, BIBREF27, and spoken language understanding BIBREF28, BIBREF29  Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows  In all of them, we represent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously described 
MPAD-sentence-att  Here, the sentence embeddings are simply combined through self-attention 
MPAD-clique  In this variant, we build a complete graph where each node represents a sentence  We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained 
MPAD-path  This variant is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text  That is, two nodes are linked by a directed edge if the two sentences they represent follow each other in the document 
Experiments ::: Datasets
We evaluate the quality of the document embeddings learned by MPAD on 10 document classification datasets, covering the topic identification, coarse and fine sentiment analysis and opinion mining, and subjectivity detection tasks  We briefly introduce the datasets next  Their statistics are reported in Table TABREF21 
(1) Reuters  This dataset contains stories collected from the Reuters news agency in 1987  Following common practice, we used the ModApte split and considered only the 10 classes with the highest number of positive training examples  We also removed documents belonging to more than one class and then classes left with no document (2 classes) 
(2) BBCSport BIBREF30 contains documents from the BBC Sport website corresponding to 2004-2005 sports news articles 
(3) Polarity BIBREF31 features positive and negative labeled snippets from Rotten Tomatoes 
(4) Subjectivity BIBREF32 contains movie review snippets from Rotten Tomatoes (subjective sentences), and Internet Movie Database plot summaries (objective sentences) 
(5) MPQA BIBREF33 is made of positive and negative phrases, annotated as part of the summer 2002 NRRC Workshop on Multi-Perspective Question Answering 
(6) IMDB BIBREF34 is a collection of highly polarized movie reviews from IMDB (positive and negative)  There are at most 30 reviews for each movie 
(7) TREC BIBREF35 consists of questions that are classified into 6 different categories 
(8) SST-1 BIBREF36 contains the same snippets as Polarity  The authors used the Stanford Parser to parse the snippets and split them into multiple sentences  They then used Amazon Mechanical Turk to annotate the resulting phrases according to their polarity (very negative, negative, neutral, positive, very positive) 
(9) SST-2 BIBREF36 is the same as SST-1 but with neutral reviews removed and snippets classified as positive or negative 
(10) Yelp2013 BIBREF26 features reviews obtained from the 2013 Yelp Dataset Challenge 
Experiments ::: Baselines
We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants 
doc2vec BIBREF37  Doc2vec (or paragraph vector) is an extension of word2vec that learns vectors for documents in a fully unsupervised manner  Document embeddings are then fed to a logistic regression classifier 
CNN BIBREF38  The convolutional neural network architecture, well-known in computer vision, is applied to text  There is one spatial dimension and the word embeddings are used as channels (depth dimensions) 
DAN BIBREF39  The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax 
Tree-LSTM BIBREF40 is a generalization of the standard LSTM architecture to constituency and dependency parse trees 
DRNN BIBREF41  Recursive neural networks are stacked and applied to parse trees 
LSTMN BIBREF42 is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations 
C-LSTM BIBREF43 combines convolutional and recurrent neural networks  The region embeddings provided by a CNN are fed to a LSTM 
SPGK BIBREF44 also models documents as word co-occurrence networks  It computes a graph kernel that compares shortest paths extracted from the word co-occurrence networks and then uses a SVM to categorize documents 
WMD BIBREF45 is an application of the well-known Earth Mover's Distance to text  A k-nearest neighbor classifier is used 
S-WMD BIBREF46 is a supervised extension of the Word Mover's Distance 
Semantic-CNN BIBREF47  Here, a CNN is applied to semantic units obtained by clustering words in the embedding space 
LSTM-GRNN BIBREF26 is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector 
HN-ATT BIBREF27 is another hierarchical model, where the same encoder architecture (a bidirectional GRU-RNN) is used for both sentences and documents, with different parameters  A self-attention mechanism is applied to the RNN annotations at each level 
Experiments ::: Model configuration and training
We preprocess all datasets using the code of BIBREF38  On Yelp2013, we also replace all tokens appearing strictly less than 6 times with a special UNK token, like in BIBREF27  We then build a directed word co-occurrence network from each document, with a window of size 2 
We use two MP iterations ($T$=2) for the basic MPAD, and two MP iterations at each level, for the hierarchical variants  We set $d$ to 64, except on IMDB and Yelp on which $d=128$, and use a two-layer MLP  The final graph representations are passed through a softmax for classification  We train MPAD in an end-to-end fashion by minimizing the cross-entropy loss function with the Adam optimizer BIBREF48 and an initial learning rate of 0 001 
To regulate potential differences in magnitude, we apply batch normalization after concatenating the feature vector of the master node with the self-attentional vector, that is, after the skip connection (see subsection SECREF16)  To prevent overfitting, we use dropout BIBREF49 with a rate of 0 5  We select the best epoch, capped at 200, based on the validation accuracy  When cross-validation is used (see 3rd column of Table TABREF21), we construct a validation set by randomly sampling 10% of the training set of each fold 
On all datasets except Yelp2013, we use the publicly available 300-dimensional pre-trained Google News vectors ($D$=300) BIBREF50 to initialize the node representations $\mathbf {H}^0$  On Yelp2013, we follow BIBREF27 and learn our own word vectors from the training and validation sets with the gensim implementation of word2vec BIBREF51 
MPAD was implemented in Python 3 6 using the PyTorch library BIBREF52  All experiments were run on a single machine consisting of a 3 4 GHz Intel Core i7 CPU with 16 GB of RAM and an NVidia GeForce Titan Xp GPU 
Results and ablations ::: Results
Experimental results are shown in Table TABREF28  For the baselines, the best scores reported in each original paper are shown  MPAD reaches best performance on 7 out of 10 datasets, and is close second elsewhere  Moreover, the 7 datasets on which MPAD ranks first widely differ in training set size, number of categories, and prediction task (topic, sentiment, subjectivity), which indicates that MPAD can perform well in different settings 
MPAD vs  hierarchical variants  On 9 datasets out of 10, one or more of the hierarchical variants outperform the vanilla MPAD architecture, highlighting the benefit of explicitly modeling the hierarchical nature of documents 
However, on Subjectivity, standard MPAD outperforms all hierarchical variants  On TREC, it reaches the same accuracy  We hypothesize that in some cases, using a different graph to separately encode each sentence might be worse than using one single graph to directly encode the document  Indeed, in the single document graph, some words that never appear in the same sentence can be connected through common neighbors, as was explained in subsection SECREF7  So, this way, some notion of cross-sentence context is captured while learning representations of words, bigrams, etc  at each MP iteration  This creates better informed representations, resulting in a better document embedding  With the hierarchical variants, on the other hand, each sentence vector is produced in isolation, without any contextual information about the other sentences in the document  Therefore, the final sentence embeddings might be of lower quality, and as a group might also contain redundant/repeated information  When the sentence vectors are finally combined into a document representation, it is too late to take context into account 
Results and ablations ::: Ablation studies
To understand the impact of some hyperparameters on performance, we conducted additional experiments on the Reuters, Polarity, and IMDB datasets, with the non-hierarchical version of MPAD  Results are shown in Table TABREF29 
Number of MP iterations  First, we varied the number of message passing iterations from 1 to 4  We can clearly see in Table TABREF29 that having more iterations improves performance  We attribute this to the fact that we are reading out at each iteration from 1 to $T$ (see Eq  DISPLAY_FORM18), which enables the final graph representation to encode a mixture of low-level and high-level features  Indeed, in initial experiments involving readout at $t$=$T$ only, setting $T\ge 2$ was always decreasing performance, despite the GRU-based updates (Eq  DISPLAY_FORM14)  These results were consistent with that of BIBREF53 and BIBREF9, who both are reading out only at $t$=$T$ too  We hypothesize that node features at $T\ge 2$ are too diffuse to be entirely relied upon during readout  More precisely, initially at $t$=0, node representations capture information about words, at $t$=1, about their 1-hop neighborhood (bigrams), at $t$=2, about compositions of bigrams, etc  Thus, pretty quickly, node features become general and diffuse  In such cases, considering also the lower-level, more precise features of the earlier iterations when reading out may be necessary 
Undirected edges  On Reuters, using an undirected graph leads to better performance, while on Polarity and IMDB, it is the opposite  This can be explained by the fact that Reuters is a topic classification task, for which the presence or absence of some patterns is important, but not necessarily the order in which they appear, while Polarity and IMDB are sentiment analysis tasks  To capture sentiment, modeling word order is crucial, e g , in detecting negation 
No master node  Removing the master node deteriorates performance across all datasets, clearly showing the value of having such a node  We hypothesize that since the special document node is connected to all other nodes, it is able to encode during message passing a summary of the document 
No renormalization  Here, we do not use the renormalization trick of BIBREF9 during MP (see subsection SECREF10)  That is, Eq  DISPLAY_FORM11 becomes $\mathbf {M}^{t+1} = \textsc {MLP}^{t+1}\big (\mathbf {A}\mathbf {H}^{t}\big )$  In other words, instead of computing a weighted average of the incoming neighbors' feature vectors, we compute a weighted sum of them  Unlike the mean, which captures distributions, the sum captures structural information BIBREF14  As shown in Table TABREF29, using sum instead of mean decreases performance everywhere, suggesting that in our application, capturing the distribution of neighbor representations is more important that capturing their structure  We hypothesize that this is the case because statistical word co-occurrence networks tend to have similar structural properties, regardless of the topic, polarity, sentiment, etc  of the corresponding documents 
Neighbors-only  In this experiment, we replaced the GRU combine function (see Eq  DISPLAY_FORM14) with the identity function  That is, we simply have $\mathbf {H}^{t+1}$=$\mathbf {M}^{t+1}$  Since $\mathbf {A}$ has zero diagonal, by doing so, we completely ignore the previous feature of the node itself when updating its representation  That is, the update is based entirely on its neighbors  Except on Reuters (almost no change), performance always suffers, stressing the need to take into account the root node during updates, not only its neighborhood 
Related work
In what follows, we offer a brief review of relevant studies, ranked by increasing order of similarity with our work 
BIBREF9, BIBREF54, BIBREF11, BIBREF10 conduct some node classification experiments on citation networks, where nodes are scientific papers, i e , textual data  However, text is only used to derive node feature vectors  The external graph structure, which plays a central role in determining node labels, is completely unrelated to text 
On the other hand, BIBREF55, BIBREF7 experiment on traditional document classification tasks  They both build $k$-nearest neighbor similarity graphs based on the Gaussian diffusion kernel  More precisely, BIBREF55 build one single graph where nodes are documents and distance is computed in the BoW space  Node features are then used for classification  Closer to our work, BIBREF7 represent each document as a graph  All document graphs are derived from the same underlying structure  Only node features, corresponding to the entries of the documents' BoW vectors, vary  The underlying, shared structure is that of a $k$-NN graph where nodes are vocabulary terms and similarity is the cosine of the word embedding vectors  BIBREF7 then perform graph classification  However they found performance to be lower than that of a naive Bayes classifier 
BIBREF56 use a GNN for hierarchical classification into a large taxonomy of topics  This task differs from traditional document classification  The authors represent documents as unweighted, undirected word co-occurrence networks with word embeddings as node features  They then use the spatial GNN of BIBREF15 to perform graph classification 
The work closest to ours is probably that of BIBREF53  The authors adopt the semi-supervised node classification approach of BIBREF9  They build one single undirected graph from the entire dataset, with both word and document nodes  Document-word edges are weighted by TF-IDF and word-word edges are weighted by pointwise mutual information derived from co-occurrence within a sliding window  There are no document-document edges  The GNN is trained based on the cross-entropy loss computed only for the labeled nodes, that is, the documents in the training set  When the final node representations are obtained, one can use that of the test documents to classify them and evaluate prediction performance 
There are significant differences between BIBREF53 and our work  First, our approach is inductive, not transductive  Indeed, while the node classification approach of BIBREF53 requires all test documents at training time, our graph classification model is able to perform inference on new, never-seen documents  The downside of representing documents as separate graphs, however, is that we lose the ability to capture corpus-level dependencies  Also, our directed graphs capture word ordering, which is ignored by BIBREF53  Finally, the approach of BIBREF53 requires computing the PMI for every word pair in the vocabulary, which may be prohibitive on datasets with very large vocabularies  On the other hand, the complexity of MPAD does not depend on vocabulary size 
Conclusion
We have proposed an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD)  Experiments conducted on 10 standard text classification datasets show that our architecture is competitive with the state-of-the-art  By processing weighted, directed word co-occurrence networks, MPAD is sensitive to word order and word-word relationship strength  To explicitly capture the hierarchical structure of documents, we also propose three hierarchical variants of MPAD, that we show bring improvements over the vanilla architecture 
Acknowledgments
We thank the NVidia corporation for the donation of a GPU as part of their GPU grant program 

ground_truth: ancient Chinese history records in several dynasties (about 1000BC-200BC) and articles written by celebrities of that era

Ancient Chinese history records in several dynasties and articles written by celebrities during 1000BC-200BC collected from the internet 

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

Please answer the questions based on the text provided below. Which component is the least impactful?
Context 3: Introduction
Automatic classification of sentiment has mainly focused on categorizing tweets in either two (binary sentiment analysis) or three (ternary sentiment analysis) categories BIBREF0   In this work we study the problem of fine-grained sentiment classification where tweets are classified according to a five-point scale ranging from VeryNegative to VeryPositive  To illustrate this, Table TABREF3 presents examples of tweets associated with each of these categories  Five-point scales are widely adopted in review sites like Amazon and TripAdvisor, where a user's sentiment is ordered with respect to its intensity  From a sentiment analysis perspective, this defines a classification problem with five categories  In particular, Sebastiani et al  BIBREF1 defined such classification problems whose categories are explicitly ordered to be ordinal classification problems  To account for the ordering of the categories, learners are penalized according to how far from the true class their predictions are 
Although considering different scales, the various settings of sentiment classification are related  First, one may use the same feature extraction and engineering approaches to represent the text spans such as word membership in lexicons, morpho-syntactic statistics like punctuation or elongated word counts BIBREF2 , BIBREF3   Second, one would expect that knowledge from one task can be transfered to the others and this would benefit the performance  Knowing that a tweet is “Positive” in the ternary setting narrows the classification decision between the VeryPositive and Positive categories in the fine-grained setting  From a research perspective this raises the question of whether and how one may benefit when tackling such related tasks and how one can transfer knowledge from one task to another during the training phase 
Our focus in this work is to exploit the relation between the sentiment classification settings and demonstrate the benefits stemming from combining them  To this end, we propose to formulate the different classification problems as a multitask learning problem and jointly learn them  Multitask learning BIBREF4 has shown great potential in various domains and its benefits have been empirically validated BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 using different types of data and learning approaches  An important benefit of multitask learning is that it provides an elegant way to access resources developed for similar tasks  By jointly learning correlated tasks, the amount of usable data increases  For instance, while for ternary classification one can label data using distant supervision with emoticons BIBREF9 , there is no straightforward way to do so for the fine-grained problem  However, the latter can benefit indirectly, if the ternary and fine-grained tasks are learned jointly 
The research question that the paper attempts to answer is the following: Can twitter sentiment classification problems, and fine-grained sentiment classification in particular, benefit from multitask learning? To answer the question, the paper brings the following two main contributions: (i) we show how jointly learning the ternary and fine-grained sentiment classification problems in a multitask setting improves the state-of-the-art performance, and (ii) we demonstrate that recurrent neural networks outperform models previously proposed without access to huge corpora while being flexible to incorporate different sources of data 
Multitask Learning for Twitter Sentiment Classification
In his work, Caruana BIBREF4 proposed a multitask approach in which a learner takes advantage of the multiplicity of interdependent tasks while jointly learning them  The intuition is that if the tasks are correlated, the learner can learn a model jointly for them while taking into account the shared information which is expected to improve its generalization ability  People express their opinions online on various subjects (events, products  ), on several languages and in several styles (tweets, paragraph-sized reviews  ), and it is exactly this variety that motivates the multitask approaches  Specifically for Twitter for instance, the different settings of classification like binary, ternary and fine-grained are correlated since their difference lies in the sentiment granularity of the classes which increases while moving from binary to fine-grained problems 
There are two main decisions to be made in our approach: the learning algorithm, which learns a decision function, and the data representation  With respect to the former, neural networks are particularly suitable as one can design architectures with different properties and arbitrary complexity  Also, as training neural network usually relies on back-propagation of errors, one can have shared parts of the network trained by estimating errors on the joint tasks and others specialized for particular tasks  Concerning the data representation, it strongly depends on the data type available  For the task of sentiment classification of tweets with neural networks, distributed embeddings of words have shown great potential  Embeddings are defined as low-dimensional, dense representations of words that can be obtained in an unsupervised fashion by training on large quantities of text BIBREF10  
Concerning the neural network architecture, we focus on Recurrent Neural Networks (RNNs) that are capable of modeling short-range and long-range dependencies like those exhibited in sequence data of arbitrary length like text  While in the traditional information retrieval paradigm such dependencies are captured using INLINEFORM0 -grams and skip-grams, RNNs learn to capture them automatically BIBREF11   To circumvent the problems with capturing long-range dependencies and preventing gradients from vanishing, the long short-term memory network (LSTM) was proposed BIBREF12   In this work, we use an extended version of LSTM called bidirectional LSTM (biLSTM)  While standard LSTMs access information only from the past (previous words), biLSTMs capture both past and future information effectively BIBREF13 , BIBREF11   They consist of two LSTM networks, for propagating text forward and backwards with the goal being to capture the dependencies better  Indeed, previous work on multitask learning showed the effectiveness of biLSTMs in a variety of problems: BIBREF14 tackled sequence prediction, while BIBREF6 and BIBREF15 used biLSTMs for Named Entity Recognition and dependency parsing respectively 
Figure FIGREF2 presents the architecture we use for multitask learning  In the top-left of the figure a biLSTM network (enclosed by the dashed line) is fed with embeddings INLINEFORM0 that correspond to the INLINEFORM1 words of a tokenized tweet  Notice, as discussed above, the biLSTM consists of two LSTMs that are fed with the word sequence forward and backwards  On top of the biLSTM network one (or more) hidden layers INLINEFORM2 transform its output  The output of INLINEFORM3 is led to the softmax layers for the prediction step  There are INLINEFORM4 softmax layers and each is used for one of the INLINEFORM5 tasks of the multitask setting  In tasks such as sentiment classification, additional features like membership of words in sentiment lexicons or counts of elongated/capitalized words can be used to enrich the representation of tweets before the classification step BIBREF3   The lower part of the network illustrates how such sources of information can be incorporated to the process  A vector “Additional Features” for each tweet is transformed from the hidden layer(s) INLINEFORM6 and then is combined by concatenation with the transformed biLSTM output in the INLINEFORM7 layer 
Experimental setup
Our goal is to demonstrate how multitask learning can be successfully applied on the task of sentiment classification of tweets  The particularities of tweets are to be short and informal text spans  The common use of abbreviations, creative language etc , makes the sentiment classification problem challenging  To validate our hypothesis, that learning the tasks jointly can benefit the performance, we propose an experimental setting where there are data from two different twitter sentiment classification problems: a fine-grained and a ternary  We consider the fine-grained task to be our primary task as it is more challenging and obtaining bigger datasets, e g  by distant supervision, is not straightforward and, hence we report the performance achieved for this task 
Ternary and fine-grained sentiment classification were part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF16   We use the high-quality datasets the challenge organizers released  The dataset for fine-grained classification is split in training, development, development_test and test parts  In the rest, we refer to these splits as train, development and test, where train is composed by the training and the development instances  Table TABREF7 presents an overview of the data  As discussed in BIBREF16 and illustrated in the Table, the fine-grained dataset is highly unbalanced and skewed towards the positive sentiment: only INLINEFORM0 of the training examples are labeled with one of the negative classes 
Feature representation We report results using two different feature sets  The first one, dubbed nbow, is a neural bag-of-words that uses text embeddings to generate low-dimensional, dense representations of the tweets

ground_truth: Based on table results provided changing directed to undirected edges had least impact - max abs difference of 0.33 points on all three datasets.

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. By how much did they improve?
Context 3:  To construct the nbow representation, given the word embeddings dictionary where each word is associated with a vector, we apply the average compositional function that averages the embeddings of the words that compose a tweet  Simple compositional functions like average were shown to be robust and efficient in previous work BIBREF17   Instead of training embeddings from scratch, we use the pre-trained on tweets GloVe embeddings of BIBREF10   In terms of resources required, using only nbow is efficient as it does not require any domain knowledge  However, previous research on sentiment analysis showed that using extra resources, like sentiment lexicons, can benefit significantly the performance BIBREF3 , BIBREF2   To validate this and examine at which extent neural networks and multitask learning benefit from such features we evaluate the models using an augmented version of nbow, dubbed nbow+  The feature space of the latter, is augmented using 1,368 extra features consisting mostly of counts of punctuation symbols ('!?#@'), emoticons, elongated words and word membership features in several sentiment lexicons  Due to space limitations, for a complete presentation of these features, we refer the interested reader to BIBREF2 , whose open implementation we used to extract them 
Evaluation measure To reproduce the setting of the SemEval challenges BIBREF16 , we optimize our systems using as primary measure the macro-averaged Mean Absolute Error ( INLINEFORM0 ) given by: INLINEFORM1
where INLINEFORM0 is the number of categories, INLINEFORM1 is the set of instances whose true class is INLINEFORM2 , INLINEFORM3 is the true label of the instance INLINEFORM4 and INLINEFORM5 the predicted label  The measure penalizes decisions far from the true ones and is macro-averaged to account for the fact that the data are unbalanced  Complementary to INLINEFORM6 , we report the performance achieved on the micro-averaged INLINEFORM7 measure, which is a commonly used measure for classification 
The models To evaluate the multitask learning approach, we compared it with several other models  Support Vector Machines (SVMs) are maximum margin classification algorithms that have been shown to achieve competitive performance in several text classification problems BIBREF16   SVM INLINEFORM0 stands for an SVM with linear kernel and an one-vs-rest approach for the multi-class problem  Also, SVM INLINEFORM1 is an SVM with linear kernel that employs the crammer-singer strategy BIBREF18 for the multi-class problem  Logistic regression (LR) is another type of linear classification method, with probabilistic motivation  Again, we use two types of Logistic Regression depending on the multi-class strategy: LR INLINEFORM2 that uses an one-vs-rest approach and multinomial Logistic Regression also known as the MaxEnt classifier that uses a multinomial criterion 
Both SVMs and LRs as discussed above treat the problem as a multi-class one, without considering the ordering of the classes  For these four models, we tuned the hyper-parameter INLINEFORM0 that controls the importance of the L INLINEFORM1 regularization part in the optimization problem with grid-search over INLINEFORM2 using 10-fold cross-validation in the union of the training and development data and then retrained the models with the selected values  Also, to account for the unbalanced classification problem we used class weights to penalize more the errors made on the rare classes  These weights were inversely proportional to the frequency of each class  For the four models we used the implementations of Scikit-learn BIBREF19  
For multitask learning we use the architecture shown in Figure FIGREF2 , which we implemented with Keras BIBREF20   The embeddings are initialized with the 50-dimensional GloVe embeddings while the output of the biLSTM network is set to dimension 50  The activation function of the hidden layers is the hyperbolic tangent  The weights of the layers were initialized from a uniform distribution, scaled as described in BIBREF21   We used the Root Mean Square Propagation optimization method  We used dropout for regularizing the network  We trained the network using batches of 128 examples as follows: before selecting the batch, we perform a Bernoulli trial with probability INLINEFORM0 to select the task to train for  With probability INLINEFORM1 we pick a batch for the fine-grained sentiment classification problem, while with probability INLINEFORM2 we pick a batch for the ternary problem  As shown in Figure FIGREF2 , the error is backpropagated until the embeddings, that we fine-tune during the learning process  Notice also that the weights of the network until the layer INLINEFORM3 are shared and therefore affected by both tasks 
To tune the neural network hyper-parameters we used 5-fold cross validation  We tuned the probability INLINEFORM0 of dropout after the hidden layers INLINEFORM1 and for the biLSTM for INLINEFORM2 , the size of the hidden layer INLINEFORM3 and the probability INLINEFORM4 of the Bernoulli trials from INLINEFORM5   During training, we monitor the network's performance on the development set and apply early stopping if the performance on the validation set does not improve for 5 consecutive epochs 
Experimental results Table TABREF9 illustrates the performance of the models for the different data representations  The upper part of the Table summarizes the performance of the baselines  The entry “Balikas et al ” stands for the winning system of the 2016 edition of the challenge BIBREF2 , which to the best of our knowledge holds the state-of-the-art  Due to the stochasticity of training the biLSTM models, we repeat the experiment 10 times and report the average and the standard deviation of the performance achieved 
Several observations can be made from the table  First notice that, overall, the best performance is achieved by the neural network architecture that uses multitask learning  This entails that the system makes use of the available resources efficiently and improves the state-of-the-art performance  In conjunction with the fact that we found the optimal probability INLINEFORM0 , this highlights the benefits of multitask learning over single task learning  Furthermore, as described above, the neural network-based models have only access to the training data as the development are hold for early stopping  On the other hand, the baseline systems were retrained on the union of the train and development sets  Hence, even with fewer resources available for training on the fine-grained problem, the neural networks outperform the baselines  We also highlight the positive effect of the additional features that previous research proposed  Adding the features both in the baselines and in the biLSTM-based architectures improves the INLINEFORM1 scores by several points 
Lastly, we compare the performance of the baseline systems with the performance of the state-of-the-art system of BIBREF2   While BIBREF2 uses n-grams (and character-grams) with INLINEFORM0 , the baseline systems (SVMs, LRs) used in this work use the nbow+ representation, that relies on unigrams  Although they perform on par, the competitive performance of nbow highlights the potential of distributed representations for short-text classification  Further, incorporating structure and distributed representations leads to the gains of the biLSTM network, in the multitask and single task setting 
Similar observations can be drawn from Figure FIGREF10 that presents the INLINEFORM0 scores  Again, the biLSTM network with multitask learning achieves the best performance  It is also to be noted that although the two evaluation measures are correlated in the sense that the ranking of the models is the same, small differences in the INLINEFORM1 have large effect on the scores of the INLINEFORM2 measure 
Conclusion
In this paper, we showed that by jointly learning the tasks of ternary and fine-grained classification with a multitask learning model, one can greatly improve the performance on the second  This opens several avenues for future research  Since sentiment is expressed in different textual types like tweets and paragraph-sized reviews, in different languages (English, German,   ) and in different granularity levels (binary, ternary,  ) one can imagine multitask approaches that could benefit from combining such resources  Also, while we opted for biLSTM networks here, one could use convolutional neural networks or even try to combine different types of networks and tasks to investigate the performance effect of multitask learning  Lastly, while our approach mainly relied on the foundations of BIBREF4 , the internal mechanisms and the theoretical guarantees of multitask learning remain to be better understood 
Acknowledgements
This work is partially supported by the CIFRE N 28/2015 

ground_truth: They decrease MAE in 0.34