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 chinese datasets were used?
Context 1: Introduction
Grammar induction is the task of inducing hierarchical syntactic structure from data  Statistical approaches to grammar induction require specifying a probabilistic grammar (e g  formalism, number and shape of rules), and fitting its parameters through optimization  Early work found that it was difficult to induce probabilistic context-free grammars (PCFG) from natural language data through direct methods, such as optimizing the log likelihood with the EM algorithm BIBREF0 , BIBREF1   While the reasons for the failure are manifold and not completely understood, two major potential causes are the ill-behaved optimization landscape and the overly strict independence assumptions of PCFGs  More successful approaches to grammar induction have thus resorted to carefully-crafted auxiliary objectives BIBREF2 , priors or non-parametric models BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , and manually-engineered features BIBREF7 , BIBREF8 to encourage the desired structures to emerge 
We revisit these aforementioned issues in light of advances in model parameterization and inference  First, contrary to common wisdom, we find that parameterizing a PCFG's rule probabilities with neural networks over distributed representations makes it possible to induce linguistically meaningful grammars by simply optimizing log likelihood  While the optimization problem remains non-convex, recent work suggests that there are optimization benefits afforded by over-parameterized models BIBREF9 , BIBREF10 , BIBREF11 , and we indeed find that this neural PCFG is significantly easier to optimize than the traditional PCFG  Second, this factored parameterization makes it straightforward to incorporate side information into rule probabilities through a sentence-level continuous latent vector, which effectively allows different contexts in a derivation to coordinate  In this compound PCFG—continuous mixture of PCFGs—the context-free assumptions hold conditioned on the latent vector but not unconditionally, thereby obtaining longer-range dependencies within a tree-based generative process 
To utilize this approach, we need to efficiently optimize the log marginal likelihood of observed sentences  While compound PCFGs break efficient inference, if the latent vector is known the distribution over trees reduces to a standard PCFG  This property allows us to perform grammar induction using a collapsed approach where the latent trees are marginalized out exactly with dynamic programming  To handle the latent vector, we employ standard amortized inference using reparameterized samples from a variational posterior approximated from an inference network BIBREF12 , BIBREF13  
On standard benchmarks for English and Chinese, the proposed approach is found to perform favorably against recent neural network-based approaches to grammar induction BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17  
Probabilistic Context-Free Grammars
We consider context-free grammars (CFG) consisting of a 5-tuple INLINEFORM0 where INLINEFORM1 is the distinguished start symbol, INLINEFORM2 is a finite set of nonterminals, INLINEFORM3 is a finite set of preterminals, INLINEFORM6 is a finite set of terminal symbols, and INLINEFORM7 is a finite set of rules of the form,
INLINEFORM0
A probabilistic context-free grammar (PCFG) consists of a grammar INLINEFORM0 and rule probabilities INLINEFORM1 such that INLINEFORM2 is the probability of the rule INLINEFORM3   Letting INLINEFORM4 be the set of all parse trees of INLINEFORM5 , a PCFG defines a probability distribution over INLINEFORM6 via INLINEFORM7 where INLINEFORM8 is the set of rules used in the derivation of INLINEFORM9   It also defines a distribution over string of terminals INLINEFORM10 via
INLINEFORM0
where INLINEFORM0 , i e  the set of trees INLINEFORM1 such that INLINEFORM2 's leaves are INLINEFORM3   We will slightly abuse notation and use
INLINEFORM0
to denote the posterior distribution over the unobserved latent trees given the observed sentence INLINEFORM0 , where INLINEFORM1 is the indicator function 
Compound PCFGs
A compound probability distribution BIBREF19 is a distribution whose parameters are themselves random variables  These distributions generalize mixture models to the continuous case, for example in factor analysis which assumes the following generative process,
INLINEFORM0
Compound distributions provide the ability to model rich generative processes, but marginalizing over the latent parameter can be computationally intractable unless conjugacy can be exploited 
In this work, we study compound probabilistic context-free grammars whose distribution over trees arises from the following generative process: we first obtain rule probabilities via
INLINEFORM0
where INLINEFORM0 is a prior with parameters INLINEFORM1 (spherical Gaussian in this paper), and INLINEFORM2 is a neural network that concatenates the input symbol embeddings with INLINEFORM3 and outputs the sentence-level rule probabilities INLINEFORM4 ,
INLINEFORM0
where INLINEFORM0 denotes vector concatenation  Then a tree/sentence is sampled from a PCFG with rule probabilities given by INLINEFORM1 ,
INLINEFORM0
This can be viewed as a continuous mixture of PCFGs, or alternatively, a Bayesian PCFG with a prior on sentence-level rule probabilities parameterized by INLINEFORM0   Importantly, under this generative model the context-free assumptions hold conditioned on INLINEFORM3 , but they do not hold unconditionally  This is shown in Figure FIGREF3 (right) where there is a dependence path through INLINEFORM4 if it is not conditioned upon  Compound PCFGs give rise to a marginal distribution over parse trees INLINEFORM5 via
INLINEFORM0
where INLINEFORM0   The subscript in INLINEFORM1 denotes the fact that the rule probabilities depend on INLINEFORM2   Compound PCFGs are clearly more expressive than PCFGs as each sentence has its own set of rule probabilities  However, it still assumes a tree-based generative process, making it possible to learn latent tree structures 
Our motivation for the compound PCFG is based on the observation that for grammar induction, context-free assumptions are generally made not because they represent an adequate model of natural language, but because they allow for tractable training  We can in principle model richer dependencies through vertical/horizontal Markovization BIBREF21 , BIBREF22 and lexicalization BIBREF23   However such dependencies complicate training due to the rapid increase in the number of rules  Under this view, we can interpret the compound PCFG as a restricted version of some lexicalized, higher-order PCFG where a child can depend on structural and lexical context through a shared latent vector  We hypothesize that this dependence among siblings is especially useful in grammar induction from words, where (for example) if we know that watched is used as a verb then the noun phrase is likely to be a movie 
In contrast to the usual Bayesian treatment of PCFGs which places priors on global rule probabilities BIBREF3 , BIBREF4 , BIBREF6 , the compound PCFG assumes a prior on local, sentence-level rule probabilities  It is therefore closely related to the Bayesian grammars studied by BIBREF25 and BIBREF26 , who also sample local rule probabilities from a logistic normal prior for training dependency models with valence (DMV) BIBREF27  
Experimental Setup
Results and Discussion
Table TABREF23 shows the unlabeled INLINEFORM0 scores for our models and various baselines  All models soundly outperform right branching baselines, and we find that the neural PCFG/compound PCFG are strong models for grammar induction  In particular the compound PCFG outperforms other models by an appreciable margin on both English and Chinese  We again note that we were unable to induce meaningful grammars through a traditional PCFG with the scalar parameterization despite a thorough hyperparameter search  See lab:full for the full results (including corpus-level INLINEFORM1 ) broken down by sentence length 
Table TABREF27 analyzes the learned tree structures  We compare similarity as measured by INLINEFORM0 against gold, left, right, and “self" trees (top), where self INLINEFORM1 score is calculated by averaging over all 6 pairs obtained from 4 different runs  We find that PRPN is particularly consistent across multiple runs  We also observe that different models are better at identifying different constituent labels, as measured by label recall (Table TABREF27 , bottom)  While left as future work, this naturally suggests an ensemble approach wherein the empirical probabilities of constituents (obtained by averaging the predicted binary constituent labels from the different models) are used either to supervise another model or directly as potentials in a CRF constituency parser  Finally, all models seemed to have some difficulty in identifying SBAR/VP constituents which typically span more words than NP constituents 
Related Work
Grammar induction has a long and rich history in natural language processing  Early work on grammar induction with pure unsupervised learning was mostly negative BIBREF0 , BIBREF1 , BIBREF74 , though BIBREF75 reported some success on partially bracketed data  BIBREF76 and BIBREF2 were some of the first successful statistical approaches to grammar induction  In particular, the constituent-context model (CCM) of BIBREF2 , which explicitly models both constituents and distituents, was the basis for much subsequent work BIBREF27 , BIBREF7 , BIBREF8   Other works have explored imposing inductive biases through Bayesian priors BIBREF4 , BIBREF5 , BIBREF6 , modified objectives BIBREF42 , and additional constraints on recursion depth BIBREF77 , BIBREF48  
While the framework of specifying the structure of a grammar and learning the parameters is common, other methods exist  BIBREF43 consider a nonparametric-style approach to unsupervised parsing by using random subsets of training subtrees to parse new sentences  BIBREF46 utilize an incremental algorithm to unsupervised parsing which makes local decisions to create constituents based on a complex set of heuristics  BIBREF47 induce parse trees through cascaded applications of finite state models 
More recently, neural network-based approaches to grammar induction have shown promising results on inducing parse trees directly from words  BIBREF14 , BIBREF15 learn tree structures through soft gating layers within neural language models, while BIBREF16 combine recursive autoencoders with the inside-outside algorithm  BIBREF17 train unsupervised recurrent neural network grammars with a structured inference network to induce latent trees, and BIBREF78 utilize image captions to identify and ground constituents 
Our work is also related to latent variable PCFGs BIBREF79 , BIBREF80 , BIBREF81 , which extend PCFGs to the latent variable setting by splitting nonterminal symbols into latent subsymbols  In particular, latent vector grammars BIBREF82 and compositional vector grammars BIBREF83 also employ continuous vectors within their grammars  However these approaches have been employed for learning supervised parsers on annotated treebanks, in contrast to the unsupervised setting of the current work 
Conclusion
This work explores grammar induction with compound PCFGs, which modulate rule probabilities with per-sentence continuous latent vectors  The latent vector induces marginal dependencies beyond the traditional first-order context-free assumptions within a tree-based generative process, leading to improved performance  The collapsed amortized variational inference approach is general and can be used for generative models which admit tractable inference through partial conditioning  Learning deep generative models which exhibit such conditional Markov properties is an interesting direction for future work 
Acknowledgments
We thank Phil Blunsom for initial discussions which seeded many of the core ideas in the present work  We also thank Yonatan Belinkov and Shay Cohen for helpful feedback, and Andrew Drozdov for providing the parsed dataset from their DIORA model  YK is supported by a Google Fellowship  AMR acknowledges the support of NSF 1704834, 1845664, AWS, and Oracle 
Model Parameterization
We associate an input embedding INLINEFORM0 for each symbol INLINEFORM1 on the left side of a rule (i e  INLINEFORM2 ) and run a neural network over INLINEFORM3 to obtain the rule probabilities  Concretely, each rule type INLINEFORM4 is parameterized as follows, INLINEFORM5
where INLINEFORM0 is the product space INLINEFORM1 , and INLINEFORM2 are MLPs with two residual layers, INLINEFORM3
The bias terms for the above expressions (including for the rule probabilities) are omitted for notational brevity  In Figure FIGREF3 we use the following to refer to rule probabilities of different rule types, INLINEFORM0
where INLINEFORM0 denotes the set of rules with INLINEFORM1 on the left hand side 
The compound PCFG rule probabilities INLINEFORM0 given a latent vector INLINEFORM1 , INLINEFORM2
Again the bias terms are omitted for brevity, and INLINEFORM0 are as before where the first layer's input dimensions are appropriately changed to account for concatenation with INLINEFORM1  
Corpus/Sentence F 1 F_1 by Sentence Length
For completeness we show the corpus-level and sentence-level INLINEFORM0 broken down by sentence length in Table TABREF44 , averaged across 4 different runs of each model 
Experiments with RNNGs
For experiments on supervising RNNGs with induced trees, we use the parameterization and hyperparameters from BIBREF17 , which uses a 2-layer 650-dimensional stack LSTM (with dropout of 0 5) and a 650-dimensional tree LSTM BIBREF88 , BIBREF90 as the composition function 
Concretely, the generative story is as follows: first, the stack representation is used to predict the next action (shift or reduce) via an affine transformation followed by a sigmoid  If shift is chosen, we obtain a distribution over the vocabulary via another affine transformation over the stack representation followed by a softmax  Then we sample the next word from this distribution and shift the generated word onto the stack using the stack LSTM  If reduce is chosen, we pop the last two elements off the stack and use the tree LSTM to obtain a new representation  This new representation is shifted onto the stack via the stack LSTM  Note that this RNNG parameterization is slightly different than the original from BIBREF53 , which does not ignore constituent labels and utilizes a bidirectional LSTM as the composition function instead of a tree LSTM  As our RNNG parameterization only works with binary trees, we binarize the gold trees with right binarization for the RNNG trained on gold trees (trees from the unsupervised methods explored in this paper are already binary)  The RNNG also trains a discriminative parser alongside the generative model for evaluation with importance sampling  We use a CRF parser whose span score parameterization is similar similar to recent works BIBREF89 , BIBREF87 , BIBREF85 : position embeddings are added to word embeddings, and a bidirectional LSTM with 256 hidden dimensions is run over the input representations to obtain the forward and backward hidden states  The score INLINEFORM0 for a constituent spanning the INLINEFORM1 -th and INLINEFORM2 -th word is given by,
INLINEFORM0
where the MLP has a single hidden layer with INLINEFORM0 nonlinearity followed by layer normalization BIBREF84  
For experiments on fine-tuning the RNNG with the unsupervised RNNG, we take the discriminative parser (which is also pretrained alongside the RNNG on induced trees) to be the structured inference network for optimizing the evidence lower bound  We refer the reader to BIBREF17 and their open source implementation for additional details  We also observe that as noted by BIBREF17 , a URNNG trained from scratch on this version of PTB without punctuation failed to outperform a right-branching baseline 
The LSTM language model baseline is the same size as the stack LSTM (i e  2 layers, 650 hidden units, dropout of 0 5), and is therefore equivalent to an RNNG with completely right branching trees  The PRPN/ON baselines for perplexity/syntactic evaluation in Table TABREF30 also have 2 layers with 650 hidden units and 0 5 dropout  Therefore all models considered in Table TABREF30 have roughly the same capacity  For all models we share input/output word embeddings BIBREF86   Perplexity estimation for the RNNGs and the compound PCFG uses 1000 importance-weighted samples 
For grammaticality judgment, we modify the publicly available dataset from BIBREF56 to only keep sentence pairs that did not have any unknown words with respect to our PTB vocabulary of 10K words  This results in 33K sentence pairs for evaluation 
Nonterminal/Preterminal Alignments
Figure FIGREF50 shows the part-of-speech alignments and Table TABREF46 shows the nonterminal label alignments for the compound PCFG/neural PCFG 
Subtree Analysis
Table TABREF53 lists more examples of constituents within each subtree as the top principical component is varied  Due to data sparsity, the subtree analysis is performed on the full dataset  See section UID36 for more details 
Context 2: Introduction
Machine Reading Comprehension (MRC), as the name suggests, requires a machine to read a passage and answer its relevant questions  Since the answer to each question is supposed to stem from the corresponding passage, a common MRC solution is to develop a neural-network-based MRC model that predicts an answer span (i e  the answer start position and the answer end position) from the passage of each given passage-question pair  To facilitate the explorations and innovations in this area, many MRC datasets have been established, such as SQuAD BIBREF0 , MS MARCO BIBREF1 , and TriviaQA BIBREF2   Consequently, many pioneering MRC models have been proposed, such as BiDAF BIBREF3 , R-NET BIBREF4 , and QANet BIBREF5   According to the leader board of SQuAD, the state-of-the-art MRC models have achieved the same performance as human beings  However, does this imply that they have possessed the same reading comprehension ability as human beings?
OF COURSE NOT  There is a huge gap between MRC models and human beings, which is mainly reflected in the hunger for data and the robustness to noise  On the one hand, developing MRC models requires a large amount of training examples (i e  the passage-question pairs labeled with answer spans), while human beings can achieve good performance on evaluation examples (i e  the passage-question pairs to address) without training examples  On the other hand, BIBREF6 revealed that intentionally injected noise (e g  misleading sentences) in evaluation examples causes the performance of MRC models to drop significantly, while human beings are far less likely to suffer from this  The reason for these phenomena, we believe, is that MRC models can only utilize the knowledge contained in each given passage-question pair, but in addition to this, human beings can also utilize general knowledge  A typical category of general knowledge is inter-word semantic connections  As shown in Table TABREF1 , such general knowledge is essential to the reading comprehension ability of human beings 
A promising strategy to bridge the gap mentioned above is to integrate the neural networks of MRC models with the general knowledge of human beings  To this end, it is necessary to solve two problems: extracting general knowledge from passage-question pairs and utilizing the extracted general knowledge in the prediction of answer spans  The first problem can be solved with knowledge bases, which store general knowledge in structured forms  A broad variety of knowledge bases are available, such as WordNet BIBREF7 storing semantic knowledge, ConceptNet BIBREF8 storing commonsense knowledge, and Freebase BIBREF9 storing factoid knowledge  In this paper, we limit the scope of general knowledge to inter-word semantic connections, and thus use WordNet as our knowledge base  The existing way to solve the second problem is to encode general knowledge in vector space so that the encoding results can be used to enhance the lexical or contextual representations of words BIBREF10 , BIBREF11   However, this is an implicit way to utilize general knowledge, since in this way we can neither understand nor control the functioning of general knowledge  In this paper, we discard the existing implicit way and instead explore an explicit (i e  understandable and controllable) way to utilize general knowledge 
The contribution of this paper is two-fold  On the one hand, we propose a data enrichment method, which uses WordNet to extract inter-word semantic connections as general knowledge from each given passage-question pair  On the other hand, we propose an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the above extracted general knowledge to assist its attention mechanisms  Based on the data enrichment method, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them  When only a subset ( INLINEFORM0 – INLINEFORM1 ) of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise 
Data Enrichment Method
In this section, we elaborate a WordNet-based data enrichment method, which is aimed at extracting inter-word semantic connections from each passage-question pair in our MRC dataset  The extraction is performed in a controllable manner, and the extracted results are provided as general knowledge to our MRC model 
Semantic Relation Chain
WordNet is a lexical database of English, where words are organized into synsets according to their senses  A synset is a set of words expressing the same sense so that a word having multiple senses belongs to multiple synsets, with each synset corresponding to a sense  Synsets are further related to each other through semantic relations  According to the WordNet interface provided by NLTK BIBREF12 , there are totally sixteen types of semantic relations (e g  hypernyms, hyponyms, holonyms, meronyms, attributes, etc )  Based on synset and semantic relation, we define a new concept: semantic relation chain  A semantic relation chain is a concatenated sequence of semantic relations, which links a synset to another synset  For example, the synset “keratin n 01” is related to the synset “feather n 01” through the semantic relation “substance holonym”, the synset “feather n 01” is related to the synset “bird n 01” through the semantic relation “part holonym”, and the synset “bird n 01” is related to the synset “parrot n 01” through the semantic relation “hyponym”, thus “substance holonym INLINEFORM0 part holonym INLINEFORM1 hyponym” is a semantic relation chain, which links the synset “keratin n 01” to the synset “parrot n 01”  We name each semantic relation in a semantic relation chain as a hop, therefore the above semantic relation chain is a 3-hop chain  By the way, each single semantic relation is equivalent to a 1-hop chain 
Inter-word Semantic Connection
The key problem in the data enrichment method is determining whether a word is semantically connected to another word  If so, we say that there exists an inter-word semantic connection between them  To solve this problem, we define another new concept: the extended synsets of a word  Given a word INLINEFORM0 , whose synsets are represented as a set INLINEFORM1 , we use another set INLINEFORM2 to represent its extended synsets, which includes all the synsets that are in INLINEFORM3 or that can be linked to from INLINEFORM4 through semantic relation chains  Theoretically, if there is no limitation on semantic relation chains, INLINEFORM5 will include all the synsets in WordNet, which is meaningless in most situations  Therefore, we use a hyper-parameter INLINEFORM6 to represent the permitted maximum hop count of semantic relation chains  That is to say, only the chains having no more than INLINEFORM7 hops can be used to construct INLINEFORM8 so that INLINEFORM9 becomes a function of INLINEFORM10 : INLINEFORM11 (if INLINEFORM12 , we will have INLINEFORM13 )  Based on the above statements, we formulate a heuristic rule for determining inter-word semantic connections: a word INLINEFORM14 is semantically connected to another word INLINEFORM15 if and only if INLINEFORM16  
General Knowledge Extraction
Given a passage-question pair, the inter-word semantic connections that connect any word to any passage word are regarded as the general knowledge we need to extract  Considering the requirements of our MRC model, we only extract the positional information of such inter-word semantic connections  Specifically, for each word INLINEFORM0 , we extract a set INLINEFORM1 , which includes the positions of the passage words that INLINEFORM2 is semantically connected to (if INLINEFORM3 itself is a passage word, we will exclude its own position from INLINEFORM4 )  We can control the amount of the extracted results by setting the hyper-parameter INLINEFORM5 : if we set INLINEFORM6 to 0, inter-word semantic connections will only exist between synonyms; if we increase INLINEFORM7 , inter-word semantic connections will exist between more words  That is to say, by increasing INLINEFORM8 within a certain range, we can usually extract more inter-word semantic connections from a passage-question pair, and thus can provide the MRC model with more general knowledge  However, due to the complexity and diversity of natural languages, only a part of the extracted results can serve as useful general knowledge, while the rest of them are useless for the prediction of answer spans, and the proportion of the useless part always rises when INLINEFORM9 is set larger  Therefore we set INLINEFORM10 through cross validation (i e  according to the performance of the MRC model on the development examples) 
Knowledge Aided Reader
In this section, we elaborate our MRC model: Knowledge Aided Reader (KAR)  The key components of most existing MRC models are their attention mechanisms BIBREF13 , which are aimed at fusing the associated representations of each given passage-question pair  These attention mechanisms generally fall into two categories: the first one, which we name as mutual attention, is aimed at fusing the question representations into the passage representations so as to obtain the question-aware passage representations; the second one, which we name as self attention, is aimed at fusing the question-aware passage representations into themselves so as to obtain the final passage representations  Although KAR is equipped with both categories, its most remarkable feature is that it explicitly uses the general knowledge extracted by the data enrichment method to assist its attention mechanisms  Therefore we separately name the attention mechanisms of KAR as knowledge aided mutual attention and knowledge aided self attention 
Task Definition
Given a passage INLINEFORM0 and a relevant question INLINEFORM1 , the task is to predict an answer span INLINEFORM2 , where INLINEFORM3 , so that the resulting subsequence INLINEFORM4 from INLINEFORM5 is an answer to INLINEFORM6  
Overall Architecture
As shown in Figure FIGREF7 , KAR is an end-to-end MRC model consisting of five layers:
Lexicon Embedding Layer  This layer maps the words to the lexicon embeddings  The lexicon embedding of each word is composed of its word embedding and character embedding  For each word, we use the pre-trained GloVe BIBREF14 word vector as its word embedding, and obtain its character embedding with a Convolutional Neural Network (CNN) BIBREF15   For both the passage and the question, we pass the concatenation of the word embeddings and the character embeddings through a shared dense layer with ReLU activation, whose output dimensionality is INLINEFORM0   Therefore we obtain the passage lexicon embeddings INLINEFORM1 and the question lexicon embeddings INLINEFORM2  
Context Embedding Layer  This layer maps the lexicon embeddings to the context embeddings  For both the passage and the question, we process the lexicon embeddings (i e  INLINEFORM0 for the passage and INLINEFORM1 for the question) with a shared bidirectional LSTM (BiLSTM) BIBREF16 , whose hidden state dimensionality is INLINEFORM2   By concatenating the forward LSTM outputs and the backward LSTM outputs, we obtain the passage context embeddings INLINEFORM3 and the question context embeddings INLINEFORM4  
Coarse Memory Layer  This layer maps the context embeddings to the coarse memories  First we use knowledge aided mutual attention (introduced later) to fuse INLINEFORM0 into INLINEFORM1 , the outputs of which are represented as INLINEFORM2   Then we process INLINEFORM3 with a BiLSTM, whose hidden state dimensionality is INLINEFORM4   By concatenating the forward LSTM outputs and the backward LSTM outputs, we obtain the coarse memories INLINEFORM5 , which are the question-aware passage representations 
Refined Memory Layer  This layer maps the coarse memories to the refined memories  First we use knowledge aided self attention (introduced later) to fuse INLINEFORM0 into themselves, the outputs of which are represented as INLINEFORM1   Then we process INLINEFORM2 with a BiLSTM, whose hidden state dimensionality is INLINEFORM3   By concatenating the forward LSTM outputs and the backward LSTM outputs, we obtain the refined memories INLINEFORM4 , which are the final passage representations 
Answer Span Prediction Layer  This layer predicts the answer start position and the answer end position based on the above layers  First we obtain the answer start position distribution INLINEFORM0 : INLINEFORM1 INLINEFORM2
where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters; INLINEFORM3 represents the refined memory of each passage word INLINEFORM4 (i e  the INLINEFORM5 -th column in INLINEFORM6 ); INLINEFORM7 represents the question summary obtained by performing an attention pooling over INLINEFORM8   Then we obtain the answer end position distribution INLINEFORM9 : INLINEFORM10 INLINEFORM11
where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters; INLINEFORM3 represents vector concatenation  Finally we construct an answer span prediction matrix INLINEFORM4 , where INLINEFORM5 represents the upper triangular matrix of a matrix INLINEFORM6   Therefore, for the training, we minimize INLINEFORM7 on each training example whose labeled answer span is INLINEFORM8 ; for the inference, we separately take the row index and column index of the maximum element in INLINEFORM9 as INLINEFORM10 and INLINEFORM11  
Knowledge Aided Mutual Attention
As a part of the coarse memory layer, knowledge aided mutual attention is aimed at fusing the question context embeddings INLINEFORM0 into the passage context embeddings INLINEFORM1 , where the key problem is to calculate the similarity between each passage context embedding INLINEFORM2 (i e  the INLINEFORM3 -th column in INLINEFORM4 ) and each question context embedding INLINEFORM5 (i e  the INLINEFORM6 -th column in INLINEFORM7 )  To solve this problem, BIBREF3 proposed a similarity function: INLINEFORM8
where INLINEFORM0 is a trainable parameter; INLINEFORM1 represents element-wise multiplication  This similarity function has also been adopted by several other works BIBREF17 , BIBREF5   However, since context embeddings contain high-level information, we believe that introducing the pre-extracted general knowledge into the calculation of such similarities will make the results more reasonable  Therefore we modify the above similarity function to the following form: INLINEFORM2
where INLINEFORM0 represents the enhanced context embedding of a word INLINEFORM1   We use the pre-extracted general knowledge to construct the enhanced context embeddings  Specifically, for each word INLINEFORM2 , whose context embedding is INLINEFORM3 , to construct its enhanced context embedding INLINEFORM4 , first recall that we have extracted a set INLINEFORM5 , which includes the positions of the passage words that INLINEFORM6 is semantically connected to, thus by gathering the columns in INLINEFORM7 whose indexes are given by INLINEFORM8 , we obtain the matching context embeddings INLINEFORM9   Then by constructing a INLINEFORM10 -attended summary of INLINEFORM11 , we obtain the matching vector INLINEFORM12 (if INLINEFORM13 , which makes INLINEFORM14 , we will set INLINEFORM15 ): INLINEFORM16 INLINEFORM17
where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters; INLINEFORM3 represents the INLINEFORM4 -th column in INLINEFORM5   Finally we pass the concatenation of INLINEFORM6 and INLINEFORM7 through a dense layer with ReLU activation, whose output dimensionality is INLINEFORM8   Therefore we obtain the enhanced context embedding INLINEFORM9  
Based on the modified similarity function and the enhanced context embeddings, to perform knowledge aided mutual attention, first we construct a knowledge aided similarity matrix INLINEFORM0 , where each element INLINEFORM1   Then following BIBREF5 , we construct the passage-attended question summaries INLINEFORM2 and the question-attended passage summaries INLINEFORM3 : INLINEFORM4 INLINEFORM5
where INLINEFORM0 represents softmax along the row dimension and INLINEFORM1 along the column dimension  Finally following BIBREF17 , we pass the concatenation of INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , and INLINEFORM5 through a dense layer with ReLU activation, whose output dimensionality is INLINEFORM6   Therefore we obtain the outputs INLINEFORM7  
Knowledge Aided Self Attention
As a part of the refined memory layer, knowledge aided self attention is aimed at fusing the coarse memories INLINEFORM0 into themselves  If we simply follow the self attentions of other works BIBREF4 , BIBREF18 , BIBREF19 , BIBREF17 , then for each passage word INLINEFORM1 , we should fuse its coarse memory INLINEFORM2 (i e  the INLINEFORM3 -th column in INLINEFORM4 ) with the coarse memories of all the other passage words  However, we believe that this is both unnecessary and distracting, since each passage word has nothing to do with many of the other passage words  Thus we use the pre-extracted general knowledge to guarantee that the fusion of coarse memories for each passage word will only involve a precise subset of the other passage words  Specifically, for each passage word INLINEFORM5 , whose coarse memory is INLINEFORM6 , to perform the fusion of coarse memories, first recall that we have extracted a set INLINEFORM7 , which includes the positions of the other passage words that INLINEFORM8 is semantically connected to, thus by gathering the columns in INLINEFORM9 whose indexes are given by INLINEFORM10 , we obtain the matching coarse memories INLINEFORM11   Then by constructing a INLINEFORM12 -attended summary of INLINEFORM13 , we obtain the matching vector INLINEFORM14 (if INLINEFORM15 , which makes INLINEFORM16 , we will set INLINEFORM17 ): INLINEFORM18 INLINEFORM19
where INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 are trainable parameters  Finally we pass the concatenation of INLINEFORM3 and INLINEFORM4 through a dense layer with ReLU activation, whose output dimensionality is INLINEFORM5   Therefore we obtain the fusion result INLINEFORM6 , and further the outputs INLINEFORM7  
Related Works
Attention Mechanisms  Besides those mentioned above, other interesting attention mechanisms include performing multi-round alignment to avoid the problems of attention redundancy and attention deficiency BIBREF20 , and using mutual attention as a skip-connector to densely connect pairwise layers BIBREF21  
Data Augmentation  It is proved that properly augmenting training examples can improve the performance of MRC models  For example, BIBREF22 trained a generative model to generate questions based on unlabeled text, which substantially boosted their performance; BIBREF5 trained a back-and-forth translation model to paraphrase training examples, which brought them a significant performance gain 
Multi-step Reasoning  Inspired by the fact that human beings are capable of understanding complex documents by reading them over and over again, multi-step reasoning was proposed to better deal with difficult MRC tasks  For example, BIBREF23 used reinforcement learning to dynamically determine the number of reasoning steps; BIBREF19 fixed the number of reasoning steps, but used stochastic dropout in the output layer to avoid step bias 
Linguistic Embeddings  It is both easy and effective to incorporate linguistic embeddings into the input layer of MRC models  For example, BIBREF24 and BIBREF19 used POS embeddings and NER embeddings to construct their input embeddings; BIBREF25 used structural embeddings based on parsing trees to constructed their input embeddings 
Transfer Learning  Several recent breakthroughs in MRC benefit from feature-based transfer learning BIBREF26 , BIBREF27 and fine-tuning-based transfer learning BIBREF28 , BIBREF29 , which are based on certain word-level or sentence-level models pre-trained on large external corpora in certain supervised or unsupervised manners 
Experimental Settings
MRC Dataset  The MRC dataset used in this paper is SQuAD 1 1, which contains over INLINEFORM0 passage-question pairs and has been randomly partitioned into three parts: a training set ( INLINEFORM1 ), a development set ( INLINEFORM2 ), and a test set ( INLINEFORM3 )  Besides, we also use two of its adversarial sets, namely AddSent and AddOneSent BIBREF6 , to evaluate the robustness to noise of MRC models  The passages in the adversarial sets contain misleading sentences, which are aimed at distracting MRC models  Specifically, each passage in AddSent contains several sentences that are similar to the question but not contradictory to the answer, while each passage in AddOneSent contains a human-approved random sentence that may be unrelated to the passage 
Implementation Details  We tokenize the MRC dataset with spaCy 2 0 13 BIBREF30 , manipulate WordNet 3 0 with NLTK 3 3, and implement KAR with TensorFlow 1 11 0 BIBREF31   For the data enrichment method, we set the hyper-parameter INLINEFORM0 to 3  For the dense layers and the BiLSTMs, we set the dimensionality unit INLINEFORM1 to 600  For model optimization, we apply the Adam BIBREF32 optimizer with a learning rate of INLINEFORM2 and a mini-batch size of 32  For model evaluation, we use Exact Match (EM) and F1 score as evaluation metrics  To avoid overfitting, we apply dropout BIBREF33 to the dense layers and the BiLSTMs with a dropout rate of INLINEFORM3   To boost the performance, we apply exponential moving average with a decay rate of INLINEFORM4  
Model Comparison in both Performance and the Robustness to Noise
We compare KAR with other MRC models in both performance and the robustness to noise  Specifically, we not only evaluate the performance of KAR on the development set and the test set, but also do this on the adversarial sets  As for the comparative objects, we only consider the single MRC models that rank in the top 20 on the SQuAD 1 1 leader board and have reported their performance on the adversarial sets  There are totally five such comparative objects, which can be considered as representatives of the state-of-the-art MRC models  As shown in Table TABREF12 , on the development set and the test set, the performance of KAR is on par with that of the state-of-the-art MRC models; on the adversarial sets, KAR outperforms the state-of-the-art MRC models by a large margin  That is to say, KAR is comparable in performance with the state-of-the-art MRC models, and significantly more robust to noise than them 
To verify the effectiveness of general knowledge, we first study the relationship between the amount of general knowledge and the performance of KAR  As shown in Table TABREF13 , by increasing INLINEFORM0 from 0 to 5 in the data enrichment method, the amount of general knowledge rises monotonically, but the performance of KAR first rises until INLINEFORM1 reaches 3 and then drops down  Then we conduct an ablation study by replacing the knowledge aided attention mechanisms with the mutual attention proposed by BIBREF3 and the self attention proposed by BIBREF4 separately, and find that the F1 score of KAR drops by INLINEFORM2 on the development set, INLINEFORM3 on AddSent, and INLINEFORM4 on AddOneSent  Finally we find that after only one epoch of training, KAR already achieves an EM of INLINEFORM5 and an F1 score of INLINEFORM6 on the development set, which is even better than the final performance of several strong baselines, such as DCN (EM / F1: INLINEFORM7 / INLINEFORM8 ) BIBREF36 and BiDAF (EM / F1: INLINEFORM9 / INLINEFORM10 ) BIBREF3   The above empirical findings imply that general knowledge indeed plays an effective role in KAR 
To demonstrate the advantage of our explicit way to utilize general knowledge over the existing implicit way, we compare the performance of KAR with that reported by BIBREF10 , which used an encoding-based method to utilize the general knowledge dynamically retrieved from Wikipedia and ConceptNet  Since their best model only achieved an EM of INLINEFORM0 and an F1 score of INLINEFORM1 on the development set, which is much lower than the performance of KAR, we have good reason to believe that our explicit way works better than the existing implicit way 
Model Comparison in the Hunger for Data
We compare KAR with other MRC models in the hunger for data  Specifically, instead of using all the training examples, we produce several training subsets (i e  subsets of the training examples) so as to study the relationship between the proportion of the available training examples and the performance  We produce each training subset by sampling a specific number of questions from all the questions relevant to each passage  By separately sampling 1, 2, 3, and 4 questions on each passage, we obtain four training subsets, which separately contain INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , and INLINEFORM3 of the training examples  As shown in Figure FIGREF15 , with KAR, SAN (re-implemented), and QANet (re-implemented without data augmentation) trained on these training subsets, we evaluate their performance on the development set, and find that KAR performs much better than SAN and QANet  As shown in Figure FIGREF16 and Figure FIGREF17 , with the above KAR, SAN, and QANet trained on the same training subsets, we also evaluate their performance on the adversarial sets, and still find that KAR performs much better than SAN and QANet  That is to say, when only a subset of the training examples are available, KAR outperforms the state-of-the-art MRC models by a large margin, and is still reasonably robust to noise 
Analysis
According to the experimental results, KAR is not only comparable in performance with the state-of-the-art MRC models, but also superior to them in terms of both the hunger for data and the robustness to noise  The reasons for these achievements, we believe, are as follows:
Conclusion
In this paper, we innovatively integrate the neural networks of MRC models with the general knowledge of human beings  Specifically, inter-word semantic connections are first extracted from each given passage-question pair by a WordNet-based data enrichment method, and then provided as general knowledge to an end-to-end MRC model named as Knowledge Aided Reader (KAR), which explicitly uses the general knowledge to assist its attention mechanisms  Experimental results show that KAR is not only comparable in performance with the state-of-the-art MRC models, but also superior to them in terms of both the hunger for data and the robustness to noise  In the future, we plan to use some larger knowledge bases, such as ConceptNet and Freebase, to improve the quality and scope of the general knowledge 
Acknowledgments
This work is partially supported by a research donation from iFLYTEK Co , Ltd , Hefei, China, and a discovery grant from Natural Sciences and Engineering Research Council (NSERC) of Canada 
Context 3: Introduction
Massive Open Online Courses (MOOCs) have strived to bridge the social gap in higher education by bringing quality education from reputed universities to students at large  Such massive scaling through online classrooms, however, disrupt co-located, synchronous two-way communication between the students and the instructor 
MOOC platforms provide discussion forums for students to talk to their classmates about the lectures, homeworks, quizzes and provide a venue to socialise  Instructors (defined here as the course instructors, their teaching assistants and the MOOC platform's technical staff) monitor the discussion forum to post (reply to their message) in discussion threads among students  We refer to this posting as intervention, following prior work BIBREF0   However, due to large student enrolment, the student–instructor ratio in MOOCs is very high Therefore, instructors are not able to monitor and participate in all student discussions  To address this problem, a number of works have proposed systems e g , BIBREF0 , BIBREF1 to aid instructors to selectively intervene on student discussions where they are needed the most 
In this paper, we improve the state-of-the-art for instructor intervention in MOOC forums  We propose the first neural models for this prediction problem  We show that modelling the thread structure and the sequence of posts explicitly improves performance  Instructors in different MOOCs from different subject areas intervene differently  For example, on a Science, Technology, Engineering and Mathematics (STEM) MOOC, instructors may often intervene early as possible to resolve misunderstanding of the subject material and prevent confusion  However, in a Humanities MOOC, instructors allow for the students to explore open-ended discussions and debate among themselves  Such instructors may prefer to intervene later in the discussion to encourage further discussion or resolve conflicts among students  We therefore propose attention models to infer the latent context, i e , the series of posts that trigger an intervention  Earlier studies on MOOC forum intervention either model the entire context or require the context size to be specified explicitly 
Problem Statement
A thread INLINEFORM0 consists of a series of posts INLINEFORM1 through INLINEFORM2 where INLINEFORM3 is an instructor's post when INLINEFORM4 is intervened, if applicable  INLINEFORM5 is considered intervened if an instructor had posted at least once  The problem of predicting instructor intervention is cast as a binary classification problem  Intervened threads are predicted as 1 given while non-intervened threads are predicted as 0 given posts INLINEFORM6 through INLINEFORM7  
The primary problem leads to a secondary problem of inferring the appropriate amount of context to intervene  We define a context INLINEFORM0 of a post INLINEFORM1 as a series of linear contiguous posts INLINEFORM2 through INLINEFORM3 where INLINEFORM4   The problem of inferring context is to identify context INLINEFORM5 from a set of candidate contexts INLINEFORM6  
Modelling Context in Forums
Context has been used and modelled in various ways for different problems in discussion forums  In a work on a closely related problem of forum thread retrieval BIBREF2 models context using inter-post discourse e g , Question-Answer  BIBREF3 models the structural dependencies and relationships between forum posts using a conditional random field in their problem to infer the reply structure  Unlike BIBREF2 , BIBREF3 can be used to model any structural dependency and is, therefore, more general  In this paper, we seek to infer general dependencies between a reply and its previous context whereas BIBREF3 inference is limited to pairs of posts  More recently BIBREF4 proposed a context based model which factorises attention over threads of different lengths  Differently, we do not model length but the context before a post  However, our attention models cater to threads of all lengths 
BIBREF5 proposed graph structured LSTM to model the explicit reply structure in Reddit forums  Our work does not assume access to such a reply structure because 1) Coursera forums do not provide one and 2) forum participants often err by posting their reply to a different post than that they intended  At the other end of the spectrum are document classification models that do not assume structure in the document layout but try to infer inherent structure in the natural language, viz, words, sentences, paragraphs and documents  Hierarchical attention BIBREF6 is a well know recent work that classifies documents using a multi-level LSTMs with attention mechanism to select important units at each hierarchical level  Differently, we propose a hierarchical model that encodes layout hierarchy between a post and a thread but also infers reply structure using a attention mechanism since the layout does not reliably encode it 
Instructor Intervention in MOOC forums
The problem of predicting instructor intervention in MOOCs was proposed by BIBREF0   Later BIBREF7 evaluated baseline models by BIBREF0 over a larger corpus and found the results to vary widely across MOOCs  Since then subsequent works have used similar diverse evaluations on the same prediction problem BIBREF1 , BIBREF8   BIBREF1 proposed models with discourse features to enable better prediction over unseen MOOCs  BIBREF8 recently showed interventions on Coursera forums to be biased by the position at which a thread appears to an instructor viewing the forum interface and proposed methods for debiased prediction 
While all works since BIBREF0 address key limitations in this line of research, they have not investigated the role of structure and sequence in the threaded discussion in predicting instructor interventions  BIBREF0 proposed probabilistic graphical models to model structure and sequence  They inferred vocabulary dependent latent post categories to model the thread sequence and infer states that triggered intervention  Their model, however, requires a hyperparameter for the number of latent states  It is likely that their empirically reported setting will not generalise due to their weak evaluation BIBREF7   In this paper, we propose models to infer the context that triggers instructor intervention that does not require context lengths to be set apriori  All our proposed models generalise over modelling assumptions made by BIBREF0  
For the purpose of comparison against a state-of-the-art and competing baselines we choose BIBREF7 since BIBREF0 's system and data are not available for replication 
Data and Preprocessing
We evaluate our proposed models over a corpus of 12 MOOC iterations (offerings) on Coursera org In partnership with Coursera and in line with its Terms of Service, we obtained the data for use in our academic research  Following prior work BIBREF7 we evaluate over a diverse dataset to represent MOOCs of varying sizes, instructor styles, instructor team sizes and number of threads intervened  We only include threads from sub-forums on Lecture, Homework, Quiz and Exam  We also normalise and label sub-forums with other non-standard names (e g , Assignments instead of Homework) into of the four said sub-forums  Threads on general discussion, meet and greet and other custom sub-forums for social chitchat are omitted as our focus is to aid instructors on intervening on discussion on the subject matter  We also exclude announcement threads and other threads started by instructors since they are not interventions  We preprocess each thread by replacing URLs, equations and other mathematical formulae and references to timestamps in lecture videos by tokens INLINEFORM0 URL INLINEFORM1 , INLINEFORM2 MATH INLINEFORM3 , INLINEFORM4 TIMEREF INLINEFORM5 respectively

ground_truth: Answer with content missing: (Data section) Chinese with version 5.1 of the Chinese Penn Treebank (CTB)

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

Please answer the questions based on the text provided below. Do the authors hypothesize that humans' robustness to noise is due to their general knowledge?
Context 3:  We also truncate intervened threads to only include posts before the first instructor post since the instructor's and subsequent posts will bias the prediction due to the instructor's post 
Model
The key innovation of our work is to decompose the intervention prediction problem into a two-stage model that first explicitly tries to discover the proper context to which a potential intervention could be replying to, and then, predict the intervention status  This model implicitly assesses the importance (or urgency) of the existing thread's context to decide whether an intervention is necessary  For example in Figure SECREF1 , prior to the instructor's intervention, the ultimate post (Post #6) by Student 2 already acknowledged the OP's gratitude for his answer  In this regard, the instructor may have decided to use this point to summarize the entire thread to consolidate all the pertinent positions  Here, we might assume that the instructor's reply takes the entire thread (Posts #1–6) as the context for her reply 
This subproblem of inferring the context scope is where our innovation centers on  To be clear, in order to make the prediction that a instruction intervention is now necessary on a thread, the instructor's reply is not yet available — the model predicts whether a reply is necessary — so in the example, only Posts #1–6 are available in the problem setting  To infer the context, we have to decide which subsequence of posts are the most plausible motivation for an intervention 
Recent work in deep neural modeling has used an attention mechanism as a focusing query to highlight specific items within the input history that significantly influence the current decision point  Our work employs this mechanism – but with a twist: due to the fact that the actual instructor intervention is not (yet) available at the decision timing, we cannot use any actual intervention to decide the context  To employ attention, we must then employ a surrogate text as the query to train our prediction model  Our model variants model assess the suitability of such surrogate texts for the attention mechanism basis 
Congruent with the representation of the input forums, in all our proposed models, we encode the discussion thread hierarchically  We first build representations for each post by passing pre-trained word vector representations from GloVe BIBREF9 for each word through an LSTM BIBREF10 , INLINEFORM0   We use the last layer output of the LSTM as a representation of the post  We refer this as the post vector INLINEFORM1  
Then each post INLINEFORM0 is passed through another LSTM, INLINEFORM1 , whose last layer output forms the encoding of the entire thread  Hidden unit outputs of INLINEFORM2 represent the contexts INLINEFORM3 ; that is, snapshots of the threads after each post, as shown in Figure FIGREF1  
The INLINEFORM0 and INLINEFORM1 together constitute the hierarchical LSTM (hLSTM) model  This general hLSTM model serves as the basis for our model exploration in the rest of this section 
Contextual Attention Models
When they intervene, instructors either pay attention to a specific post or a series of posts, which trigger their reply  However, instructors rarely explicitly indicate to which post(s) their intervention is in relation to  This is the case in our corpus, party due to Coursera's user interface which only allows for single level comments (see Figure FIGREF2 )  Based solely on the binary, thread-level intervention signal, our secondary objective seeks to infer the appropriate context – represented by a sequence of posts – as the basis for the intervention 
We only consider linear contiguous series of posts starting with the thread's original post to constitute to a context; e g , INLINEFORM0   This is a reasonable as MOOC forum posts always reply to the original post or to a subsequent post, which in turn replies to the original post  This is in contrast to forums such as Reddit that have a tree or graph-like structure that require forum structure to be modelled explicitly, such as in BIBREF5  
We propose three neural attention BIBREF11 variants based on how an instructor might attend and reply to a context in a thread: the ultimate, penultimate and any post attention models  We review each of these in turn 
Ultimate Post Attention (UPA) Model  In this model we attend to the context represented by hidden state of the INLINEFORM0   We use the post prior to the instructor's reply as a query over the contexts INLINEFORM1 to compute attention weights INLINEFORM2 , which are then used to compute the attended context representation INLINEFORM3 (recall again that the intervention text itself is not available for this purpose)  This attention formulation makes an equivalence between the final INLINEFORM4 post and the prospective intervention, using Post INLINEFORM5 as the query for finding the appropriate context INLINEFORM6 , inclusive of itself INLINEFORM7   Said in another way, UPA uses the most recent content in the thread as the attentional query for context 
For example, if post INLINEFORM0 is the instructor's reply, post INLINEFORM1 will query over the contexts INLINEFORM2 and INLINEFORM3   The model schematic is shown in Figure FIGREF12  
The attended context representations are computed as: DISPLAYFORM0
The INLINEFORM0 representation is then passed through a fully connected softmax layer to yield the binary prediction 
Penultimate Post Attention (PPA) Model  While the UPA model uses the most recent text and makes the ultimate post itself available as potential context, our the ultimate post may be better modeled as having any of its prior posts as potential context  Penultimate Post Attention (PPA) variant does this  The schematic and the equations for the PPA model are obtained by summing over contexts INLINEFORM0 in Equation EQREF10 and Figure FIGREF12   While we could properly model such a context inference decision with any post INLINEFORM1 and prospective contexts INLINEFORM2 (where INLINEFORM3 is a random post), it makes sense to use the penultimate post, as we can make the most information available to the model for the context inference 
The attended context representations are computed as: DISPLAYFORM0
Any Post Attention (APA) Model  APA further relaxes both UPA and PPA, allowing APA to generalize and hypothesize that the prospective instructor intervention is based on the context that any previous post INLINEFORM0 replied to  In this model, each post INLINEFORM1 is set as a query to attend to its previous context INLINEFORM2   For example, INLINEFORM3 will attend to INLINEFORM4   Different from standard attention mechanisms, APA attention weights INLINEFORM5 are obtained by normalising interaction matrix over the different queries 
In APA, the attention context INLINEFORM0 is computed via: DISPLAYFORM0
Evaluation
The baseline and the models are evaluated on a corpus of 12 MOOC discussion forums  We train on 80% of the training data and report evaluation results on the held-out 20% of test data  We report INLINEFORM0 scores on the positive class (interventions), in line with prior work  We also argue that recall of the positive class is more important than precision, since it is costlier for instructors to miss intervening on a thread than spending irrelevant time intervening on a less critical threads due to false positives 
Model hyperpameter settings  All proposed and baseline neural models are trained using Adam optimizer with a learning rate of 0 001  We used cross-entropy as loss function  Importantly we updated the model parameters during training after each instance as in vanilla stochastic gradient descent; this setting was practical since data on most courses had only a few hundred instances enabling convergence within a reasonable training time of a few hours (see Table TABREF15 , column 2)  Models were trained for a single epoch as most of our courses with a few hundred thread converged after a single epoch  We used 300-dimensional GloVe vectors and permitted the embeddings to be updated during the model's end-to-end training  The hidden dimension size of both INLINEFORM0 and INLINEFORM1 are set to 128 for all the models 
Baselines  We compare our models against a neural baseline models, hierarchical LSTM (hLSTM), with the attention ablated but with access to the complete context, and a strong, open-sourced feature-rich baseline BIBREF7   We choose BIBREF7 over other prior works such as BIBREF0 since we do not have access to the dataset or the system used in their papers for replication

ground_truth: Yes

Yes

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 was the previous state of the art for this task?
Context 3:  BIBREF7 is a logistic regression classifier with features inclusive of bag-of-words representation of the unigrams and thread length, normalised counts of agreements to previous posts, counts of non-lexical reference items such as URLs, and the Coursera forum type in which a thread appeared  We also report aggregated results from a hLSTM model with access only to the last post as context for comparison  Table TABREF17 compares the performance of these baselines against our proposed methods 
Results
Table TABREF15 shows performance of all our proposed models and the neural baseline over our 12 MOOC dataset  Our models of UPA, PPA individually better the baseline by 5 and 2% on INLINEFORM0 and 3 and 6% on recall respectively  UPA performs the best in terms of INLINEFORM1 on average while PPA performs the best in terms of recall on average  At the individual course level, however, the results are mixed  UPA performs the best on INLINEFORM2 on 5 out of 12 courses, PPA on 3 out 12 courses, APA 1 out of 12 courses and the baseline hLSTM on 1  PPA performs the best on recall on 7 out of the 12 courses  We also note that course level performance differences correlate with the course size and intervention ratio (hereafter, i ratio), which is the ratio of intervened to non-intervened threads  UPA performs better than PPA and APA on low intervention courses (i ratio INLINEFORM3 0 25) mainly because PPA and APA's performance drops steeply when i ratio drops (see col 2 parenthesis and INLINEFORM4 of PPA and APA)  While all the proposed models beat the baseline on every course except casebased-2  On medicalneuro-2 and compilers-4 which have the lowest i ratio among the 12 courses none of the neural models better the reported baseline BIBREF7 (course level not scores not shown in this paper)  The effect is pronounced in compilers-4 course where none of the neural models were able to predict any intervened threads  This is due to the inherent weakness of standard neural models, which are unable to learn features well enough when faced with sparse data 
The best performance of UPA indicates that the reply context of the instructor's post INLINEFORM0 correlates strongly with that of the previous post INLINEFORM1   This is not surprising since normal conversations are typically structured that way 
Discussion
In order to further understand the models' ability to infer the context and its effect on intervention prediction, we further investigate the following research questions 
RQ1  Does context inference help intervention prediction?
In order to understand if context inference is useful to intervention prediction, we ablate the attention components and experiment with the vanilla hierarchical LSTM model  Row 3 of Table TABREF17 shows the macro averaged result from this experiment  The UPA and PPA attention models better the vanilla hLSTM by 5% and 2% on average in INLINEFORM0 respectively  Recall that the vanilla hLSTM already has access to a context consisting of all posts (from INLINEFORM1 through INLINEFORM2 )  In contrast, the UPA and PPA models selectively infers a context for INLINEFORM3 and INLINEFORM4 posts, respectively, and use it to predict intervention  The improved performance of our attention models that actively select their optimal context, over a model with the complete thread as context, hLSTM, shows that the context inference improves intervention prediction over using the default full context 
RQ2  How well do the models perform across threads of different lengths? To understand the models' prediction performance across threads of different lengths, we bin threads by length and study the models' recall  We choose three courses, ml-5, rprog-3 and calc-1, from our corpus of 12 with the highest number of positive instances ( INLINEFORM0 100 threads)  We limit our analysis to these since binning renders courses with fewer positive instances sparse  Figure FIGREF18 shows performance across thread lengths from 1 through 7 posts and INLINEFORM1 posts  Clearly, the UPA model performs much better on shorter threads than on longer threads while PPA and APA works better on longer threads  Although, UPA is the best performing model in terms of overall INLINEFORM2 its performance drops steeply on threads of length INLINEFORM3   UPA's overall best performance is because most of the interventions in the corpus happen after one post  To highlight the performance of APA we show an example from smac-1 in Figure FIGREF22 with nine posts which was predicted correctly as intervened by APA but not by other models  Threads shows students confused over a missing figure in a homework  The instructor finally shows up, though late, to resolve the confusion 
RQ3  Do models trained with different context lengths perform better than when trained on a single context length?
We find that context length has a regularising effect on the model's performance at test time  This is not surprising since models trained with threads of single context length will not generalise to infer different context lengths  Row 4 of Table TABREF17 shows a steep performance drop in training by classifier with all threads truncated to a context of just one post, INLINEFORM0 , the post immediately preceding the intervened post  We also conducted an experiment with a multi-objective loss function with an additive cross-entropy term where each term computes loss from a model with context limited to a length of 3  We chose 3 since intervened threads in all the courses had a median length between 3 and 4  We achieved an INLINEFORM1 of 0 45 with a precision of 0 47 and recall of 0 43  This achieves a performance comparable to that of the BIBREF7 with context length set to only to 3  This approach of using infinitely many loss terms for each context length from 1 through the maximum thread length in a course is naive and not practical  We only use this model to show the importance of training the model with loss from threads of different lengths to prevent models overfitting to threads of specific context lengths 
Conclusion
We predict instructor intervention on student discussions by first inferring the optimal size of the context needed to decide on the intervention decision for the intervened post  We first show that a structured representation of the complete thread as the context is better than a bag-of-words, feature-rich representation  We then propose attention-based models to infer and select a context – defined as a contiguous subsequence of student posts – to improve over a model that always takes the complete thread as a context to prediction intervention  Our Any Post Attention (APA) model enables instructors to tune the model to predict intervention early or late  We posit our APA model will enable MOOC instructors employing varying pedagogical styles to use the model equally well  We introspect the attention models' performance across threads of varying lengths and show that APA predicts intervention on longer threads, which possesses more candidate contexts, better 
We note that the recall of the predictive models for longer threads (that is, threads of length greater 2) can still be improved  Models perform differently between shorter and longer length  An ensemble model or a multi-objective loss function is thus planned in our future work to better prediction on such longer threads 

ground_truth: hLSTM

hLSTM