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. How is the political bias of different sources included in the model?
Context 1: Introduction and related work
In recent years there has been increasing interest on the issue of disinformation spreading on online social media  Global concern over false (or "fake") news as a threat to modern democracies has been frequently raised–ever since 2016 US Presidential elections–in correspondence of events of political relevance, where the proliferation of manipulated and low-credibility content attempts to drive and influence people opinions BIBREF0BIBREF1BIBREF2BIBREF3 
Researchers have highlighted several drivers for the diffusion of such malicious phenomenon, which include human factors (confirmation bias BIBREF4, naive realism BIBREF5), algorithmic biases (filter bubble effect BIBREF0), the presence of deceptive agents on social platforms (bots and trolls BIBREF6) and, lastly, the formation of echo chambers BIBREF7 where people polarize their opinions as they are insulated from contrary perspectives 
The problem of automatically detecting online disinformation news has been typically formulated as a binary classification task (i e  credible vs non-credible articles), and tackled with a variety of different techniques, based on traditional machine learning and/or deep learning, which mainly differ in the dataset and the features they employ to perform the classification  We may distinguish three approaches: those built on content-based features, those based on features extracted from the social context, and those which combine both aspects  A few main challenges hinder the task, namely the impossibility to manually verify all news items, the lack of gold-standard datasets and the adversarial setting in which malicious content is created BIBREF3BIBREF6 
In this work we follow the direction pointed out in a few recent contributions on the diffusion of disinformation compared to traditional and objective information  These have shown that false news spread faster and deeper than true news BIBREF8, and that social bots and echo chambers play an important role in the diffusion of malicious content BIBREF6, BIBREF7  Therefore we focus on the analysis of spreading patterns which naturally arise on social platforms as a consequence of multiple interactions between users, due to the increasing trend in online sharing of news BIBREF0 
A deep learning framework for detection of fake news cascades is provided in BIBREF9, where the authors refer to BIBREF8 in order to collect Twitter cascades pertaining to verified false and true rumors  They employ geometric deep learning, a novel paradigm for graph-based structures, to classify cascades based on four categories of features, such as user profile, user activity, network and spreading, and content  They also observe that a few hours of propagation are sufficient to distinguish false news from true news with high accuracy  Diffusion cascades on Weibo and Twitter are analyzed in BIBREF10, where authors focus on highlighting different topological properties, such as the number of hops from the source or the heterogeneity of the network, to show that fake news shape diffusion networks which are highly different from credible news, even at early stages of propagation 
In this work, we consider the results of BIBREF11 as our baseline  The authors use off-the-shelf machine learning classifiers to accurately classify news articles leveraging Twitter diffusion networks  To this aim, they consider a set of basic features which can be qualitatively interpreted w r t to the social behavior of users sharing credible vs non-credible information  Their methodology is overall in accordance with BIBREF12, where authors successfully detect Twitter astroturfing content, i e  political campaigns disguised as spontaneous grassroots, with a machine learning framework based on network features 
In this paper, we propose a classification framework based on a multi-layer formulation of Twitter diffusion networks  For each article we disentangle different social interactions on Twitter, namely tweets, retweets, mentions, replies and quotes, to accordingly build a diffusion network composed of multiple layers (on for each type of interaction), and we compute structural features separately for each layer  We pick a set of global network properties from the network science toolbox which can be qualitatively explained in terms of social dimensions and allow us to encode different networks with a tuple of features  These include traditional indicators, e g  network density, number of strong/weak connected components and diameter, and more elaborated ones such as main K-core number BIBREF13 and structural virality BIBREF14  Our main research question is whether the use of a multi-layer, disentangled network yields a significant advance in terms of classification accuracy over a conventional single-layer diffusion network  Additionally, we are interested in understanding which of the above features, and in which layer, are most effective in the classification task 
We perform classification experiments with an off-the-shelf Logistic Regression model on two different datasets of mainstream and disinformation news shared on Twitter respectively in the United States and in Italy during 2019  In the former case we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets  Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles)  We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains  We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries 
The outline of this paper is the following: we first formulate the problem and describe data collection, network representation and structural properties employed for the classification; then we provide experimental results–classification performances, layer and feature importance analyses and a temporal classification evaluation–and finally we draw conclusions and future directions 
Methodology ::: Disinformation and mainstream news
In this work we formulate our classification problem as follows: given two classes of news articles, respectively $D$ (disinformation) and $M$ (mainstream), a set of news articles $A_i$ and associated class labels $C_i \in \lbrace D,M\rbrace $, and a set of tweets $\Pi _i=\lbrace T_i^1, T_i^2,    \rbrace $ each of which contains an Uniform Resource Locator (URL) pointing explicitly to article $A_i$, predict the class $C_i$ of each article $A_i$  There is huge debate and controversy on a proper taxonomy of malicious and deceptive information BIBREF1BIBREF2BIBREF15BIBREF16BIBREF17BIBREF3BIBREF11  In this work we prefer the term disinformation to the more specific fake news to refer to a variety of misleading and harmful information  Therefore, we follow a source-based approach, a consolidated strategy also adopted by BIBREF6BIBREF16BIBREF2BIBREF1, in order to obtain relevant data for our analysis  We collected:
Disinformation articles, published by websites which are well-known for producing low-credibility content, false and misleading news reports as well as extreme propaganda and hoaxes and flagged as such by reputable journalists and fact-checkers;
Mainstream news, referring to traditional news outlets which deliver factual and credible information 
We believe that this is currently the most reliable classification approach, but it entails obvious limitations, as disinformation outlets may also publish true stories and likewise misinformation is sometimes reported on mainstream media  Also, given the choice of news sources, we cannot test whether our methodology is able to classify disinformation vs factual but not mainstream news which are published on niche, non-disinformation outlets 
Methodology ::: US dataset
We collected tweets associated to a dozen US mainstream news websites, i e  most trusted sources described in BIBREF18, with the Streaming API, and we referred to Hoaxy API BIBREF16 for what concerns tweets containing links to 100+ US disinformation outlets  We filtered out articles associated to less than 50 tweets  The resulting dataset contains overall $\sim $1 7 million tweets for mainstream news, collected in a period of three weeks (February 25th, 2019-March 18th, 2019), which are associated to 6,978 news articles, and $\sim $1 6 million tweets for disinformation, collected in a period of three months (January 1st, 2019-March 18th, 2019) for sake of balance of the two classes, which hold 5,775 distinct articles  Diffusion censoring effects BIBREF14 were correctly taken into account in both collection procedures  We provide in Figure FIGREF4 the distribution of articles by source and political bias for both news domains 
As it is reported that conservatives and liberals exhibit different behaviors on online social platforms BIBREF19BIBREF20BIBREF21, we further assigned a political bias label to different US outlets (and therefore news articles) following the procedure described in BIBREF2  In order to assess the robustness of our method, we performed classification experiments by training only on left-biased (or right-biased) outlets of both disinformation and mainstream domains and testing on the entire set of sources, as well as excluding particular sources that outweigh the others in terms of samples to avoid over-fitting 
Methodology ::: Italian dataset
For what concerns the Italian scenario we first collected tweets with the Streaming API in a 3-week period (April 19th, 2019-May 5th, 2019), filtering those containing URLs pointing to Italian official newspapers websites as described in BIBREF22; these correspond to the list provided by the association for the verification of newspaper circulation in Italy (Accertamenti Diffusione Stampa)  We instead referred to the dataset provided by BIBREF23 to obtain a set of tweets, collected continuously since January 2019 using the same Twitter endpoint, which contain URLs to 60+ Italian disinformation websites  In order to get balanced classes (April 5th, 2019-May 5th, 2019), we retained data collected in a longer period w r t to mainstream news  In both cases we filtered out articles with less than 50 tweets; overall this dataset contains $\sim $160k mainstream tweets, corresponding to 227 news articles, and $\sim $100k disinformation tweets, corresponding to 237 news articles  We provide in Figure FIGREF5 the distribution of articles according to distinct sources for both news domains  As in the US dataset, we took into account censoring effects BIBREF14 by excluding tweets published before (left-censoring) or after two weeks (right-censoring) from the beginning of the collection process 
The different volumes of news shared on Twitter in the two countries are due both to the different population size of US and Italy (320 vs 60 millions) but also to the different usage of Twitter platform (and social media in general) for news consumption BIBREF24  Both datasets analyzed in this work are available from the authors on request 
A crucial aspect in our approach is the capability to fully capturing sharing cascades on Twitter associated to news articles  It has been reported BIBREF25 that the Twitter streaming endpoint filters out tweets matching a given query if they exceed 1% of the global daily volume of shared tweets, which nowadays is approximately $5\cdot 10^8$; however, as we always collected less than $10^6$ tweets per day, we did not incur in this issue and we thus gathered 100% of tweets matching our query 
Methodology ::: Building diffusion networks
We built Twitter diffusion networks following an approach widely adopted in the literature BIBREF6BIBREF17BIBREF2  We remark that there is an unavoidable limitation in Twitter Streaming API, which does not allow to retrieve true re-tweeting cascades because re-tweets always point to the original source and not to intermediate re-tweeting users BIBREF8BIBREF14; thus we adopt the only viable approach based on Twitter's public availability of data  Besides, by disentangling different interactions with multiple layers we potentially reduce the impact of this limitation on the global network properties compared to the single-layer approach used in our baseline 
Using the notation described in BIBREF26  we employ a multi-layer representation for Twitter diffusion networks  Sociologists have indeed recognized decades ago that it is crucial to study social systems by constructing multiple social networks where different types of ties among same individuals are used BIBREF27  Therefore, for each news article we built a multi-layer diffusion network composed of four different layers, one for each type of social interaction on Twitter platform, namely retweet (RT), reply (R), quote (Q) and mention (M), as shown in Figure FIGREF11  These networks are not necessarily node-aligned, i e  users might be missing in some layers  We do not insert "dummy" nodes to represent all users as it would have severe impact on the global network properties (e g  number of weakly connected components)  Alternatively one may look at each multi-layer diffusion network as an ensemble of individual graphs BIBREF26; since global network properties are computed separately for each layer, they are not affected by the presence of any inter-layer edges 
In our multi-layer representation, each layer is a directed graph where we add edges and nodes for each tweet of the layer type, e g  for the RT layer: whenever user $a$ retweets account $b$ we first add nodes $a$ and $b$ if not already present in the RT layer, then we build an edge that goes from $b$ to $a$ if it does not exists or we increment the weight by 1  Similarly for the other layers: for the R layer edges go from user $a$ (who replies) to user $b$, for the Q layer edges go from user $b$ (who is quoted by) to user $a$ and for the M layer edges go from user $a$ (who mentions) to user $b$ 
Note that, by construction, our layers do not include isolated nodes; they correspond to "pure tweets", i e  tweets which have not originated any interactions with other users  However, they are present in our dataset, and their number is exploited for classification, as described below 
Methodology ::: Global network properties
We used a set of global network indicators which allow us to encode each network layer by a tuple of features  Then we simply concatenated tuples as to represent each multi-layer network with a single feature vector  We used the following global network properties:
Number of Strongly Connected Components (SCC): a Strongly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $u,v$ there is a path in each direction ($u\rightarrow v$, $v\rightarrow u$) 
Size of the Largest Strongly Connected Component (LSCC): the number of nodes in the largest strongly connected component of a given graph 
Number of Weakly Connected Components (WCC): a Weakly Connected Component of a directed graph is a maximal (sub)graph where for each pair of vertices $(u, v)$ there is a path $u \leftrightarrow v$ ignoring edge directions 
Size of the Largest Weakly Connected Component (LWCC): the number of nodes in the largest weakly connected component of a given graph 
Diameter of the Largest Weakly Connected Component (DWCC): the largest distance (length of the shortest path) between two nodes in the (undirected version of) largest weakly connected component of a graph 
Average Clustering Coefficient (CC): the average of the local clustering coefficients of all nodes in a graph; the local clustering coefficient of a node quantifies how close its neighbours are to being a complete graph (or a clique)  It is computed according to BIBREF28 
Main K-core Number (KC): a K-core BIBREF13 of a graph is a maximal sub-graph that contains nodes of internal degree $k$ or more; the main K-core number is the highest value of $k$ (in directed graphs the total degree is considered) 
Density (d): the density for directed graphs is $d=\frac{|E|}{|V||V-1|}$, where $|E|$ is the number of edges and $|N|$ is the number of vertices in the graph; the density equals 0 for a graph without edges and 1 for a complete graph 
Structural virality of the largest weakly connected component (SV): this measure is defined in BIBREF14 as the average distance between all pairs of nodes in a cascade tree or, equivalently, as the average depth of nodes, averaged over all nodes in turn acting as a root; for $|V| > 1$ vertices, $SV=\frac{1}{|V||V-1|}\sum _i\sum _j d_{ij}$ where $d_{ij}$ denotes the length of the shortest path between nodes $i$ and $j$  This is equivalent to compute the Wiener's index BIBREF29 of the graph and multiply it by a factor $\frac{1}{|V||V-1|}$  In our case we computed it for the undirected equivalent graph of the largest weakly connected component, setting it to 0 whenever $V=1$ 
We used networkx Python package BIBREF30 to compute all features  Whenever a layer is empty  we simply set to 0 all its features  In addition to computing the above nine features for each layer, we added two indicators for encoding information about pure tweets, namely the number T of pure tweets (containing URLs to a given news article) and the number U of unique users authoring those tweets  Therefore, a single diffusion network is represented by a vector with $9\cdot 4+2=38$ entries 
Methodology ::: Interpretation of network features and layers
Aforementioned network properties can be qualitatively explained in terms of social footprints as follows: SCC correlates with the size of the diffusion network, as the propagation of news occurs in a broadcast manner most of the time, i e  re-tweets dominate on other interactions, while LSCC allows to distinguish cases where such mono-directionality is somehow broken  WCC equals (approximately) the number of distinct diffusion cascades pertaining to each news article, with exceptions corresponding to those cases where some cascades merge together via Twitter interactions such as mentions, quotes and replies, and accordingly LWCC and DWCC equals the size and the depth of the largest cascade  CC corresponds to the level of connectedness of neighboring users in a given diffusion network whereas KC identifies the set of most influential users in a network and describes the efficiency of information spreading BIBREF17  Finally, d describes the proportions of potential connections between users which are actually activated and SV indicates whether a news item has gained popularity with a single and large broadcast or in a more viral fashion through multiple generations 
For what concerns different Twitter actions, users primarily interact with each other using retweets and mentions BIBREF20 
The former are the main engagement activity and act as a form of endorsement, allowing users to rebroadcast content generated by other users BIBREF31  Besides, when node B retweets node A we have an implicit confirmation that information from A appeared in B's Twitter feed BIBREF12  Quotes are simply a special case of retweets with comments 
Mentions usually include personal conversations as they allow someone to address a specific user or to refer to an individual in the third person; in the first case they are located at the beginning of a tweet and they are known as replies, otherwise they are put in the body of a tweet BIBREF20  The network of mentions is usually seen as a stronger version of interactions between Twitter users, compared to the traditional graph of follower/following relationships BIBREF32 
Experiments ::: Setup
We performed classification experiments using a basic off-the-shelf classifier, namely Logistic Regression (LR) with L2 penalty; this also allows us to compare results with our baseline  We applied a standardization of the features and we used the default configuration for parameters as described in scikit-learn package BIBREF33  We also tested other classifiers (such as K-Nearest Neighbors, Support Vector Machines and Random Forest) but we omit results as they give comparable performances  We remark that our goal is to show that a very simple machine learning framework, with no parameter tuning and optimization, allows for accurate results with our network-based approach 
We used the following evaluation metrics to assess the performances of different classifiers (TP=true positives, FP=false positives, FN=false negatives):
Precision = $\frac{TP}{TP+FP}$, the ability of a classifier not to label as positive a negative sample 
Recall = $\frac{TP}{TP+FN}$, the ability of a classifier to retrieve all positive samples 
F1-score = $2 \frac{\mbox{Precision} \cdot \mbox{Recall}}{\mbox{Precision} + \mbox{Recall}}$, the harmonic average of Precision and Recall 
Area Under the Receiver Operating Characteristic curve (AUROC); the Receiver Operating Characteristic (ROC) curve BIBREF34, which plots the TP rate versus the FP rate, shows the ability of a classifier to discriminate positive samples from negative ones as its threshold is varied; the AUROC value is in the range $[0, 1]$, with the random baseline classifier holding AUROC$=0 5$ and the ideal perfect classifier AUROC$=1$; thus larger AUROC values (and steeper ROCs) correspond to better classifiers 
In particular we computed so-called macro average–simple unweighted mean–of these metrics evaluated considering both labels (disinformation and mainstream)  We employed stratified shuffle split cross validation (with 10 folds) to evaluate performances 
Finally, we partitioned networks according to the total number of unique users involved in the sharing, i e  the number of nodes in the aggregated network represented with a single-layer representation considering together all layers and also pure tweets  A breakdown of both datasets according to size class (and political biases for the US scenario) is provided in Table 1 and Table 2 
Experiments ::: Classification performances
In Table 3 we first provide classification performances on the US dataset for the LR classifier evaluated on the size class described in Table 1  We can observe that in all instances our methodology performs better than a random classifier (50% AUROC), with AUROC values above 85% in all cases 
For what concerns political biases, as the classes of mainstream and disinformation networks are not balanced (e g , 1,292 mainstream and 4,149 disinformation networks with right bias) we employ a Balanced Random Forest with default parameters (as provided in imblearn Python package BIBREF35)  In order to test the robustness of our methodology, we trained only on left-biased networks or right-biased networks and tested on the entire set of sources (relative to the US dataset); we provide a comparison of AUROC values for both biases in Figure 4  We can notice that our multi-layer approach still entails significant results, thus showing that it can accurately distinguish mainstream news from disinformation regardless of the political bias  We further corroborated this result with additional classification experiments, that show similar performances, in which we excluded from the training/test set two specific sources (one at a time and both at the same time) that outweigh the others in terms of data samples–respectively "breitbart com" for right-biased sources and "politicususa com" for left-biased ones 
We performed classification experiments on the Italian dataset using the LR classifier and different size classes (we excluded $[1000, +\infty )$ which is empty); we show results for different evaluation metrics in Table 3  We can see that despite the limited number of samples (one order of magnitude smaller than the US dataset) the performances are overall in accordance with the US scenario  As shown in Table 4, we obtain results which are much better than our baseline in all size classes (see Table 4):
In the US dataset our multi-layer methodology performs much better in all size classes except for large networks ($[1000, +\infty )$ size class), reaching up to 13% improvement on smaller networks ($[0, 100)$ size class);
In the IT dataset our multi-layer methodology outperforms the baseline in all size classes, with the maximum performance gain (20%) on medium networks ($[100, 1000)$ size class); the baseline generally reaches bad performances compared to the US scenario 
Overall, our performances are comparable with those achieved by two state-of-the-art deep learning models for "fake news" detection BIBREF9BIBREF36 
Experiments ::: Layer importance analysis
In order to understand the impact of each layer on the performances of classifiers, we performed additional experiments considering separately each layer (we ignored T and U features relative to pure tweets)  In Table 5 we show metrics for each layer and all size classes, computed with a 10-fold stratified shuffle split cross validation, evaluated on the US dataset; in Figure 5 we show AUROC values for each layer compared with the general multi-layer approach  We can notice that both Q and M layers alone capture adequately the discrepancies of the two distinct news domains in the United States as they obtain good results with AUROC values in the range 75%-86%; these are comparable with those of the multi-layer approach which, nevertheless, outperforms them across all size classes 
We obtained similar performances for the Italian dataset, as the M layer obtains comparable performances w r t multi-layer approach with AUROC values in the range 72%-82%  We do not show these results for sake of conciseness 
Experiments ::: Feature importance analysis
We further investigated the importance of each feature by performing a $\chi ^2$ test, with 10-fold stratified shuffle split cross validation, considering the entire range of network sizes $[0, +\infty )$  We show the Top-5 most discriminative features for each country in Table 6 
We can notice the exact same set of features (with different relative orderings in the Top-3) in both countries; these correspond to two global network propertie–LWCC, which indicates the size of the largest cascade in the layer, and SCC, which correlates with the size of the network–associated to the same set of layers (Quotes, Retweets and Mentions) 
We further performed a $\chi ^2$ test to highlight the most discriminative features in the M layer of both countries, which performed equally well in the classification task as previously highlighted; also in this case we focused on the entire range of network sizes $[0, +\infty )$  Interestingly, we discovered exactly the same set of Top-3 features in both countries, namely LWCC, SCC and DWCC (which indicates the depth of the largest cascade in the layer) 
An inspection of the distributions of all aforementioned features revealed that disinformation news exhibit on average larger values than mainstream news 
We can qualitatively sum up these results as follows:
Sharing patterns in the two news domains exhibit discrepancies which might be country-independent and due to the content that is being shared 
Interactions in disinformation sharing cascades tends to be broader and deeper than in mainstream news, as widely reported in the literature BIBREF8BIBREF2BIBREF7 
Users likely make a different usage of mentions when sharing news belonging to the two domains, consequently shaping different sharing patterns 
Experiments ::: Temporal analysis
Similar to BIBREF9, we carried out additional experiments to answer the following question: how long do we need to observe a news spreading on Twitter in order to accurately classify it as disinformation or mainstream?
With this goal, we built several versions of our original dataset of multi-layer networks by considering in turn the following lifetimes: 1 hour, 6 hours, 12 hours, 1 day, 2 days, 3 days and 7 days; for each case, we computed the global network properties of the corresponding network and evaluated the LR classifier with 10-fold cross validation, separately for each lifetime (and considering always the entire set of networks)  We show corresponding AUROC values for both US and IT datasets in Figure 6 
We can see that in both countries news diffusion networks can be accurately classified after just a few hours of spreading, with AUROC values which are larger than 80% after only 6 hours of diffusion  These results are very promising and suggest that articles pertaining to the two news domains exhibit discrepancies in their sharing patterns that can be timely exploited in order to rapidly detect misleading items from factual information 
Conclusions
In this work we tackled the problem of the automatic classification of news articles in two domains, namely mainstream and disinformation news, with a language-independent approach which is based solely on the diffusion of news items on Twitter social platform  We disentangled different types of interactions on Twitter to accordingly build a multi-layer representation of news diffusion networks, and we computed a set of global network properties–separately for each layer–in order to encode each network with a tuple of features  Our goal was to investigate whether a multi-layer representation performs better than one layer BIBREF11, and to understand which of the features, observed at given layers, are most effective in the classification task 
Experiments with an off-the-shelf classifier such as Logistic Regression on datasets pertaining to two different media landscapes (US and Italy) yield very accurate classification results (AUROC up to 94%), even when accounting for the different political bias of news sources, which are far better than our baseline BIBREF11 with improvements up to 20%  Classification performances using single layers show that the layer of mentions alone entails better performance w r t other layers in both countries 
We also highlighted the most discriminative features across different layers in both countries; the results suggest that differences between the two news domains might be country-independent but rather due only to the typology of content shared, and that disinformation news shape broader and deeper cascades 
Additional experiments involving the temporal evolution of Twitter diffusion networks show that our methodology can accurate classify mainstream and disinformation news after a few hours of propagation on the platform 
Overall, our results prove that the topological features of multi-layer diffusion networks might be effectively exploited to detect online disinformation  We do not deny the presence of deceptive efforts to orchestrate the regular spread of information on social media via content amplification and manipulation BIBREF37BIBREF38  On the contrary, we postulate that such hidden forces might play to accentuate the discrepancies between the diffusion patterns of disinformation and mainstream news (and thus to make our methodology effective) 
In the future we aim to further investigate three directions: (1) employ temporal networks to represent news diffusion and apply classification techniques that take into account the sequential aspect of data (e g  recurrent neural networks); (2) carry out an extensive comparison of the diffusion of disinformation and mainstream news across countries to investigate deeper the presence of differences and similarities in sharing patterns; (3) leverage our network-based features in addition to state-of-the-art text-based approaches for "fake news" dete ction in order to deliver a real-world system to detect misleading and harmful information spreading on social media 
Context 2: Introduction
Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing  Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3   Recent neural conversation models BIBREF4 , BIBREF5 , BIBREF6 are even able to perform open-ended conversations  However, since they do not use explicit knowledge bases and do not perform inference, they often suffer from generic and dull responses BIBREF5 , BIBREF7   More recently, BIBREF8 and BIBREF9 proposed to use knowledge bases (KBs) to help generate responses for knowledge-grounded conversation  However, one major weakness of all existing chat systems is that they do not explicitly or implicitly learn new knowledge in the conversation process  This seriously limits the scope of their applications  In contrast, we humans constantly learn new knowledge in our conversations  Even if some existing systems can use very large knowledge bases either harvested from a large data source such as the Web or built manually, these KBs still miss a large number of facts (knowledge) BIBREF10   It is thus important for a chatbot to continuously learn new knowledge in the conversation process to expand its KB and to improve its conversation ability 
In recent years, researchers have studied the problem of KB completion, i e , inferring new facts (knowledge) automatically from existing facts in a KB  KB completion (KBC) is defined as a binary classification problem: Given a query triple, ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we want to predict whether the source entity INLINEFORM3 and target entity INLINEFORM4 can be linked by the relation INLINEFORM5   However, existing approaches BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 solve this problem under the closed-world assumption, i e , INLINEFORM6 , INLINEFORM7 and INLINEFORM8 are all known to exist in the KB  This is a major weakness because it means that no new knowledge or facts may contain unknown entities or relations  Due to this limitation, KBC is clearly not sufficient for knowledge learning in conversations because in a conversation, the user can say anything, which may contain entities and relations that are not already in the KB 
In this paper, we remove this assumption of KBC, and allow all INLINEFORM0 , INLINEFORM1 and INLINEFORM2 to be unknown  We call the new problem open-world knowledge base completion (OKBC)  OKBC generalizes KBC  Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations  In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting 
From the perspective of knowledge learning in conversations, essentially we can extract two key types of information, true facts and queries, from the user utterances  Queries are facts whose truth values need to be determined  Note that we do not study fact or relation extraction in this paper as there is an extensive work on the topic  (1) For a true fact, we will incorporate it into the KB  Here we need to make sure that it is not already in the KB, which involves relation resolution and entity linking  After a fact is added to the KB, we may predict that some related facts involving some existing relations in the KB may also be true (not logical implications as they can be automatically inferred)  For example, if the user says “Obama was born in USA,” the system may guess that (Obama, CitizenOf, USA) (meaning that Obama is a citizen of USA) could also be true based on the current KB  To verify this fact, it needs to solve a KBC problem by treating (Obama, CitizenOf, USA) as a query  This is a KBC problem because the fact (Obama, BornIn, USA) extracted from the original sentence has been added to the KB  Then Obama and USA are in the KB  If the KBC problem is solved, it learns a new fact (Obama, CitizenOf, USA) in addition to the extracted fact (Obama, BornIn, USA)  (2) For a query fact, e g , (Obama, BornIn, USA) extracted from the user question “Was Obama born in USA?” we need to solve the OKBC problem if any of “Obama, “BornIn”, or “USA" is not already in the KB 
We can see that OKBC is the core of a knowledge learning engine for conversation  Thus, in this paper, we focus on solving it  We assume that other tasks such as fact/relation extraction and resolution and guessing of related facts of an extracted fact are solved by other sub-systems 
We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation  Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge  If our knowledge does not allow us to draw a conclusion, we typically ask questions to others to acquire related knowledge and use it in inference  The process typically involves an inference strategy (a sequence of actions), which interleaves a sequence of processing and interactive actions  A processing action can be the selection of related facts, deriving inference chain, etc , that advances the inference process  An interactive action can be deciding what to ask, formulating a suitable question, etc , that enable us to interact  The process helps grow the knowledge over time and the gained knowledge enables us to communicate better in the future  We call this lifelong interactive learning and inference (LiLi)  Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning  LiLi should have the following capabilities:
This setting is ideal for many NLP applications like dialog and question-answering systems that naturally provide the scope for human interaction and demand real-time inference 
LiLi starts with the closed-world KBC approach path-ranking (PR) BIBREF11 , BIBREF17 and extends KBC in a major way to open-world knowledge base completion (OKBC)  For a relation INLINEFORM0 , PR works by enumerating paths (except single-link path INLINEFORM1 ) between entity-pairs linked by INLINEFORM2 in the KB and use them as features to train a binary classifier to predict whether a query INLINEFORM3 should be in the KB  Here, a path between two entities is a sequence of relations linking them  In our work, we adopt the latest PR method, C-PR BIBREF16 and extend it to make it work in the open-world setting  C-PR enumerates paths by performing bidirectional random walks over the KB graph while leveraging the context of the source-target entity-pair  We also adopt and extend the compositional vector space model BIBREF20 , BIBREF21 with continual learning capability for prediction 
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (e g , “(Obama, CitizenOf, USA), which means whether Obama a citizen of USA), LiLi interacts with the user (if needed) by dynamically formulating questions (see the interaction example in Figure 1, which will be further explained in §3) and leverages the interactively acquired knowledge (supporting facts (SFs) in the figure) for continued inference  To do so, LiLi formulates a query-specific inference strategy and executes it  We design LiLi in a Reinforcement Learning (RL) setting that performs sub-tasks like formulating and executing strategy, training a prediction model for inference, and knowledge retention for future use  To the best of our knowledge, our work is the first to address the OKBC problem and to propose an interactive learning mechanism to solve it in a continuous or lifelong manner  We empirically verify the effectiveness of LiLi on two standard real-world KBs: Freebase and WordNet  Experimental results show that LiLi is highly effective in terms of its predictive performance and strategy formulation ability 
Related Work
To the best of our knowledge, we are not aware of any knowledge learning system that can learn new knowledge in the conversation process  This section thus discusses other related work 
Among existing KB completion approaches, BIBREF20 extended the vector space model for zero-shot KB inference  However, the model cannot handle unknown entities and can only work on fixed set of unknown relations with known embeddings  Recently, BIBREF22 proposed a method using external text corpus to perform inference on unknown entities  However, the method cannot handle unknown relations  Thus, these methods are not suitable for our open-world setting  None of the existing KB inference methods perform interactive knowledge learning like LiLi  NELL BIBREF23 continuously updates its KB using facts extracted from the Web  Our task is very different as we do not do Web fact extraction (which is also useful)  We focus on user interactions in this paper  Our work is related to interactive language learning (ILL) BIBREF24 , BIBREF25 , but these are not about KB completion  The work in BIBREF26 allows a learner to ask questions in dialogue  However, this work used RL to learn about whether to ask the user or not  The “what to ask aspect" was manually designed by modeling synthetic tasks  LiLi formulates query-specific inference strategies which embed interaction behaviors  Also, no existing dialogue systems BIBREF4 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 employ lifelong learning to train prediction models by using information/knowledge retained in the past 
Our work is related to general lifelong learning in BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35 , BIBREF36   However, they learn only one type of tasks, e g , supervised, topic modeling or reinforcement learning (RL) tasks  None of them is suitable for our setting, which involves interleaving of RL, supervised and interactive learning  More details about lifelong learning can be found in the book BIBREF31  
Interactive Knowledge Learning (LiLi)
We design LiLi as a combination of two interconnected models: (1) a RL model that learns to formulate a query-specific inference strategy for performing the OKBC task, and (2) a lifelong prediction model to predict whether a triple should be in the KB, which is invoked by an action while executing the inference strategy and is learned for each relation as in C-PR  The framework improves its performance over time through user interaction and knowledge retention  Compared to the existing KB inference methods, LiLi overcomes the following three challenges for OKBC:
1  Mapping open-world to close-world  Being a closed-world method, C-PR cannot extract path features and learn a prediction model when any of INLINEFORM0 , INLINEFORM1 or INLINEFORM2 is unknown  LiLi solves this problem through interactive knowledge acquisition  If INLINEFORM3 is unknown, LiLi asks the user to provide a clue (an example of INLINEFORM4 )  And if INLINEFORM5 or INLINEFORM6 is unknown, LiLi asks the user to provide a link (relation) to connect the unknown entity with an existing entity (automatically selected) in the KB  We refer to such a query as a connecting link query (CLQ)  The acquired knowledge reduces OKBC to KBC and makes the inference task feasible 
2  Spareseness of KB  A main issue of all PR methods like C-PR is the connectivity of the KB graph  If there is no path connecting INLINEFORM0 and INLINEFORM1 in the graph, path enumeration of C-PR gets stuck and inference becomes infeasible  In such cases, LiLi uses a template relation (“@-?-@") as the missing link marker to connect entity-pairs and continues feature extraction  A path containing “@-?-@" is called an incomplete path  Thus, the extracted feature set contains both complete (no missing link) and incomplete paths  Next, LiLi selects an incomplete path from the feature set and asks the user to provide a link for path completion  We refer to such a query as missing link query (MLQ) 
3  Limitation in user knowledge  If the user is unable to respond to MLQs or CLQs, LiLi uses a guessing mechanism (discussed later) to fill the gap  This enables LiLi to continue its inference even if the user cannot answer a system question 
Components of LiLi
As lifelong learning needs to retain knowledge learned from past tasks and use it to help future learning BIBREF31 , LiLi uses a Knowledge Store (KS) for knowledge retention  KS has four components: (i) Knowledge Graph ( INLINEFORM0 ): INLINEFORM1 (the KB) is initialized with base KB triples (see §4) and gets updated over time with the acquired knowledge  (ii) Relation-Entity Matrix ( INLINEFORM2 ): INLINEFORM3 is a sparse matrix, with rows as relations and columns as entity-pairs and is used by the prediction model  Given a triple ( INLINEFORM4 , INLINEFORM5 , INLINEFORM6 ) INLINEFORM7 , we set INLINEFORM8 [ INLINEFORM9 , ( INLINEFORM10 , INLINEFORM11 )] = 1 indicating INLINEFORM12 occurs for pair ( INLINEFORM13 , INLINEFORM14 )  (iii) Task Experience Store ( INLINEFORM15 ): INLINEFORM16 stores the predictive performance of LiLi on past learned tasks in terms of Matthews correlation coefficient (MCC) that measures the quality of binary classification  So, for two tasks INLINEFORM17 and INLINEFORM18 (each relation is a task), if INLINEFORM19 [ INLINEFORM20 ] INLINEFORM21 INLINEFORM22 [ INLINEFORM23 ] [where INLINEFORM24 [ INLINEFORM25 ]=MCC( INLINEFORM26 )], we say C-PR has learned INLINEFORM27 well compared to INLINEFORM28   (iv) Incomplete Feature DB ( INLINEFORM29 ): INLINEFORM30 stores the frequency of an incomplete path INLINEFORM31 in the form of a tuple ( INLINEFORM32 , INLINEFORM33 , INLINEFORM34 ) and is used in formulating MLQs  INLINEFORM35 [( INLINEFORM36 , INLINEFORM37 , INLINEFORM38 )] = INLINEFORM39 implies LiLi has extracted incomplete path INLINEFORM40 INLINEFORM41 times involving entity-pair INLINEFORM42 [( INLINEFORM43 , INLINEFORM44 )] for query relation INLINEFORM45  
The RL model learns even after training whenever it encounters an unseen state (in testing) and thus, gets updated over time  KS is updated continuously over time as a result of the execution of LiLi and takes part in future learning  The prediction model uses lifelong learning (LL), where we transfer knowledge (parameter values) from the model for a past most similar task to help learn for the current task  Similar tasks are identified by factorizing INLINEFORM0 and computing a task similarity matrix INLINEFORM1   Besides LL, LiLi uses INLINEFORM2 to identify poorly learned past tasks and acquire more clues for them to improve its skillset over time 
LiLi also uses a stack, called Inference Stack ( INLINEFORM0 ) to hold query and its state information for RL  LiLi always processes stack top ( INLINEFORM1 [top])  The clues from the user get stored in INLINEFORM2 on top of the query during strategy execution and processed first  Thus, the prediction model for INLINEFORM3 is learned before performing inference on query, transforming OKBC to a KBC problem  Table 1 shows the parameters of LiLi used in the following sections 
Working of LiLi
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we represent it as a data instance INLINEFORM3   INLINEFORM4 consists of INLINEFORM5 (the query triple), INLINEFORM6 (interaction limit set for INLINEFORM7 ), INLINEFORM8 (experience list storing the transition history of MDP for INLINEFORM9 in RL) and INLINEFORM10 (mode of INLINEFORM11 ) denoting if INLINEFORM12 is ` INLINEFORM13 ' (training), ` INLINEFORM14 ' (validation), ` INLINEFORM15 ' (evaluation) or ` INLINEFORM16 ' (clue) instance and INLINEFORM17 (feature set)  We denote INLINEFORM18 ( INLINEFORM19 ) as the set of all complete (incomplete) path features in INLINEFORM20   Given a data instance INLINEFORM21 , LiLi starts its initialization as follows: it sets the state as INLINEFORM22 (based on INLINEFORM23 , explained later), pushes the query tuple ( INLINEFORM24 , INLINEFORM25 ) into INLINEFORM26 and feeds INLINEFORM27 [top] to the RL-model for strategy formulation from INLINEFORM28  
Inference Strategy Formulation  We view solving the strategy formulation problem as learning to play an inference game, where the goal is to formulate a strategy that "makes the inference task possible"  Considering PR methods, inference is possible, iff (1) INLINEFORM0 becomes known to its KB (by acquiring clues when INLINEFORM1 is unknown) and (2) path features are extracted between INLINEFORM2 and INLINEFORM3 (which inturn requires INLINEFORM4 and INLINEFORM5 to be known to KB)  If these conditions are met at the end of an episode (when strategy formulation finishes for a given query) of the game, LiLi wins and thus, it trains the prediction model for INLINEFORM6 and uses it for inference 
LiLi's strategy formulation is modeled as a Markov Decision Process (MDP) with finite state ( INLINEFORM0 ) and action ( INLINEFORM1 ) spaces  A state INLINEFORM2 consists of 10 binary state variables (Table 2), each of which keeps track of results of an action INLINEFORM3 taken by LiLi and thus, records the progress in inference process made so far  INLINEFORM4 is the initial state with all state bits set as 0  If the data instance (query) is a clue [ INLINEFORM5 ], INLINEFORM6 [CLUE] is set as 1  INLINEFORM7 consists of 6 actions (Table 3)  INLINEFORM8 , INLINEFORM9 , INLINEFORM10 are processing actions and INLINEFORM11 , INLINEFORM12 , INLINEFORM13 are interactive actions  Whenever INLINEFORM14 is executed, the MDP reaches the terminal state  Given an action INLINEFORM15 in state INLINEFORM16 , if INLINEFORM17 is invalid in INLINEFORM21 or the objective of INLINEFORM22 is unsatisfied (* marked the condition in INLINEFORM23 ), RL receives a negative reward (empirically set); else receives a positive reward   We use Q-learning BIBREF38 with INLINEFORM24 -greedy strategy to learn the optimal policy for training the RL model  Note that, the inference strategy is independent of KB type and correctness of prediction  Thus, the RL-model is trained only once from scratch (reused thereafter for other KBs) and also, independently of the prediction model 
Sometimes the training dataset may not be enough to learn optimal policy for all INLINEFORM0   Thus, encountering an unseen state during test can make RL-model clueless about the action  Given a state INLINEFORM1 , whenever an invalid INLINEFORM2 is chosen, LiLi remains in INLINEFORM3   For INLINEFORM4 , LiLi remains in INLINEFORM5 untill INLINEFORM6 (see Table 1 for INLINEFORM7 )  So, if the state remains the same for ( INLINEFORM8 +1) times, it implies LiLi has encountered a fault (an unseen state)  RL-model instantly switches to the training mode and randomly explores INLINEFORM9 to learn the optimal action (fault-tolerant learning)  While exploring INLINEFORM10 , the model chooses INLINEFORM11 only when it has tried all other INLINEFORM12 to avoid abrupt end of episode 
Execution of Actions  At any given point in time, let ( INLINEFORM0 , INLINEFORM1 ) be the current INLINEFORM2 [top], INLINEFORM3 is the chosen action and the current version of KS components are INLINEFORM4 , INLINEFORM5 , INLINEFORM6 and INLINEFORM7   Then, if INLINEFORM8 is invalid in INLINEFORM9 , LiLi only updates INLINEFORM10 [top] with ( INLINEFORM11 , INLINEFORM12 ) and returns INLINEFORM13 [top] to RL-model
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: By assigning a political bias label to each news article and training only on left-biased or right-biased outlets of both disinformation and mainstream domains

we also account for political biases inherent to different news sources, referring to the procedure proposed in BIBREF2 to label different outlets. Overall we show that we are able to classify credible vs non-credible diffusion networks (and consequently news articles) with high accuracy (AUROC up to 94%), even when accounting for the political bias of sources (and training only on left-biased or right-biased articles). We observe that the layer of mentions alone conveys useful information for the classification, denoting a different usage of this functionality when sharing news belonging to the two news domains. We also show that most discriminative features, which are relative to the breadth and depth of largest cascades in different layers, are the same across the two countries.

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 metrics are used to establish that this makes chatbots more knowledgeable and better at learning and conversation? 
Context 2:  In this process, LiLi adds experience ( INLINEFORM14 , INLINEFORM15 , INLINEFORM16 , INLINEFORM17 ) in INLINEFORM18 and then, replaces INLINEFORM19 [top] with ( INLINEFORM20 , INLINEFORM21 )  If INLINEFORM22 is valid in INLINEFORM23 , LiLi first sets the next state INLINEFORM24 and performs a sequence of operations INLINEFORM25 based on INLINEFORM26 (discussed below)  Unless specified, in INLINEFORM27 , LiLi always monitors INLINEFORM28 and if INLINEFORM29 becomes 0, LiLi sets INLINEFORM30   Also, whenever LiLi asks the user a query, INLINEFORM31 is decremented by 1  Once INLINEFORM32 ends, LiLi updates INLINEFORM33 [top] with ( INLINEFORM34 , INLINEFORM35 ) and returns INLINEFORM36 [top] to RL-model for choosing the next action 
In INLINEFORM0 , LiLi searches INLINEFORM1 , INLINEFORM2 , INLINEFORM3 in INLINEFORM4 and sets appropriate bits in INLINEFORM5 (see Table 2)  If INLINEFORM6 was unknown before and is just added to INLINEFORM7 or is in the bottom INLINEFORM8 % (see Table 1 for INLINEFORM9 ) of INLINEFORM10 , LiLi randomly sets INLINEFORM14 with probability INLINEFORM15   If INLINEFORM16 is a clue and INLINEFORM17 , LiLi updates KS with triple INLINEFORM18 , where ( INLINEFORM19 , INLINEFORM20 , INLINEFORM21 ) and ( INLINEFORM22 , INLINEFORM23 , INLINEFORM24 ) gets added to INLINEFORM25 and INLINEFORM26 , INLINEFORM27 are set as 1 
In INLINEFORM0 , LiLi asks the user to provide a clue (+ve instance) for INLINEFORM1 and corrupts INLINEFORM2 and INLINEFORM3 of the clue once at a time, to generate -ve instances by sampling nodes from INLINEFORM4   These instances help in training prediction model for INLINEFORM5 while executing INLINEFORM6  
In INLINEFORM0 , LiLi selects an incomplete path INLINEFORM1 from INLINEFORM2 to formulate MLQ, such that INLINEFORM3 is most frequently observed for INLINEFORM4 and INLINEFORM5 is high, given by INLINEFORM6   Here, INLINEFORM7 denotes the contextual similarity BIBREF16 of entity-pair INLINEFORM8   If INLINEFORM9 is high, INLINEFORM10 is more likely to possess a relation between them and so, is a good candidate for formulating MLQ  When the user does not respond to MLQ (or CLQ in INLINEFORM11 ), the guessing mechanism is used, which works as follows: Since contextual similarity of entity-pairs is highly correlated with their class labels BIBREF16 , LiLi divides the similarity range [-1, 1] into three segments, using a low ( INLINEFORM12 ) and high ( INLINEFORM13 ) similarity threshold and replaces the missing link with INLINEFORM14 in INLINEFORM15 to make it complete as follows: If INLINEFORM16 , INLINEFORM17 = “@-LooselyRelatedTo-@"; else if INLINEFORM18 , INLINEFORM19 =“@-NotRelatedTo-@"; Otherwise, INLINEFORM20 =“@-RelatedTo-@" 
In INLINEFORM0 , LiLi asks CLQs for connecting unknown entities INLINEFORM1 and/or INLINEFORM2 with INLINEFORM3 by selecting the most contextually relevant node (wrt INLINEFORM4 , INLINEFORM5 ) from INLINEFORM6 , given by link INLINEFORM7   We adopt the contextual relevance idea in BIBREF16 which is computed using word embedding BIBREF39
In INLINEFORM0 , LiLi extracts path features INLINEFORM1 between ( INLINEFORM2 , INLINEFORM3 ) and updates INLINEFORM4 with incomplete features from INLINEFORM5   LiLi always trains the prediction model with complete features INLINEFORM6 and once INLINEFORM7 or INLINEFORM8 , LiLi stops asking MLQs  Thus, in both INLINEFORM9 and INLINEFORM10 , LiLi always monitors INLINEFORM11 to check for the said requirements and sets INLINEFORM12 to control interactions 
In INLINEFORM0 , if LiLi wins the episode, it adds INLINEFORM1 in one of data buffers INLINEFORM2 based on its mode INLINEFORM3   E g , if INLINEFORM4 or INLINEFORM5 , INLINEFORM6 is used for training and added to INLINEFORM7   Similarly validation buffer INLINEFORM8 and evaluation buffer INLINEFORM9 are populated  If INLINEFORM10 , LiLi invokes the prediction model for INLINEFORM11  
Lifelong Relation Prediction  Given a relation INLINEFORM0 , LiLi uses INLINEFORM1 and INLINEFORM2 (see INLINEFORM3 ) to train a prediction model (say, INLINEFORM4 ) with parameters INLINEFORM5   For a unknown INLINEFORM6 , the clue instances get stored in INLINEFORM7 and INLINEFORM8   Thus, LiLi populates INLINEFORM9 by taking 10% (see §4) of the instances from INLINEFORM10 and starts the training  For INLINEFORM11 , LiLi uses a LSTM BIBREF40 to compose the vector representation of each feature INLINEFORM12 as INLINEFORM13 and vector representation of INLINEFORM14 as INLINEFORM15   Next, LiLi computes the prediction value, INLINEFORM16 as sigmoid of the mean cosine similarity of all features and INLINEFORM17 , given by INLINEFORM18 ) and maximize the log-likelihood of INLINEFORM19 for training  Once INLINEFORM20 is trained, LiLi updates INLINEFORM21 [ INLINEFORM22 ] using INLINEFORM23   We also train an inverse model for INLINEFORM24 , INLINEFORM25 by reversing the path features in INLINEFORM26 and INLINEFORM27 which help in lifelong learning (discussed below)  Unlike BIBREF20 , BIBREF21 , while predicting the label for INLINEFORM28 , we compute a relation-specific prediction threshold INLINEFORM29 corresponding to INLINEFORM30 using INLINEFORM31 as: INLINEFORM32 and infer INLINEFORM33 as +ve if INLINEFORM34 and -ve otherwise  Here, INLINEFORM35 ( INLINEFORM36 ) is the mean prediction value for all +ve (-ve) examples in INLINEFORM37  
Models trained on a few examples (e g , clues acquired for unknown INLINEFORM0 ) with randomly initialized weights often perform poorly due to underfitting  Thus, we transfer knowledge (weights) from the past most similar (wrt INLINEFORM1 ) task in a lifelong learning manner BIBREF31   LiLi uses INLINEFORM2 to find the past most similar task for INLINEFORM3 as follows: LiLi computes trancated SVD of INLINEFORM4 as INLINEFORM5 and then, the similarity matrix INLINEFORM6   INLINEFORM7 provides the similarity between relations INLINEFORM8 and INLINEFORM9 in INLINEFORM10   Thus, LiLi chooses a source relation INLINEFORM11 to transfer weights  Here, INLINEFORM12 is the set of all INLINEFORM13 and INLINEFORM14 for which LiLi has already learned a prediction model  Now, if INLINEFORM15 or INLINEFORM16 , LiLi randomly initializes the weights INLINEFORM17 for INLINEFORM18 and proceeds with the training  Otherwise, LiLi uses INLINEFORM19 as initial weights and fine-tunes INLINEFORM20 with a low learning rate 
A Running Example  Considering the example shown in Figure 1, LiLi works as follows: first, LiLi executes INLINEFORM0 and detects that the source entity “Obama" and query relation “CitizenOf" are unknown  Thus, LiLi executes INLINEFORM1 to acquire clue (SF1) for “CitizenOf" and pushes the clue (+ve example) and two generated -ve examples into INLINEFORM2   Once the clues are processed and a prediction model is trained for “CitizenOf" by formulating separate strategies for them, LiLi becomes aware of “CitizenOf"  Now, as the clues have already been popped from INLINEFORM3 , the query becomes INLINEFORM4 and the strategy formulation process for the query resumes  Next, LiLi asks user to provide a connecting link for “Obama" by performing INLINEFORM5   Now, the query entities and relation being known, LiLi enumerates paths between “Obama" and “USA" by performing INLINEFORM6   Let an extracted path be “ INLINEFORM7 " with missing link between ( INLINEFORM8 , INLINEFORM9 )  LiLi asks the user to fill the link by performing INLINEFORM10 and then, extracts the complete feature “ INLINEFORM11 "  The feature set is then fed to the prediction model and inference is made as a result of INLINEFORM12   Thus, the formulated inference strategy is: “ INLINEFORM13 " 
Experiments
We now evaluate LiLi in terms of its predictive performance and strategy formulation abilities 
Data: We use two standard datasets (see Table 4): (1) Freebase FB15k, and (2) WordNet INLINEFORM0   Using each dataset, we build a fairly large graph and use it as the original KB ( INLINEFORM1 ) for evaluation  We also augment INLINEFORM2 with inverse triples ( INLINEFORM3 , INLINEFORM4 , INLINEFORM5 ) for each ( INLINEFORM6 , INLINEFORM7 , INLINEFORM8 ) following existing KBC methods 
Parameter Settings  Unless specified, the empirically set parameters (see Table 1) of LiLi are: INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 , INLINEFORM7 , INLINEFORM8 , INLINEFORM9 , INLINEFORM10   For training RL-model with INLINEFORM11 -greedy strategy, we use INLINEFORM12 , INLINEFORM13 , pre-training steps=50000  We used Keras deep learning library to implement and train the prediction model  We set batch-size as 128, max  training epoch as 150, dropout as 0 2, hidden units and embedding size as 300 and learning rate as 5e-3 which is reduced gradually on plateau with factor 0 5 and patience 5  Adam optimizer and early stopping were used in training  We also shuffle INLINEFORM14 in each epoch and adjust class weights inversely proportional to class frequencies in INLINEFORM15  
Labeled Dataset Generation and Simulated User Creation  We create a simulated user for each KB to evaluate LiLi  We create the labeled datasets, the simulated user’s knowledge base ( INLINEFORM0 ), and the base KB ( INLINEFORM1 ) from INLINEFORM2   INLINEFORM3 used as the initial KB graph ( INLINEFORM4 ) of LiLi 
We followed BIBREF16 for labeled dataset generation  For Freebase, we found 86 relations with INLINEFORM0 triples and randomly selected 50 from various domains  We randomly shuffle the list of 50 relations, select 25% of them as unknown relations and consider the rest (75%) as known relations  For each known relation INLINEFORM1 , we randomly shuffle the list of distinct triples for INLINEFORM2 , choose 1000 triples and split them into 60% training, 10% validation and 20% test  Rest 10% along with the leftover (not included in the list of 1000) triples are added to INLINEFORM3   For each unknown relation INLINEFORM4 , we remove all triples of INLINEFORM5 from INLINEFORM6 and add them to INLINEFORM7   In this process, we also randomly choose 20% triples as test instances for unknown INLINEFORM8 which are excluded from INLINEFORM9   Note that, now INLINEFORM10 has at least 10% of chosen triples for each INLINEFORM11 (known and unknown) and so, user is always able to provide clues for both cases  For each labeled dataset, we randomly choose 10% of the entities present in dataset triples, remove triples involving those entities from INLINEFORM12 and add to INLINEFORM13   At this point, INLINEFORM14 gets reduced to INLINEFORM15 and is used as INLINEFORM16 for LiLi  The dataset stats in Table 4 shows that the base KB (60% triples of INLINEFORM17 ) is highly sparse (compared to original KB) which makes the inference task much harder  WordNet dataset being small, we select all 18 relations for evaluation and create labeled dataset, INLINEFORM18 and INLINEFORM19 following Freebase  Although the user may provide clues 100% of the time, it often cannot respond to MLQs and CLQs (due to lack of required triples/facts)  Thus, we further enrich INLINEFORM20 with external KB triples 
Given a relation INLINEFORM0 and an observed triple ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 ) in training or testing, the pair ( INLINEFORM4 , INLINEFORM5 ) is regarded as a +ve instance for INLINEFORM6   Following BIBREF18 , for each +ve instance ( INLINEFORM7 , INLINEFORM8 ), we generate two negative ones, one by randomly corrupting the source INLINEFORM9 , and the other by corrupting the target INLINEFORM10   Note that, the test triples are not in INLINEFORM11 or INLINEFORM12 and none of the -ve instances overlap with the +ve ones 
Baselines  As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines 
Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations 
Sep: We do not transfer (past learned) weights for initializing INLINEFORM0 , i e , we disable LL 
F-th): Here, we use a fixed prediction threshold 0 5 instead of relation-specific threshold INLINEFORM0  
BG: The missing or connecting links (when the user does not respond) are filled with “@-RelatedTo-@" blindly, no guessing mechanism 
w/o PTS: LiLi does not ask for additional clues via past task selection for skillset improvement 
Evaluation Metrics  To evaluate the strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), defined as the fraction of total query data instances, for which LiLi has successfully formulated strategies that lead to winning  If LiLi wins on all episodes for a given dataset, INLINEFORM1 is 1 0  To evaluate the predictive performance, we use Avg  MCC and avg  +ve F1 score 
Results and Analysis
Evaluation-I: Strategy Formulation Ability  Table 5 shows the list of inference strategies formulated by LiLi for various INLINEFORM0 and INLINEFORM1 , which control the strategy formulation of LiLi  When INLINEFORM2 , LiLi cannot interact with user and works like a closed-world method  Thus, INLINEFORM3 drops significantly (0 47)  When INLINEFORM4 , i e  with only one interaction per query, LiLi acquires knowledge well for instances where either of the entities or relation is unknown  However, as one unknown entity may appear in multiple test triples, once the entity becomes known, LiLi doesn’t need to ask for it again and can perform inference on future triples causing significant increase in INLINEFORM5 (0 97)  When INLINEFORM6 , LiLi is able to perform inference on all instances and INLINEFORM7 becomes 1  For INLINEFORM8 , LiLi uses INLINEFORM9 only once (as only one MLQ satisfies INLINEFORM10 ) compared to INLINEFORM11   In summary, LiLi’s RL-model can effectively formulate query-specific inference strategies (based on specified parameter values)  Evaluation-II: Predictive Performance  Table 6 shows the comparative performance of LiLi with baselines  To judge the overall improvements, we performed paired t-test considering +ve F1 scores on each relation as paired data  Considering both KBs and all relation types, LiLi outperforms Sep with INLINEFORM12   If we set INLINEFORM13 (training with very few clues), LiLi outperforms Sep with INLINEFORM14 on Freebase considering MCC  Thus, the lifelong learning mechanism is effective in transferring helpful knowledge  Single model performs better than Sep for unknown relations due to the sharing of knowledge (weights) across tasks  However, for known relations, performance drops because, as a new relation arrives to the system, old weights get corrupted and catastrophic forgetting occurs  For unknown relations, as the relations are evaluated just after training, there is no chance for catastrophic forgetting  The performance improvement ( INLINEFORM15 ) of LiLi over F-th on Freebase signifies that the relation-specific threshold INLINEFORM16 works better than fixed threshold 0 5 because, if all prediction values for test instances lie above (or below) 0 5, F-th predicts all instances as +ve (-ve) which degrades its performance  Due to the utilization of contextual similarity (highly correlated with class labels) of entity-pairs, LiLi’s guessing mechanism works better ( INLINEFORM17 ) than blind guessing (BG)  The past task selection mechanism of LiLi also improves its performance over w/o PTS, as it acquires more clues during testing for poorly performed tasks (evaluated on validation set)  For Freebase, due to a large number of past tasks [9 (25% of 38)], the performance difference is more significant ( INLINEFORM18 )  For WordNet, the number is relatively small [3 (25% of 14)] and hence, the difference is not significant 
Evaluation-III: User Interaction vs  Performance  Table 7 shows the results of LiLi by varying clue acquisition rate ( INLINEFORM0 )  We use Freebase for tuning INLINEFORM1 due to its higher number of unknown test relations compared to WordNet  LiLi’s performance improves significantly as it acquires more clues from the user  The results on INLINEFORM2 outperforms ( INLINEFORM3 ) that on INLINEFORM4   Table 8 shows the results of LiLi on user responses to MLQ’s and CLQ’s  Answering MLQ’s and CLQ’s is very hard for simulated users (unlike crowd-sourcing) as often INLINEFORM5 lacks the required triple  Thus, we attempt to analyze how the performance is effected if the user does not respond at all  The results show a clear trend in overall performance improvement when the user responds  However, the improvement is not significant as the simulated user’s query satisfaction rate (1% MLQs and 10% CLQs) is very small  But, the analysis shows the effectiveness of LiLi’s guessing mechanism and continual learning ability that help in achieving avg  +ve F1 of 0 57 and 0 62 on FB and WN respectively with minimal participation of the user 
Conclusion
In this paper, we are interested in building a generic engine for continuous knowledge learning in human-machine conversations  We first showed that the problem underlying the engine can be formulated as an open-world knowledge base completion (OKBC) problem  We then proposed an lifelong interactive learning and inference (LiLi) approach to solving the OKBC problem  OKBC is a generalization of KBC  LiLi solves the OKBC problem by first formulating a query-specific inference strategy using RL and then executing it to solve the problem by interacting with the user in a lifelong learning manner  Experimental results showed the effectiveness of LiLi in terms of both predictive quality and strategy formulation ability  We believe that a system with the LiLi approach can serve as a knowledge learning engine for conversations  Our future work will improve LiLi to make more accurate 
Acknowledgments
This work was supported in part by National Science Foundation (NSF) under grant no  IIS-1407927 and IIS-1650900, and a gift from Huawei Technologies Co Ltd 
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: Coverage, Avg. MCC and avg. +ve F1 score

strategy formulation ability, we introduce a measure called Coverage( INLINEFORM0 ), To evaluate the predictive performance, we use Avg. MCC and avg. +ve F1 score

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