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 does their ensemble method work?
Context 1: Introduction
Since humans amass more and more generally available data in the form of unstructured text it would be very useful to teach machines to read and comprehend such data and then use this understanding to answer our questions  A significant amount of research has recently focused on answering one particular kind of questions the answer to which depends on understanding a context document  These are cloze-style questions BIBREF0 which require the reader to fill in a missing word in a sentence  An important advantage of such questions is that they can be generated automatically from a suitable text corpus which allows us to produce a practically unlimited amount of them  That opens the task to notoriously data-hungry deep-learning techniques which now seem to outperform all alternative approaches 
Two such large-scale datasets have recently been proposed by researchers from Google DeepMind and Facebook AI: the CNN/Daily Mail dataset BIBREF1 and the Children's Book Test (CBT) BIBREF2 respectively  These have attracted a lot of attention from the research community BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 with a new state-of-the-art model coming out every few weeks 
However if our goal is a production-level system actually capable of helping humans, we want the model to use all available resources as efficiently as possible  Given that
we believe that if the community is striving to bring the performance as far as possible, it should move its work to larger data 
This thinking goes in line with recent developments in the area of language modelling  For a long time models were being compared on several "standard" datasets with publications often presenting minuscule improvements in performance  Then the large-scale One Billion Word corpus dataset appeared BIBREF15 and it allowed Jozefowicz et al  to train much larger LSTM models BIBREF16 that almost halved the state-of-the-art perplexity on this dataset 
We think it is time to make a similar step in the area of text comprehension  Hence we are introducing the BookTest, a new dataset very similar to the Children's Book test but more than 60 times larger to enable training larger models even in the domain of text comprehension  Furthermore the methodology used to create our data can later be used to create even larger datasets when the need arises thanks to further technological progress 
We show that if we evaluate a model trained on the new dataset on the now standard Children's Book Test dataset, we see an improvement in accuracy much larger than other research groups achieved by enhancing the model architecture itself (while still using the original CBT training data)  By training on the new dataset, we reduce the prediction error by almost one third  On the named-entity version of CBT this brings the ensemble of our models to the level of human baseline as reported by Facebook BIBREF2   However in the final section we show in our own human study that there is still room for improvement on the CBT beyond the performance of our model 
Task Description
A natural way of testing a reader's comprehension of a text is to ask her a question the answer to which can be deduced from the text  Hence the task we are trying to solve consists of answering a cloze-style question, the answer to which depends on the understanding of a context document provided with the question  The model is also provided with a set of possible answers from which the correct one is to be selected  This can be formalized as follows:
The training data consist of tuples INLINEFORM0 , where INLINEFORM1 is a question, INLINEFORM2 is a document that contains the answer to question INLINEFORM3 , INLINEFORM4 is a set of possible answers and INLINEFORM5 is the ground-truth answer  Both INLINEFORM6 and INLINEFORM7 are sequences of words from vocabulary INLINEFORM8   We also assume that all possible answers are words from the vocabulary, that is INLINEFORM9   In the CBT and CNN/Daily Mail datasets it is also true that the ground-truth answer INLINEFORM10 appears in the document  This is exploited by many machine learning models BIBREF2 , BIBREF4 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF10 , BIBREF11 , BIBREF12 , however some do not explicitly depend on this property BIBREF1 , BIBREF3 , BIBREF5 , BIBREF9
Current Landscape
We will now briefly review what datasets for text comprehension have been published up to date and look at models which have been recently applied to solving the task we have just described 
Datasets
A crucial condition for applying deep-learning techniques is to have a huge amount of data available for training  For question answering this specifically means having a large number of document-question-answer triples available  While there is an unlimited amount of text available, coming up with relevant questions and the corresponding answers can be extremely labour-intensive if done by human annotators  There were efforts to provide such human-generated datasets, e g  Microsoft's MCTest BIBREF17 , however their scale is not suitable for deep learning without pre-training on other data BIBREF18 (such as using pre-trained word embedding vectors) 
Google DeepMind managed to avoid this scale issue with their way of generating document-question-answer triples automatically, closely followed by Facebook with a similar method  Let us now briefly introduce the two resulting datasets whose properties are summarized in Table TABREF8  
These two datasets BIBREF1 exploit a useful feature of online news articles – many articles include a short summarizing sentence near the top of the page  Since all information in the summary sentence is also presented in the article body, we get a nice cloze-style question about the article contents by removing a word from the short summary 
The dataset's authors also replaced all named entities in the dataset by anonymous tokens which are further shuffled for each new batch  This forces the model to rely solely on information from the context document, not being able to transfer any meaning of the named entities between documents 
This restricts the task to one specific aspect of context-dependent question answering which may be useful however it moves the task further from the real application scenario, where we would like the model to use all information available to answer questions  Furthermore Chen et al  BIBREF5 have suggested that this can make about 17% of the questions unanswerable even by humans  They also claim that more than a half of the question sentences are mere paraphrases or exact matches of a single sentence from the context document  This raises a question to what extent the dataset can test deeper understanding of the articles 
The Children's Book Test BIBREF2 uses a different source - books freely available thanks to Project Gutenberg  Since no summary is available, each example consists of a context document formed from 20 consecutive sentences from the story together with a question formed from the subsequent sentence 
The dataset comes in four flavours depending on what type of word is omitted from the question sentence  Based on human evaluation done in BIBREF2 it seems that NE and CN are more context dependent than the other two types – prepositions and verbs  Therefore we (and all of the recent publications) focus only on these two word types 
Several new datasets related to the (now almost standard) ones above emerged recently  We will now briefly present them and explain how the dataset we are introducing in this article differs from them 
The LAMBADA dataset BIBREF19 is designed to measure progress in understanding common-sense questions about short stories that can be easily answered by humans but cannot be answered by current standard machine-learning models (e g  plain LSTM language models)  This dataset is useful for measuring the gap between humans and machine learning algorithms  However, by contrast to our BookTest dataset, it will not allow us to track progress towards the performance of the baseline systems or on examples where machine learning may show super-human performance  Also LAMBADA is just a diagnostic dataset and does not provide ready-to-use question-answering training data, just a plain-text corpus which may moreover include copyrighted books making its use potentially problematic for some purposes  We are providing ready training data consisting of copyright-free books only 
The SQuAD dataset BIBREF20 based on Wikipedia and the Who-did-What dataset BIBREF21 based on Gigaword news articles are factoid question-answering datasets where a multi-word answer should be extracted from a context document  This is in contrast to the previous datasets, including CNN/DM, CBT, LAMBADA and our new dataset, which require only single-word answers  Both these datasets however provide less than 130,000 training questions, two orders of magnitude less than our dataset does 
The Story Cloze Test BIBREF22 provides a crowd-sourced corpus of 49,255 commonsense stories for training and 3,744 testing stories with right and wrong endings  Hence the dataset is again rather small  Similarly to LAMBADA, the Story Cloze Test was designed to be easily answerable by humans 
In the WikiReading BIBREF23 dataset the context document is formed from a Wikipedia article and the question-answer pair is taken from the corresponding WikiData page  For each entity (e g  Hillary Clinton), WikiData contain a number of property-value pairs (e g  place of birth: Chicago) which form the datasets's question-answer pairs  The dataset is certainly relevant to the community, however the questions are of very limited variety with only 20 properties (and hence unique questions) covering INLINEFORM0 of the dataset  Furthermore many of the frequent properties are mentioned at a set spot within the article (e g  the date of birth is almost always in brackets behind the name of a person) which may make the task easier for machines  We are trying to provide a more varied dataset 
Although there are several datasets related to task we are aiming to solve, they differ sufficiently for our dataset to bring new value to the community  Its biggest advantage is its size which can furthermore be easily upscaled without expensive human annotation  Finally while we are emphasizing the differences, models could certainly benefit from as diverse a collection of datasets as possible 
Machine Learning Models
A first major work applying deep-learning techniques to text comprehension was Hermann et al  BIBREF1   This work was followed by the application of Memory Networks to the same task BIBREF2   Later three models emerged around the same time BIBREF3 , BIBREF4 , BIBREF5 including our psr model BIBREF4   The AS Reader inspired several subsequent models that use it as a sub-component in a diverse ensemble BIBREF8 ; extend it with a hierarchical structure BIBREF6 , BIBREF24 , BIBREF7 ; compute attention over the context document for every word in the query BIBREF10 or use two-way context-query attention mechanism for every word in the context and the query BIBREF11 that is similar in its spirit to models recently proposed in different domains, e g  BIBREF25 in information retrieval  Other neural approaches to text comprehension are explored in BIBREF9 , BIBREF12  
Possible Directions for Improvements
Accuracy in any machine learning tasks can be enhanced either by improving a machine learning model or by using more in-domain training data  Current state of the art models BIBREF6 , BIBREF7 , BIBREF8 , BIBREF11 improve over AS Reader's accuracy on CBT NE and CN datasets by 1-2 percent absolute  This suggests that with current techniques there is only limited room for improvement on the algorithmic side 
The other possibility to improve performance is simply to use more training data  The importance of training data was highlighted by the frequently quoted Mercer's statement that “There is no data like more data ” The observation that having more data is often more important than having better algorithms has been frequently stressed since then BIBREF13 , BIBREF14  
As a step in the direction of exploiting the potential of more data in the domain of text comprehension, we created a new dataset called BookTest similar to, but much larger than the widely used CBT and CNN/DM datasets 
BookTest
Similarly to the CBT, our BookTest dataset is derived from books available through project Gutenberg  We used 3555 copyright-free books to extract CN examples and 10507 books for NE examples, for comparison the CBT dataset was extracted from just 108 books 
When creating our dataset we follow the same procedure as was used to create the CBT dataset BIBREF2   That is, we detect whether each sentence contains either a named entity or a common noun that already appeared in one of the preceding twenty sentences  This word is then replaced by a gap tag (XXXXX) in this sentence which is hence turned into a cloze-style question  The preceding 20 sentences are used as the context document  For common noun and named entity detection we use the Stanford POS tagger BIBREF27 and Stanford NER BIBREF28  
The training dataset consists of the original CBT NE and CN data extended with new NE and CN examples  The new BookTest dataset hence contains INLINEFORM0 training examples and INLINEFORM1 tokens 
The validation dataset consists of INLINEFORM0 NE and INLINEFORM1 CN questions  We have one test set for NE and one for CN, each containing INLINEFORM2 examples  The training, validation and test sets were generated from non-overlapping sets of books 
When generating the dataset we removed all editions of books used to create CBT validation and test sets from our training dataset  Therefore the models trained on the BookTest corpus can be evaluated on the original CBT data and they can be compared with recent text-comprehension models utilizing this dataset BIBREF2 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11  
Baselines
We will now use our psr model to evaluate the performance gain from increasing the dataset size 
AS Reader
In BIBREF4 we introduced the psr , which at the time of publication significantly outperformed all other architectures on the CNN, DM and CBT datasets  This model is built to leverage the fact that the answer is a single word from the context document  Similarly to many other models it uses attention over the document – intuitively a measure of how relevant each word is to answering the question  However while most previous models used this attention as weights to calculate a blended representation of the answer word, we simply sum the attention across all occurrences of each unique words and then simply select the word with the highest sum as the final answer  While simple, this trick seems both to improve accuracy and to speed-up training  It was adopted by many subsequent models BIBREF8 , BIBREF6 , BIBREF7 , BIBREF10 , BIBREF11 , BIBREF24  
Let us now describe the model in more detail  Figure FIGREF21 may help you in understanding the following paragraphs 
The words from the document and the question are first converted into vector embeddings using a look-up matrix INLINEFORM0   The document is then read by a bidirectional GRU network BIBREF29   A concatenation of the hidden states of the forward and backward GRUs at each word is then used as a contextual embedding of this word, intuitively representing the context in which the word is appearing  We can also understand it as representing the set of questions to which this word may be an answer 
Similarly the question is read by a bidirectional GRU but in this case only the final hidden states are concatenated to form the question embedding 
The attention over each word in the context is then calculated as the dot product of its contextual embedding with the question embedding  This attention is then normalized by the softmax function and summed across all occurrences of each answer candidate  The candidate with most accumulated attention is selected as the final answer 
For a more detailed description of the model including equations check BIBREF4   More details about the training setup and model hyperparameters can be found in the Appendix 
During our past experiments on the CNN, DM and CBT datasets BIBREF4 each unique word from the training, validation and test datasets had its row in the look-up matrix INLINEFORM0   However as we radically increased the dataset size, this would result in an extremely large number of model parameters so we decided to limit the vocabulary size to INLINEFORM1 most frequent words  For each example, each unique out-of-vocabulary word is now mapped on one of 1000 anonymous tokens which are randomly initialized and untrained  Fixing the embeddings of these anonymous tags proved to significantly improve the performance 
While mostly using the original AS Reader model, we have also tried introducing a minor tweak in some instances of the model  We tried initializing the context encoder GRU's hidden state by letting the encoder read the question first before proceeding to read the context document  Intuitively this allows the encoder to know in advance what to look for when reading over the context document 
Including models of this kind in the ensemble helped to improve the performance 
Results
Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data  The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data 
If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively  By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model  The ensemble of our models even exceeded the human baseline provided by Facebook BIBREF2 on the Common Noun dataset 
Our model takes approximately two weeks to converge when trained on the BookTest dataset on a single Nvidia Tesla K40 GPU 
Discussion
Embracing the abundance of data may mean focusing on other aspects of system design than with smaller data  Here are some of the challenges that we need to face in this situation 
Firstly, since the amount of data is practically unlimited – we could even generate them on the fly resulting in continuous learning similar to the Never-Ending Language Learning by Carnegie Mellon University BIBREF30 – it is now the speed of training that determines how much data the model is able to see  Since more training data significantly help the model performance, focusing on speeding up the algorithm may be more important than ever before  This may for instance influence the decision whether to use regularization such as dropout which does seem to somewhat improve the model performance, however usually at a cost of slowing down training 
Thanks to its simplicity, the psr seems to be training fast - for example around seven times faster than the models proposed by Chen et al  BIBREF5   Hence the psr may be particularly suitable for training on large datasets 
The second challenge is how to generalize the performance gains from large data to a specific target domain  While there are huge amounts of natural language data in general, it may not be the case in the domain where we may want to ultimately apply our model 
Hence we are usually not facing a scenario of simply using a larger amount of the same training data, but rather extending training to a related domain of data, hoping that some of what the model learns on the new data will still help it on the original task 
This is highlighted by our observations from applying a model trained on the BookTest to Children's Book Test test data  If we move model training from joint CBT NE+CN training data to a subset of the BookTest of the same size (230k examples), we see a drop in accuracy of around 10% on the CBT test datasets 
Hence even though the Children's Book Test and BookTest datasets are almost as close as two disjoint datasets can get, the transfer is still very imperfect   Rightly choosing data to augment the in-domain training data is certainly a problem worth exploring in future work 
Our results show that given enough data the AS Reader was able to exceed the human performance on CBT CN reported by Facebook  However we hypothesized that the system is still not achieving its full potential so we decided to examine the room for improvement in our own small human study 
Human Study
After adding more data we have the performance on the CBT validation and test datasets soaring  However is there still potential for much further growth beyond the results we have observed?
We decided to explore the remaining space for improvement on the CBT by testing humans on a random subset of 50 named entity and 50 common noun validation questions that the psr ensemble could not answer correctly  These questions were answered by 10 non-native English speakers from our research laboratory, each on a disjoint subset of questions   Participants had unlimited time to answer the questions and were told that these questions were not correctly answered by a machine, providing additional motivation to prove they are better than computers  The results of the human study are summarized in Table TABREF28   They show that a majority of questions that our system could not answer so far are in fact answerable  This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement 
A system that would answer correctly every time when either our ensemble or human answered correctly would achieve accuracy over 92% percent on both validation and test NE datasets and over 96% on both CN datasets  Hence it still makes sense to use CBT dataset to study further improvements of text-comprehension systems 
Conclusion
Few ways of improving model performance are as solidly established as using more training data  Yet we believe this principle has been somewhat neglected by recent research in text comprehension  While there is a practically unlimited amount of data available in this field, most research was performed on unnecessarily small datasets 
As a gentle reminder to the community we have shown that simply infusing a model with more data can yield performance improvements of up to INLINEFORM0 where several attempts to improve the model architecture on the same training data have given gains of at most INLINEFORM1 compared to our best ensemble result  Yes, experiments on small datasets certainly can bring useful insights  However we believe that the community should also embrace the real-world scenario of data abundance 
The BookTest dataset we are proposing gives the reading-comprehension community an opportunity to make a step in that direction 
Training Details
The training details are similar to those in BIBREF4 however we are including them here for completeness 
To train the model we used stochastic gradient descent with the ADAM update rule BIBREF32 and learning rates of INLINEFORM0 , INLINEFORM1 and INLINEFORM2   The best learning rate in our experiments was INLINEFORM3   We minimized negative log-likelihood as the training objective 
The initial weights in the word-embedding matrix were drawn randomly uniformly from the interval INLINEFORM0   Weights in the GRU networks were initialized by random orthogonal matrices BIBREF34 and biases were initialized to zero  We also used a gradient clipping BIBREF33 threshold of 10 and batches of sizes between 32 or 256  Increasing the batch from 32 to 128 seems to significantly improve performance on the large dataset - something we did not observe on the original CBT data  Increasing the batch size much above 128 is currently difficult due to memory constraints of the GPU 
During training we randomly shuffled all examples at the beginning of each epoch  To speed up training, we always pre-fetched 10 batches worth of examples and sorted them according to document length  Hence each batch contained documents of roughly the same length 
We also did not use pre-trained word embeddings 
We did not perform any text pre-processing since the datasets were already tokenized 
During training we evaluated the model performance every 12 hours and at the end of each epoch and stopped training when the error on the 20k BookTest validation set started increasing  We explored the hyperparameter space by training 67 different models The region of the parameter space that we explored together with the parameters of the model with best validation accuracy are summarized in Table TABREF29  
Our model was implemented using Theano BIBREF31 and Blocks BIBREF35  
The ensembles were formed by simply averaging the predictions from the constituent single models  These single models were selected using the following algorithm 
We started with the best performing model according to validation performance  Then in each step we tried adding the best performing model that had not been previously tried  We kept it in the ensemble if it did improve its validation performance and discarded it otherwise  This way we gradually tried each model once  We call the resulting model a greedy ensemble  We used the INLINEFORM0 BookTest validation dataset for this procedure 
The algorithm was offered 10 models and selected 5 of them for the final ensemble 
Context 2: Introduction
Deep learning systems have shown a lot of promise for extractive Question Answering (QA), with performance comparable to humans when large scale data is available  However, practitioners looking to build QA systems for specific applications may not have the resources to collect tens of thousands of questions on corpora of their choice  At the same time, state-of-the-art machine reading systems do not lend well to low-resource QA settings where the number of labeled question-answer pairs are limited (c f  Table 2 )  Semi-supervised QA methods like BIBREF0 aim to improve this performance by leveraging unlabeled data which is easier to collect 
In this work, we present a semi-supervised QA system which requires the end user to specify a set of base documents and only a small set of question-answer pairs over a subset of these documents  Our proposed system consists of three stages  First, we construct cloze-style questions (predicting missing spans of text) from the unlabeled corpus; next, we use the generated clozes to pre-train a powerful neural network model for extractive QA BIBREF1 , BIBREF2 ; and finally, we fine-tune the model on the small set of provided QA pairs 
Our cloze construction process builds on a typical writing phenomenon and document structure: an introduction precedes and summarizes the main body of the article  Many large corpora follow such a structure, including Wikipedia, academic papers, and news articles  We hypothesize that we can benefit from the un-annotated corpora to better answer various questions – at least ones that are lexically similar to the content in base documents and directly require factual information 
We apply the proposed system on three datasets from different domains – SQuAD BIBREF3 , TriviaQA-Web BIBREF4 and the BioASQ challenge BIBREF5   We observe significant improvements in a low-resource setting across all three datasets  For SQuAD and TriviaQA, we attain an F1 score of more than 50% by merely using 1% of the training data  Our system outperforms the approaches for semi-supervised QA presented in BIBREF0 , and a baseline which uses the same unlabeled data but with a language modeling objective for pretraining  In the BioASQ challenge, we outperform the best performing system from previous year's challenge, improving over a baseline which does transfer learning from the SQuAD dataset  Our analysis reveals that questions which ask for factual information and match to specific parts of the context documents benefit the most from pretraining on automatically constructed clozes 
Related Work
Semi-supervised learning augments the labeled dataset $L$ with a potentially larger unlabeled dataset $U$   BIBREF0 presented a model, GDAN, which trained an auxiliary neural network to generate questions from passages by reinforcement learning, and augment the labeled dataset with the generated questions to train the QA model  Here we use a much simpler heuristic to generate the auxiliary questions, which also turns out to be more effective as we show superior performance compared to GDAN  Several approaches have been suggested for generating natural questions BIBREF6 , BIBREF7 , BIBREF8 , however none of them show a significant improvement of using the generated questions in a semi-supervised setting  Recent papers also use unlabeled data for QA by training large language models and extracting contextual word vectors from them to input to the QA model BIBREF9 , BIBREF10 , BIBREF11   The applicability of this method in the low-resource setting is unclear as the extra inputs increase the number of parameters in the QA model, however, our pretraining can be easily applied to these models as well 
Domain adaptation (and Transfer learning) leverage existing large scale datasets from a source domain (or task) to improve performance on a target domain (or task)  For deep learning and QA, a common approach is to pretrain on the source dataset and then fine-tune on the target dataset BIBREF12 , BIBREF13   BIBREF14 used SQuAD as a source for the target BioASQ dataset, and BIBREF15 used Book Test BIBREF16 as source for the target SQuAD dataset  BIBREF17 transfer learned model layers from the tasks of sequence labeling, text classification and relation classification to show small improvements on SQuAD  All these works use manually curated source datatset, which in themselves are expensive to collect  Instead, we show that it is possible to automatically construct the source dataset from the same domain as the target, which turns out to be more beneficial in terms of performance as well (c f  Section "Experiments & Results" )  Several cloze datasets have been proposed in the literature which use heuristics for construction BIBREF18 , BIBREF19 , BIBREF20   We further see the usability of such a dataset in a semi-supervised setting 
Methodology
Our system comprises of following three steps:
Cloze generation: Most of the documents typically follow a template, they begin with an introduction that provides an overview and a brief summary for what is to follow  We assume such a structure while constructing our cloze style questions  When there is no clear demarcation, we treat the first $K\%$ (hyperparameter, in our case 20%) of the document as the introduction  While noisy, this heuristic generates a large number of clozes given any corpus, which we found to be beneficial for semi-supervised learning despite the noise 
We use a standard NLP pipeline based on Stanford CoreNLP (for SQuAD, TrivaQA and PubMed) and the BANNER Named Entity Recognizer (only for PubMed articles) to identify entities and phrases  Assume that a document comprises of introduction sentences $\lbrace q_1, q_2,     q_n\rbrace $ , and the remaining passages $\lbrace p_1, p_2,    p_m\rbrace $   Additionally, let's say that each sentence $q_i$ in introduction is composed of words $\lbrace w_1, w_2,     w_{l_{q_i}}\rbrace $ , where $l_{q_i}$ is the length of $q_i$   We consider a $\text{match} (q_i, p_j)$ , if there is an exact string match of a sequence of words $\lbrace w_k, w_{k+1},    w_{l_{q_i}}\rbrace $ between the sentence $q_i$ and passage $p_j$   If this sequence is either a noun phrase, verb phrase, adjective phrase or a named entity in $\lbrace p_1, p_2,    p_m\rbrace $0 , as recognized by CoreNLP or BANNER, we select it as an answer span $\lbrace p_1, p_2,    p_m\rbrace $1   Additionally, we use $\lbrace p_1, p_2,    p_m\rbrace $2 as the passage $\lbrace p_1, p_2,    p_m\rbrace $3 and form a cloze question $\lbrace p_1, p_2,    p_m\rbrace $4 from the answer bearing sentence $\lbrace p_1, p_2,    p_m\rbrace $5 by replacing $\lbrace p_1, p_2,    p_m\rbrace $6 with a placeholder  As a result, we obtain passage-question-answer ( $\lbrace p_1, p_2,    p_m\rbrace $7 ) triples (Table 1 shows an example)  As a post-processing step, we prune out $\lbrace p_1, p_2, 
Context 3: Introduction
Microblogging such as Twitter and Weibo is a popular social networking service, which allows users to post messages up to 140 characters  There are millions of active users on the platform who stay connected with friends  Unfortunately, spammers also use it as a tool to post malicious links, send unsolicited messages to legitimate users, etc  A certain amount of spammers could sway the public opinion and cause distrust of the social platform  Despite the use of rigid anti-spam rules, human-like spammers whose homepages having photos, detailed profiles etc  have emerged  Unlike previous "simple" spammers, whose tweets contain only malicious links, those "smart" spammers are more difficult to distinguish from legitimate users via content-based features alone BIBREF0  
There is a considerable amount of previous work on spammer detection on social platforms  Researcher from Twitter Inc  BIBREF1 collect bot accounts and perform analysis on the user behavior and user profile features  Lee et al  lee2011seven use the so-called social honeypot by alluring social spammers' retweet to build a benchmark dataset, which has been extensively explored in our paper  Some researchers focus on the clustering of urls in tweets and network graph of social spammers BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , showing the power of social relationship features As for content information modeling, BIBREF6 apply improved sparse learning methods  However, few studies have adopted topic-based features  Some researchers BIBREF7 discuss topic characteristics of spamming posts, indicating that spammers are highly likely to dwell on some certain topics such as promotion  But this may not be applicable to the current scenario of smart spammers 
In this paper, we propose an efficient feature extraction method  In this method, two new topic-based features are extracted and used to discriminate human-like spammers from legitimate users  We consider the historical tweets of each user as a document and use the Latent Dirichlet Allocation (LDA) model to compute the topic distribution for each user  Based on the calculated topic probability, two topic-based features, the Local Outlier Standard Score (LOSS) which captures the user's interests on different topics and the Global Outlier Standard Score (GOSS) which reveals the user's interests on specific topic in comparison with other users', are extracted  The two features contain both local and global information, and the combination of them can distinguish human-like spammers effectively 
To the best of our knowledge, it is the first time that features based on topic distributions are used in spammer classification  Experimental results on one public dataset and one self-collected dataset further validate that the two sets of extracted topic-based features get excellent performance on human-like spammer classification problem compared with other state-of-the-art methods  In addition, we build a Weibo dataset, which contains both legitimate users and spammers 
To summarize, our major contributions are two-fold:
In the following sections, we first propose the topic-based features extraction method in Section 2, and then introduce the two datasets in Section 3  Experimental results are discussed in Section 4, and we conclude the paper in Section 5  Future work is presented in Section 6 
Methodology
In this section, we first provide some observations we obtained after carefully exploring the social network, then the LDA model is introduced  Based on the LDA model, the ways to obtain the topic probability vector for each user and the two topic-based features are provided 
Observation
After exploring the homepages of a substantial number of spammers, we have two observations  1) social spammers can be divided into two categories  One is content polluters, and their tweets are all about certain kinds of advertisement and campaign  The other is fake accounts, and their tweets resemble legitimate users' but it seems they are simply random copies of others to avoid being detected by anti-spam rules  2) For legitimate users, content polluters and fake accounts, they show different patterns on topics which interest them 
Legitimate users mainly focus on limited topics which interest him  They seldom post contents unrelated to their concern 
Content polluters concentrate on certain topics 
Fake accounts focus on a wide range of topics due to random copying and retweeting of other users' tweets 
Spammers and legitimate users show different interests on some topics e g  commercial, weather, etc 
To better illustrate our observation, Figure  1 shows the topic distribution of spammers and legitimate users in two employed datasets(the Honeypot dataset and Weibo dataset)  We can see that on both topics (topic-3 and topic-11) there exists obvious difference between the red bars and green bars, representing spammers and legitimate users  On the Honeypot dataset, spammers have a narrower shape of distribution (the outliers on the red bar tail are not counted) than that of legitimate users  This is because there are more content polluters than fake accounts  In other word, spammers in this dataset tend to concentrate on limited topics  While on the Weibo dataset, fake accounts who are interested in different topics take large proportion of spammers  Their distribution is more flat (i e  red bars) than that of the legitimate users  Therefore we can detect spammers by means of the difference of their topic distribution patterns 
LDA model
Blei et al blei2003latent first presented Latent Dirichlet Allocation(LDA) as an example of topic model 
Each document $i$ is deemed as a bag of words $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $ and $M$ is the number of words  Each word is attributable to one of the document's topics $Z=\left\lbrace  z_{i1},z_{i2},   ,z_{iK}\right\rbrace $ and $K$ is the number of topics  $\psi _{k}$ is a multinomial distribution over words for topic $k$   $\theta _i$ is another multinomial distribution over topics for document $i$   The smoothed generative model is illustrated in Figure  2   $\alpha $ and $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $0 are hyper parameter that affect scarcity of the document-topic and topic-word distributions  In this paper, $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $1 , $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $2 and $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $3 are empirically set to 0 3, 0 01 and 15  The entire content of each Twitter user is regarded as one document  We adopt Gibbs Sampling BIBREF8 to speed up the inference of LDA  Based on LDA, we can get the topic probabilities for all users in the employed dataset as: $W=\left\lbrace  w_{i1},w_{i2},  

ground_truth: simply averaging the predictions from the constituent single models

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. Is it possible to convert a cloze-style questions to a naturally-looking questions?
Context 2:   p_m\rbrace $8 triples where the word overlap between the question (Q) and passage (P) is less than 2 words (after excluding the stop words) 
The process relies on the fact that answer candidates from the introduction are likely to be discussed in detail in the remainder of the article  In effect, the cloze question from the introduction and the matching paragraph in the body forms a question and context passage pair  We create two cloze datasets, one each from Wikipedia corpus (for SQuAD and TriviaQA) and PUBMed academic papers (for the BioASQ challenge), consisting of 2 2M and 1M clozes respectively  From analyzing the cloze data manually, we were able to answer 76% times for the Wikipedia set and 80% times for the PUBMed set using the information in the passage  In most cases the cloze paraphrased the information in the passage, which we hypothesized to be a useful signal for the downstream QA task 
We also investigate the utility of forming subsets of the large cloze corpus, where we select the top passage-question-answer triples, based on the different criteria, like i) jaccard similarity of answer bearing sentence in introduction and the passage ii) the tf-idf scores of answer candidates and iii) the length of answer candidates  However, we empirically find that we were better off using the entire set rather than these subsets 
Pre-training: We make use of the generated cloze dataset to pre-train an expressive neural network designed for the task of reading comprehension  We work with two publicly available neural network models – the GA Reader BIBREF2 (to enable comparison with prior work) and BiDAF + Self-Attention (SA) model from BIBREF1 (which is among the best performing models on SQuAD and TriviaQA)  After pretraining, the performance of BiDAF+SA on a dev set of the (Wikipedia) cloze questions is 0 58 F1 score and 0 55 Exact Match (EM) score  This implies that the cloze corpus is neither too easy, nor too difficult to answer 
Fine Tuning: We fine tune the pre-trained model, from the previous step, over a small set of labelled question-answer pairs  As we shall later see, this step is crucial, and it only requires a handful of labelled questions to achieve a significant proportion of the performance typically attained by training on tens of thousands of questions 
Datasets
We apply our system to three datasets from different domains  SQuAD BIBREF3 consists of questions whose answers are free form spans of text from passages in Wikipedia articles  We follow the same setting as in BIBREF0 , and split $10\%$ of training questions as the test set, and report performance when training on subsets of the remaining data ranging from $1\%$ to $90\%$ of the full set  We also report the performance on the dev set when trained on the full training set ( $1^\ast $ in Table 2 )  We use the same hyperparameter settings as in prior work  We compare and study four different settings: 1) the Supervised Learning (SL) setting, which is only trained on the supervised data, 2) the best performing GDAN model from BIBREF0 , 3) pretraining on a Language Modeling (LM) objective and fine-tuning on the supervised data, and 4) pretraining on the Cloze dataset and fine-tuning on the supervised data  The LM and Cloze methods use exactly the same data for pretraining, but differ in the loss functions used  We report F1 and EM scores on our test set using the official evaluation scripts provided by the authors of the dataset 
TriviaQA BIBREF4 comprises of over 95K web question-answer-evidence triples  Like SQuAD, the answers are spans of text  Similar to the setting in SQuAD, we create multiple smaller subsets of the entire set  For our semi-supervised QA system, we use the BiDAF+SA model BIBREF1 – the highest performing publicly available system for TrivaQA  Here again, we compare the supervised learning (SL) settings against the pretraining on Cloze set and fine tuning on the supervised set  We report F1 and EM scores on the dev set 
We also test on the BioASQ 5b dataset, which consists of question-answer pairs from PubMed abstracts  We use the publicly available system from BIBREF14 , and follow the exact same setup as theirs, focusing only on factoid and list questions  For this setting, there are only 899 questions for training  Since this is already a low-resource problem we only report results using 5-fold cross-validation on all the available data  We report Mean Reciprocal Rank (MRR) on the factoid questions, and F1 score for the list questions 
Main Results
Table 2 shows a comparison of the discussed settings on both SQuAD and TriviaQA  Without any fine-tuning (column 0) the performance is low, probably because the model never saw a real question, but we see significant gains with Cloze pretraining even with very little labeled data  The BiDAF+SA model, exceeds an F1 score of $50\%$ with only $1\%$ of the training data (454 questions for SQuAD, and 746 questions for TriviaQA), and approaches $90\%$ of the best performance with only $10\%$ labeled data  The gains over the SL setting, however, diminish as the size of the labeled set increases and are small when the full dataset is available 
Cloze pretraining outperforms the GDAN baseline from BIBREF0 using the same SQuAD dataset splits  Additionally, we show improvements in the $90\%$ data case unlike GDAN  Our approach is also applicable in the extremely low-resource setting of $1\%$ data, which we suspect GDAN might have trouble with since it uses the labeled data to do reinforcement learning  Furthermore, we are able to use the same cloze dataset to improve performance on both SQuAD and TriviaQA datasets  When we use the same unlabeled data to pre-train with a language modeling objective, the performance is worse, showing the bias we introduce by constructing clozes is important 
On the BioASQ dataset (Table 3 ) we again see a significant improvement when pretraining with the cloze questions over the supervised baseline  The improvement is smaller than what we observe with SQuAD and TriviaQA datasets – we believe this is because questions are generally more difficult in BioASQ  BIBREF14 showed that pretraining on SQuAD dataset improves the downstream performance on BioASQ  Here, we show a much larger improvement by pretraining on cloze questions constructed in an unsupervised manner from the same domain 
Analysis
Regression Analysis: To understand which types of questions benefit from pre-training, we pre-specified certain features (see Figure 1 right) for each of the dev set questions in SQuAD, and then performed linear regression to predict the F1 score for that question from these features  We predict the F1 scores from the cloze pretrained model ( $y^{\text{cloze}}$ ), the supervised model ( $y^{\text{sl}}$ ), and the difference of the two ( $y^{\text{cloze}}-y^{\text{sl}}$ ), when using $10\%$ of labeled data  The coefficients of the fitted model are shown in Figure 1 (left) along with their std errors  Positive coefficients indicate that a high value of that feature is predictive of a high F1 score, and a negative coefficient indicates that a small value of that feature is predictive of a high F1 score (or a high difference of F1 scores from the two models in the case of $y^{\text{cloze}}-y^{\text{sl}}$ ) 
The two strongest effects we observe are that a high lexical overlap between the question and the sentence containing the answer is indicative of high boost with pretraining, and that a high lexical overlap between the question and the whole passage is indicative of the opposite  This is hardly surprising, since our cloze construction process is biased towards questions which have a similar phrasing to the answer sentences in context  Hence, test questions with a similar property are answered correctly after pretraining, whereas those with a high overlap with the whole passage tend to have lower performance  The pretraining also favors questions with short answers because the cloze construction process produces short answer spans  Also passages and questions which consist of tokens infrequent in the SQuAD training corpus receive a large boost after pretraining, since the unlabeled data covers a larger domain 
Performance on question types: Figure 2 shows the average gain in F1 score for different types of questions, when we pretrain on the clozes compared to the supervised case  This analysis is done on the $10\%$ split of the SQuAD training set  We consider two classifications of each question – one determined on the first word (usually a wh-word) of the question (Figure 2 (bottom)) and one based on the output of a separate question type classifier adapted from BIBREF21   We use the coarse grain labels namely Abbreviation (ABBR), Entity (ENTY), Description (DESC), Human (HUM), Location (LOC), Numeric (NUM) trained on a Logistic Regression classification system   While there is an improvement across the board, we find that abbreviation questions in particular receive a large boost  Also, "why" questions show the least improvement, which is in line with our expectation, since these usually require reasoning or world knowledge which cloze questions rarely require 
Conclusion
In this paper, we show that pre-training QA models with automatically constructed cloze questions improves the performance of the models significantly, especially when there are few labeled examples  The performance of the model trained only on the cloze questions is poor, validating the need for fine-tuning  Through regression analysis, we find that pretraining helps with questions which ask for factual information located in a specific part of the context  For future work, we plan to explore the active learning setup for this task – specifically, which passages and / or types of questions can we select to annotate, such that there is a maximum performance gain from fine-tuning  We also want to explore how to adapt cloze style pre-training to NLP tasks other than QA 
Acknowledgments
Bhuwan Dhingra is supported by NSF under grants CCF-1414030 and IIS-1250956 and by grants from Google  Danish Pruthi and Dheeraj Rajagopal are supported by the DARPA Big Mechanism program under ARO contract W911NF-14-1-0436 
Context 3: ,w_{iM}\right\rbrace $4 , where $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $5 is the number of users  Each element $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $6 is a topic probability vector for the $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $7 document  $W=\left\lbrace  w_{i1},w_{i2},   ,w_{iM}\right\rbrace $8 is the raw topic probability vector and our features are developed on top of it 
Topic-based Features
Using the LDA model, each person in the dataset is with a topic probability vector $X_i$   Assume $x_{ik}\in X_{i}$ denotes the likelihood that the $\emph {i}^{th}$ tweet account favors $\emph {k}^{th}$ topic in the dataset  Our topic based features can be calculated as below 
Global Outlier Standard Score measures the degree that a user's tweet content is related to a certain topic compared to the other users  Specifically, the "GOSS" score of user $i$ on topic $k$ can be calculated as Eq ( 12 ):
$$\centering \begin{array}{ll} \mu \left(x_{k}\right)=\frac{\sum _{i=1}^{n} x_{ik}}{n},\\ GOSS\left(x_{ik}\right)=\frac{x_{ik}-\mu \left(x_k\right)}{\sqrt{\underset{i}{\sum }\left(x_{ik}-\mu \left(x_{k}\right)\right)^{2}}}  \end{array}$$   (Eq  12)
The value of $GOSS\left(x_{ik}\right)$ indicates the interesting degree of this person to the $\emph {k}^{th}$ topic  Specifically, if $GOSS\left(x_{ik}\right)$ > $GOSS\left(x_{jk}\right)$ , it means that the $\emph {i}^{th}$ person has more interest in topic $k$ than the $\emph {j}^{th}$ person  If the value $GOSS\left(x_{ik}\right)$ is extremely high or low, the $\emph {i}^{th}$ person showing extreme interest or no interest on topic $k$ which will probably be a distinctive pattern in the fowllowing classfication  Therefore, the topics interested or disliked by the $\emph {k}^{th}$0 person can be manifested by $\emph {k}^{th}$1 , from which the pattern of the interested topics with regarding to this person is found  Denote $\emph {k}^{th}$2 our first topic-based feature, and it hopefully can get good performance on spammer detection 
Local Outlier Standard Score measures the degree of interest someone shows to a certain topic by considering his own homepage content only  For instance, the "LOSS" score of account $i$ on topic $k$ can be calculated as Eq ( 13 ):
$$\centering \begin{array}{ll} \mu \left(x_{i}\right)=\frac{\sum _{k=1}^{K} x_{ik}}{K},\\ LOSS\left(x_{ik}\right)=\frac{x_{ik}-\mu \left(x_i\right)}{\sqrt{\underset{k}{\sum }\left(x_{ik}-\mu \left(x_{i}\right)\right)^{2}}}  \end{array}$$   (Eq  13)
$\mu (x_i)$ represents the averaged interesting degree for all topics with regarding to $\emph {i}^{th}$ user and his tweet content  Similarly to $GOSS$ , the topics interested or disliked by the $\emph {i}^{th}$ person via considering his single post information can be manifested by $f_{LOSS}^{i}=[LOSS(x_{i1})\cdots LOSS(x_{iK})]$ , and $LOSS$ becomes our second topic-based features for the $\emph {i}^{th}$ person 
Dataset
We use one public dataset Social Honeypot dataset and one self-collected dataset Weibo dataset to validate the effectiveness of our proposed features 
Social Honeypot Dataset: Lee et al  lee2010devils created and deployed 60 seed social accounts on Twitter to attract spammers by reporting back what accounts interact with them  They collected 19,276 legitimate users and 22,223 spammers in their datasets along with their tweet content in 7 months  This is our first test dataset 
Our Weibo Dataset: Sina Weibo is one of the most famous social platforms in China  It has implemented many features from Twitter  The 2197 legitimate user accounts in this dataset are provided by the Tianchi Competition held by Sina Weibo  The spammers are all purchased commercially from multiple vendors on the Internet  We checked them manually and collected 802 suitable "smart" spammers accounts 
Preprocessing: Before directly performing the experiments on the employed datasets, we first delete some accounts with few posts in the two employed since the number of tweets is highly indicative of spammers  For the English Honeypot dataset, we remove stopwords, punctuations, non-ASCII words and apply stemming  For the Chinese Weibo dataset, we perform segmentation with "Jieba", a Chinese text segmentation tool  After preprocessing steps, the Weibo dataset contains 2197 legitimate users and 802 spammers, and the honeypot dataset contains 2218 legitimate users and 2947 spammers  It is worth mentioning that the Honeypot dataset has been slashed because most of the Twitter accounts only have limited number of posts, which are not enough to show their interest inclination 
Evaluation Metrics
The evaluating indicators in our model are show in 2   We calculate precision, recall and F1-score (i e  F1 score) as in Eq  ( 19 )  Precision is the ratio of selected accounts that are spammers  Recall is the ratio of spammers that are detected so  F1-score is the harmonic mean of precision and recall 
$$precision =\frac{TP}{TP+FP}, recall =\frac{TP}{TP+FN}\nonumber \\ F1-score = \frac{2\times precision \times recall}{precision + recall}$$   (Eq  19)
Performance Comparisons with Baseline
Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features  We test each classification algorithm with scikit-learn BIBREF9 and run a 10-fold cross validation  On each dataset, the employed classifiers are trained with individual feature first, and then with the combination of the two features  From 1 , we can see that GOSS+LOSS achieves the best performance on F1-score among all others  Besides, the classification by combination of LOSS and GOSS can increase accuracy by more than 3% compared with raw topic distribution probability 
Comparison with Other Features
To compare our extracted features with previously used features for spammer detection, we use three most discriminative feature sets according to Lee et al  lee2011seven( 4 )  Two classifiers (Adaboost and SVM) are selected to conduct feature performance comparisons  Using Adaboost, our LOSS+GOSS features outperform all other features except for UFN which is 2% higher than ours with regard to precision on the Honeypot dataset  It is caused by the incorrectly classified spammers who are mostly news source after our manual check  They keep posting all kinds of news pieces covering diverse topics, which is similar to the behavior of fake accounts  However, UFN based on friendship networks is more useful for public accounts who possess large number of followers  The best recall value of our LOSS+GOSS features using SVM is up to 6% higher than the results by other feature groups  Regarding F1-score, our features outperform all other features  To further show the advantages of our proposed features, we compare our combined LOSS+GOSS with the combination of all the features from Lee et al  lee2011seven (UFN+UC+UH)  It's obvious that LOSS+GOSS have a great advantage over UFN+UC+UH in terms of recall and F1-score  Moreover, by combining our LOSS+GOSS features and UFN+UC+UH features together, we obtained another 7 1% and 2 3% performance gain with regard to precision and F1-score by Adaboost  Though there is a slight decline in terms of recall  By SVM, we get comparative results on recall and F1-score but about 3 5% improvement on precision 
Conclusion
In this paper, we propose a novel feature extraction method to effectively detect "smart" spammers who post seemingly legitimate tweets and are thus difficult to identify by existing spammer classification methods  Using the LDA model, we obtain the topic probability for each Twitter user  By utilizing the topic probability result, we extract our two topic-based features: GOSS and LOSS which represent the account with global and local information  Experimental results on a public dataset and a self-built Chinese microblog dataset validate the effectiveness of the proposed features 
Future Work
In future work, the combination method of local and global information can be further improved to maximize their individual strengths  We will also apply decision theory to enhancing the performance of our proposed features  Moreover, we are also building larger datasets on both Twitter and Weibo to validate our method  Moreover, larger datasets on both Twitter and Weibo will be built to further validate our method 

ground_truth: Unanswerable

Unanswerable

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. LDA is an unsupervised method; is this paper introducing an unsupervised approach to spam detection?
Context 4:  Overall, the 16 languages considered in our experiments are typologically, morphologically and syntactically fairly diverse 
Lexicons
We generate our external lexicons using the set of source lexicons listed in Table TABREF3   Since external lexical information is exploited via features, there is no need for the external lexicons and the annotated corpora to use the same PoS inventory  Therefore, for each language, we simply extracted from the corresponding lexicon the PoS of each word based on its morphological tags, by removing all information provided except for its coarsest-level category  We also added entries for punctuations when the source lexicons did not contain any 
We also performed experiments in which we retained the full original tags provided by the lexicons, with all morphological features included  On average, results were slightly better than those presented in the paper, although not statistically significantly  Moreover, the granularity of tag inventories in the lexicons is diverse, which makes it difficult to draw general conclusions about results based on full tags  This is why we only report results based on (coarse) PoS extracted from the original lexicons 
Baseline models
In order to assess the respective contributions of external lexicons and word vector representations, we first compared the results of the three above-mentioned systems when trained without such additional lexical information  Table TABREF11 provides the results of MElt and MarMoT retrained on UD1 2 corpora, together with the results publised on the same corpora by BIBREF20 , using their best model not enhanced by external word vector representations —i e  the model they call INLINEFORM0 , which is a bidirectional LSTM that combines both word and character embeddings 
These results show that Plank et al 's (2016) bi-LSTM performs extremely well, surpassed by MarMoT on only 3 out of 16 datasets (Czech, French and Italian), and by MElt only once (Indonesian) 
Models enriched with external lexical information
Table TABREF13 provides the results of four systems enriched with lexical information  The feature-based systems MElt and MarMoT, respectively based on MEMMs and CRFs, are extended with the lexical information provided by our morphosyntactic lexicons  This extension takes the form of additional features, as described in Section SECREF2 for MElt  The results reported by BIBREF20 for their bidirectional LSTM when initialised with Polyglot embeddings trained on full wikipedias are also included, together with their new system FREQBIN, also initialised with Polyglot embeddings  FREQBIN trains bi-LSTMs to predict for each input word both a PoS and a label that represents its log frequency in the training data  As they word it, “the idea behind this model is to make the representation predictive for frequency, which encourages the model not to share representations between common and rare words, thus benefiting the handling of rare tokens ”
The results, which are also displayed in Figures FIGREF14 and FIGREF15 , show that all systems reach very similar results on average, although discrepancies can be observed from one dataset to another, on which we shall comment shortly  The best performing system in terms of macro-average is MElt (96 60%)  Both bi-LSTM systems reach the same score (96 58%), the difference with MElt's results being non significant, whereas MarMoT is only 0 14% behind (96 46%)  Given the better baseline scores of the neural approaches, these results show that the benefit of using external lexicons in the feature-based models MElt and MarMoT are much higher than those using Polyglot word vector representations as initialisations for bi-LSTMs 
Yet these very similar overall results reflect a different picture when focusing on OOV tagging accuracy  The best models for OOV tagging accuracy are, by far, FREQBIN models, which are beaten by MarMoT and by MElt only once each (on English and Danish respectively)  The comparison on OOV tagging between MElt and MarMoT shows that MElt performs better on average than MarMoT, despite the fact that MarMoT's baseline results were better than those reached by MElt  This shows that the information provided by external morphosyntactic lexicons is better exploited by MElt's lexical features than by those used by MarMoT  On the other hand, the comparison of both bi-LSTM-based approaches confirm that the FREQBIN models is better by over 10% absolute on OOV tagging accuracy (94 28% vs  83 59%), with 65% lower error rate 
One of the important differences between the lexical information provided by an external lexicon and word vectors built from raw corpora, apart from the very nature of the lexical information provided, is the coverage and accuracy of this lexical information on rare words  All words in a morphosyntactic lexicon are associated with information of a same granularity and quality, which is not the case with word representations such as provided by Polyglot  Models that take advantage of external lexicons should therefore perform comparatively better on datasets containing a higher proportion of rarer words, provided the lexicons' coverage is high  In order to confirm this intuition, we have used a lexical richness metric based on the type/token ratio  Since this ratio is well-known for being sensitive to corpus length, we normalised it by computing it over the 60,000 first tokens of each training set  When this normalised type/token ratio is plotted against the difference between the results of MElt and both bi-LSTM-based models, the expected correlation is clearly visible (see Figure FIGREF16 )  This explains why MElt obtains better results on the morphologically richer Slavic datasets (average normalised type/token ratio: 0 28, average accuracy difference: 0 32 compared to both bi-LSTM+Polyglot and FREQBIN+Polyglot) and, at the other end of the spectrum, significantly worse results on the English dataset (normalised type/token ratio: 0 15, average accuracy difference: -0 56 compared to bi-LSTM+Polyglot, -0 57 compared to FREQBIN+Polyglot) 
Conclusion
Two main conclusions can be drawn from our comparative results  First, feature-based tagging models adequately enriched with external morphosyntactic lexicons perform, on average, as well as bi-LSTMs enriched with word embeddings  Per-language results show that the best accuracy levels are reached by feature-based models, and in particular by our improved version of the MEMM-based system MElt, on datasets with high lexical variability (in short, for morphologically rich languages), whereas neural-based results perform better on datatsets with lower lexical variability (e g  for English) 
We have only compared the contribution of morphosyntactic lexicons to feature-based models (MEMMs, CRFs) and that of word vector representations to bi-LSTM-based models as reported by BIBREF20   As mentioned above, work on the contribution of word vector representations to feature-based approaches has been carried out by BIBREF18   However, the exploitation of existing morphosyntactic or morphological lexicons in neural models is a less studied question  Improvements over the state of the art might be achieved by integrating lexical information both from an external lexicon and from word vector representations into tagging models 
In that regard, further work will be required to understand which class of models perform the best  An option would be to integrate feature-based models such as a CRF with an LSTM-based layer, following recent proposals such as the one proposed by BIBREF45 for named entity recognition 

ground_truth: No

No