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

Please answer the questions based on the text provided below. Do the answered questions measure for the usefulness of the answer?
Context 1: Introduction
From a group of small users at the time of its inception in 2009, Quora has evolved in the last few years into one of the largest community driven Q&A sites with diverse user communities  With the help of efficient content moderation/review policies and active in-house review team, efficient Quora bots, this site has emerged into one of the largest and reliable sources of Q&A on the Internet  On Quora, users can post questions, follow questions, share questions, tag them with relevant topics, follow topics, follow users apart from answering, commenting, upvoting/downvoting etc  The integrated social structure at the backbone of it and the topical organization of its rich content have made Quora unique with respect to other Q&A sites like Stack Overflow, Yahoo! Answers etc  and these are some of the prime reasons behind its popularity in recent times  Quality question posting and getting them answered are the key objectives of any Q&A site  In this study we focus on the answerability of questions on Quora, i e , whether a posted question shall eventually get answered  In Quora, the questions with no answers are referred to as “open questions”  These open questions need to be studied separately to understand the reason behind their not being answered or to be precise, are there any characteristic differences between `open' questions and the answered ones  For example, the question “What are the most promising advances in the treatment of traumatic brain injuries?” was posted on Quora on 23rd June, 2011 and got its first answer after almost 2 years on 22nd April, 2013  The reason that this question remained open so long might be the hardness of answering it and the lack of visibility and experts in the domain  Therefore, it is important to identify the open questions and take measures based on the types - poor quality questions can be removed from Quora and the good quality questions can be promoted so that they get more visibility and are eventually routed to topical experts for better answers 
Characterization of the questions based on question quality requires expert human interventions often judging if a question would remain open based on factors like if it is subjective, controversial, open-ended, vague/imprecise, ill-formed, off-topic, ambiguous, uninteresting etc  Collecting judgment data for thousands of question posts is a very expensive process  Therefore, such an experiment can be done only for a small set of questions and it would be practically impossible to scale it up for the entire collection of posts on the Q&A site  In this work, we show that appropriate quantification of various linguistic activities can naturally correspond to many of the judgment factors mentioned above (see table 2 for a collection of examples)  These quantities encoding such linguistic activities can be easily measured for each question post and thus helps us to have an alternative mechanism to characterize the answerability on the Q&A site 
There are several research works done in Q&A focusing on content of posts  BIBREF0 exploit community feedback to identify high quality content on Yahoo! Answers  BIBREF1 use textual features to predict answer quality on Yahoo! Answers  BIBREF2 , investigate predictors of answer quality through a comparative, controlled field study of user responses  BIBREF3 study the problem of how long questions remain unanswered  BIBREF4 propose a prediction model on how many answers a question shall receive  BIBREF5 analyze and predict unanswered questions on Yahoo Answers  BIBREF6 study question quality in Yahoo! Answers 
Dataset description
We obtained our Quora dataset BIBREF7 through web-based crawls between June 2014 to August 2014  This crawling exercise has resulted in the accumulation of a massive Q&A dataset spanning over a period of over four years starting from January 2010 to May 2014  We initiated crawling with 100 questions randomly selected from different topics so that different genre of questions can be covered  The crawling of the questions follow a BFS pattern through the related question links  We obtained 822,040 unique questions across 80,253 different topics with a total of 1,833,125 answers to these questions  For each question, we separately crawl their revision logs that contain different types of edit information for the question and the activity log of the question asker 
Linguistic activities on Quora
In this section, we identify various linguistic activities on Quora and propose quantifications of the language usage patterns in this Q&A site  In particular, we show that there exists significant differences in the linguistic structure of the open and the answered questions  Note that most of the measures that we define are simple, intuitive and can be easily obtained automatically from the data (without manual intervention)  Therefore the framework is practical, inexpensive and highly scalable 
Content of a question text is important to attract people and make them engage more toward it  The linguistic structure (i e , the usage of POS tags, the use of Out-of-Vocabulary words, character usage etc ) one adopts are key factors for answerability of questions  We shall discuss the linguistic structure that often represents the writing style of a question asker 
In fig 1 (a), we observe that askers of open questions generally use more no  of words compared to answered questions  To understand the nature of words (standard English words or chat-like words frequently used in social media) used in the text, we compare the words with GNU Aspell dictionary to see whether they are present in the dictionary or not  We observe that both open questions and answered questions follow similar distribution (see fig 1 (b))  Part-of-Speech (POS) tags are indicators of grammatical aspects of texts  To observe how the Part-of-Speech tags are distributed in the question texts, we define a diversity metric  We use the standard CMU POS tagger BIBREF8 for identifying the POS tags of the constituent words in the question  We define the POS tag diversity (POSDiv) of a question $q_i$ as follows: $POSDiv(q_i) = -\sum _{j \in pos_{set}}p_j\times \log (p_j)$ where $p_j$ is the probability of the $j^{th}$ POS in the set of POS tags  Fig 1 (c) shows that the answered questions have lower POS tag diversity compared to open questions  Question texts undergo several edits so that their readability and the engagement toward them are enhanced  It is interesting to identify how far such edits can make the question different from the original version of it  To capture this phenomena, we have adopted ROUGE-LCS recall BIBREF9 from the domain of text summarization  Higher the recall value, lesser are the changes in the question text  From fig 1 (d), we observe that open questions tend to have higher recall compared to the answered ones which suggests that they have not gone through much of text editing thus allowing for almost no scope of readability enhancement 
Psycholinguistic analysis:
The way an individual talks or writes, give us clue to his/her linguistic, emotional, and cognitive states  A question asker's linguistic, emotional, cognitive states are also revealed through the language he/she uses in the question text  In order to capture such psycholinguistic aspects of the asker, we use Linguistic Inquiry and Word Count (LIWC) BIBREF10 that analyzes various emotional, cognitive, and structural components present in individuals' written texts  LIWC takes a text document as input and outputs a score for the input for each of the LIWC categories such as linguistic (part-of-speech of the words, function words etc ) and psychological categories (social, anger, positive emotion, negative emotion, sadness etc ) based on the writing style and psychometric properties of the document  In table 1 , we perform a comparative analysis of the asker's psycholinguistic state while asking an open question and an answered question 
Askers of open questions use more function words, impersonal pronouns, articles on an average whereas asker of answered questions use more personal pronouns, conjunctions and adverbs to describe their questions  Essentially, open questions lack content words compared to answered questions which, in turn, affects the readability of the question  As far as the psychological aspects are concerned, answered question askers tend to use more social, family, human related words on average compared to an open question asker  The open question askers express more positive emotions whereas the answered question asker tend to express more negative emotions in their texts  Also, answered question askers are more emotionally involved and their questions reveal higher usage of anger, sadness, anxiety related words compared to that of open questions  Open questions, on the other hand, contains more sexual, body, health related words which might be reasons why they do not attract answers 
In table 2 , we show a collection of examples of open questions to illustrate that many of the above quantities based on the linguistic activities described in this section naturally correspond to the factors that human judges consider responsible for a question remaining unanswered  This is one of the prime reasons why these quantities qualify as appropriate indicators of answerability 
Prediction model
In this section, we describe the prediction framework in detail  Our goal is to predict whether a given question after a time period $t$ will be answered or not 
Linguistic styles of the question asker
The content and way of posing a question is important to attract answers  We have observed in the previous section that these linguistic as well as psycholinguistic aspects of the question asker are discriminatory factors  For the prediction, we use the following features:
Context 2: Introduction
Assembling training corpora of annotated natural language examples in specialized domains such as biomedicine poses considerable challenges  Experts with the requisite domain knowledge to perform high-quality annotation tend to be expensive, while lay annotators may not have the necessary knowledge to provide high-quality annotations  A practical approach for collecting a sufficiently large corpus would be to use crowdsourcing platforms like Amazon Mechanical Turk (MTurk)  However, crowd workers in general are likely to provide noisy annotations BIBREF0 , BIBREF1 , BIBREF2 , an issue exacerbated by the technical nature of specialized content  Some of this noise may reflect worker quality and can be modeled BIBREF0 , BIBREF1 , BIBREF3 , BIBREF4 , but for some instances lay people may simply lack the domain knowledge to provide useful annotation 
In this paper we report experiments on the EBM-NLP corpus comprising crowdsourced annotations of medical literature BIBREF5   We operationalize the concept of annotation difficulty and show how it can be exploited during training to improve information extraction models  We then obtain expert annotations for the abstracts predicted to be most difficult, as well as for a similar number of randomly selected abstracts  The annotation of highly specialized data and the use of lay and expert annotators allow us to examine the following key questions related to lay and expert annotations in specialized domains:
Can we predict item difficulty? We define a training instance as difficult if a lay annotator or an automated model disagree on its labeling  We show that difficulty can be predicted, and that it is distinct from inter-annotator agreement  Further, such predictions can be used during training to improve information extraction models 
Are there systematic differences between expert and lay annotations? We observe decidedly lower agreement between lay workers as compared to domain experts  Lay annotations have high precision but low recall with respect to expert annotations in the new data that we collected  More generally, we expect lay annotations to be lower quality, which may translate to lower precision, recall, or both, compared to expert annotations  Can one rely solely on lay annotations? Reasonable models can be trained using lay annotations alone, but similar performance can be achieved using markedly less expert data  This suggests that the optimal ratio of expert to crowd annotations for specialized tasks will depend on the cost and availability of domain experts  Expert annotations are preferable whenever its collection is practical  But in real-world settings, a combination of expert and lay annotations is better than using lay data alone 
Does it matter what data is annotated by experts? We demonstrate that a system trained on combined data achieves better predictive performance when experts annotate difficult examples rather than instances selected at i i d  random 
Our contributions in this work are summarized as follows  We define a task difficulty prediction task and show how this is related to, but distinct from, inter-worker agreement  We introduce a new model for difficulty prediction combining learned representations induced via a pre-trained `universal' sentence encoder BIBREF6 , and a sentence encoder learned from scratch for this task  We show that predicting annotation difficulty can be used to improve the task routing and model performance for a biomedical information extraction task  Our results open up a new direction for ensuring corpus quality  We believe that item difficulty prediction will likely be useful in other, non-specialized tasks as well, and that the most effective data collection in specialized domains requires research addressing the fundamental questions we examine here 
Related Work
Crowdsourcing annotation is now a well-studied problem BIBREF7 , BIBREF0 , BIBREF1 , BIBREF2   Due to the noise inherent in such annotations, there have also been considerable efforts to develop aggregation models that minimize noise BIBREF0 , BIBREF1 , BIBREF3 , BIBREF4  
There are also several surveys of crowdsourcing in biomedicine specifically BIBREF8 , BIBREF9 , BIBREF10   Some work in this space has contrasted model performance achieved using expert vs  crowd annotated training data BIBREF11 , BIBREF12 , BIBREF13   Dumitrache et al  Dumitrache:2018:CGT:3232718 3152889 concluded that performance is similar under these supervision types, finding no clear advantage from using expert annotators  This differs from our findings, perhaps owing to differences in design  The experts we used already hold advanced medical degrees, for instance, while those in prior work were medical students  Furthermore, the task considered here would appear to be of greater difficulty: even a system trained on $\sim $ 5k instances performs reasonably, but far from perfect  By contrast, in some of the prior work where experts and crowd annotations were deemed equivalent, a classifier trained on 300 examples can achieve very high accuracy BIBREF12  
More relevant to this paper, prior work has investigated methods for `task routing' in active learning scenarios in which supervision is provided by heterogeneous labelers with varying levels of expertise BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF14   The related question of whether effort is better spent collecting additional annotations for already labeled (but potentially noisily so) examples or novel instances has also been addressed BIBREF18   What distinguishes the work here is our focus on providing an operational definition of instance difficulty, showing that this can be predicted, and then using this to inform task routing 
Application Domain
Our specific application concerns annotating abstracts of articles that describe the conduct and results of randomized controlled trials (RCTs)  Experimentation in this domain has become easy with the recent release of the EBM-NLP BIBREF5 corpus, which includes a reasonably large training dataset annotated via crowdsourcing, and a modest test set labeled by individuals with advanced medical training  More specifically, the training set comprises 4,741 medical article abstracts with crowdsourced annotations indicating snippets (sequences) that describe the Participants (p), Interventions (i), and Outcome (o) elements of the respective RCT, and the test set is composed of 191 abstracts with p, i, o sequence annotations from three medical experts 
Table 1 shows an example of difficult and easy examples according to our definition of difficulty  The underlined text demarcates the (consensus) reference label provided by domain experts  In the difficult examples, crowd workers marked text distinct from these reference annotations; whereas in the easy cases they reproduced them with reasonable fidelity  The difficult sentences usually exhibit complicated structure and feature jargon 
An abstract may contain some `easy' and some `difficult' sentences  We thus perform our analysis at the sentence level  We split abstracts into sentences using spaCy  We excluded sentences that comprise fewer than two tokens, as these are likely an artifact of errors in sentence splitting  In total, this resulted in 57,505 and 2,428 sentences in the train and test set abstracts, respectively 
Quantifying Task Difficulty
The test set includes annotations from both crowd workers and domain experts  We treat the latter as ground truth and then define the difficulty of sentences in terms of the observed agreement between expert and lay annotators  Formally, for annotation task $t$ and instance $i$ :
$$\text{Difficulty}_{ti} = \frac{\sum _{j=1}^n{f(\text{label}_{ij}, y_i})}{n}$$   (Eq  3)
where $f$ is a scoring function that measures the quality of the label from worker $j$ for sentence $i$ , as compared to a ground truth annotation, $y_i$   The difficulty score of sentence $i$ is taken as an average over the scores for all $n$ layworkers  We use Spearmans' correlation coefficient as a scoring function  Specifically, for each sentence we create two vectors comprising counts of how many times each token was annotated by crowd and expert workers, respectively, and calculate the correlation between these  Sentences with no labels are treated as maximally easy; those with only either crowd worker or expert label(s) are assumed maximally difficult 
The training set contains only crowdsourced annotations  To label the training data, we use a 10-fold validation like setting  We iteratively retrain the LSTM-CRF-Pattern sequence tagger of Patel et al  patel2018syntactic on 9 folds of the training data and use that trained model to predict labels for the 10th  In this way we obtain predictions on the full training set  We then use predicted spans as proxy `ground truth' annotations to calculate the difficulty score of sentences as described above; we normalize these to the [ $0, 1$ ] interval  We validate this approximation by comparing the proxy scores against reference scores over the test set, the Pearson's correlation coefficients are 0 57 for Population, 0 71 for Intervention and 0 68 for Outcome 
There exist many sentences that contain neither manual nor predicted annotations  We treat these as maximally easy sentences (with difficulty scores of 0)  Such sentences comprise 51%, 42% and 36% for Population, Interventions and Outcomes data respectively, indicating that it is easier to identify sentences that have no Population spans, but harder to identify sentences that have no Interventions or Outcomes spans  This is intuitive as descriptions of the latter two tend to be more technical and dense with medical jargon 
We show the distribution of the automatically labeled scores for sentences that do contain spans in Figure 1   The mean of the Population (p) sentence scores is significantly lower than that for other types of sentences (i and o), again indicating that they are easier on average to annotate  This aligns with a previous finding that annotating Interventions and Outcomes is more difficult than annotating Participants BIBREF5  
Many sentences contain spans tagged by the LSTM-CRF-Pattern model, but missed by all crowd workers, resulting in a maximally difficult score (1)  Inspection of such sentences revealed that some are truly difficult examples, but others are tagging model errors  In either case, such sentences have confused workers and/or the model, and so we retain them all as `difficult' sentences 
Content describing the p, i and o, respectively, is quite different  As such, one sentence usually contains (at most) only one of these three content types  We thus treat difficulty prediction for the respective label types as separate tasks 
Difficulty is not Worker Agreement
Our definition of difficulty is derived from agreement between expert and crowd annotations for the test data, and agreement between a predictive model and crowd annotations in the training data  It is reasonable to ask if these measures are related to inter-annotator agreement, a metric often used in language technology research to identify ambiguous or difficult items  Here we explicitly verify that our definition of difficulty only weakly correlates with inter-annotator agreement 
We calculate inter-worker agreement between crowd and expert annotators using Spearman's correlation coefficient  As shown in Table 2 , average agreement between domain experts are considerably higher than agreements between crowd workers for all three label types  This is a clear indication that the crowd annotations are noisier 
Furthermore, we compare the correlation between inter-annotator agreement and difficulty scores in the training data  Given that the majority of sentences do not contain a PICO span, we only include in these calculations those that contain a reference label  Pearson's r are 0 34, 0 30 and 0 31 for p, i and o, respectively, confirming that inter-worker agreement and our proposed difficulty score are quite distinct 
Predicting Annotation Difficulty
We treat difficulty prediction as a regression problem, and propose and evaluate neural model variants for the task  We first train RNN BIBREF19 and CNN BIBREF20 models 
We also use the universal sentence encoder (USE) BIBREF6 to induce sentence representations, and train a model using these as features  Following BIBREF6 , we then experiment with an ensemble model that combines the `universal' and task-specific representations to predict annotation difficulty  We expect these universal embeddings to capture general, high-level semantics, and the task specific representations to capture more granular information  Figure 2 depicts the model architecture  Sentences are fed into both the universal sentence encoder and, separately, a task specific neural encoder, yielding two representations  We concatenate these and pass the combined vector to the regression layer 
Experimental Setup and Results
We trained models for each label type separately  Word embeddings were initialized to 300d GloVe vectors BIBREF21 trained on common crawl data; these are fine-tuned during training  We used the Adam optimizer BIBREF22 with learning rate and decay set to 0 001 and 0 99, respectively  We used batch sizes of 16 
We used the large version of the universal sentence encoder with a transformer BIBREF23   We did not update the pretrained sentence encoder parameters during training  All hyperparamaters for all models (including hidden layers, hidden sizes, and dropout) were tuned using Vizier BIBREF24 via 10-fold cross validation on the training set maximizing for F1 
As a baseline, we also trained a linear Support-Vector Regression BIBREF25 model on $n$ -gram features ( $n$ ranges from 1 to 3) 
Table 3 reports Pearson correlation coefficients between the predictions with each of the neural models and the ground truth difficulty scores  Rows 1-4 correspond to individual models, and row 5 reports the ensemble performance  Columns correspond to label type  Results from all models outperform the baseline SVR model: Pearson's correlation coefficients range from 0 550 to 0 622  The regression correlations are the lowest 
The RNN model realizes the strongest performance among the stand-alone (non-ensemble) models, outperforming variants that exploit CNN and USE representations  Combining the RNN and USE further improves results  We hypothesize that this is due to complementary sentence information encoded in universal representations 
For all models, correlations for Intervention and Outcomes are higher than for Population, which is expected given the difficulty distributions in Figure 1   In these, the sentences are more uniformly distributed, with a fair number of difficult and easier sentences  By contrast, in Population there are a greater number of easy sentences and considerably fewer difficult sentences, which makes the difficulty ranking task particularly challenging
Context 3: Introduction
Deep Learning approaches have achieved impressive results on various NLP tasks BIBREF0 , BIBREF1 , BIBREF2 and have become the de facto approach for any NLP task  However, these deep learning techniques have found to be less effective for low-resource languages when the available training data is very less BIBREF3   Recently, several approaches like Multi-task learning BIBREF4 , multilingual learning BIBREF5 , semi-supervised learning BIBREF2 , BIBREF6 and transfer learning BIBREF7 , BIBREF3 have been explored by the deep learning community to overcome data sparsity in low-resource languages  Transfer learning trains a model for a parent task and fine-tunes the learned parent model weights (features) for a related child task BIBREF7 , BIBREF8   This effectively reduces the requirement on training data for the child task as the model would have learned relevant features from the parent task data thereby, improving the performance on the child task 
Transfer learning has also been explored in the multilingual Neural Machine Translation BIBREF3 , BIBREF9 , BIBREF10   The goal is to improve the NMT performance on the source to target language pair (child task) using an assisting source language (assisting to target translation is the parent task)  Here, the parent model is trained on the assisting and target language parallel corpus and the trained weights are used to initialize the child model  The child model can now be fine-tuned on the source-target language pairs, if parallel corpus is available  The divergence between the source and the assisting language can adversely impact the benefits obtained from transfer learning  Multiple studies have shown that transfer learning works best when the languages are related BIBREF3 , BIBREF10 , BIBREF9   Several studies have tried to address lexical divergence between the source and the target languages BIBREF10 , BIBREF11 , BIBREF12   However, the effect of word order divergence and its mitigation has not been explored  In a practical setting, it is not uncommon to have source and assisting languages with different word order  For instance, it is possible to find parallel corpora between English and some Indian languages, but very little parallel corpora between Indian languages  Hence, it is natural to use English as an assisting language for inter-Indian language translation 
To see how word order divergence can be detrimental, let us consider the case of the standard RNN (Bi-LSTM) encoder-attention-decoder architecture BIBREF13   The encoder generates contextual representations (annotation vectors) for each source word, which are used by the attention network to match the source words to the current decoder state  The contextual representation is word-order dependent  Hence, if the assisting and the source languages do not have similar word order the generated contextual representations will not be consistent  The attention network (and hence the decoder) sees different contextual representations for similar words in parallel sentences across different languages  This makes it difficult to transfer knowledge learned from the assisting language to the source language 
We illustrate this by visualizing the contextual representations generated by the encoder of an English to Hindi NMT system for two versions of the English input: (a) original word order (SVO) (b) word order of the source language (SOV, for Bengali)  Figure FIGREF1 shows that the encoder representations obtained are very different  The attention network and the decoder now have to work with very different representations  Note that the plot below does not take into account further lexical and other divergences between source and assisting languages, since we demonstrated word order divergence with the same language on the source side 
To address this word order divergence, we propose to pre-order the assisting language sentences to match the word order of the source language  We consider an extremely resource constrained scenario, where we do not have any parallel corpus for the child task  We are limited to a bilingual dictionary for transfer information from the assisting to the source language  From our experiments, we show that there is a significant increase in the translation accuracy for the unseen source-target language pair 
Addressing Lexical Divergence
BIBREF3 explored transfer learning for NMT on low-resource languages  They studied the influence of language divergence between languages chosen for training the parent and child model, and showed that choosing similar languages for training the parent and child model leads to better improvements from transfer learning  A limitation of BIBREF3 approach is that they ignore the lexical similarity between languages and also the source language embeddings are randomly initialized  BIBREF10 , BIBREF11 , BIBREF12 take advantage of lexical similarity between languages in their work  BIBREF10 proposed to use Byte-Pair Encoding (BPE) to represent the sentences in both the parent and the child language to overcome the above limitation  They show using BPE benefits transfer learning especially when the involved languages are closely-related agglutinative languages  Similarly, BIBREF11 utilize lexical similarity between the source and assisting languages by training a character-level NMT system  BIBREF12 address lexical divergence by using bilingual embeddings and mixture of universal token embeddings

ground_truth: No

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 much data is needed to train the task-specific encoder?
Context 2: 
Better IE with Difficulty Prediction
We next present experiments in which we attempt to use the predicted difficulty during training to improve models for information extraction of descriptions of Population, Interventions and Outcomes from medical article abstracts  We investigate two uses: (1) simply removing the most difficult sentences from the training set, and, (2) re-weighting the most difficult sentences 
We again use LSTM-CRF-Pattern as the base model and experimenting on the EBM-NLP corpus BIBREF5   This is trained on either (1) the training set with difficult sentences removed, or (2) the full training set but with instances re-weighted in proportion to their predicted difficulty score  Following BIBREF5 , we use the Adam optimizer with learning rate of 0 001, decay 0 9, batch size 20 and dropout 0 5  We use pretrained 200d GloVe vectors BIBREF21 to initialize word embeddings, and use 100d hidden char representations  Each word is thus represented with 300 dimensions in total  The hidden size is 100 for the LSTM in the character representation component, and 200 for the LSTM in the information extraction component  We train for 15 epochs, saving parameters that achieve the best F1 score on a nested development set 
Removing Difficult Examples
We first evaluate changes in performance induced by training the sequence labeling model using less data by removing difficult sentences prior to training  The hypothesis here is that these difficult instances are likely to introduce more noise than signal  We used a cross-fold approach to predict sentence difficulties, training on 9/10ths of the data and scoring the remaining 1/10th at a time  We then sorted sentences by predicted difficulty scores, and experimented with removing increasing numbers of these (in order of difficulty) prior to training the LSTM-CRF-Pattern model 
Figure 3 shows the results achieved by the LSTM-CRF-Pattern model after discarding increasing amounts of the training data: the $x$ and $y$ axes correspond to the the percentage of data removed and F1 scores, respectively  We contrast removing sentences predicted to be difficult with removing them (a) randomly (i i d ), and, (b) in inverse order of predicted inter-annotator agreement  The agreement prediction model is trained exactly the same like difficult prediction model, with simply changing the difficult score to annotation agreement  F1 scores actually improve (marginally) when we remove the most difficult sentences, up until we drop 4% of the data for Population and Interventions, and 6% for Outcomes  Removing training points at i i d  random degrades performance, as expected  Removing sentences in order of disagreement seems to have similar effect as removing them by difficulty score when removing small amount of the data, but the F1 scores drop much faster when removing more data  These findings indicate that sentences predicted to be difficult are indeed noisy, to the extent that they do not seem to provide the model useful signal 
Re-weighting by Difficulty
We showed above that removing a small number of the most difficult sentences does not harm, and in fact modestly improves, medical IE model performance  However, using the available data we are unable to test if this will be useful in practice, as we would need additional data to determine how many difficult sentences should be dropped 
We instead explore an alternative, practical means of exploiting difficulty predictions: we re-weight sentences during training inversely to their predicted difficulty  Formally, we weight sentence $i$ with difficulty scores above $\tau $ according to: $1-a\cdot (d_i-\tau )/(1-\tau )$ , where $d_i$ is the difficulty score for sentence $i$ , and $a$ is a parameter codifying the minimum weight value  We set $\tau $ to 0 8 so as to only re-weight sentences with difficulty in the top 20th percentile, and we set $a$ to 0 5  The re-weighting is equivalent to down-sampling the difficult sentences  LSTM-CRF-Pattern is our base model 
Table 4 reports the precision, recall and F1 achieved both with and without sentence re-weighting  Re-weighting improves all metrics modestly but consistently  All F1 differences are statistically significant under a sign test ( $p<0 01$ )  The model with best precision is different for Patient, Intervention and Outcome labels  However re-weighting by difficulty does consistently yield the best recall for all three extraction types, with the most notable improvement for i and o, where recall improved by 10 percentage points  This performance increase translated to improvements in F1 across all types, as compared to the base model and to re-weighting by agreement 
Involving Expert Annotators
The preceding experiments demonstrate that re-weighting difficult sentences annotated by the crowd generally improves the extraction models  Presumably the performance is influenced by the annotation quality 
We now examine the possibility that the higher quality and more consistent annotations of domain experts on the difficult instances will benefit the extraction model  This simulates an annotation strategy in which we route difficult instances to domain experts and easier ones to crowd annotators  We also contrast the value of difficult data to that of an i i d  random sample of the same size, both annotated by experts 
Expert annotations of Random and Difficult Instances
We re-annotate by experts a subset of most difficult instances and the same number of random instances  As collecting annotations from experts is slow and expensive, we only re-annotate the difficult instances for the interventions extraction task  We re-annotate the abstracts which cover the sentences with predicted difficulty scores in the top 5 percentile  We rank the abstracts from the training set by the count of difficult sentences, and re-annotate the abstracts that contain the most difficult sentences  Constrained by time and budget, we select only 2000 abstracts for re-annotation; 1000 of these are top-ranked, and 1000 are randomly sampled  This re-annotation cost $3,000  We have released the new annotation data at: https://github com/bepnye/EBM-NLP 
Following BIBREF5 , we recruited five medical experts via Up-work with advanced medical training and strong technical reading/writing skills  The expert annotator were asked to read the entire abstract and highlight, using the BRAT toolkit BIBREF26 , all spans describing medical Interventions  Each abstract is only annotated by one expert  We examined 30 re-annotated abstracts to ensure the annotation quality before hiring the annotator 
Table 5 presents the results of LSTM-CRF-Pattern model trained on the reannotated difficult subset and the random subset  The first two rows show the results for models trained with expert annotations  The model trained on random data has a slightly better F1 than that trained on the same amount of difficult data  The model trained on random data has higher precision but lower recall 
Rows 3 and 4 list the results for models trained on the same data but with crowd annotation  Models trained with expert-annotated data are clearly superior to those trained with crowd labels with respect to F1, indicating that the experts produced higher quality annotations  For crowdsourced annotations, training the model with data sampled at i i d  random achieves 2% higher F1 than when difficult instances are used  When expert annotations are used, this difference is less than 1%  This trend in performance may be explained by differences in annotation quality: the randomly sampled set was more consistently annotated by both experts and crowd because the difficult set is harder  However, in both cases expert annotations are better, with a bigger difference between the expert and crowd models on the difficult set 
The last row is the model trained on all 5k abstracts with crowd annotations  Its F1 score is lower than either expert model trained on only 20% of data, suggesting that expert annotations should be collected whenever possible  Again the crowd model on complete data has higher precision than expert models but its recall is much lower 
Routing To Experts or Crowd
So far a system was trained on one type of data, either labeled by crowd or experts  We now examine the performance of a system trained on data that was routed to either experts or crowd annotators depending on their predicted difficult  Given the results presented so far mixing annotators may be beneficial given their respective trade-offs of precision and recall  We use the annotations from experts for an abstract if it exists otherwise use crowd annotations  The results are presented in Table 6  
Rows 1 and 2 repeat the performance of the models trained on difficult subset and random subset with expert annotations only respectively  The third row is the model trained by combining difficult and random subsets with expert annotations  There are around 250 abstracts in the overlap of these two sets, so there are total 1 75k abstracts used for training the D+R model  Rows 4 to 6 are the models trained on all 5k abstracts with mixed annotations, where Other means the rest of the abstracts with crowd annotation only 
The results show adding more training data with crowd annotation still improves at least 1 point F1 score in all three extraction tasks  The improvement when the difficult subset with expert annotations is mixed with the remaining crowd annotation is 3 5 F1 score, much larger than when a random set of expert annotations are added  The model trained with re-annotating the difficult subset (D+Other) also outperforms the model with re-annotating the random subset (R+Other) by 2 points in F1  The model trained with re-annotating both of difficult and random subsets (D+R+Other), however, achieves only marginally higher F1 than the model trained with the re-annotated difficult subset (D+Other)  In sum, the results clearly indicate that mixing expert and crowd annotations leads to better models than using solely crowd data, and better than using expert data alone  More importantly, there is greater gain in performance when instances are routed according to difficulty, as compared to randomly selecting the data for expert annotators  These findings align with our motivating hypothesis that annotation quality for difficult instances is important for final model performance  They also indicate that mixing annotations from expert and crowd could be an effective way to achieve acceptable model performance given a limited budget 
How Many Expert Annotations?
We established that crowd annotation are still useful in supplementing expert annotations for medical IE  Obtaining expert annotations for the one thousand most difficult instances greatly improved the model performance  However the choice of how many difficult instances to annotate was an uninformed choice  Here we check if less expert data would have yielded similar gains  Future work will need to address how best to choose this parameter for a routing system 
We simulate a routing scenario in which we send consecutive batches of the most difficult examples to the experts for annotation  We track changes in performance as we increase the number of most-difficult-articles sent to domain experts  As shown in Figure 4 , adding expert annotations for difficult articles consistently increases F1 scores  The performance gain is mostly from increased recall; the precision changes only a bit with higher quality annotation  This observation implies that crowd workers often fail to mark target tokens, but do not tend to produce large numbers of false positives  We suspect such failures to identify relevant spans/tokens are due to insufficient domain knowledge possessed by crowd workers 
The F1 score achieved after re-annotating the 600 most-difficult articles reaches 68 1%, which is close to the performance when re-annotating 1000 random articles  This demonstrates the effectiveness of recognizing difficult instances  The trend when we use up all expert data is still upward, so adding even more expert data is likely to further improve performance  Unfortunately we exhausted our budget and were not able to obtain additional expert annotations  It is likely that as the size of the expert annotations increases, the value of crowd annotations will diminish  This investigation is left for future work 
Conclusions
We have introduced the task of predicting annotation difficulty for biomedical information extraction (IE)  We trained neural models using different learned representations to score texts in terms of their difficulty  Results from all models were strong with Pearson’s correlation coefficients higher than 0 45 in almost all evaluations, indicating the feasibility of this task  An ensemble model combining universal and task specific feature sentence vectors yielded the best results 
Experiments on biomedical IE tasks show that removing up to $\sim $ 10% of the sentences predicted to be most difficult did not decrease model performance, and that re-weighting sentences inversely to their difficulty score during training improves predictive performance  Simulations in which difficult examples are routed to experts and other instances to crowd annotators yields the best results, outperforming the strategy of randomly selecting data for expert annotation, and substantially improving upon the approach of relying exclusively on crowd annotations  In future work, routing strategies based on instance difficulty could be further investigated for budget-quality trade-off 
Acknowledgements
This work has been partially supported by NSF1748771 grant  Wallace was support in part by NIH/NLM R01LM012086 
Context 3:  One of the languages' vocabulary, usually English vocabulary is considered as universal tokens and every word in the other languages is represented as a mixture of universal tokens  They show results on extremely low-resource languages 
Addressing Word Order Divergence
To the best of our knowledge, no work has addressed word order divergence in transfer learning for multilingual NMT  However, some work exists for other NLP tasks that could potentially address word order  For Named Entity Recognition (NER), BIBREF14 use a self-attention layer after the Bi-LSTM layer to address word-order divergence for Named Entity Recognition (NER) task  The approach does not show any significant improvements over multiple languages  A possible reason is that the divergence has to be addressed before/during construction of the contextual embeddings in the Bi-LSTM layer, and the subsequent self-attention layer does not address word-order divergence  BIBREF15 use adversarial training for cross-lingual question-question similarity ranking in community question answering  The adversarial training tries to force the encoder representations of similar sentences from different input languages to have similar representations 
Use of Pre-ordering
Pre-ordering the source language sentences to match the target language word order has been useful in addressing word-order divergence for Phrase-Based SMT BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19   Recently, BIBREF20 proposed a way to measure and reduce the divergence between the source and target languages based on morphological and syntactic properties, also termed as anisomorphism  They demonstrated that by reducing the anisomorphism between the source and target languages, consistent improvements in NMT performance were obtained  The NMT system used additional features like word forms, POS tags and dependency relations in addition to parallel corpora  On the other hand, BIBREF21 observed a drop in performance due to pre-ordering for NMT  Unlike BIBREF20 , the NMT system was trained on pre-ordered sentences and no additional features were provided to the system  Note that all these works address source-target divergence, not divergence between source languages in multilingual NMT 
Proposed Solution
Consider the task of translating from an extremely low-resource language (source) to a target language  The parallel corpus between the two languages if available may be too small to train a NMT model  Similar to existing works BIBREF3 , BIBREF10 , BIBREF12 , we use transfer learning to overcome data sparsity and train a NMT model between the source and the target languages  Specifically, the NMT model (parent model) is trained on the assisting language and target language pairs  We choose English as the assisting language in all our experiments  In our resource-scarce scenario, we have no parallel corpus for the child task  Hence, at test time, the source language sentence is translated using the parent model after performing a word-by-word translation into the assisting language 
Since the source language and the assisting language (English) have different word order, we hypothesize that it leads to inconsistencies in the contextual representations generated by the encoder for the two languages  In this paper, we propose to pre-order English sentences (assisting language sentences) to match the word-order of the source language and train the parent model on this pre-ordered corpus  In our experiments, we look at scenarios where the assisting language has SVO word order and the source language has SOV word order 
For instance, consider the English sentence Anurag will meet Thakur  One of the pre-ordering rule swaps the position of the noun phrase followed by a transitive verb with the transitive verb  The original and the resulting re-ordered parse tree will be as shown in the Table TABREF5   Applying this reordering rule to the above sentence Anurag will meet Thakur will yield the reordered sentence Anurag Thakur will meet  Additionally, the Table TABREF5 shows the parse trees for the above sentence with and without pre-ordering 
Pre-ordering should also be beneficial for other word order divergence scenarios (e g , SOV to SVO), but we leave verification of these additional scenarios for future work 
Experimental Setup
In this section, we describe the languages experimented with, datasets used, the network hyper-parameters used in our experiments 
Languages
We experimented with English INLINEFORM0 Hindi translation as the parent task  English is the assisting source language  Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks

ground_truth: 57,505 sentences

57,505 sentences

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 do they match words before reordering them?
Context 3:  Hindi, Bengali, Gujarati and Marathi are Indo-Aryan languages, while Malayalam and Tamil are Dravidian languages  All these languages have a canonical SOV word order 
Datasets
For training English-Hindi NMT systems, we use the IITB English-Hindi parallel corpus BIBREF22 ( INLINEFORM0 sentences from the training set) and the ILCI English-Hindi parallel corpus ( INLINEFORM1 sentences)  The ILCI (Indian Language Corpora Initiative) multilingual parallel corpus BIBREF23 spans multiple Indian languages from the health and tourism domains  We use the 520-sentence dev-set of the IITB parallel corpus for validation  For each child task, we use INLINEFORM2 sentences from ILCI corpus as the test set 
Network
We use OpenNMT-Torch BIBREF24 to train the NMT system  We use the standard sequence-to-sequence architecture with attention BIBREF13   We use an encoder which contains two layers of bidirectional LSTMs with 500 neurons each  The decoder contains two LSTM layers with 500 neurons each  Input feeding approach BIBREF1 is used where the previous attention hidden state is fed as input to the decoder LSTM  We use a mini-batch of size 50 and use a dropout layer  We begin with an initial learning rate of INLINEFORM0 and decay the learning rate by a factor of INLINEFORM1 when the perplexity on validation set increases  The training is stopped when the learning rate falls below INLINEFORM2 or number of epochs=22  The English input is initialized with pre-trained embeddings trained using fastText BIBREF25  
English vocabulary consists of INLINEFORM0 tokens appearing at least 2 times in the English training corpus  For constructing the Hindi vocabulary we considered only those tokens appearing at least 5 times in the training split resulting in a vocabulary size of INLINEFORM1 tokens  For representing English and other source languages into a common space, we translate each word in the source language into English using a bilingual dictionary (Google Translate word translation in our case)  In an end-to-end solution, it would have been ideal to use bilingual embeddings or obtain word-by-word translations via bilingual embeddings BIBREF14   But, the quality of publicly available bilingual embeddings for English-Indian languages is very low for obtaining good-quality, bilingual representations BIBREF26 , BIBREF27   We also found that these embeddings were not useful for transfer learning 
We use the CFILT-preorder system for reordering English sentences to match the Indian language word order  It contains two re-ordering systems: (1) generic rules that apply to all Indian languages BIBREF17 , and (2) hindi-tuned rules which improve the generic rules by incorporating improvements found through an error analysis of English-Hindi reordering BIBREF28   These Hindi-tuned rules have been found to improve reordering for many English to Indian language pairs BIBREF29  
Results
In this section, we describe the results from our experiments on NMT task  We report the results on X-Hindi pair, where X is one of Bengali, Gujarati, Marathi, Tamil, and Malayalam  The results are presented in the Table TABREF6   We report BLEU scores and LeBLEU scores BIBREF30   We observe that both the pre-ordering configurations significantly improve the BLEU scores over the baseline scores  We observe larger gains when generic pre-ordering rules are used compared to the Hindi-tuned pre-ordering rules 
These results support our hypothesis that word-order divergence can limit the benefits of multilingual translation  Reducing the word order divergence can improve translation in extremely low-resource scenarios 
An analysis of the outputs revealed that pre-ordering significantly reducing the number of UNK tokens (placeholder for unknown words) in the test output (Table TABREF14 )  We hypothesize that due to word order divergence between English and Indian languages, the encoder representation generated is not consistent leading to decoder generating unknown words  However, the pre-ordered models generate better contextual representations leading to less number of unknown tokens and better translation which is also reflected in the BLEU scores 
Conclusion
In this paper, we show that handling word-order divergence between source and assisting languages is crucial for the success of multilingual NMT in an extremely low-resource setting  We show that pre-ordering the assisting language to match the word order of the source language significantly improves translation quality in an extremely low-resource setting  While the current work focused on Indian languages, we would like to validate the hypothesis on a more diverse set of languages 

ground_truth: Unanswerable

CFILT-preorder system