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

Please answer the questions based on the text provided below. How is non-standard pronunciation identified?
Context 1: Introduction
Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask  Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks  Low-resource languages still lag behind 
Endangered languages pose an additional challenge  The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives  This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources 
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina  We hope to ameliorate the resource gap and the transcription bottleneck in two ways  First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation)  In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set  Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities 
The Mapudungun Language
Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina BIBREF0  It is an isolate language and is classified as threatened by Ethnologue, hence the critical importance of all documentary efforts  Although the morphology of nouns is relatively simple, Mapudungun verb morphology is highly agglutinative and complex  Some analyses provide as many as 36 verb suffix slots BIBREF1  A typical complex verb form occurring in our corpus of spoken Mapudungun consists of five or six morphemes 
Mapudungun has several interesting grammatical properties  It is a polysynthetic language in the sense of BIBREF2; see BIBREF3 for explicit argumentation  As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages BIBREF4  One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself  One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role BIBREF5 
The Resource
The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005  The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio  We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines 
The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera  The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche  The three dialects are quite similar, with some minor semantic and phonetic differences  The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct 
The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine  The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach  The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time  We also followed human subject protocol  Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized  Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation  Similarly, we deleted any names or any information that may identify the participants 
The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations  They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured  The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun 
The Resource ::: Orthography
At the time of the collection and transcription of the corpus, the orthography of Mapudungun was not standardized  The Mapuche team at the Instituto de Estudios Indígenas (IEI – Institute for Indigenous Studies) developed a supra-dialectal alphabet that comprises 28 letters that cover 32 phones used in the three Mapudungun variants  The main criterion for choosing alphabetic characters was to use the current Spanish keyboard that was available on all computers in Chilean offices and schools  The alphabet used the same letters used in Spanish for those phonemes that sound like Spanish phonemes  Diacritics such as apostrophes were used for sounds that are not found in Spanish 
As a result, certain orthographic conventions that were made at the time deviate from the now-standard orthography of Mapudungun, Azumchefe  We plan to normalize the orthography of the corpus, and in fact a small sample has already been converted to the modern orthography  However, we believe that the original transcriptions will also be invaluable for academic, historical, and cultural purposes, hence we release the corpus using these conventions 
The Resource ::: Additional Annotations
In addition, the transcription includes annotations for noises and disfluencies including aborted words, mispronunciations, poor intelligibility, repeated and corrected words, false starts, hesitations, undefined sound or pronunciations, non-verbal articulations, and pauses  Foreign words, in this case Spanish words, are also labelled as such 
The Resource ::: Cleaning
The dialogues were originally recorded using a Sony DAT recorder (48kHz), model TCD-D8, and Sony digital stereo microphone, model ECM-DS70P  Transcription was performed with the TransEdit transcription tool v 1 1 beta 10, which synchronizes the transcribed text and the wave files 
However, we found that a non-trivial number of the utterance boundaries and speaker annotations were flawed  Also some recording sessions did not have a complete set of matching audio, transcription, and translation files  Hence, in an effort to provide a relatively “clean" corpus for modern computational experiments, we converted the encoding of the textual transcription from Latin-1 to Unicode, DOS to UNIX line endings, a now more standard text encoding format than what was used when the data was first collected  Additionally, we renamed a small portion of files which had been misnamed and removed several duplicate files 
Although all of the data was recorded with similar equipment in relatively quiet environments, the acoustics are not as uniform as we would like for building speech synthesizers  Thus we applied standardized power normalization  We also moved the boundaries of the turns to standardize the amount of leading and trailing silence in each turn  This is a standard procedure for speech recognition and synthesis datasets  Finally we used the techniques in BIBREF7 for found data to re-align the text to the audio and find out which turns are best (or worst) aligned so that we can select segments that give the most accurate alignments  Some of the misalignments may in part be due to varied orthography, and we intend, but have not yet, to investigate normalization of orthography (i e  spelling correction) to mitigate this 
The Resource ::: Training, Dev, and Test Splits
We create two training sets, one appropriate for single-speaker speech synthesis experiments, and one appropriate for multiple-speaker speech recognition and machine translation experiments  In both cases, our training, development, and test splits are performed at the dialogue level, so that all examples from each dialogue belong to exactly one of these sets 
For single-speaker speech synthesis, we only use the dialog turns of the speaker with the largest volume of data (nmlch – one of the interviewers)  The training set includes $221 8$ thousand sentences from 285 dialogues, with 12 and 46 conversations reserved for the development and test set 
For speech recognition experiments, we ensure that our test set includes unique speakers as well as speakers that overlap with the training set, in order to allow for comparisons of the ability of the speech recognition system to generalize over seen and new speakers  For consistency, we use the same dataset splits for the machine translation experiments  The statistics in Table reflect this split 
Applications
Our resource has the potential to be the basis of computational research in Mapudungun across several areas  Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis  The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation  We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction 
Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community  In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems  The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun  Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications 
The potential applications of our resource, however, are not exhausted in language technologies  The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture 
In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8  As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work 
Baseline Results
Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish 
Baseline Results ::: Speech Synthesis
In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription)  We used the same techniques here, as described above 
For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech  Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch  This gave us around 18250 segments  We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0)  This gave us 13 hours and 48 minutes of speech 
We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data  We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10  We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7 
The initial build gave an MCD on held out data of 6 483  While the 2000 best segment dataset gives an MCD of 5 551, which is a large improvement  The quality of the generated speech goes from understandable, only if you can see the text, to understandable, and transcribable even for non-Mapudungun speakers  We do not believe we are building the best synthesizer with our current (non-neural) techniques, but we do believe we are selecting the best training data for other statistical and neural training techniques in both speech synthesis and speech recognition 
Baseline Results ::: Speech Recognition
For speech recognition (ASR) we used Kaldi BIBREF11  As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings  In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme  In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques  The train/dev/test splits are across conversations, as described above 
Under the first setting, we obtained a 60% character error rate, while the generated lexicon significantly boosts performance, as our systems achieve a notably reduced 30% phone error rate  Naturally, these results are relatively far from the quality of ASR systems trained on large amounts of clean data such as those available in English  Given the quality of the recordings, and the lack of additional resources, we consider our results fairly reasonable and they would still be usable for simple dialog-like tasks  We anticipate, though, that one could significantly improve ASR quality over our dataset, by using in-domain language models, or by training end-to-end neural recognizers leveraging languages with similar phonetic inventories BIBREF12 or by using the available Spanish translations in a multi-source scenario BIBREF13 
Baseline Results ::: Mapudungun–Spanish Machine Translation
We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15  We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages  We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads  We use dropout BIBREF17 with $0 4$ probability as well as label smoothing set to $0 1$  We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs 
The baseline results using different portions of the training set (10k, 50k, 100k, and all (220k) parallel sentences) on both translation directions are presented in Table , using detokenized BLEU BIBREF19 (a standard MT metric) and chrF BIBREF20 (a metric that we consider to be more appropriate for polysynthetic languages, as it does not rely on word n-grams) computed with the sacreBLEU toolkit BIBREF21  It it worth noting the difference in quality between the two directions, with translation into Spanish reaching 20 4 (almost 21) BLEU points in the development set, while the opposite direction (translating into Mapudungun) shows about a 7 BLEU points worse performance  This is most likely due to Mapudungun being a polysynthetic language, with its complicated morphology posing a challenge for proper generation 
Related Work
Mapudungun grammar has been studied since the arrival of European missionaries and colonizers hundreds of years ago  More recent descriptions of Mapudungun grammar BIBREF1 and BIBREF0 informed the collection of the resource that we are presenting in this paper 
Portions of our resource have been used in early efforts to build language systems for Mapudungun  In particular, BIBREF22 focused on Mapudungun morphology in order to create spelling correction systems, while BIBREF23, BIBREF6, BIBREF24, and BIBREF25 developed hybrid rule- and phrase-based Statistical Machine Translation systems 
Naturally, similar works in collecting corpora in Indigenous languages of Latin America are abundant, but very few, if any, have the scale and potential of our resource to be useful in many downstream language-specific and inter-disciplinary applications  A general overview of the state of NLP for the under-represented languages of the Americas can be found at BIBREF26  To name a few of the many notable works, BIBREF27 created a parallel Mixtec-Spanish corpus for Machine Translation and BIBREF28 created lexical resources for Arapaho, while BIBREF29 and BIBREF30 focused on building speech corpora for Southern Quechua and Chatino respectively 
Conclusion
With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun  We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results  The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource 
Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community 
Acknowledgements
The data collection described in this paper was supported by NSF grants IIS-0121631 (AVENUE) and IIS-0534217 (LETRAS), with supplemental funding from NSF's Office of International Science and Education  Preliminary funding for work on Mapudungun was also provided by DARPA The experimental material is based upon work generously supported by the National Science Foundation under grant 1761548 
Context 2: Introduction
Decoding intended speech or motor activity from brain signals is one of the major research areas in Brain Computer Interface (BCI) systems BIBREF0 , BIBREF1   In particular, speech-related BCI technologies attempt to provide effective vocal communication strategies for controlling external devices through speech commands interpreted from brain signals BIBREF2   Not only do they provide neuro-prosthetic help for people with speaking disabilities and neuro-muscular disorders like locked-in-syndrome, nasopharyngeal cancer, and amytotropic lateral sclerosis (ALS), but also equip people with a better medium to communicate and express thoughts, thereby improving the quality of rehabilitation and clinical neurology BIBREF3 , BIBREF4   Such devices also have applications in entertainment, preventive treatments, personal communication, games, etc  Furthermore, BCI technologies can be utilized in silent communication, as in noisy environments, or situations where any sort of audio-visual communication is infeasible 
Among the various brain activity-monitoring modalities in BCI, electroencephalography (EEG) BIBREF5 , BIBREF6 has demonstrated promising potential to differentiate between various brain activities through measurement of related electric fields  EEG is non-invasive, portable, low cost, and provides satisfactory temporal resolution  This makes EEG suitable to realize BCI systems  EEG data, however, is challenging: these data are high dimensional, have poor SNR, and suffer from low spatial resolution and a multitude of artifacts  For these reasons, it is not particularly obvious how to decode the desired information from raw EEG signals  Although the area of BCI based speech intent recognition has received increasing attention among the research community in the past few years, most research has focused on classification of individual speech categories in terms of discrete vowels, phonemes and words BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15   This includes categorization of imagined EEG signal into binary vowel categories like /a/, /u/ and rest BIBREF7 , BIBREF8 , BIBREF9 ; binary syllable classes like /ba/ and /ku/ BIBREF1 , BIBREF10 , BIBREF11 , BIBREF12 ; a handful of control words like 'up', 'down', 'left', 'right' and 'select' BIBREF15 or others like 'water', 'help', 'thanks', 'food', 'stop' BIBREF13 , Chinese characters BIBREF14 , etc  Such works mostly involve traditional signal processing or manual feature handcrafting along with linear classifiers (e g , SVMs)  In our recent work BIBREF16 , we introduced deep learning models for classification of vowels and words that achieved 23 45% improvement of accuracy over the baseline 
Production of articulatory speech is an extremely complicated process, thereby rendering understanding of the discriminative EEG manifold corresponding to imagined speech highly challenging  As a result, most of the existing approaches failed to achieve satisfactory accuracy on decoding speech tokens from the speech imagery EEG data  Perhaps, for these reasons, very little work has been devoted to relating the brain signals to the underlying articulation  The few exceptions include BIBREF17 , BIBREF18   In BIBREF17 , Zhao et al  used manually handcrafted features from EEG data, combined with speech audio and facial features to achieve classification of the phonological categories varying based on the articulatory steps  However, the imagined speech classification accuracy based on EEG data alone, as reported in BIBREF17 , BIBREF18 , are not satisfactory in terms of accuracy and reliability  We now turn to describing our proposed models 
Proposed Framework
Cognitive learning process underlying articulatory speech production involves incorporation of intermediate feedback loops and utilization of past information stored in the form of memory as well as hierarchical combination of several feature extractors  To this end, we develop our mixed neural network architecture composed of three supervised and a single unsupervised learning step, discussed in the next subsections and shown in Fig  FIGREF1   We formulate the problem of categorizing EEG data based on speech imagery as a non-linear mapping INLINEFORM0 of a multivariate time-series input sequence INLINEFORM1 to fixed output INLINEFORM2 , i e, mathematically INLINEFORM3 : INLINEFORM4 , where c and t denote the EEG channels and time instants respectively 
Preprocessing step
We follow similar pre-processing steps on raw EEG data as reported in BIBREF17 (ocular artifact removal using blind source separation, bandpass filtering and subtracting mean value from each channel) except that we do not perform Laplacian filtering step since such high-pass filtering may decrease information content from the signals in the selected bandwidth 
Joint variability of electrodes
Multichannel EEG data is high dimensional multivariate time series data whose dimensionality depends on the number of electrodes  It is a major hurdle to optimally encode information from these EEG data into lower dimensional space  In fact, our investigation based on a development set (as we explain later) showed that well-known deep neural networks (e g , fully connected networks such as convolutional neural networks, recurrent neural networks and autoencoders) fail to individually learn such complex feature representations from single-trial EEG data  Besides, we found that instead of using the raw multi-channel high-dimensional EEG requiring large training times and resource requirements, it is advantageous to first reduce its dimensionality by capturing the information transfer among the electrodes  Instead of the conventional approach of selecting a handful of channels as BIBREF17 , BIBREF18 , we address this by computing the channel cross-covariance, resulting in positive, semi-definite matrices encoding the connectivity of the electrodes  We define channel cross-covariance (CCV) between any two electrodes INLINEFORM0 and INLINEFORM1 as: INLINEFORM2   Next, we reject the channels which have significantly lower cross-covariance than auto-covariance values (where auto-covariance implies CCV on same electrode)  We found this measure to be essential as the higher cognitive processes underlying speech planning and synthesis involve frequent information exchange between different parts of the brain  Hence, such matrices often contain more discriminative features and hidden information than mere raw signals  This is essentially different than our previous work BIBREF16 where we extract per-channel 1-D covariance information and feed it to the networks  We present our sample 2-D EEG cross-covariance matrices (of two individuals) in Fig  FIGREF2  
CNN & LSTM
In order to decode spatial connections between the electrodes from the channel covariance matrix, we use a CNN BIBREF19 , in particular a four-layered 2D CNN stacking two convolutional and two fully connected hidden layers  The INLINEFORM0 feature map at a given CNN layer with input INLINEFORM1 , weight matrix INLINEFORM2 and bias INLINEFORM3 is obtained as: INLINEFORM4   At this first level of hierarchy, the network is trained with the corresponding labels as target outputs, optimizing a cross-entropy cost function  In parallel, we apply a four-layered recurrent neural network on the channel covariance matrices to explore the hidden temporal features of the electrodes  Namely, we exploit an LSTM BIBREF20 consisting of two fully connected hidden layers, stacked with two LSTM layers and trained in a similar manner as CNN 
Deep autoencoder for spatio-temporal information
As we found the individually-trained parallel networks (CNN and LSTM) to be useful (see Table TABREF12 ), we suspected the combination of these two networks could provide a more powerful discriminative spatial and temporal representation of the data than each independent network  As such, we concatenate the last fully-connected layer from the CNN with its counterpart in the LSTM to compose a single feature vector based on these two penultimate layers  Ultimately, this forms a joint spatio-temporal encoding of the cross-covariance matrix 
In order to further reduce the dimensionality of the spatio-temporal encodings and cancel background noise effects BIBREF21 , we train an unsupervised deep autoenoder (DAE) on the fused heterogeneous features produced by the combined CNN and LSTM information  The DAE forms our second level of hierarchy, with 3 encoding and 3 decoding layers, and mean squared error (MSE) as the cost function 
Classification with Extreme Gradient Boost
At the third level of hierarchy, the discrete latent vector representation of the deep autoencoder is fed into an Extreme Gradient Boost based classification layer BIBREF22 , BIBREF23 motivated by BIBREF21   It is a regularized gradient boosted decision tree that performs well on structured problems

ground_truth: Unanswerable

Original transcription was labeled with additional labels in [] brackets with nonstandard pronunciation.

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

Please answer the questions based on the text provided below. What data was presented to the subjects to elicit event-related responses?
Context 2:  Since our EEG-phonological pairwise classification has an internal structure involving individual phonemes and words, it seems to be a reasonable choice of classifier  The classifier receives its input from the latent vectors of the deep autoencoder and is trained in a supervised manner to output the final predicted classes corresponding to the speech imagery 
Dataset
We evaluate our model on a publicly available dataset, KARA ONE BIBREF17 , composed of multimodal data for stimulus-based, imagined and articulated speech state corresponding to 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)  The dataset consists of 14 participants, with each prompt presented 11 times to each individual  Since our intention is to classify the phonological categories from human thoughts, we discard the facial and audio information and only consider the EEG data corresponding to imagined speech  It is noteworthy that given the mixed nature of EEG signals, it is reportedly challenging to attain a pairwise EEG-phoneme mapping BIBREF18   In order to explore the problem space, we thus specifically target five binary classification problems addressed in BIBREF17 , BIBREF18 , i e presence/absence of consonants, phonemic nasal, bilabial, high-front vowels and high-back vowels 
Training and hyperparameter selection
We performed two sets of experiments with the single-trial EEG data  In PHASE-ONE, our goals was to identify the best architectures and hyperparameters for our networks with a reasonable number of runs  For PHASE-ONE, we randomly shuffled and divided the data (1913 signals from 14 individuals) into train (80%), development (10%) and test sets (10%)  In PHASE-TWO, in order to perform a fair comparison with the previous methods reported on the same dataset, we perform a leave-one-subject out cross-validation experiment using the best settings we learn from PHASE-ONE 
The architectural parameters and hyperparameters listed in Table TABREF6 were selected through an exhaustive grid-search based on the validation set of PHASE-ONE  We conducted a series of empirical studies starting from single hidden-layered networks for each of the blocks and, based on the validation accuracy, we increased the depth of each given network and selected the optimal parametric set from all possible combinations of parameters  For the gradient boosting classification, we fixed the maximum depth at 10, number of estimators at 5000, learning rate at 0 1, regularization coefficient at 0 3, subsample ratio at 0 8, and column-sample/iteration at 0 4  We did not find any notable change of accuracy while varying other hyperparameters while training gradient boost classifier 
Performance analysis and discussion
To demonstrate the significance of the hierarchical CNN-LSTM-DAE method, we conducted separate experiments with the individual networks in PHASE-ONE of experiments and summarized the results in Table TABREF12 From the average accuracy scores, we observe that the mixed network performs much better than individual blocks which is in agreement with the findings in BIBREF21   A detailed analysis on repeated runs further shows that in most of the cases, LSTM alone does not perform better than chance  CNN, on the other hand, is heavily biased towards the class label which sees more training data corresponding to it  Though the situation improves with combined CNN-LSTM, our analysis clearly shows the necessity of a better encoding scheme to utilize the combined features rather than mere concatenation of the penultimate features of both networks 
The very fact that our combined network improves the classification accuracy by a mean margin of 14 45% than the CNN-LSTM network indeed reveals that the autoencoder contributes towards filtering out the unrelated and noisy features from the concatenated penultimate feature set  It also proves that the combined supervised and unsupervised neural networks, trained hierarchically, can learn the discriminative manifold better than the individual networks and it is crucial for improving the classification accuracy  In addition to accuracy, we also provide the kappa coefficients BIBREF24 of our method in Fig  FIGREF14   Here, a higher mean kappa value corresponding to a task implies that the network is able to find better discriminative information from the EEG data beyond random decisions  The maximum above-chance accuracy (75 92%) is recorded for presence/absence of the vowel task and the minimum (49 14%) is recorded for the INLINEFORM0  
To further investigate the feature representation achieved by our model, we plot T-distributed Stochastic Neighbor Embedding (tSNE) corresponding to INLINEFORM0 and V/C classification tasks in Fig  FIGREF8   We particularly select these two tasks as our model exhibits respectively minimum and maximum performance for these two  The tSNE visualization reveals that the second set of features are more easily separable than the first one, thereby giving a rationale for our performance 
Next, we provide performance comparison of the proposed approach with the baseline methods for PHASE-TWO of our study (cross-validation experiment) in Table TABREF15   Since the model encounters the unseen data of a new subject for testing, and given the high inter-subject variability of the EEG data, a reduction in the accuracy was expected  However, our network still managed to achieve an improvement of 18 91, 9 95, 67 15, 2 83 and 13 70 % over BIBREF17   Besides, our best model shows more reliability compared to previous works: The standard deviation of our model's classification accuracy across all the tasks is reduced from 22 59% BIBREF17 and 17 52% BIBREF18 to a mere 5 41% 
Conclusion and future direction
In an attempt to move a step towards understanding the speech information encoded in brain signals, we developed a novel mixed deep neural network scheme for a number of binary classification tasks from speech imagery EEG data  Unlike previous approaches which mostly deal with subject-dependent classification of EEG into discrete vowel or word labels, this work investigates a subject-invariant mapping of EEG data with different phonological categories, varying widely in terms of underlying articulator motions (eg: involvement or non-involvement of lips and velum, variation of tongue movements etc)  Our model takes an advantage of feature extraction capability of CNN, LSTM as well as the deep learning benefit of deep autoencoders  We took BIBREF17 , BIBREF18 as the baseline works investigating the same problem and compared our performance with theirs  Our proposed method highly outperforms the existing methods across all the five binary classification tasks by a large average margin of 22 51% 
Acknowledgments
This work was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada and Canadian Institutes for Health Research (CIHR) 
Context 3: 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

ground_truth: 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)

KARA ONE BIBREF17 , composed of multimodal data for stimulus-based, imagined and articulated speech state corresponding to 7 phonemic/syllabic ( /iy/, /piy/, /tiy/, /diy/, /uw/, /m/, /n/ ) as well as 4 words(pat, pot, knew and gnaw)

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 3: 
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 

ground_truth: 57,505 sentences

57,505 sentences