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 language do they explore?
Context 1: Winograd Schemas
A Winograd schema (Levesque, Davis, and Morgenstern 2012) is a pair of sentences, or of short texts, called the elements of the schema, that satisfy the following constraints:
The following is an example of a Winograd schema:
Here, the two sentences differ only in the last word: `large' vs  `small'  The ambiguous pronoun is `it'  The two antecedents are `trophy' and `brown suitcase'  A human reader will naturally interpret `it' as referring to the trophy in the first sentence and to the suitcase in the second sentence, using the world knowledge that a small object can fit in a large container, but a large object cannot fit in a small container (Davis 2013)  Condition 4 is satisfied because either a trophy or a suitcase can be either large or small, and there is no reason to suppose that there would be a meaningful correlation of `small' or `large' with `trophy' or `suitcase' in a typical corpus 
We will say that an element of a Winograd schema is “solved” if the referent of the pronoun is identified 
An example of a pair of sentences satisfying conditions 1-3 but not 4 would be
Since women cannot be carcinogenic and pills cannot be pregnant, the pronoun `they' in these sentences is easily disambiguated using selectional restrictions  This pair is therefore not a valid Winograd schema 
The Winograd Schema Challenge (WSC) is a challenge for AI programs  The program is presented with a collection of sentences, each of which is one element of a Winograd schema, and is required to find the correct referent for the ambiguous pronoun  An AI program passes the challenge if its success rate is comparable to a human reader  The challenge is administered by commonsensereasoning org and sponsored by Nuance Inc  It was offered for the first time at IJCAI-2016 (Morgenstern, Davis, and Ortiz, in preparation); the organizers plan to continue to offer it roughly once a year 
Winograd schemas as the basis for challenges for machine translation programs
In many cases, the identification of the referent of the prounoun in a Winograd schema is critical for finding the correct translation of that pronoun in a different language  Therefore, Winograd schemas can be used as a very difficult challenge for the depth of understanding achieved by a machine translation program 
The third person plural pronoun `they' has no gender in English and most other languages (unlike the third person singular pronouns `he', `she', and `it')  However Romance languages such as French, Italian, and Spanish, and Semitic languages such as Hebrew and Arabic distinguish between the masculine and the feminine third-person plural pronouns, at least in some grammatical cases  For instance in French, the masculine pronoun is `ils'; the feminine pronoun is `elles'  In all of these cases, the masculine pronoun is standardly used for groups of mixed or unknown gender 
In order to correctly translate a sentence in English containing the word `they' into one of these languages, it is necessary to determine whether or not the referent is a group of females  If it is, then the translation must be the feminine pronoun; otherwise, it must be the masculine pronoun  Therefore, if one can create a Winograd schema in English where the ambiguous pronoun is `they' and the correct referent for one element is a collection of men and for the other is a collection of women, then to translate both elements correctly requires solving the Winograd schema 
As an example, consider the Winograd schema:
If these sentences are translated into French, then `they' in the first sentence should be translated `elles', as referring to Jane and Susan, and `they' in the second sentence should be translated `ils', as referring to Fred and George 
A number of the Winograd schemas already published can be “converted” quite easily to this form  Indeed, the above example was constructed in this way; the original form was “Jane knocked on Susan's door, but she did not [answer/get an answer] ” Of the 144 schemas in the collection at http://www cs nyu edu/faculty/davise/papers/WinogradSchemas/WSCollection html there are 33 that can plausibly be translated this way  (#'s 3, 4, 12, 15, 17, 18, 23, 25, 26, 27, 42, 43, 44, 45, 46, 57, 58, 60, 69, 70, 71, 77, 78, 79, 86, 97, 98, 106, 111, 114, 127, 132, 144) — essentially any schema that involves two people as possible referents where the content of the sentences makes sense as applied to groups of people rather than individuals 
A similar device, in the opposite direction, relies on the fact that French does not distinguish between the possessive pronouns `his' and `her'  The pronouns `son' and `sa' are gendered, but the gender agrees with the possession, not the possessor  Therefore a Winograd schema that relies on finding a referent for a possessive pronoun can be turned into a hard pair of French-to-English translation problems by making one possible referent male and the other one female  For example, schema #124 in the online collection reads “The man lifted the boy onto his [bunk bed/shoulders] ” Changing the boy to a girl and translating into French gives “L'homme leva la fille sur [ses épaules/son lit superposé]”  In the first sentence “ses” is translated “his”, in the second “son” is translated “her”  The eleven Winograd schemas #'s 112, 117, 118, 119, 124, 125, 127, 129, 130, 131, and 143 can be converted this way 
Care must be taken to avoid relying on, or seeming to rely on, objectionable stereotypes about men and women  One mechanism that can sometimes be used is to include a potentially problematic sentence in both directions  For instance, schema 23 from the WSC collection can be translated into both “The girls were bullying the boys so we [punished/rescued] them” and “The boys were bullying the girls, so we [punished/rescued] them,” thus avoiding any presupposition of whether girls are more likely to bully boys or vice versa 
A historical note: Winograd schemas were named after Terry Winograd because of a well-known example in his doctoral thesis (Winograd 1970)  In his thesis, this example followed the above form, and the importance of the example was justified in terms of machine translation  Winograd's original schema was: “The city councilmen refused to give the women a permit for a demonstration because they [feared/advocated] violence", and Winograd explained that, in the sentence with `feared', `they' would refer to the councilmen and would be translated as `ils' in French, whereas in the sentence with `advocated', `they' would refer to the women and would be translated as `elles'  In the later versions of the thesis, published as (Winograd 1972), he changed `women' to `demonstrators'; this made the disambiguation clearer, but lost the point about translation 
Current state of the art
No one familiar with the state of the art in machine translation technology or the state of the art of artificial intelligence generally will be surprised to learn that currently machine translation program are unable to solve these Winograd schema challenge problems 
What may be more surprising is that currently (July 2016), machine translation programs are unable to choose the feminine plural pronoun even when the only possible antecedent is a group of women  For instance, the sentence “The girls sang a song and they danced” is translated into French as “Les filles ont chanté une chanson et ils ont dansé” by Google Translate (GT), Bing Translate, and Yandex  In fact, I have been unable to construct any sentence in English that is translated into any language using the feminine plural pronun  Note that, since the masculine plural pronoun is used for groups of mixed gender in all these languages, it is almost certainly more common in text than the feminine plural; hence this strategy is reasonable faute de mieux  (It also seems likely that the erroneous use of a feminine plural for a masculine antecedent sounds even more jarring than the reverse )
The same thing sometimes occurs in translating the feminine plural pronoun between languages that have it  GT translates the French word `elles' into Spanish as `ellos' (the masculine form)  Curiously, in the opposite direction, it gets the right answer; the Spanish `ellas' (fem ) is translated into French as `elles' 
Language-specific issues
The masculine and feminine plural pronouns are distinguished in the Romance languages (French, Spanish, Italian, Portuguese etc ) and in Semitic languages (Arabic, Hebrew, etc ) I have consulted with native speakers and experts in these languages about the degree to which the gender distinction is observed in practice  The experts say that in French, Spanish, Italian, and Portuguese, the distinction is very strictly observed; the use of a masculine pronoun for a feminine antecedent is jarringly wrong to a native or fluent speaker  “Les filles ont chanté une chanson et ils ont dansé” sounds as wrong to a French speaker as “The girl sang a song and he danced” sounds to an English speaker; in both cases, the hearer will interpret the pronoun as referrinig to some other persons or person, who is male  In Hebrew and Arabic, this is much less true; in speech, and even, increasingly, in writing, the masculine pronoun is often used for a feminine antecedent 
Looking further ahead, it is certainly possible that gender distinctions will be abandoned in the Romance languages, or even that English will have driven all other languages out of existence, sooner than AI systems will be able to do pronoun resolution in Winograd schemas; at that point, this test will no longer be useful 
In some cases, a translation program can side-step the issue by omitting the pronoun altogether  For example, GT translates the above sentence “The girls sang a song and they danced” into Spanish as “Las chicas cantaron una canción y bailaban” and into Italian as “Le ragazze hanno cantato una canzone e ballavano ” However, with the more complex sentences of the Winograd Schemas, this strategy will rarely give a plausible translation for both elements of the schema 
Other languages, other ambiguities
Broadly speaking, whenever a target language INLINEFORM0 requires some distinction that is optional or non-existent in source language INLINEFORM1 , it is possible to create a sentence INLINEFORM2 in INLINEFORM3 where the missing information is not explicit but can be inferred from background knowledge  Translating INLINEFORM4 from INLINEFORM5 to INLINEFORM6 thus requires using the background knowledge to resolve the ambiguity, and will therefore be challenging for automatic machine translation 
A couple of examples: The word `sie' in German serves as both the formal second person prounoun (always capitalized), the third person feminine singular, and the third person plural  Therefore, it can be translated into English as either “you”, “she”, “it”, or “they”; and into French as either `vous', `il', `elle', `ils', or `elles'  (The feminine third-person singular German `sie' can be translated as neuter in English and as masculine in French because the three languages do not slice up the worlds into genders in the same way ) Likewise, the possessive pronoun `ihr' in all its declensions can mean either `her' or `their'  In some cases, the disambiguation can be carried out on purely syntactic ground; e g  if `sie' is the subject of a third-person singular verb, it must mean `she'  However, in many case, the disambiguation requires a deeper level of understanding  Thus, it should be possible to construct German Winograd schemas based on the words `sie' or `ihr' that have to be solved in order to translate them into English  For example,
Marie fand fünf verlassene Kätzchen im Keller  Ihre Mutter war gestorben 
(Marie found five abandoned kittens in the cellar  Their mother was dead )
vs 
Marie fand fünf verlassene Kätzchen im Keller  Ihre Mutter war unzufrieden 
(Marie found five abandoned kittens in the cellar  Her mother was displeased )
Another example: French distinguishes between a male friend `ami' and a female friend `amie'  Therefore, in translating the word “friend” into French, it is necessary, if possible, to determine the sex of the friend; and the clue for that can involve an inference that, as far as AI programs are concerned, is quite remote  Human readers are awfully good at picking up on those, of course  In general, with this kind of thing, if you want to break GT or another translation program, the trick is to place a large separation between the evidence and the word being disambiguated  In this case, at the present time, the separation can be pretty small  GT correctly translates “my friend Pierre” and “my friend Marie” as “mon ami Pierre” and “mon amie Marie” and it translates “She is my friend” as “Elle est mon amie”, but rather surpringly it breaks down at “Marie is my friend,” which it translates “Marie est mon ami ” As for something like “Jacques said to Marie, `You have always been a true friend,' ” that is quite hopeless  GT can surprise one, though, in both directions; sometimes it misses a very close clue, as in “Marie is my friend”, but other times it can carry a clue further than one would have guessed 
Acknowledgements
Thanks to Arianna Bisazza, Gerhard Brewka, Antoine Cerfon, Joseph Davis, Gigi Dopico-Black, Nizar Habash, Leora Morgenstern, Oded Regev, Francesca Rossi, Vesna Sabljakovic-Fritz, and Manuela Veloso for help with the various languages and discussion of gendered pronouns 
References
E  Davis, “Qualitative Spatial Reasoning in Interpreting Text and Narrative” Spatial Cognition and Computation, 13:4, 2013, 264-294 
H  Levesque, E  Davis, and L  Morgenstern, “The Winograd Schema Challenge,” KR 2012 
L  Morgenstern, E  Davis, and C  Ortiz, “Report on the Winograd Schema Challenge, 2016”, in preparation 
T  Winograd, “Procedures as a Representation for Data in a Computer Program for Understanding Natural Language," Ph D  thesis, Department of Mathematics, MIT, August 1970  Published as MIT AITR-235, January 1971 
T  Winograd, Understanding Natural Language, Academic Press, 1972 

ground_truth: English, French, German 

French, English, Spanish, Italian, Portuguese, Hebrew, Arabic

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 large is the corpus?
Context 2: Introduction
The automatic processing of medical texts and documents plays an increasingly important role in the recent development of the digital health area  To enable dedicated Natural Language Processing (NLP) that is highly accurate with respect to medically relevant categories, manually annotated data from this domain is needed  One category of high interest and relevance are medical entities  Only very few annotated corpora in the medical domain exist  Many of them focus on the relation between chemicals and diseases or proteins and diseases, such as the BC5CDR corpus BIBREF0, the Comparative Toxicogenomics Database BIBREF1, the FSU PRotein GEne corpus BIBREF2 or the ADE (adverse drug effect) corpus BIBREF3  The NCBI Disease Corpus BIBREF4 contains condition mention annotations along with annotations of symptoms  Several new corpora of annotated case reports were made available recently  grouin-etal-2019-clinical presented a corpus with medical entity annotations of clinical cases written in French, copdPhenotype presented a corpus focusing on phenotypic information for chronic obstructive pulmonary disease while 10 1093/database/bay143 presented a corpus focusing on identifying main finding sentences in case reports 
The corpus most comparable to ours is the French corpus of clinical case reports by grouin-etal-2019-clinical  Their annotations are based on UMLS semantic types  Even though there is an overlap in annotated entities, semantic classes are not the same  Lab results are subsumed under findings in our corpus and are not annotated as their own class  Factors extend beyond gender and age and describe any kind of risk factor that contributes to a higher probability of having a certain disease  Our corpus includes additional entity types  We annotate conditions, findings (including medical findings such as blood values), factors, and also modifiers which indicate the negation of other entities as well as case entities, i  e , entities specific to one case report  An overview is available in Table TABREF3 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation tasks
Case reports are standardized in the CARE guidelines BIBREF5  They represent a detailed description of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient  We focus on documents freely available through PubMed Central (PMC)  The presentation of the patient's case can usually be found in a dedicated section or the abstract  We perform a manual annotation of all mentions of case entities, conditions, findings, factors and modifiers  The scope of our manual annotation is limited to the presentation of a patient's signs and symptoms  In addition, we annotate the title of the case report 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation Guidelines
We annotate the following entities:
case entity marks the mention of a patient  A case report can contain more than one case description  Therefore, all the findings, factors and conditions related to one patient are linked to the respective case entity  Within the text, this entity is often represented by the first mention of the patient and overlaps with the factor annotations which can, e  g , mark sex and age (cf  Figure FIGREF12) 
condition marks a medical disease such as pneumothorax or dislocation of the shoulder 
factor marks a feature of a patient which might influence the probability for a specific diagnosis  It can be immutable (e  g , sex and age), describe a specific medical history (e  g , diabetes mellitus) or a behaviour (e  g , smoking) 
finding marks a sign or symptom a patient shows  This can be visible (e  g , rash), described by a patient (e  g , headache) or measurable (e  g , decreased blood glucose level) 
negation modifier explicitly negate the presence of a certain finding usually setting the case apart from common cases 
We also annotate relations between these entities, where applicable  Since we work on case descriptions, the anchor point of these relations is the case that is described  The following relations are annotated:
has relations exist between a case entity and factor, finding or condition entities 
modifies relations exist between negation modifiers and findings 
causes relations exist between conditions and findings 
Example annotations are shown in Figure FIGREF16 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotators
We asked medical doctors experienced in extracting knowledge related to medical entities from texts to annotate the entities described above  Initially, we asked four annotators to test our guidelines on two texts  Subsequently, identified issues were discussed and resolved  Following this pilot annotation phase, we asked two different annotators to annotate two case reports according to our guidelines  The same annotators annotated an overall collection of 53 case reports 
Inter-annotator agreement is calculated based on two case reports  We reach a Cohen's kappa BIBREF6 of 0 68  Disagreements mainly appear for findings that are rather unspecific such as She no longer eats out with friends which can be seen as a finding referring to “avoidance behaviour” 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Annotation Tools and Format
The annotation was performed using WebAnno BIBREF7, a web-based tool for linguistic annotation  The annotators could choose between a pre-annotated version or a blank version of each text  The pre-annotated versions contained suggested entity spans based on string matches from lists of conditions and findings synonym lists  Their quality varied widely throughout the corpus  The blank version was preferred by the annotators  We distribute the corpus in BioC JSON format  BioC was chosen as it allows us to capture the complexities of the annotations in the biomedical domain  It represented each documents properties ranging from full text, individual passages/sentences along with captured annotations and relationships in an organized manner  BioC is based on character offsets of annotations and allows the stacking of different layers 
A Corpus of Medical Case Reports with Medical Entity Annotation ::: Corpus Overview
The corpus consists of 53 documents, which contain an average number of 156 1 sentences per document, each with 19 55 tokens on average  The corpus comprises 8,275 sentences and 167,739 words in total  However, as mentioned above, only case presentation sections, headings and abstracts are annotated  The numbers of annotated entities are summarized in Table TABREF24 
Findings are the most frequently annotated type of entity  This makes sense given that findings paint a clinical picture of the patient's condition  The number of tokens per entity ranges from one token for all types to 5 tokens for cases (average length 3 1), nine tokens for conditions (average length 2 0), 16 tokens for factors (average length 2 5), 25 tokens for findings (average length 2 6) and 18 tokens for modifiers (average length 1 4) (cf  Table TABREF24)  Examples of rather long entities are given in Table TABREF25 
Entities can appear in a discontinuous way  We model this as a relation between two spans which we call “discontinuous” (cf  Figure FIGREF26)  Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations  The numbers for conditions and factors are lower with seven and two, respectively  Entities can also be nested within one another  This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf  Figure FIGREF12), or when there is a partial overlapping between the spans of two different entities (partially-nested; cf  Figure FIGREF12)  There is a high number of inter-sentential relations in the corpus (cf  Table TABREF27)  This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences 
The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case  This correlates with the high number of finding entities  The relations contained in our corpus are summarized in Table TABREF27 
Baseline systems for Named Entity Recognition in medical case reports
We evaluate the corpus using Named Entity Recognition (NER), i  e , the task of finding mentions of concepts of interest in unstructured text  We focus on detecting cases, conditions, factors, findings and modifiers in case reports (cf  Section SECREF6)  We approach this as a sequence labeling problem  Four systems were developed to offer comparable robust baselines 
The original documents are pre-processed (sentence splitting and tokenization with ScispaCy)  We do not perform stop word removal or lower-casing of the tokens  The BIO labeling scheme is used to capture the order of tokens belonging to the same entity type and enable span-level detection of entities  Detection of nested and/or discontinuous entities is not supported  The annotated corpus is randomized and split in five folds using scikit-learn BIBREF9  Each fold has a train, test and dev split with the test split defined as  15% of the train split  This ensures comparability between the presented systems 
Baseline systems for Named Entity Recognition in medical case reports ::: Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling  We use a combination of linguistic and semantic features, with a context window of size five, to describe each of the tokens and the dependencies between them  Hyper-parameter optimization is performed using randomized search and cross validation  Span-based F1 score is used as the optimization metric 
Baseline systems for Named Entity Recognition in medical case reports ::: BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling  We use a BiLSTM-CRF model with both word-level and character-level input  BioWordVec BIBREF12 pre-trained word embeddings are used in the embedding layer for the input representation  A bidirectional LSTM layer is applied to a multiplication of the two input representations  Finally, a CRF layer is applied to predict the sequence of labels  Dropout and L1/L2 regularization is used where applicable  He (uniform) initialization BIBREF13 is used to initialize the kernels of the individual layers  As the loss metric, CRF-based loss is used, while optimizing the model based on the CRF Viterbi accuracy  Additionally, span-based F1 score is used to serialize the best performing model  We train for a maximum of 100 epochs, or until an early stopping criterion is reached (no change in validation loss value grater than 0 01 for ten consecutive epochs)  Furthermore, Adam BIBREF14 is used as the optimizer  The learning rate is reduced by a factor of 0 3 in case no significant increase of the optimization metric is achieved in three consecutive epochs 
Baseline systems for Named Entity Recognition in medical case reports ::: Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning  This model family is characterized by simultaneous optimization of multiple loss functions and transfer of knowledge achieved this way  The knowledge is transferred through the use of one or multiple shared layers  Through finding supporting patterns in related tasks, MTL provides better generalization on unseen cases and the main tasks we are trying to solve 
We rely on the model presented by bekoulis2018joint and reuse the implementation provided by the authors  The model jointly trains two objectives supported by the dataset: the main task of NER and a supporting task of Relation Extraction (RE)  Two separate models are developed for each of the tasks  The NER task is solved with the help of a BiLSTM-CRF model, similar to the one presented in Section SECREF32 The RE task is solved by using a multi-head selection approach, where each token can have none or more relationships to in-sentence tokens  Additionally, this model also leverages the output of the NER branch model (the CRF prediction) to learn label embeddings  Shared layers consist of a concatenation of word and character embeddings followed by two bidirectional LSTM layers  We keep most of the parameters suggested by the authors and change (1) the number of training epochs to 100 to allow the comparison to other deep learning approaches in this work, (2) use label embeddings of size 64, (3) allow gradient clipping and (4) use $d=0 8$ as the pre-trained word embedding dropout and $d=0 5$ for all other dropouts  $\eta =1^{-3}$ is used as the learning rate with the Adam optimizer and tanh activation functions across layers  Although it is possible to use adversarial training BIBREF16, we omit from using it  We also omit the publication of results for the task of RE as we consider it to be a supporting task and no other competing approaches have been developed 
Baseline systems for Named Entity Recognition in medical case reports ::: BioBERT
Deep neural language models have recently evolved to a successful method for representing text  In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17  For our experiments, we use BioBERT, an adaptation of BERT for the biomedical domain, pre-trained on PubMed abstracts and PMC full-text articles BIBREF18  The BERT architecture for deriving text representations uses 12 hidden layers, consisting of 768 units each  For NER, token level BIO-tag probabilities are computed with a single output layer based on the representations from the last layer of BERT  We fine-tune the model on the entity recognition task during four training epochs with batch size $b=32$, dropout probability $d=0 1$ and learning rate $\eta =2^{-5}$  These hyper-parameters are proposed by Devlin2018 for BERT fine-tuning 
Baseline systems for Named Entity Recognition in medical case reports ::: Evaluation
To evaluate the performance of the four systems, we calculate the span-level precision (P), recall (R) and F1 scores, along with corresponding micro and macro scores  The reported values are shown in Table TABREF29 and are averaged over five folds, utilising the seqeval framework 
With a macro avg  F1-score of 0 59, MTL achieves the best result with a significant margin compared to CRF, BiLSTM-CRF and BERT  This confirms the usefulness of jointly training multiple objectives (minimizing multiple loss functions), and enabling knowledge transfer, especially in a setting with limited data (which is usually the case in the biomedical NLP domain)  This result also suggest the usefulness of BioBERT for other biomedical datasets as reported by Lee2019  Despite being a rather standard approach, CRF outperforms the more elaborated BiLSTM-CRF, presumably due to data scarcity and class imbalance  We hypothesize that an increase in training data would yield better results for BiLSTM-CRF but not outperform transfer learning approach of MTL (or even BioBERT)  In contrast to other common NER corpora, like CoNLL 2003, even the best baseline system only achieves relatively low scores  This outcome is due to the inherent difficulty of the task (annotators are experienced medical doctors) and the small number of training samples 
Conclusion
We present a new corpus, developed to facilitate the processing of case reports  The corpus focuses on five distinct entity types: cases, conditions, factors, findings and modifiers  Where applicable, relationships between entities are also annotated  Additionally, we annotate discontinuous entities with a special relationship type (discontinuous)  The corpus presented in this paper is the very first of its kind and a valuable addition to the scarce number of corpora available in the field of biomedical NLP  Its complexity, given the discontinuous nature of entities and a high number of nested and multi-label entities, poses new challenges for NLP methods applied for NER and can, hence, be a valuable source for insights into what entities “look like in the wild”  Moreover, it can serve as a playground for new modelling techniques such as the resolution of discontinuous entities as well as multi-task learning given the combination of entities and their relations  We provide an evaluation of four distinct NER systems that will serve as robust baselines for future work but which are, as of yet, unable to solve all the complex challenges this dataset holds  A functional service based on the presented corpus is currently being integrated, as a NER service, in the QURATOR platform BIBREF20 
Acknowledgments
The research presented in this article is funded by the German Federal Ministry of Education and Research (BMBF) through the project QURATOR (Unternehmen Region, Wachstumskern, grant no  03WKDA1A), see http://qurator ai  We want to thank our medical experts for their help annotating the data set, especially Ashlee Finckh and Sophie Klopfenstein 
Context 3: 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  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) 

ground_truth: 8,275 sentences and 167,739 words in total

The corpus comprises 8,275 sentences and 167,739 words in total.

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 4: 
For example, for the sentence:
a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , have anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   (from BIBREF1 )
I feed into BERT:
[CLS] a 2002 systemic review of herbal products found that several herbs , including peppermint and caraway , [MASK] anti-dyspeptic effects for non-ulcer dyspepsia with “ encouraging safety profiles ”   and look for the score assigned to the words have and has at the masked position 
Similarly, for the pair
the game that the guard hates is bad  
the game that the guard hates are bad  
(from BIBREF3 ), I feed into BERT:
[CLS] the game that the guard hates [MASK] bad  
and compare the scores predicted for is and are 
This differs from BIBREF1 and BIBREF2 by considering the entire sentence (excluding the verb) and not just its prefix leading to the verb, and differs from BIBREF3 by conditioning the focus verb on bidirectional context 
I use the PyTorch implementation of BERT, with the pre-trained models supplied by Google  I experiment with the bert-large-uncased and bert-base-uncased models 
The bi-directional setup precludes using using the NPI stimuli of BIBREF3 , in which the minimal pair differs in two words position, which I discard from the evaluation  I also discard the agreement cases involving the verbs is or are in BIBREF1 and in BIBREF2 , because some of them are copular construction, in which strong agreement hints can be found also on the object following the verb  This is not an issue in the manually constructed BIBREF3 stimuli due to the patterns they chose 
Finally, I discard stimuli in which the focus verb or its plural/singular inflection does not appear as a single word in the BERT word-piece-based vocabulary (and hence cannot be predicted by the model)  This include discarding BIBREF3 stimuli involving the words swims or admires, resulting in 23,368 discarded pairs (out of 152,300)  I similarly discard 680 sentences from BIBREF1 where the focus verb or its inflection were one of 108 out-of-vocabulary tokens, and 28 sentence-pairs (8 tokens) from BIBREF2  
The BERT results are not directly comparable to the numbers reported in previous work  Beyond the differences due to bidirectionality and the discarded stimuli, the BERT models are also trained on a different and larger corpus (covering both wikipedia and books) 
Code is available at https://github com/yoavg/bert-syntax 
Results
Tables 1 , 2 and 3 show the results  All cases exhibit high scores—in the vast majority of the cases substantially higher than reported in previous work  As discussed above, the results are not directly comparable to previous work: the BERT models are trained on different (and larger) data, are allowed to access the suffix of the sentence in addition to its prefix, and are evaluated on somewhat different data due to discarding OOV items  Still, taken together, the high performance numbers indicate that the purely attention-based BERT models are likely capable of capturing the same kind of syntactic regularities that LSTM-based models are capable of capturing, at least as well as the LSTM models and probably better 
Another noticeable and interesting trend is that larger is not necessarily better: the BERT-Base model outperforms the BERT-Large model on many of the syntactic conditions 
Discussion
The BERT models perform remarkably well on all the syntactic test cases  I expected the attention-based mechanism to fail on these (compared to the LSTM-based models), and am surprised by these results  The BIBREF2 and BIBREF3 conditions rule out the possibility of overly relying on selectional preference cues or memorizing the wikipedia training data, and suggest real syntactic generalization is taking place  Exploring the extent to which deep purely-attention-based architectures such as BERT are capable of capturing hierarchy-sensitive and syntactic dependencies—as well as the mechanisms by which this is achieved—is a fascinating area for future research 

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)