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 big is Augmented LibriSpeech dataset?
Context 1: Introduction
End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems  The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems  Several corpora have been developed in recent years  post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations  di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only  beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks  godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort  woldeyohannis provided an Amharic-English ST corpus in the tourism domain  boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7  Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs  This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9  Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings  The corpus we introduce has larger speech durations and more translation tokens  It is diversified with multiple speakers per transcript/translation  Finally, we provide additional out-of-domain test sets 
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora  We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9 3 hours of speech  Both corpora are created at the sentence level and do not require additional alignments or segmentation  Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models  CoVoST is released under CC0 license and free to use  The Tatoeba evaluation samples are also available under friendly CC licenses  All the data can be acquired at https://github com/facebookresearch/covost 
Data Collection and Processing ::: Common Voice (CoVo)
Common Voice BIBREF10 is a crowdsourcing speech recognition corpus with an open CC0 license  Contributors record voice clips by reading from a bank of donated sentences  Each voice clip was validated by at least two other users  Most of the sentences are covered by multiple speakers, with potentially different genders, age groups or accents 
Raw CoVo data contains samples that passed validation as well as those that did not  To build CoVoST, we only use the former one and reuse the official train-development-test partition of the validated data  As of January 2020, the latest CoVo 2019-06-12 release includes 29 languages  CoVoST is currently built on that release and covers the following 11 languages: French, German, Dutch, Russian, Spanish, Italian, Turkish, Persian, Swedish, Mongolian and Chinese 
Validated transcripts were sent to professional translators  Note that the translators had access to the transcripts but not the corresponding voice clips since clips would not carry additional information  Since transcripts were duplicated due to multiple speakers, we deduplicated the transcripts before sending them to translators  As a result, different voice clips of the same content (transcript) will have identical translations in CoVoST for train, development and test splits 
In order to control the quality of the professional translations, we applied various sanity checks to the translations BIBREF11  1) For German-English, French-English and Russian-English translations, we computed sentence-level BLEU BIBREF12 with the NLTK BIBREF13 implementation between the human translations and the automatic translations produced by a state-of-the-art system BIBREF14 (the French-English system was a Transformer big BIBREF15 separately trained on WMT14)  We applied this method to these three language pairs only as we are confident about the quality of the corresponding systems  Translations with a score that was too low were manually inspected and sent back to the translators when needed  2) We manually inspected examples where the source transcript was identical to the translation  3) We measured the perplexity of the translations using a language model trained on a large amount of clean monolingual data BIBREF14  We manually inspected examples where the translation had a high perplexity and sent them back to translators accordingly  4) We computed the ratio of English characters in the translations  We manually inspected examples with a low ratio and sent them back to translators accordingly  5) Finally, we used VizSeq BIBREF16 to calculate similarity scores between transcripts and translations based on LASER cross-lingual sentence embeddings BIBREF17  Samples with low scores were manually inspected and sent back for translation when needed 
We also sanity check the overlaps of train, development and test sets in terms of transcripts and voice clips (via MD5 file hashing), and confirm they are totally disjoint 
Data Collection and Processing ::: Tatoeba (TT)
Tatoeba (TT) is a community built language learning corpus having sentences aligned across multiple languages with the corresponding speech partially available  Its sentences are on average shorter than those in CoVoST (see also Table TABREF2) given the original purpose of language learning  Sentences in TT are licensed under CC BY 2 0 FR and part of the speeches are available under various CC licenses 
We construct an evaluation set from TT (for French, German, Dutch, Russian and Spanish) as a complement to CoVoST development and test sets  We collect (speech, transcript, English translation) triplets for the 5 languages and do not include those whose speech has a broken URL or is not CC licensed  We further filter these samples by sentence lengths (minimum 4 words including punctuations) to reduce the portion of short sentences  This makes the resulting evaluation set closer to real-world scenarios and more challenging 
We run the same quality checks for TT as for CoVoST but we do not find poor quality translations according to our criteria  Finally, we report the overlap between CoVo transcripts and TT sentences in Table TABREF5  We found a minimal overlap, which makes the TT evaluation set a suitable additional test set when training on CoVoST 
Data Analysis ::: Basic Statistics
Basic statistics for CoVoST and TT are listed in Table TABREF2 including (unique) sentence counts, speech durations, speaker demographics (partially available) as well as vocabulary and token statistics (based on Moses-tokenized sentences by sacreMoses) on both transcripts and translations  We see that CoVoST has over 327 hours of German speeches and over 171 hours of French speeches, which, to our knowledge, corresponds to the largest corpus among existing public ST corpora (the second largest is 110 hours BIBREF18 for German and 38 hours BIBREF19 for French)  Moreover, CoVoST has a total of 18 hours of Dutch speeches, to our knowledge, contributing the first public Dutch ST resource  CoVoST also has around 27-hour Russian speeches, 37-hour Italian speeches and 67-hour Persian speeches, which is 1 8 times, 2 5 times and 13 3 times of the previous largest public one BIBREF7  Most of the sentences (transcripts) in CoVoST are covered by multiple speakers with potentially different accents, resulting in a rich diversity in the speeches  For example, there are over 1,000 speakers and over 10 accents in the French and German development / test sets  This enables good coverage of speech variations in both model training and evaluation 
Data Analysis ::: Speaker Diversity
As we can see from Table TABREF2, CoVoST is diversified with a rich set of speakers and accents  We further inspect the speaker demographics in terms of sample distributions with respect to speaker counts, accent counts and age groups, which is shown in Figure FIGREF6, FIGREF7 and FIGREF8  We observe that for 8 of the 11 languages, at least 60% of the sentences (transcripts) are covered by multiple speakers  Over 80% of the French sentences have at least 3 speakers  And for German sentences, even over 90% of them have at least 5 speakers  Similarly, we see that a large portion of sentences are spoken in multiple accents for French, German, Dutch and Spanish  Speakers of each language also spread widely across different age groups (below 20, 20s, 30s, 40s, 50s, 60s and 70s) 
Baseline Results
We provide baselines using the official train-development-test split on the following tasks: automatic speech recognition (ASR), machine translation (MT) and speech translation (ST) 
Baseline Results ::: Experimental Settings ::: Data Preprocessing
We convert raw MP3 audio files from CoVo and TT into mono-channel waveforms, and downsample them to 16,000 Hz  For transcripts and translations, we normalize the punctuation, we tokenize the text with sacreMoses and lowercase it  For transcripts, we further remove all punctuation markers except for apostrophes  We use character vocabularies on all the tasks, with 100% coverage of all the characters  Preliminary experimentation showed that character vocabularies provided more stable training than BPE  For MT, the vocabulary is created jointly on both transcripts and translations  We extract 80-channel log-mel filterbank features, computed with a 25ms window size and 10ms window shift using torchaudio  The features are normalized to 0 mean and 1 0 standard deviation  We remove samples having more than 3,000 frames or more than 256 characters for GPU memory efficiency (less than 25 samples are removed for all languages) 
Baseline Results ::: Experimental Settings ::: Model Training
Our ASR and ST models follow the architecture in berard2018end, but have 3 decoder layers like that in pino2019harnessing  For MT, we use a Transformer base architecture BIBREF15, but with 3 encoder layers, 3 decoder layers and 0 3 dropout  We use a batch size of 10,000 frames for ASR and ST, and a batch size of 4,000 tokens for MT  We train all models using Fairseq BIBREF20 for up to 200,000 updates  We use SpecAugment BIBREF21 for ASR and ST to alleviate overfitting 
Baseline Results ::: Experimental Settings ::: Inference and Evaluation
We use a beam size of 5 for all models  We use the best checkpoint by validation loss for MT, and average the last 5 checkpoints for ASR and ST  For MT and ST, we report case-insensitive tokenized BLEU BIBREF22 using sacreBLEU BIBREF23  For ASR, we report word error rate (WER) and character error rate (CER) using VizSeq 
Baseline Results ::: Automatic Speech Recognition (ASR)
For simplicity, we use the same model architecture for ASR and ST, although we do not leverage ASR models to pretrain ST model encoders later  Table TABREF18 shows the word error rate (WER) and character error rate (CER) for ASR models  We see that French and German perform the best given they are the two highest resource languages in CoVoST  The other languages are relatively low resource (especially Turkish and Swedish) and the ASR models are having difficulties to learn from this data 
Baseline Results ::: Machine Translation (MT)
MT models take transcripts (without punctuation) as inputs and outputs translations (with punctuation)  For simplicity, we do not change the text preprocessing methods for MT to correct this mismatch  Moreover, this mismatch also exists in cascading ST systems, where MT model inputs are the outputs of an ASR model  Table TABREF20 shows the BLEU scores of MT models  We notice that the results are consistent with what we see from ASR models  For example thanks to abundant training data, French has a decent BLEU score of 29 8/25 4  German doesn't perform well, because of less richness of content (transcripts)  The other languages are low resource in CoVoST and it is difficult to train decent models without additional data or pre-training techniques 
Baseline Results ::: Speech Translation (ST)
CoVoST is a many-to-one multilingual ST corpus  While end-to-end one-to-many and many-to-many multilingual ST models have been explored very recently BIBREF8, BIBREF9, many-to-one multilingual models, to our knowledge, have not  We hence use CoVoST to examine this setting  Table TABREF22 and TABREF23 show the BLEU scores for both bilingual and multilingual end-to-end ST models trained on CoVoST  We observe that combining speeches from multiple languages is consistently bringing gains to low-resource languages (all besides French and German)  This includes combinations of distant languages, such as Ru+Fr, Tr+Fr and Zh+Fr  Moreover, some combinations do bring gains to high-resource language (French) as well: Es+Fr, Tr+Fr and Mn+Fr  We simply provide the most basic many-to-one multilingual baselines here, and leave the full exploration of the best configurations to future work  Finally, we note that for some language pairs, absolute BLEU numbers are relatively low as we restrict model training to the supervised data  We encourage the community to improve upon those baselines, for example by leveraging semi-supervised training 
Baseline Results ::: Multi-Speaker Evaluation
In CoVoST, large portion of transcripts are covered by multiple speakers with different genders, accents and age groups  Besides the standard corpus-level BLEU scores, we also want to evaluate model output variance on the same content (transcript) but different speakers  We hence propose to group samples (and their sentence BLEU scores) by transcript, and then calculate average per-group mean and average coefficient of variation defined as follows:
and
where $G$ is the set of sentence BLEU scores grouped by transcript and $G^{\prime } = \lbrace g | g\in G, |g|>1, \textrm {Mean}(g) > 0 \rbrace $ 
$\textrm {BLEU}_{MS}$ provides a normalized quality score as oppose to corpus-level BLEU or unnormalized average of sentence BLEU  And $\textrm {CoefVar}_{MS}$ is a standardized measure of model stability against different speakers (the lower the better)  Table TABREF24 shows the $\textrm {BLEU}_{MS}$ and $\textrm {CoefVar}_{MS}$ of our ST models on CoVoST test set  We see that German and Persian have the worst $\textrm {CoefVar}_{MS}$ (least stable) given their rich speaker diversity in the test set and relatively small train set (see also Figure FIGREF6 and Table TABREF2)  Dutch also has poor $\textrm {CoefVar}_{MS}$ because of the lack of training data  Multilingual models are consistantly more stable on low-resource languages  Ru+Fr, Tr+Fr, Fa+Fr and Zh+Fr even have better $\textrm {CoefVar}_{MS}$ than all individual languages 
Conclusion
We introduce a multilingual speech-to-text translation corpus, CoVoST, for 11 languages into English, diversified with over 11,000 speakers and over 60 accents  We also provide baseline results, including, to our knowledge, the first end-to-end many-to-one multilingual model for spoken language translation  CoVoST is free to use with a CC0 license, and the additional Tatoeba evaluation samples are also CC-licensed 
Context 2: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International Licence  Licence details: http://creativecommons org/licenses/by/4 0/
In the spirit of the brevity of social media's messages and reactions, people have got used to express feelings minimally and symbolically, as with hashtags on Twitter and Instagram  On Facebook, people tend to be more wordy, but posts normally receive more simple “likes” than longer comments  Since February 2016, Facebook users can express specific emotions in response to a post thanks to the newly introduced reaction feature (see Section SECREF2 ), so that now a post can be wordlessly marked with an expression of say “joy" or “surprise" rather than a generic “like” 
It has been observed that this new feature helps Facebook to know much more about their users and exploit this information for targeted advertising BIBREF0 , but interest in people's opinions and how they feel isn't limited to commercial reasons, as it invests social monitoring, too, including health care and education BIBREF1   However, emotions and opinions are not always expressed this explicitly, so that there is high interest in developing systems towards their automatic detection  Creating manually annotated datasets large enough to train supervised models is not only costly, but also—especially in the case of opinions and emotions—difficult, due to the intrinsic subjectivity of the task BIBREF2 , BIBREF3   Therefore, research has focused on unsupervised methods enriched with information derived from lexica, which are manually created BIBREF3 , BIBREF4   Since go2009twitter have shown that happy and sad emoticons can be successfully used as signals for sentiment labels, distant supervision, i e  using some reasonably safe signals as proxies for automatically labelling training data BIBREF5 , has been used also for emotion recognition, for example exploiting both emoticons and Twitter hashtags BIBREF6 , but mainly towards creating emotion lexica  mohammad2015using use hashtags, experimenting also with highly fine-grained emotion sets (up to almost 600 emotion labels), to create the large Hashtag Emotion Lexicon  Emoticons are used as proxies also by hallsmarmulti, who use distributed vector representations to find which words are interchangeable with emoticons but also which emoticons are used in a similar context 
We take advantage of distant supervision by using Facebook reactions as proxies for emotion labels, which to the best of our knowledge hasn't been done yet, and we train a set of Support Vector Machine models for emotion recognition  Our models, differently from existing ones, exploit information which is acquired entirely automatically, and achieve competitive or even state-of-the-art results for some of the emotion labels on existing, standard evaluation datasets  For explanatory purposes, related work is discussed further and more in detail when we describe the benchmarks for evaluation (Section SECREF3 ) and when we compare our models to existing ones (Section SECREF5 )  We also explore and discuss how choosing different sets of Facebook pages as training data provides an intrinsic domain-adaptation method 
Facebook reactions as labels
For years, on Facebook people could leave comments to posts, and also “like” them, by using a thumbs-up feature to explicitly express a generic, rather underspecified, approval  A “like” could thus mean “I like what you said", but also “I like that you bring up such topic (though I find the content of the article you linked annoying)" 
In February 2016, after a short trial, Facebook made a more explicit reaction feature available world-wide  Rather than allowing for the underspecified “like” as the only wordless response to a post, a set of six more specific reactions was introduced, as shown in Figure FIGREF1 : Like, Love, Haha, Wow, Sad and Angry  We use such reactions as proxies for emotion labels associated to posts 
We collected Facebook posts and their corresponding reactions from public pages using the Facebook API, which we accessed via the Facebook-sdk python library  We chose different pages (and therefore domains and stances), aiming at a balanced and varied dataset, but we did so mainly based on intuition (see Section SECREF4 ) and with an eye to the nature of the datasets available for evaluation (see Section SECREF5 )  The choice of which pages to select posts from is far from trivial, and we believe this is actually an interesting aspect of our approach, as by using different Facebook pages one can intrinsically tackle the domain-adaptation problem (See Section SECREF6 for further discussion on this)  The final collection of Facebook pages for the experiments described in this paper is as follows: FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney 
Note that thankful was only available during specific time spans related to certain events, as Mother's Day in May 2016 
For each page, we downloaded the latest 1000 posts, or the maximum available if there are fewer, from February 2016, retrieving the counts of reactions for each post  The output is a JSON file containing a list of dictionaries with a timestamp, the post and a reaction vector with frequency values, which indicate how many users used that reaction in response to the post (Figure FIGREF3 )  The resulting emotion vectors must then be turned into an emotion label 
In the context of this experiment, we made the simple decision of associating to each post the emotion with the highest count, ignoring like as it is the default and most generic reaction people tend to use  Therefore, for example, to the first post in Figure FIGREF3 , we would associate the label sad, as it has the highest score (284) among the meaningful emotions we consider, though it also has non-zero scores for other emotions  At this stage, we didn't perform any other entropy-based selection of posts, to be investigated in future work 
Emotion datasets
Three datasets annotated with emotions are commonly used for the development and evaluation of emotion detection systems, namely the Affective Text dataset, the Fairy Tales dataset, and the ISEAR dataset  In order to compare our performance to state-of-the-art results, we have used them as well  In this Section, in addition to a description of each dataset, we provide an overview of the emotions used, their distribution, and how we mapped them to those we obtained from Facebook posts in Section SECREF7   A summary is provided in Table TABREF8 , which also shows, in the bottom row, what role each dataset has in our experiments: apart from the development portion of the Affective Text, which we used to develop our models (Section SECREF4 ), all three have been used as benchmarks for our evaluation 
Affective Text dataset
Task 14 at SemEval 2007 BIBREF7 was concerned with the classification of emotions and valence in news headlines  The headlines where collected from several news websites including Google news, The New York Times, BBC News and CNN  The used emotion labels were Anger, Disgust, Fear, Joy, Sadness, Surprise, in line with the six basic emotions of Ekman's standard model BIBREF8   Valence was to be determined as positive or negative  Classification of emotion and valence were treated as separate tasks  Emotion labels were not considered as mututally exclusive, and each emotion was assigned a score from 0 to 100  Training/developing data amounted to 250 annotated headlines (Affective development), while systems were evaluated on another 1000 (Affective test)  Evaluation was done using two different methods: a fine-grained evaluation using Pearson's r to measure the correlation between the system scores and the gold standard; and a coarse-grained method where each emotion score was converted to a binary label, and precision, recall, and f-score were computed to assess performance  As it is done in most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , we also treat this as a classification problem (coarse-grained)  This dataset has been extensively used for the evaluation of various unsupervised methods BIBREF2 , but also for testing different supervised learning techniques and feature portability BIBREF10  
Fairy Tales dataset
This is a dataset collected by alm2008affect, where about 1,000 sentences from fairy tales (by B  Potter, H C  Andersen and Grimm) were annotated with the same six emotions of the Affective Text dataset, though with different names: Angry, Disgusted, Fearful, Happy, Sad, and Surprised  In most works that use this dataset BIBREF3 , BIBREF4 , BIBREF9 , only sentences where all annotators agreed are used, and the labels angry and disgusted are merged  We adopt the same choices 
ISEAR
The ISEAR (International Survey on Emotion Antecedents and Reactions BIBREF11 , BIBREF12 ) is a dataset created in the context of a psychology project of the 1990s, by collecting questionnaires answered by people with different cultural backgrounds  The main aim of this project was to gather insights in cross-cultural aspects of emotional reactions  Student respondents, both psychologists and non-psychologists, were asked to report situations in which they had experienced all of seven major emotions (joy, fear, anger, sadness, disgust, shame and guilt)  In each case, the questions covered the way they had appraised a given situation and how they reacted  The final dataset contains reports by approximately 3000 respondents from all over the world, for a total of 7665 sentences labelled with an emotion, making this the largest dataset out of the three we use 
Overview of datasets and emotions
We summarise datasets and emotion distribution from two viewpoints  First, because there are different sets of emotions labels in the datasets and Facebook data, we need to provide a mapping and derive a subset of emotions that we are going to use for the experiments  This is shown in Table TABREF8 , where in the “Mapped” column we report the final emotions we use in this paper: anger, joy, sadness, surprise  All labels in each dataset are mapped to these final emotions, which are therefore the labels we use for training and testing our models 
Second, the distribution of the emotions for each dataset is different, as can be seen in Figure FIGREF9  
In Figure FIGREF9 we also provide the distribution of the emotions anger, joy, sadness, surprise per Facebook page, in terms of number of posts (recall that we assign to a post the label corresponding to the majority emotion associated to it, see Section SECREF2 )

ground_truth: Unanswerable

Unanswerable

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

Please answer the questions based on the text provided below. Which Facebook pages did they look at?
Context 2:  We can observe that for example pages about news tend to have more sadness and anger posts, while pages about cooking and tv-shows have a high percentage of joy posts  We will use this information to find the best set of pages for a given target domain (see Section SECREF5 ) 
Model
There are two main decisions to be taken in developing our model: (i) which Facebook pages to select as training data, and (ii) which features to use to train the model, which we discuss below  Specifically, we first set on a subset of pages and then experiment with features  Further exploration of the interaction between choice of pages and choice of features is left to future work, and partly discussed in Section SECREF6   For development, we use a small portion of the Affective data set described in Section SECREF4 , that is the portion that had been released as development set for SemEval's 2007 Task 14 BIBREF7 , which contains 250 annotated sentences (Affective development, Section SECREF4 )  All results reported in this section are on this dataset  The test set of Task 14 as well as the other two datasets described in Section SECREF3 will be used to evaluate the final models (Section SECREF4 ) 
Selecting Facebook pages
Although page selection is a crucial ingredient of this approach, which we believe calls for further and deeper, dedicated investigation, for the experiments described here we took a rather simple approach  First, we selected the pages that would provide training data based on intuition and availability, then chose different combinations according to results of a basic model run on development data, and eventually tested feature combinations, still on the development set 
For the sake of simplicity and transparency, we first trained an SVM with a simple bag-of-words model and default parameters as per the Scikit-learn implementation BIBREF13 on different combinations of pages  Based on results of the attempted combinations as well as on the distribution of emotions in the development dataset (Figure FIGREF9 ), we selected a best model (B-M), namely the combined set of Time, The Guardian and Disney, which yields the highest results on development data  Time and The Guardian perform well on most emotions but Disney helps to boost the performance for the Joy class 
Features
In selecting appropriate features, we mainly relied on previous work and intuition  We experimented with different combinations, and all tests were still done on Affective development, using the pages for the best model (B-M) described above as training data  Results are in Table TABREF20   Future work will further explore the simultaneous selection of features and page combinations 
We use a set of basic text-based features to capture the emotion class  These include a tf-idf bag-of-words feature, word (2-3) and character (2-5) ngrams, and features related to the presence of negation words, and to the usage of punctuation 
This feature is used in all unsupervised models as a source of information, and we mainly include it to assess its contribution, but eventually do not use it in our final model 
We used the NRC10 Lexicon because it performed best in the experiments by BIBREF10 , which is built around the emotions anger, anticipation, disgust, fear, joy, sadness, and surprise, and the valence values positive and negative  For each word in the lexicon, a boolean value indicating presence or absence is associated to each emotion  For a whole sentence, a global score per emotion can be obtained by summing the vectors for all content words of that sentence included in the lexicon, and used as feature 
As additional feature, we also included Word Embeddings, namely distributed representations of words in a vector space, which have been exceptionally successful in boosting performance in a plethora of NLP tasks  We use three different embeddings:
Google embeddings: pre-trained embeddings trained on Google News and obtained with the skip-gram architecture described in BIBREF14   This model contains 300-dimensional vectors for 3 million words and phrases 
Facebook embeddings: embeddings that we trained on our scraped Facebook pages for a total of 20,000 sentences  Using the gensim library BIBREF15 , we trained the embeddings with the following parameters: window size of 5, learning rate of 0 01 and dimensionality of 100  We filtered out words with frequency lower than 2 occurrences 
Retrofitted embeddings: Retrofitting BIBREF16 has been shown as a simple but efficient way of informing trained embeddings with additional information derived from some lexical resource, rather than including it directly at the training stage, as it's done for example to create sense-aware BIBREF17 or sentiment-aware BIBREF18 embeddings  In this work, we retrofit general embeddings to include information about emotions, so that emotion-similar words can get closer in space  Both the Google as well as our Facebook embeddings were retrofitted with lexical information obtained from the NRC10 Lexicon mentioned above, which provides emotion-similarity for each token  Note that differently from the previous two types of embeddings, the retrofitted ones do rely on handcrafted information in the form of a lexical resource 
Results on development set
We report precision, recall, and f-score on the development set  The average f-score is reported as micro-average, to better account for the skewed distribution of the classes as well as in accordance to what is usually reported for this task BIBREF19  
From Table TABREF20 we draw three main observations  First, a simple tf-idf bag-of-word mode works already very well, to the point that the other textual and lexicon-based features don't seem to contribute to the overall f-score (0 368), although there is a rather substantial variation of scores per class  Second, Google embeddings perform a lot better than Facebook embeddings, and this is likely due to the size of the corpus used for training  Retrofitting doesn't seem to help at all for the Google embeddings, but it does boost the Facebook embeddings, leading to think that with little data, more accurate task-related information is helping, but corpus size matters most  Third, in combination with embeddings, all features work better than just using tf-idf, but removing the Lexicon feature, which is the only one based on hand-crafted resources, yields even better results  Then our best model (B-M) on development data relies entirely on automatically obtained information, both in terms of training data as well as features 
Results
In Table TABREF26 we report the results of our model on the three datasets standardly used for the evaluation of emotion classification, which we have described in Section SECREF3  
Our B-M model relies on subsets of Facebook pages for training, which were chosen according to their performance on the development set as well as on the observation of emotions distribution on different pages and in the different datasets, as described in Section SECREF4   The feature set we use is our best on the development set, namely all the features plus Google-based embeddings, but excluding the lexicon  This makes our approach completely independent of any manual annotation or handcrafted resource  Our model's performance is compared to the following systems, for which results are reported in the referred literature  Please note that no other existing model was re-implemented, and results are those reported in the respective papers 
Discussion, conclusions and future work
We have explored the potential of using Facebook reactions in a distant supervised setting to perform emotion classification  The evaluation on standard benchmarks shows that models trained as such, especially when enhanced with continuous vector representations, can achieve competitive results without relying on any handcrafted resource  An interesting aspect of our approach is the view to domain adaptation via the selection of Facebook pages to be used as training data 
We believe that this approach has a lot of potential, and we see the following directions for improvement  Feature-wise, we want to train emotion-aware embeddings, in the vein of work by tang:14, and iacobacci2015sensembed  Retrofitting FB-embeddings trained on a larger corpus might also be successful, but would rely on an external lexicon 
The largest room for yielding not only better results but also interesting insights on extensions of this approach lies in the choice of training instances, both in terms of Facebook pages to get posts from, as well as in which posts to select from the given pages  For the latter, one could for example only select posts that have a certain length, ignore posts that are only quotes or captions to images, or expand posts by including content from linked html pages, which might provide larger and better contexts BIBREF23   Additionally, and most importantly, one could use an entropy-based measure to select only posts that have a strong emotion rather than just considering the majority emotion as training label  For the former, namely the choice of Facebook pages, which we believe deserves the most investigation, one could explore several avenues, especially in relation to stance-based issues BIBREF24   In our dataset, for example, a post about Chile beating Colombia in a football match during the Copa America had very contradictory reactions, depending on which side readers would cheer for  Similarly, the very same political event, for example, would get very different reactions from readers if it was posted on Fox News or The Late Night Show, as the target audience is likely to feel very differently about the same issue  This also brings up theoretical issues related more generally to the definition of the emotion detection task, as it's strongly dependent on personal traits of the audience  Also, in this work, pages initially selected on availability and intuition were further grouped into sets to make training data according to performance on development data, and label distribution  Another criterion to be exploited would be vocabulary overlap between the pages and the datasets 
Lastly, we could develop single models for each emotion, treating the problem as a multi-label task  This would even better reflect the ambiguity and subjectivity intrinsic to assigning emotions to text, where content could be at same time joyful or sad, depending on the reader 
Acknowledgements
In addition to the anonymous reviewers, we want to thank Lucia Passaro and Barbara Plank for insightful discussions, and for providing comments on draft versions of this paper 
Context 3: Introduction
Since humans amass more and more generally available data in the form of unstructured text it would be very useful to teach machines to read and comprehend such data and then use this understanding to answer our questions  A significant amount of research has recently focused on answering one particular kind of questions the answer to which depends on understanding a context document  These are cloze-style questions BIBREF0 which require the reader to fill in a missing word in a sentence  An important advantage of such questions is that they can be generated automatically from a suitable text corpus which allows us to produce a practically unlimited amount of them  That opens the task to notoriously data-hungry deep-learning techniques which now seem to outperform all alternative approaches 
Two such large-scale datasets have recently been proposed by researchers from Google DeepMind and Facebook AI: the CNN/Daily Mail dataset BIBREF1 and the Children's Book Test (CBT) BIBREF2 respectively  These have attracted a lot of attention from the research community BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 with a new state-of-the-art model coming out every few weeks 
However if our goal is a production-level system actually capable of helping humans, we want the model to use all available resources as efficiently as possible  Given that
we believe that if the community is striving to bring the performance as far as possible, it should move its work to larger data 
This thinking goes in line with recent developments in the area of language modelling  For a long time models were being compared on several "standard" datasets with publications often presenting minuscule improvements in performance  Then the large-scale One Billion Word corpus dataset appeared BIBREF15 and it allowed Jozefowicz et al  to train much larger LSTM models BIBREF16 that almost halved the state-of-the-art perplexity on this dataset 
We think it is time to make a similar step in the area of text comprehension  Hence we are introducing the BookTest, a new dataset very similar to the Children's Book test but more than 60 times larger to enable training larger models even in the domain of text comprehension  Furthermore the methodology used to create our data can later be used to create even larger datasets when the need arises thanks to further technological progress 
We show that if we evaluate a model trained on the new dataset on the now standard Children's Book Test dataset, we see an improvement in accuracy much larger than other research groups achieved by enhancing the model architecture itself (while still using the original CBT training data)  By training on the new dataset, we reduce the prediction error by almost one third  On the named-entity version of CBT this brings the ensemble of our models to the level of human baseline as reported by Facebook BIBREF2   However in the final section we show in our own human study that there is still room for improvement on the CBT beyond the performance of our model 
Task Description
A natural way of testing a reader's comprehension of a text is to ask her a question the answer to which can be deduced from the text  Hence the task we are trying to solve consists of answering a cloze-style question, the answer to which depends on the understanding of a context document provided with the question  The model is also provided with a set of possible answers from which the correct one is to be selected  This can be formalized as follows:
The training data consist of tuples INLINEFORM0 , where INLINEFORM1 is a question, INLINEFORM2 is a document that contains the answer to question INLINEFORM3 , INLINEFORM4 is a set of possible answers and INLINEFORM5 is the ground-truth answer  Both INLINEFORM6 and INLINEFORM7 are sequences of words from vocabulary INLINEFORM8   We also assume that all possible answers are words from the vocabulary, that is INLINEFORM9   In the CBT and CNN/Daily Mail datasets it is also true that the ground-truth answer INLINEFORM10 appears in the document  This is exploited by many machine learning models BIBREF2 , BIBREF4 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF10 , BIBREF11 , BIBREF12 , however some do not explicitly depend on this property BIBREF1 , BIBREF3 , BIBREF5 , BIBREF9
Current Landscape
We will now briefly review what datasets for text comprehension have been published up to date and look at models which have been recently applied to solving the task we have just described 
Datasets
A crucial condition for applying deep-learning techniques is to have a huge amount of data available for training  For question answering this specifically means having a large number of document-question-answer triples available  While there is an unlimited amount of text available, coming up with relevant questions and the corresponding answers can be extremely labour-intensive if done by human annotators  There were efforts to provide such human-generated datasets, e g  Microsoft's MCTest BIBREF17 , however their scale is not suitable for deep learning without pre-training on other data BIBREF18 (such as using pre-trained word embedding vectors) 
Google DeepMind managed to avoid this scale issue with their way of generating document-question-answer triples automatically, closely followed by Facebook with a similar method  Let us now briefly introduce the two resulting datasets whose properties are summarized in Table TABREF8  
These two datasets BIBREF1 exploit a useful feature of online news articles – many articles include a short summarizing sentence near the top of the page  Since all information in the summary sentence is also presented in the article body, we get a nice cloze-style question about the article contents by removing a word from the short summary 
The dataset's authors also replaced all named entities in the dataset by anonymous tokens which are further shuffled for each new batch  This forces the model to rely solely on information from the context document, not being able to transfer any meaning of the named entities between documents 
This restricts the task to one specific aspect of context-dependent question answering which may be useful however it moves the task further from the real application scenario, where we would like the model to use all information available to answer questions  Furthermore Chen et al  BIBREF5 have suggested that this can make about 17% of the questions unanswerable even by humans  They also claim that more than a half of the question sentences are mere paraphrases or exact matches of a single sentence from the context document  This raises a question to what extent the dataset can test deeper understanding of the articles 
The Children's Book Test BIBREF2 uses a different source - books freely available thanks to Project Gutenberg  Since no summary is available, each example consists of a context document formed from 20 consecutive sentences from the story together with a question formed from the subsequent sentence 
The dataset comes in four flavours depending on what type of word is omitted from the question sentence  Based on human evaluation done in BIBREF2 it seems that NE and CN are more context dependent than the other two types – prepositions and verbs  Therefore we (and all of the recent publications) focus only on these two word types 
Several new datasets related to the (now almost standard) ones above emerged recently  We will now briefly present them and explain how the dataset we are introducing in this article differs from them 
The LAMBADA dataset BIBREF19 is designed to measure progress in understanding common-sense questions about short stories that can be easily answered by humans but cannot be answered by current standard machine-learning models (e g  plain LSTM language models)  This dataset is useful for measuring the gap between humans and machine learning algorithms  However, by contrast to our BookTest dataset, it will not allow us to track progress towards the performance of the baseline systems or on examples where machine learning may show super-human performance  Also LAMBADA is just a diagnostic dataset and does not provide ready-to-use question-answering training data, just a plain-text corpus which may moreover include copyrighted books making its use potentially problematic for some purposes  We are providing ready training data consisting of copyright-free books only 
The SQuAD dataset BIBREF20 based on Wikipedia and the Who-did-What dataset BIBREF21 based on Gigaword news articles are factoid question-answering datasets where a multi-word answer should be extracted from a context document  This is in contrast to the previous datasets, including CNN/DM, CBT, LAMBADA and our new dataset, which require only single-word answers  Both these datasets however provide less than 130,000 training questions, two orders of magnitude less than our dataset does 
The Story Cloze Test BIBREF22 provides a crowd-sourced corpus of 49,255 commonsense stories for training and 3,744 testing stories with right and wrong endings  Hence the dataset is again rather small  Similarly to LAMBADA, the Story Cloze Test was designed to be easily answerable by humans 
In the WikiReading BIBREF23 dataset the context document is formed from a Wikipedia article and the question-answer pair is taken from the corresponding WikiData page  For each entity (e g  Hillary Clinton), WikiData contain a number of property-value pairs (e g  place of birth: Chicago) which form the datasets's question-answer pairs  The dataset is certainly relevant to the community, however the questions are of very limited variety with only 20 properties (and hence unique questions) covering INLINEFORM0 of the dataset  Furthermore many of the frequent properties are mentioned at a set spot within the article (e g  the date of birth is almost always in brackets behind the name of a person) which may make the task easier for machines  We are trying to provide a more varied dataset 
Although there are several datasets related to task we are aiming to solve, they differ sufficiently for our dataset to bring new value to the community  Its biggest advantage is its size which can furthermore be easily upscaled without expensive human annotation  Finally while we are emphasizing the differences, models could certainly benefit from as diverse a collection of datasets as possible 
Machine Learning Models
A first major work applying deep-learning techniques to text comprehension was Hermann et al  BIBREF1   This work was followed by the application of Memory Networks to the same task BIBREF2   Later three models emerged around the same time BIBREF3 , BIBREF4 , BIBREF5 including our psr model BIBREF4   The AS Reader inspired several subsequent models that use it as a sub-component in a diverse ensemble BIBREF8 ; extend it with a hierarchical structure BIBREF6 , BIBREF24 , BIBREF7 ; compute attention over the context document for every word in the query BIBREF10 or use two-way context-query attention mechanism for every word in the context and the query BIBREF11 that is similar in its spirit to models recently proposed in different domains, e g  BIBREF25 in information retrieval  Other neural approaches to text comprehension are explored in BIBREF9 , BIBREF12  
Possible Directions for Improvements
Accuracy in any machine learning tasks can be enhanced either by improving a machine learning model or by using more in-domain training data  Current state of the art models BIBREF6 , BIBREF7 , BIBREF8 , BIBREF11 improve over AS Reader's accuracy on CBT NE and CN datasets by 1-2 percent absolute  This suggests that with current techniques there is only limited room for improvement on the algorithmic side 
The other possibility to improve performance is simply to use more training data  The importance of training data was highlighted by the frequently quoted Mercer's statement that “There is no data like more data ” The observation that having more data is often more important than having better algorithms has been frequently stressed since then BIBREF13 , BIBREF14  
As a step in the direction of exploiting the potential of more data in the domain of text comprehension, we created a new dataset called BookTest similar to, but much larger than the widely used CBT and CNN/DM datasets 
BookTest
Similarly to the CBT, our BookTest dataset is derived from books available through project Gutenberg  We used 3555 copyright-free books to extract CN examples and 10507 books for NE examples, for comparison the CBT dataset was extracted from just 108 books 
When creating our dataset we follow the same procedure as was used to create the CBT dataset BIBREF2   That is, we detect whether each sentence contains either a named entity or a common noun that already appeared in one of the preceding twenty sentences  This word is then replaced by a gap tag (XXXXX) in this sentence which is hence turned into a cloze-style question  The preceding 20 sentences are used as the context document  For common noun and named entity detection we use the Stanford POS tagger BIBREF27 and Stanford NER BIBREF28 

ground_truth: FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney

FoxNews, CNN, ESPN, New York Times, Time magazine, Huffington Post Weird News, The Guardian, Cartoon Network, Cooking Light, Home Cooking Adventure, Justin Bieber, Nickelodeon, Spongebob, Disney.

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

Please answer the questions based on the text provided below. How does their ensemble method work?
Context 3: 
The training dataset consists of the original CBT NE and CN data extended with new NE and CN examples  The new BookTest dataset hence contains INLINEFORM0 training examples and INLINEFORM1 tokens 
The validation dataset consists of INLINEFORM0 NE and INLINEFORM1 CN questions  We have one test set for NE and one for CN, each containing INLINEFORM2 examples  The training, validation and test sets were generated from non-overlapping sets of books 
When generating the dataset we removed all editions of books used to create CBT validation and test sets from our training dataset  Therefore the models trained on the BookTest corpus can be evaluated on the original CBT data and they can be compared with recent text-comprehension models utilizing this dataset BIBREF2 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11  
Baselines
We will now use our psr model to evaluate the performance gain from increasing the dataset size 
AS Reader
In BIBREF4 we introduced the psr , which at the time of publication significantly outperformed all other architectures on the CNN, DM and CBT datasets  This model is built to leverage the fact that the answer is a single word from the context document  Similarly to many other models it uses attention over the document – intuitively a measure of how relevant each word is to answering the question  However while most previous models used this attention as weights to calculate a blended representation of the answer word, we simply sum the attention across all occurrences of each unique words and then simply select the word with the highest sum as the final answer  While simple, this trick seems both to improve accuracy and to speed-up training  It was adopted by many subsequent models BIBREF8 , BIBREF6 , BIBREF7 , BIBREF10 , BIBREF11 , BIBREF24  
Let us now describe the model in more detail  Figure FIGREF21 may help you in understanding the following paragraphs 
The words from the document and the question are first converted into vector embeddings using a look-up matrix INLINEFORM0   The document is then read by a bidirectional GRU network BIBREF29   A concatenation of the hidden states of the forward and backward GRUs at each word is then used as a contextual embedding of this word, intuitively representing the context in which the word is appearing  We can also understand it as representing the set of questions to which this word may be an answer 
Similarly the question is read by a bidirectional GRU but in this case only the final hidden states are concatenated to form the question embedding 
The attention over each word in the context is then calculated as the dot product of its contextual embedding with the question embedding  This attention is then normalized by the softmax function and summed across all occurrences of each answer candidate  The candidate with most accumulated attention is selected as the final answer 
For a more detailed description of the model including equations check BIBREF4   More details about the training setup and model hyperparameters can be found in the Appendix 
During our past experiments on the CNN, DM and CBT datasets BIBREF4 each unique word from the training, validation and test datasets had its row in the look-up matrix INLINEFORM0   However as we radically increased the dataset size, this would result in an extremely large number of model parameters so we decided to limit the vocabulary size to INLINEFORM1 most frequent words  For each example, each unique out-of-vocabulary word is now mapped on one of 1000 anonymous tokens which are randomly initialized and untrained  Fixing the embeddings of these anonymous tags proved to significantly improve the performance 
While mostly using the original AS Reader model, we have also tried introducing a minor tweak in some instances of the model  We tried initializing the context encoder GRU's hidden state by letting the encoder read the question first before proceeding to read the context document  Intuitively this allows the encoder to know in advance what to look for when reading over the context document 
Including models of this kind in the ensemble helped to improve the performance 
Results
Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data  The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data 
If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively  By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model  The ensemble of our models even exceeded the human baseline provided by Facebook BIBREF2 on the Common Noun dataset 
Our model takes approximately two weeks to converge when trained on the BookTest dataset on a single Nvidia Tesla K40 GPU 
Discussion
Embracing the abundance of data may mean focusing on other aspects of system design than with smaller data  Here are some of the challenges that we need to face in this situation 
Firstly, since the amount of data is practically unlimited – we could even generate them on the fly resulting in continuous learning similar to the Never-Ending Language Learning by Carnegie Mellon University BIBREF30 – it is now the speed of training that determines how much data the model is able to see  Since more training data significantly help the model performance, focusing on speeding up the algorithm may be more important than ever before  This may for instance influence the decision whether to use regularization such as dropout which does seem to somewhat improve the model performance, however usually at a cost of slowing down training 
Thanks to its simplicity, the psr seems to be training fast - for example around seven times faster than the models proposed by Chen et al  BIBREF5   Hence the psr may be particularly suitable for training on large datasets 
The second challenge is how to generalize the performance gains from large data to a specific target domain  While there are huge amounts of natural language data in general, it may not be the case in the domain where we may want to ultimately apply our model 
Hence we are usually not facing a scenario of simply using a larger amount of the same training data, but rather extending training to a related domain of data, hoping that some of what the model learns on the new data will still help it on the original task 
This is highlighted by our observations from applying a model trained on the BookTest to Children's Book Test test data  If we move model training from joint CBT NE+CN training data to a subset of the BookTest of the same size (230k examples), we see a drop in accuracy of around 10% on the CBT test datasets 
Hence even though the Children's Book Test and BookTest datasets are almost as close as two disjoint datasets can get, the transfer is still very imperfect   Rightly choosing data to augment the in-domain training data is certainly a problem worth exploring in future work 
Our results show that given enough data the AS Reader was able to exceed the human performance on CBT CN reported by Facebook  However we hypothesized that the system is still not achieving its full potential so we decided to examine the room for improvement in our own small human study 
Human Study
After adding more data we have the performance on the CBT validation and test datasets soaring  However is there still potential for much further growth beyond the results we have observed?
We decided to explore the remaining space for improvement on the CBT by testing humans on a random subset of 50 named entity and 50 common noun validation questions that the psr ensemble could not answer correctly  These questions were answered by 10 non-native English speakers from our research laboratory, each on a disjoint subset of questions   Participants had unlimited time to answer the questions and were told that these questions were not correctly answered by a machine, providing additional motivation to prove they are better than computers  The results of the human study are summarized in Table TABREF28   They show that a majority of questions that our system could not answer so far are in fact answerable  This suggests that 1) the original human baselines might have been underestimated, however, it might also be the case that there are some examples that can be answered by machines and not by humans; 2) there is still space for improvement 
A system that would answer correctly every time when either our ensemble or human answered correctly would achieve accuracy over 92% percent on both validation and test NE datasets and over 96% on both CN datasets  Hence it still makes sense to use CBT dataset to study further improvements of text-comprehension systems 
Conclusion
Few ways of improving model performance are as solidly established as using more training data  Yet we believe this principle has been somewhat neglected by recent research in text comprehension  While there is a practically unlimited amount of data available in this field, most research was performed on unnecessarily small datasets 
As a gentle reminder to the community we have shown that simply infusing a model with more data can yield performance improvements of up to INLINEFORM0 where several attempts to improve the model architecture on the same training data have given gains of at most INLINEFORM1 compared to our best ensemble result  Yes, experiments on small datasets certainly can bring useful insights  However we believe that the community should also embrace the real-world scenario of data abundance 
The BookTest dataset we are proposing gives the reading-comprehension community an opportunity to make a step in that direction 
Training Details
The training details are similar to those in BIBREF4 however we are including them here for completeness 
To train the model we used stochastic gradient descent with the ADAM update rule BIBREF32 and learning rates of INLINEFORM0 , INLINEFORM1 and INLINEFORM2   The best learning rate in our experiments was INLINEFORM3   We minimized negative log-likelihood as the training objective 
The initial weights in the word-embedding matrix were drawn randomly uniformly from the interval INLINEFORM0   Weights in the GRU networks were initialized by random orthogonal matrices BIBREF34 and biases were initialized to zero  We also used a gradient clipping BIBREF33 threshold of 10 and batches of sizes between 32 or 256  Increasing the batch from 32 to 128 seems to significantly improve performance on the large dataset - something we did not observe on the original CBT data  Increasing the batch size much above 128 is currently difficult due to memory constraints of the GPU 
During training we randomly shuffled all examples at the beginning of each epoch  To speed up training, we always pre-fetched 10 batches worth of examples and sorted them according to document length  Hence each batch contained documents of roughly the same length 
We also did not use pre-trained word embeddings 
We did not perform any text pre-processing since the datasets were already tokenized 
During training we evaluated the model performance every 12 hours and at the end of each epoch and stopped training when the error on the 20k BookTest validation set started increasing  We explored the hyperparameter space by training 67 different models The region of the parameter space that we explored together with the parameters of the model with best validation accuracy are summarized in Table TABREF29  
Our model was implemented using Theano BIBREF31 and Blocks BIBREF35  
The ensembles were formed by simply averaging the predictions from the constituent single models  These single models were selected using the following algorithm 
We started with the best performing model according to validation performance  Then in each step we tried adding the best performing model that had not been previously tried  We kept it in the ensemble if it did improve its validation performance and discarded it otherwise  This way we gradually tried each model once  We call the resulting model a greedy ensemble  We used the INLINEFORM0 BookTest validation dataset for this procedure 
The algorithm was offered 10 models and selected 5 of them for the final ensemble 

ground_truth: simply averaging the predictions from the constituent single models