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 were their results on the new dataset?
Context 1: Introduction
In the kitchen, we increasingly rely on instructions from cooking websites: recipes  A cook with a predilection for Asian cuisine may wish to prepare chicken curry, but may not know all necessary ingredients apart from a few basics  These users with limited knowledge cannot rely on existing recipe generation approaches that focus on creating coherent recipes given all ingredients and a recipe name BIBREF0  Such models do not address issues of personal preference (e g  culinary tastes, garnish choices) and incomplete recipe details  We propose to approach both problems via personalized generation of plausible, user-specific recipes using user preferences extracted from previously consumed recipes 
Our work combines two important tasks from natural language processing and recommender systems: data-to-text generation BIBREF1 and personalized recommendation BIBREF2  Our model takes as user input the name of a specific dish, a few key ingredients, and a calorie level  We pass these loose input specifications to an encoder-decoder framework and attend on user profiles—learned latent representations of recipes previously consumed by a user—to generate a recipe personalized to the user's tastes  We fuse these `user-aware' representations with decoder output in an attention fusion layer to jointly determine text generation  Quantitative (perplexity, user-ranking) and qualitative analysis on user-aware model outputs confirm that personalization indeed assists in generating plausible recipes from incomplete ingredients 
While personalized text generation has seen success in conveying user writing styles in the product review BIBREF3, BIBREF4 and dialogue BIBREF5 spaces, we are the first to consider it for the problem of recipe generation, where output quality is heavily dependent on the content of the instructions—such as ingredients and cooking techniques 
To summarize, our main contributions are as follows:
We explore a new task of generating plausible and personalized recipes from incomplete input specifications by leveraging historical user preferences;
We release a new dataset of 180K+ recipes and 700K+ user reviews for this task;
We introduce new evaluation strategies for generation quality in instructional texts, centering on quantitative measures of coherence  We also show qualitatively and quantitatively that personalized models generate high-quality and specific recipes that align with historical user preferences 
Related Work
Large-scale transformer-based language models have shown surprising expressivity and fluency in creative and conditional long-text generation BIBREF6, BIBREF7  Recent works have proposed hierarchical methods that condition on narrative frameworks to generate internally consistent long texts BIBREF8, BIBREF9, BIBREF10  Here, we generate procedurally structured recipes instead of free-form narratives 
Recipe generation belongs to the field of data-to-text natural language generation BIBREF1, which sees other applications in automated journalism BIBREF11, question-answering BIBREF12, and abstractive summarization BIBREF13, among others  BIBREF14, BIBREF15 model recipes as a structured collection of ingredient entities acted upon by cooking actions  BIBREF0 imposes a `checklist' attention constraint emphasizing hitherto unused ingredients during generation  BIBREF16 attend over explicit ingredient references in the prior recipe step  Similar hierarchical approaches that infer a full ingredient list to constrain generation will not help personalize recipes, and would be infeasible in our setting due to the potentially unconstrained number of ingredients (from a space of 10K+) in a recipe  We instead learn historical preferences to guide full recipe generation 
A recent line of work has explored user- and item-dependent aspect-aware review generation BIBREF3, BIBREF4  This work is related to ours in that it combines contextual language generation with personalization  Here, we attend over historical user preferences from previously consumed recipes to generate recipe content, rather than writing styles 
Approach
Our model's input specification consists of: the recipe name as a sequence of tokens, a partial list of ingredients, and a caloric level (high, medium, low)  It outputs the recipe instructions as a token sequence: $\mathcal {W}_r=\lbrace w_{r,0}, \dots , w_{r,T}\rbrace $ for a recipe $r$ of length $T$  To personalize output, we use historical recipe interactions of a user $u \in \mathcal {U}$ 
Encoder: Our encoder has three embedding layers: vocabulary embedding $\mathcal {V}$, ingredient embedding $\mathcal {I}$, and caloric-level embedding $\mathcal {C}$  Each token in the (length $L_n$) recipe name is embedded via $\mathcal {V}$; the embedded token sequence is passed to a two-layered bidirectional GRU (BiGRU) BIBREF17, which outputs hidden states for names $\lbrace \mathbf {n}_{\text{enc},j} \in \mathbb {R}^{2d_h}\rbrace $, with hidden size $d_h$  Similarly each of the $L_i$ input ingredients is embedded via $\mathcal {I}$, and the embedded ingredient sequence is passed to another two-layered BiGRU to output ingredient hidden states as $\lbrace \mathbf {i}_{\text{enc},j} \in \mathbb {R}^{2d_h}\rbrace $  The caloric level is embedded via $\mathcal {C}$ and passed through a projection layer with weights $W_c$ to generate calorie hidden representation $\mathbf {c}_{\text{enc}} \in \mathbb {R}^{2d_h}$ 
Ingredient Attention: We apply attention BIBREF18 over the encoded ingredients to use encoder outputs at each decoding time step  We define an attention-score function $\alpha $ with key $K$ and query $Q$:
with trainable weights $W_{\alpha }$, bias $\mathbf {b}_{\alpha }$, and normalization term $Z$  At decoding time $t$, we calculate the ingredient context $\mathbf {a}_{t}^{i} \in \mathbb {R}^{d_h}$ as:
Decoder: The decoder is a two-layer GRU with hidden state $h_t$ conditioned on previous hidden state $h_{t-1}$ and input token $w_{r, t}$ from the original recipe text  We project the concatenated encoder outputs as the initial decoder hidden state:
To bias generation toward user preferences, we attend over a user's previously reviewed recipes to jointly determine the final output token distribution  We consider two different schemes to model preferences from user histories: (1) recipe interactions, and (2) techniques seen therein (defined in data)  BIBREF19, BIBREF20, BIBREF21 explore similar schemes for personalized recommendation 
Prior Recipe Attention: We obtain the set of prior recipes for a user $u$: $R^+_u$, where each recipe can be represented by an embedding from a recipe embedding layer $\mathcal {R}$ or an average of the name tokens embedded by $\mathcal {V}$  We attend over the $k$-most recent prior recipes, $R^{k+}_u$, to account for temporal drift of user preferences BIBREF22  These embeddings are used in the `Prior Recipe' and `Prior Name' models, respectively 
Given a recipe representation $\mathbf {r} \in \mathbb {R}^{d_r}$ (where $d_r$ is recipe- or vocabulary-embedding size depending on the recipe representation) the prior recipe attention context $\mathbf {a}_{t}^{r_u}$ is calculated as
Prior Technique Attention: We calculate prior technique preference (used in the `Prior Tech` model) by normalizing co-occurrence between users and techniques seen in $R^+_u$, to obtain a preference vector $\rho _{u}$  Each technique $x$ is embedded via a technique embedding layer $\mathcal {X}$ to $\mathbf {x}\in \mathbb {R}^{d_x}$  Prior technique attention is calculated as
where, inspired by copy mechanisms BIBREF23, BIBREF24, we add $\rho _{u,x}$ for technique $x$ to emphasize the attention by the user's prior technique preference 
Attention Fusion Layer: We fuse all contexts calculated at time $t$, concatenating them with decoder GRU output and previous token embedding:
We then calculate the token probability:
and maximize the log-likelihood of the generated sequence conditioned on input specifications and user preferences  fig:ex shows a case where the Prior Name model attends strongly on previously consumed savory recipes to suggest the usage of an additional ingredient (`cilantro') 
Recipe Dataset: Food com
We collect a novel dataset of 230K+ recipe texts and 1M+ user interactions (reviews) over 18 years (2000-2018) from Food com  Here, we restrict to recipes with at least 3 steps, and at least 4 and no more than 20 ingredients  We discard users with fewer than 4 reviews, giving 180K+ recipes and 700K+ reviews, with splits as in tab:recipeixnstats 
Our model must learn to generate from a diverse recipe space: in our training data, the average recipe length is 117 tokens with a maximum of 256  There are 13K unique ingredients across all recipes  Rare words dominate the vocabulary: 95% of words appear $<$100 times, accounting for only 1 65% of all word usage  As such, we perform Byte-Pair Encoding (BPE) tokenization BIBREF25, BIBREF26, giving a training vocabulary of 15K tokens across 19M total mentions  User profiles are similarly diverse: 50% of users have consumed $\le $6 recipes, while 10% of users have consumed $>$45 recipes 
We order reviews by timestamp, keeping the most recent review for each user as the test set, the second most recent for validation, and the remainder for training (sequential leave-one-out evaluation BIBREF27)  We evaluate only on recipes not in the training set 
We manually construct a list of 58 cooking techniques from 384 cooking actions collected by BIBREF15; the most common techniques (bake, combine, pour, boil) account for 36 5% of technique mentions  We approximate technique adherence via string match between the recipe text and technique list 
Experiments and Results
For training and evaluation, we provide our model with the first 3-5 ingredients listed in each recipe  We decode recipe text via top-$k$ sampling BIBREF7, finding $k=3$ to produce satisfactory results  We use a hidden size $d_h=256$ for both the encoder and decoder  Embedding dimensions for vocabulary, ingredient, recipe, techniques, and caloric level are 300, 10, 50, 50, and 5 (respectively)  For prior recipe attention, we set $k=20$, the 80th %-ile for the number of user interactions  We use the Adam optimizer BIBREF28 with a learning rate of $10^{-3}$, annealed with a decay rate of 0 9 BIBREF29  We also use teacher-forcing BIBREF30 in all training epochs 
In this work, we investigate how leveraging historical user preferences can improve generation quality over strong baselines in our setting  We compare our personalized models against two baselines  The first is a name-based Nearest-Neighbor model (NN)  We initially adapted the Neural Checklist Model of BIBREF0 as a baseline; however, we ultimately use a simple Encoder-Decoder baseline with ingredient attention (Enc-Dec), which provides comparable performance and lower complexity  All personalized models outperform baseline in BPE perplexity (tab:metricsontest) with Prior Name performing the best  While our models exhibit comparable performance to baseline in BLEU-1/4 and ROUGE-L, we generate more diverse (Distinct-1/2: percentage of distinct unigrams and bigrams) and acceptable recipes  BLEU and ROUGE are not the most appropriate metrics for generation quality  A `correct' recipe can be written in many ways with the same main entities (ingredients)  As BLEU-1/4 capture structural information via n-gram matching, they are not correlated with subjective recipe quality  This mirrors observations from BIBREF31, BIBREF8 
We observe that personalized models make more diverse recipes than baseline  They thus perform better in BLEU-1 with more key entities (ingredient mentions) present, but worse in BLEU-4, as these recipes are written in a personalized way and deviate from gold on the phrasal level  Similarly, the `Prior Name' model generates more unigram-diverse recipes than other personalized models and obtains a correspondingly lower BLEU-1 score 
Qualitative Analysis: We present sample outputs for a cocktail recipe in tab:samplerecipes, and additional recipes in the appendix  Generation quality progressively improves from generic baseline output to a blended cocktail produced by our best performing model  Models attending over prior recipes explicitly reference ingredients  The Prior Name model further suggests the addition of lemon and mint, which are reasonably associated with previously consumed recipes like coconut mousse and pork skewers 
Personalization: To measure personalization, we evaluate how closely the generated text corresponds to a particular user profile  We compute the likelihood of generated recipes using identical input specifications but conditioned on ten different user profiles—one `gold' user who consumed the original recipe, and nine randomly generated user profiles  Following BIBREF8, we expect the highest likelihood for the recipe conditioned on the gold user  We measure user matching accuracy (UMA)—the proportion where the gold user is ranked highest—and Mean Reciprocal Rank (MRR) BIBREF32 of the gold user  All personalized models beat baselines in both metrics, showing our models personalize generated recipes to the given user profiles  The Prior Name model achieves the best UMA and MRR by a large margin, revealing that prior recipe names are strong signals for personalization  Moreover, the addition of attention mechanisms to capture these signals improves language modeling performance over a strong non-personalized baseline 
Recipe Level Coherence: A plausible recipe should possess a coherent step order, and we evaluate this via a metric for recipe-level coherence  We use the neural scoring model from BIBREF33 to measure recipe-level coherence for each generated recipe  Each recipe step is encoded by BERT BIBREF34  Our scoring model is a GRU network that learns the overall recipe step ordering structure by minimizing the cosine similarity of recipe step hidden representations presented in the correct and reverse orders  Once pretrained, our scorer calculates the similarity of a generated recipe to the forward and backwards ordering of its corresponding gold label, giving a score equal to the difference between the former and latter  A higher score indicates better step ordering (with a maximum score of 2)  tab:coherencemetrics shows that our personalized models achieve average recipe-level coherence scores of 1 78-1 82, surpassing the baseline at 1 77 
Recipe Step Entailment: Local coherence is also crucial to a user following a recipe: it is crucial that subsequent steps are logically consistent with prior ones  We model local coherence as an entailment task: predicting the likelihood that a recipe step follows the preceding  We sample several consecutive (positive) and non-consecutive (negative) pairs of steps from each recipe  We train a BERT BIBREF34 model to predict the entailment score of a pair of steps separated by a [SEP] token, using the final representation of the [CLS] token  The step entailment score is computed as the average of scores for each set of consecutive steps in each recipe, averaged over every generated recipe for a model, as shown in tab:coherencemetrics 
Human Evaluation: We presented 310 pairs of recipes for pairwise comparison BIBREF8 (details in appendix) between baseline and each personalized model, with results shown in tab:metricsontest  On average, human evaluators preferred personalized model outputs to baseline 63% of the time, confirming that personalized attention improves the semantic plausibility of generated recipes  We also performed a small-scale human coherence survey over 90 recipes, in which 60% of users found recipes generated by personalized models to be more coherent and preferable to those generated by baseline models 
Conclusion
In this paper, we propose a novel task: to generate personalized recipes from incomplete input specifications and user histories  On a large novel dataset of 180K recipes and 700K reviews, we show that our personalized generative models can generate plausible, personalized, and coherent recipes preferred by human evaluators for consumption  We also introduce a set of automatic coherence measures for instructional texts as well as personalization metrics to support our claims  Our future work includes generating structured representations of recipes to handle ingredient properties, as well as accounting for references to collections of ingredients (e g  “dry mix") 
Acknowledgements  This work is partly supported by NSF #1750063  We thank all reviewers for their constructive suggestions, as well as Rei M , Sujoy P , Alicia L , Eric H , Tim S , Kathy C , Allen C , and Micah I  for their feedback 
Appendix ::: Food com: Dataset Details
Our raw data consists of 270K recipes and 1 4M user-recipe interactions (reviews) scraped from Food com, covering a period of 18 years (January 2000 to December 2018)  See tab:int-stats for dataset summary statistics, and tab:samplegk for sample information about one user-recipe interaction and the recipe involved 
Appendix ::: Generated Examples
See tab:samplechx for a sample recipe for chicken chili and tab:samplewaffle for a sample recipe for sweet waffles 
Human Evaluation
We prepared a set of 15 pairwise comparisons per evaluation session, and collected 930 pairwise evaluations (310 per personalized model) over 62 sessions  For each pair, users were given a partial recipe specification (name and 3-5 key ingredients), as well as two generated recipes labeled `A' and `B'  One recipe is generated from our baseline encoder-decoder model and one recipe is generated by one of our three personalized models (Prior Tech, Prior Name, Prior Recipe)  The order of recipe presentation (A/B) is randomly selected for each question  A screenshot of the user evaluation interface is given in fig:exeval  We ask the user to indicate which recipe they find more coherent, and which recipe best accomplishes the goal indicated by the recipe name  A screenshot of this survey interface is given in fig:exeval2 
Context 3: Introduction
There is a recent spark of interest in the task of Question Answering (QA) over unstructured textual data, also referred to as Machine Reading Comprehension (MRC)  This is mostly due to wide-spread success of advances in various facets of deep learning related research, such as novel architectures BIBREF0, BIBREF1 that allow for efficient optimisation of neural networks consisting of multiple layers, hardware designed for deep learning purposes and software frameworks BIBREF2, BIBREF3 that allow efficient development and testing of novel approaches  These factors enable researchers to produce models that are pre-trained on large scale corpora and provide contextualised word representations BIBREF4 that are shown to be a vital component towards solutions for a variety of natural language understanding tasks, including MRC BIBREF5  Another important factor that led to the recent success in MRC-related tasks is the widespread availability of various large datasets, e g , SQuAD BIBREF6, that provide sufficient examples for optimising statistical models  The combination of these factors yields notable results, even surpassing human performance BIBREF7 
MRC is a generic task format that can be used to probe for various natural language understanding capabilities BIBREF8  Therefore it is crucially important to establish a rigorous evaluation methodology in order to be able to draw reliable conclusions from conducted experiments  While increasing effort is put into the evaluation of novel architectures, such as keeping the evaluation data from public access to prevent unintentional overfitting to test data, performing ablation and error studies and introducing novel metrics BIBREF9, surprisingly little is done to establish the quality of the data itself  Additionally, recent research arrived at worrisome findings: the data of those gold standards, which is usually gathered involving a crowd-sourcing step, suffers from flaws in design BIBREF10 or contains overly specific keywords BIBREF11  Furthermore, these gold standards contain “annotation artefacts”, cues that lead models into focusing on superficial aspects of text, such as lexical overlap and word order, instead of actual language understanding BIBREF12, BIBREF13  These weaknesses cast some doubt on whether the data can reliably evaluate the reading comprehension performance of the models they evaluate, i e  if the models are indeed being assessed for their capability to read 
Figure FIGREF3 shows an example from HotpotQA BIBREF14, a dataset that exhibits the last kind of weakness mentioned above, i e , the presence of unique keywords in both the question and the passage (in close proximity to the expected answer) 
An evaluation methodology is vital to the fine-grained understanding of challenges associated with a single gold standard, in order to understand in greater detail which capabilities of MRC models it evaluates  More importantly, it allows to draw comparisons between multiple gold standards and between the results of respective state-of-the-art models that are evaluated on them 
In this work, we take a step back and propose a framework to systematically analyse MRC evaluation data, typically a set of questions and expected answers to be derived from accompanying passages  Concretely, we introduce a methodology to categorise the linguistic complexity of the textual data and the reasoning and potential external knowledge required to obtain the expected answer  Additionally we propose to take a closer look at the factual correctness of the expected answers, a quality dimension that appears under-explored in literature 
We demonstrate the usefulness of the proposed framework by applying it to precisely describe and compare six contemporary MRC datasets  Our findings reveal concerns about their factual correctness, the presence of lexical cues that simplify the task of reading comprehension and the lack of semantic altering grammatical modifiers  We release the raw data comprised of 300 paragraphs, questions and answers richly annotated under the proposed framework as a resource for researchers developing natural language understanding models and datasets to utilise further 
To the best of our knowledge this is the first attempt to introduce a common evaluation methodology for MRC gold standards and the first across-the-board qualitative evaluation of MRC datasets with respect to the proposed categories 
Framework for MRC Gold Standard Analysis ::: Problem definition
We define the task of machine reading comprehension, the target application of the proposed methodology as follows: Given a paragraph $P$ that consists of tokens (words) $p_1, \ldots , p_{n_P}$ and a question $Q$ that consists of tokens $q_1 \ldots q_{n_Q}$, the goal is to retrieve an answer $A$ with tokens $a_1 \ldots a_{n_A}$  $A$ is commonly constrained to be one of the following cases BIBREF15, illustrated in Figure FIGREF9:
Multiple choice, where the goal is to predict $A$ from a given set of choices $\mathcal {A}$ 
Cloze-style, where $S$ is a sentence, and $A$ and $Q$ are obtained by removing a sequence of words such that $Q = S - A$  The task is to fill in the resulting gap in $Q$ with the expected answer $A$ to form $S$ 
Span, where is a continuous subsequence of tokens from the paragraph ($A \subseteq P$)  Flavours include multiple spans as the correct answer or $A \subseteq Q$ 
Free form, where $A$ is an unconstrained natural language string 
A gold standard $G$ is composed of $m$ entries $(Q_i, A_i, P_i)_{i\in \lbrace 1,\ldots , m\rbrace }$ 
The performance of an approach is established by comparing its answer predictions $A^*_{i}$ on the given input $(Q_i, T_i)$ (and $\mathcal {A}_i$ for the multiple choice setting) against the expected answer $A_i$ for all $i\in \lbrace 1,\ldots , m\rbrace $ under a performance metric  Typical performance metrics are exact match (EM) or accuracy, i e  the percentage of exactly predicted answers, and the F1 score – the harmonic mean between the precision and the recall of the predicted tokens compared to expected answer tokens  The overall F1 score can either be computed by averaging the F1 scores for every instance or by first averaging the precision and recall and then computing the F1 score from those averages (macro F1)  Free-text answers, meanwhile, are evaluated by means of text generation and summarisation metrics such as BLEU BIBREF16 or ROUGE-L BIBREF17 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest
In this section we describe a methodology to categorise gold standards according to linguistic complexity, required reasoning and background knowledge, and their factual correctness  Specifically, we use those dimensions as high-level categories of a qualitative annotation schema for annotating question, expected answer and the corresponding context  We further enrich the qualitative annotations by a metric based on lexical cues in order to approximate a lower bound for the complexity of the reading comprehension task  By sampling entries from each gold standard and annotating them, we obtain measurable results and thus are able to make observations about the challenges present in that gold standard data 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Problem setting
We are interested in different types of the expected answer  We differentiate between Span, where an answer is a continuous span taken from the passage, Paraphrasing, where the answer is a paraphrase of a text span, Unanswerable, where there is no answer present in the context, and Generated, if it does not fall into any of the other categories  It is not sufficient for an answer to restate the question or combine multiple Span or Paraphrasing answers to be annotated as Generated  It is worth mentioning that we focus our investigations on answerable questions  For a complementary qualitative analysis that categorises unanswerable questions, the reader is referred to Yatskar2019 
Furthermore, we mark a sentence as Supporting Fact if it contains evidence required to produce the expected answer, as they are used further in the complexity analysis 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Factual Correctness
An important factor for the quality of a benchmark is its factual correctness, because on the one hand, the presence of factually wrong or debatable examples introduces an upper bound for the achievable performance of models on those gold standards  On the other hand, it is hard to draw conclusions about the correctness of answers produced by a model that is evaluated on partially incorrect data 
One way by which developers of modern crowd-sourced gold standards ensure quality is by having the same entry annotated by multiple workers BIBREF18 and keeping only those with high agreement  We investigate whether this method is enough to establish a sound ground truth answer that is unambiguously correct  Concretely we annotate an answer as Debatable when the passage features multiple plausible answers, when multiple expected answers contradict each other, or an answer is not specific enough with respect to the question and a more specific answer is present  We annotate an answer as Wrong when it is factually wrong and a correct answer is present in the context 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Required Reasoning
It is important to understand what types of reasoning the benchmark evaluates, in order to be able to accredit various reasoning capabilities to the models it evaluates  Our proposed reasoning categories are inspired by those found in scientific question answering literature BIBREF19, BIBREF20, as research in this area focuses on understanding the required reasoning capabilities  We include reasoning about the Temporal succession of events, Spatial reasoning about directions and environment, and Causal reasoning about the cause-effect relationship between events  We further annotate (multiple-choice) answers that can only be answered By Exclusion of every other alternative 
We further extend the reasoning categories by operational logic, similar to those required in semantic parsing tasks BIBREF21, as solving those tasks typically requires “multi-hop” reasoning BIBREF14, BIBREF22  When an answer can only be obtained by combining information from different sentences joined by mentioning a common entity, concept, date, fact or event (from here on called entity), we annotate it as Bridge  We further annotate the cases, when the answer is a concrete entity that satisfies a Constraint specified in the question, when it is required to draw a Comparison of multiple entities' properties or when the expected answer is an Intersection of their properties (e g  “What do Person A and Person B have in common?”)
We are interested in the linguistic reasoning capabilities probed by a gold standard, therefore we include the appropriate category used by Wang2019  Specifically, we annotate occurrences that require understanding of Negation, Quantifiers (such as “every”, “some”, or “all”), Conditional (“if    then”) statements and the logical implications of Con-/Disjunction (i e  “and” and “or”) in order to derive the expected answer 
Finally, we investigate whether arithmetic reasoning requirements emerge in MRC gold standards as this can probe for reasoning that is not evaluated by simple answer retrieval BIBREF23  To this end, we annotate the presence of of Addition and Subtraction, answers that require Ordering of numerical values, Counting and Other occurrences of simple mathematical operations 
An example can exhibit multiple forms of reasoning  Notably, we do not annotate any of the categories mentioned above if the expected answer is directly stated in the passage  For example, if the question asks “How many total points were scored in the game?” and the passage contains a sentence similar to “The total score of the game was 51 points”, it does not require any reasoning, in which case we annotate it as Retrieval 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Knowledge
Worthwhile knowing is whether the information presented in the context is sufficient to answer the question, as there is an increase of benchmarks deliberately designed to probe a model's reliance on some sort of background knowledge BIBREF24  We seek to categorise the type of knowledge required  Similar to Wang2019, on the one hand we annotate the reliance on factual knowledge, that is (Geo)political/Legal, Cultural/Historic, Technical/Scientific and Other Domain Specific knowledge about the world that can be expressed as a set of facts  On the other hand, we denote Intuitive knowledge requirements, which is challenging to express as a set of facts, such as the knowledge that a parenthetic numerical expression next to a person's name in a biography usually denotes his life span 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Linguistic Complexity
Another dimension of interest is the evaluation of various linguistic capabilities of MRC models BIBREF25, BIBREF26, BIBREF27  We aim to establish which linguistic phenomena are probed by gold standards and to which degree  To that end, we draw inspiration from the annotation schema used by Wang2019, and adapt it around lexical semantics and syntax 
More specifically, we annotate features that introduce variance between the supporting facts and the question  With regard to lexical semantics, we focus on the use of redundant words that do not alter the meaning of a sentence for the task of retrieving the expected answer (Redundancy), requirements on the understanding of words' semantic fields (Lexical Entailment) and the use of Synonyms and Paraphrases with respect to the question wording  Furthermore we annotate cases where supporting facts contain Abbreviations of concepts introduced in the question (and vice versa) and when a Dative case substitutes the use of a preposition (e g  “I bought her a gift” vs “I bought a gift for her”)  Regarding syntax, we annotate changes from passive to active Voice, the substitution of a Genitive case with a preposition (e g  “of”) and changes from nominal to verbal style and vice versa (Nominalisation) 
We recognise features that add ambiguity to the supporting facts, for example when information is only expressed implicitly by using an Ellipsis  As opposed to redundant words, we annotate Restrictivity and Factivity modifiers, words and phrases whose presence does change the meaning of a sentence with regard to the expected answer, and occurrences of intra- or inter-sentence Coreference in supporting facts (that is relevant to the question)  Lastly, we mark ambiguous syntactic features, when their resolution is required in order to obtain the answer  Concretely, we mark argument collection with con- and disjunctions (Listing) and ambiguous Prepositions, Coordination Scope and Relative clauses/Adverbial phrases/Appositions 
Framework for MRC Gold Standard Analysis ::: Dimensions of Interest ::: Complexity
Finally, we want to approximate the presence of lexical cues that might simplify the reading required in order to arrive at the answer  Quantifying this allows for more reliable statements about and comparison of the complexity of gold standards, particularly regarding the evaluation of comprehension that goes beyond simple lexical matching  We propose the use of coarse metrics based on lexical overlap between question and context sentences  Intuitively, we aim to quantify how much supporting facts “stand out” from their surrounding passage context  This can be used as proxy for the capability to retrieve the answer BIBREF10  Specifically, we measure (i) the number of words jointly occurring in a question and a sentence, (ii) the length of the longest n-gram shared by question and sentence and (iii) whether a word or n-gram from the question uniquely appears in a sentence 
The resulting taxonomy of the framework is shown in Figure FIGREF10  The full catalogue of features, their description, detailed annotation guideline as well as illustrating examples can be found in Appendix  
Application of the Framework ::: Candidate Datasets
We select contemporary MRC benchmarks to represent all four commonly used problem definitions BIBREF15

ground_truth: average recipe-level coherence scores of 1.78-1.82, human evaluators preferred personalized model outputs to baseline 63% of the time

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 datasets do they evaluate on?
Context 2: Introduction
Data annotation is a major bottleneck for the application of supervised learning approaches to many problems  As a result, unsupervised methods that learn directly from unlabeled data are increasingly important  For tasks related to unsupervised syntactic analysis, discrete generative models have dominated in recent years – for example, for both part-of-speech (POS) induction BIBREF0 , BIBREF1 and unsupervised dependency parsing BIBREF2 , BIBREF3 , BIBREF4   While similar models have had success on a range of unsupervised tasks, they have mostly ignored the apparent utility of continuous word representations evident from supervised NLP applications BIBREF5 , BIBREF6   In this work, we focus on leveraging and explicitly representing continuous word embeddings within unsupervised models of syntactic structure 
Pre-trained word embeddings from massive unlabeled corpora offer a compact way of injecting a prior notion of word similarity into models that would otherwise treat words as discrete, isolated categories  However, the specific properties of language captured by any particular embedding scheme can be difficult to control, and, further, may not be ideally suited to the task at hand  For example, pre-trained skip-gram embeddings BIBREF7 with small context window size are found to capture the syntactic properties of language well BIBREF8 , BIBREF9   However, if our goal is to separate syntactic categories, this embedding space is not ideal – POS categories correspond to overlapping interspersed regions in the embedding space, evident in Figure SECREF4  
In our approach, we propose to learn a new latent embedding space as a projection of pre-trained embeddings (depicted in Figure SECREF5 ), while jointly learning latent syntactic structure – for example, POS categories or syntactic dependencies  To this end, we introduce a new generative model (shown in Figure FIGREF6 ) that first generates a latent syntactic representation (e g  a dependency parse) from a discrete structured prior (which we also call the “syntax model”), then, conditioned on this representation, generates a sequence of latent embedding random variables corresponding to each word, and finally produces the observed (pre-trained) word embeddings by projecting these latent vectors through a parameterized non-linear function  The latent embeddings can be jointly learned with the structured syntax model in a completely unsupervised fashion 
By choosing an invertible neural network as our non-linear projector, and then parameterizing our model in terms of the projection's inverse, we are able to derive tractable exact inference and marginal likelihood computation procedures so long as inference is tractable in the underlying syntax model  In sec:learn-with-inv we show that this derivation corresponds to an alternate view of our approach whereby we jointly learn a mapping of observed word embeddings to a new embedding space that is more suitable for the syntax model, but include an additional Jacobian regularization term to prevent information loss 
Recent work has sought to take advantage of word embeddings in unsupervised generative models with alternate approaches BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12   BIBREF9 build an HMM with Gaussian emissions on observed word embeddings, but they do not attempt to learn new embeddings  BIBREF10 , BIBREF11 , and BIBREF12 extend HMM or dependency model with valence (DMV) BIBREF2 with multinomials that use word (or tag) embeddings in their parameterization  However, they do not represent the embeddings as latent variables 
In experiments, we instantiate our approach using both a Markov-structured syntax model and a tree-structured syntax model – specifically, the DMV  We evaluate on two tasks: part-of-speech (POS) induction and unsupervised dependency parsing without gold POS tags  Experimental results on the Penn Treebank BIBREF13 demonstrate that our approach improves the basic HMM and DMV by a large margin, leading to the state-of-the-art results on POS induction, and state-of-the-art results on unsupervised dependency parsing in the difficult training scenario where neither gold POS annotation nor punctuation-based constraints are available 
Model
As an illustrative example, we first present a baseline model for Markov syntactic structure (POS induction) that treats a sequence of pre-trained word embeddings as observations  Then, we propose our novel approach, again using Markov structure, that introduces latent word embedding variables and a neural projector  Lastly, we extend our approach to more general syntactic structures 
Example: Gaussian HMM
We start by describing the Gaussian hidden Markov model introduced by BIBREF9 , which is a locally normalized model with multinomial transitions and Gaussian emissions  Given a sentence of length INLINEFORM0 , we denote the latent POS tags as INLINEFORM1 , observed (pre-trained) word embeddings as INLINEFORM2 , transition parameters as INLINEFORM3 , and Gaussian emission parameters as INLINEFORM4   The joint distribution of data and latent variables factors as:
DISPLAYFORM0
where INLINEFORM0 is the multinomial transition probability and INLINEFORM1 is the multivariate Gaussian emission probability 
While the observed word embeddings do inform this model with a notion of word similarity – lacking in the basic multinomial HMM – the Gaussian emissions may not be sufficiently flexible to separate some syntactic categories in the complex pre-trained embedding space – for example the skip-gram embedding space as visualized in Figure SECREF4 where different POS categories overlap  Next we introduce a new approach that adds flexibility to the emission distribution by incorporating new latent embedding variables 
Markov Structure with Neural Projector
To flexibly model observed embeddings and yield a new representation space that is more suitable for the syntax model, we propose to cascade a neural network as a projection function, deterministically transforming the simple space defined by the Gaussian HMM to the observed embedding space  We denote the latent embedding of the INLINEFORM0 word in a sentence as INLINEFORM1 , and the neural projection function as INLINEFORM2 , parameterized by INLINEFORM3   In the case of sequential Markov structure, our new model corresponds to the following generative process:
For each time step INLINEFORM0 ,
[noitemsep, leftmargin=*]
Draw the latent state INLINEFORM0
Draw the latent embedding INLINEFORM0
Deterministically produce embedding
INLINEFORM0
The graphical model is depicted in Figure FIGREF6   The deterministic projection can also be viewed as sampling each observation from a point mass at INLINEFORM0   The joint distribution of our model is: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, and INLINEFORM1 is the Dirac delta function centered at INLINEFORM2 : DISPLAYFORM0
General Structure with Neural Projector
Our approach can be applied to a broad family of structured syntax models  We denote latent embedding variables as INLINEFORM0 , discrete latent variables in the syntax model as INLINEFORM1 ( INLINEFORM2 ), where INLINEFORM3 are conditioned to generate INLINEFORM4   The joint probability of our model factors as:
DISPLAYFORM0
where INLINEFORM0 represents the probability of the syntax model, and can encode any syntactic structure – though, its factorization structure will determine whether inference is tractable in our full model  As shown in Figure FIGREF6 , we focus on two syntax models for syntactic analysis in this paper  The first is Markov-structured, which we use for POS induction, and the second is DMV-structured, which we use to learn dependency parses without supervision 
The marginal data likelihood of our model is: DISPLAYFORM0
While the discrete variables INLINEFORM0 can be marginalized out with dynamic program in many cases, it is generally intractable to marginalize out the latent continuous variables, INLINEFORM1 , for an arbitrary projection INLINEFORM2 in Eq  ( EQREF17 ), which means inference and learning may be difficult  In sec:opt, we address this issue by constraining INLINEFORM3 to be invertible, and show that this constraint enables tractable exact inference and marginal likelihood computation 
Learning & Inference
In this section, we introduce an invertibility condition for our neural projector to tackle the optimization challenge  Specifically, we constrain our neural projector with two requirements: (1) INLINEFORM0 and (2) INLINEFORM1 exists  Invertible transformations have been explored before in independent components analysis BIBREF14 , gaussianization BIBREF15 , and deep density models BIBREF16 , BIBREF17 , BIBREF18 , for unstructured data  Here, we generalize this style of approach to structured learning, and augment it with discrete latent variables ( INLINEFORM2 )  Under the invertibility condition, we derive a learning algorithm and give another view of our approach revealed by the objective function  Then, we present the architecture of a neural projector we use in experiments: a volume-preserving invertible neural network proposed by BIBREF16 for independent components estimation 
Learning with Invertibility
For ease of exposition, we explain the learning algorithm in terms of Markov structure without loss of generality  As shown in Eq  ( EQREF17 ), the optimization challenge in our approach comes from the intractability of the marginalized emission factor INLINEFORM0   If we can marginalize out INLINEFORM1 and compute INLINEFORM2 , then the posterior and marginal likelihood of our Markov-structured model can be computed with the forward-backward algorithm  We can apply Eq  ( EQREF14 ) and obtain : INLINEFORM3
By using the change of variable rule to the integration, which allows the integration variable INLINEFORM0 to be replaced by INLINEFORM1 , the marginal emission factor can be computed in closed-form when the invertibility condition is satisfied: DISPLAYFORM0
where INLINEFORM0 is a conditional Gaussian distribution, INLINEFORM1 is the Jacobian matrix of function INLINEFORM2 at INLINEFORM3 , and INLINEFORM4 represents the absolute value of its determinant  This Jacobian term is nonzero and differentiable if and only if INLINEFORM5 exists 
Eq  ( EQREF19 ) shows that we can directly calculate the marginal emission distribution INLINEFORM0   Denote the marginal data likelihood of Gaussian HMM as INLINEFORM1 , then the log marginal data likelihood of our model can be directly written as: DISPLAYFORM0
where INLINEFORM0 represents the new sequence of embeddings after applying INLINEFORM1 to each INLINEFORM2   Eq  ( EQREF20 ) shows that the training objective of our model is simply the Gaussian HMM log likelihood with an additional Jacobian regularization term  From this view, our approach can be seen as equivalent to reversely projecting the data through INLINEFORM3 to another manifold INLINEFORM4 that is directly modeled by the Gaussian HMM, with a regularization term  Intuitively, we optimize the reverse projection INLINEFORM5 to modify the INLINEFORM6 space, making it more appropriate for the syntax model  The Jacobian regularization term accounts for the volume expansion or contraction behavior of the projection  Maximizing it can be thought of as preventing information loss  In the extreme case, the Jacobian determinant is equal to zero, which means the projection is non-invertible and thus information is being lost through the projection  Such “information preserving” regularization is crucial during optimization, otherwise the trivial solution of always projecting data to the same single point to maximize likelihood is viable 
More generally, for an arbitrary syntax model the data likelihood of our approach is: DISPLAYFORM0
If the syntax model itself allows for tractable inference and marginal likelihood computation, the same dynamic program can be used to marginalize out INLINEFORM0   Therefore, our joint model inherits the tractability of the underlying syntax model 
Invertible Volume-Preserving Neural Net
For the projection we can use an arbitrary invertible function, and given the representational power of neural networks they seem a natural choice  However, calculating the inverse and Jacobian of an arbitrary neural network can be difficult, as it requires that all component functions be invertible and also requires storage of large Jacobian matrices, which is memory intensive  To address this issue, several recent papers propose specially designed invertible networks that are easily trainable yet still powerful BIBREF16 , BIBREF17 , BIBREF19   Inspired by these works, we use the invertible transformation proposed by BIBREF16 , which consists of a series of “coupling layers”  This architecture is specially designed to guarantee a unit Jacobian determinant (and thus the invertibility property) 
From Eq  ( EQREF22 ) we know that only INLINEFORM0 is required for accomplishing learning and inference; we never need to explicitly construct INLINEFORM1   Thus, we directly define the architecture of INLINEFORM2   As shown in Figure FIGREF24 , the nonlinear transformation from the observed embedding INLINEFORM3 to INLINEFORM4 represents the first coupling layer  The input in this layer is partitioned into left and right halves of dimensions, INLINEFORM5 and INLINEFORM6 , respectively  A single coupling layer is defined as: DISPLAYFORM0
where INLINEFORM0 is the coupling function and can be any nonlinear form  This transformation satisfies INLINEFORM1 , and BIBREF16 show that its Jacobian matrix is triangular with all ones on the main diagonal  Thus the Jacobian determinant is always equal to one (i e  volume-preserving) and the invertibility condition is naturally satisfied 
To be sufficiently expressive, we compose multiple coupling layers as suggested in BIBREF16   Specifically, we exchange the role of left and right half vectors at each layer as shown in Figure FIGREF24   For instance, from INLINEFORM0 to INLINEFORM1 the left subset INLINEFORM2 is unchanged, while from INLINEFORM3 to INLINEFORM4 the right subset INLINEFORM5 remains the same  Also note that composing multiple coupling layers does not change the volume-preserving and invertibility properties  Such a sequence of invertible transformations from the data space INLINEFORM6 to INLINEFORM7 is also called normalizing flow BIBREF20  
Experiments
In this section, we first describe our datasets and experimental setup  We then instantiate our approach with Markov and DMV-structured syntax models, and report results on POS tagging and dependency grammar induction respectively  Lastly, we analyze the learned latent embeddings 
Data
For both POS tagging and dependency parsing, we run experiments on the Wall Street Journal (WSJ) portion of the Penn Treebank  To create the observed data embeddings, we train skip-gram word embeddings BIBREF7 that are found to capture syntactic properties well when trained with small context window BIBREF8 , BIBREF9   Following BIBREF9 , the dimensionality INLINEFORM0 is set to 100, and the training context window size is set to 1 to encode more syntactic information  The skip-gram embeddings are trained on the one billion word language modeling benchmark dataset BIBREF21 in addition to the WSJ corpus 
General Experimental Setup
For the neural projector, we employ rectified networks as coupling function INLINEFORM0 following BIBREF16   We use a rectified network with an input layer, one hidden layer, and linear output units, the number of hidden units is set to the same as the number of input units  The number of coupling layers are varied as 4, 8, 16 for both tasks  We optimize marginal data likelihood directly using Adam BIBREF22   For both tasks in the fully unsupervised setting, we do not tune the hyper-parameters using supervised data 
Unsupervised POS tagging
For unsupervised POS tagging, we use a Markov-structured syntax model in our approach, which is a popular structure for unsupervised tagging tasks BIBREF9 , BIBREF10  
Following existing literature, we train and test on the entire WSJ corpus (49208 sentences, 1M tokens)  We use 45 tag clusters, the number of POS tags that appear in WSJ corpus  We train the discrete HMM and the Gaussian HMM BIBREF9 as baselines  For the Gaussian HMM, mean vectors of Gaussian emissions are initialized with the empirical mean of all word vectors with an additive noise  We assume diagonal covariance matrix for INLINEFORM0 and initialize it with the empirical variance of the word vectors  Following BIBREF9 , the covariance matrix is fixed during training  The multinomial probabilities are initialized as INLINEFORM1 , where INLINEFORM2   For our approach, we initialize the syntax model and Gaussian parameters with the pre-trained Gaussian HMM  The weights of layers in the rectified network are initialized from a uniform distribution with mean zero and a standard deviation of INLINEFORM3 , where INLINEFORM4 is the input dimension  We evaluate the performance of POS tagging with both Many-to-One (M-1) accuracy BIBREF23 and V-Measure (VM) BIBREF24   Given a model we found that the tagging performance is well-correlated with the training data likelihood, thus we use training data likelihood as a unsupervised criterion to select the trained model over 10 random restarts after training 50 epochs  We repeat this process 5 times and report the mean and standard deviation of performance 
We compare our approach with basic HMM, Gaussian HMM, and several state-of-the-art systems, including sophisticated HMM variants and clustering techniques with hand-engineered features  The results are presented in Table TABREF32   Through the introduced latent embeddings and additional neural projection, our approach improves over the Gaussian HMM by 5 4 points in M-1 and 5 6 points in VM  Neural HMM (NHMM) BIBREF10 is a baseline that also learns word representation jointly  Both their basic model and extended Conv version does not outperform the Gaussian HMM  Their best model incorporates another LSTM to model long distance dependency and breaks the Markov assumption, yet our approach still achieves substantial improvement over it without considering more context information  Moreover, our method outperforms the best published result that benefits from hand-engineered features BIBREF27 by 2 0 points on VM 
We found that most tagging errors happen in noun subcategories  Therefore, we do the one-to-one mapping between gold POS tags and induced clusters and plot the normalized confusion matrix of noun subcategories in Figure FIGREF35   The Gaussian HMM fails to identify “NN” and “NNS” correctly for most cases, and it often recognizes “NNPS” as “NNP”  In contrast, our approach corrects these errors well 
Unsupervised Dependency Parsing without gold POS tags
For the task of unsupervised dependency parse induction, we employ the Dependency Model with Valence (DMV) BIBREF2 as the syntax model in our approach  DMV is a generative model that defines a probability distribution over dependency parse trees and syntactic categories, generating tokens and dependencies in a head-outward fashion  While, traditionally, DMV is trained using gold POS tags as observed syntactic categories, in our approach, we treat each tag as a latent variable, as described in sec:general-neural 
Most existing approaches to this task are not fully unsupervised since they rely on gold POS tags following the original experimental setup for DMV  This is partially because automatically parsing from words is difficult even when using unsupervised syntactic categories BIBREF29   However, inducing dependencies from words alone represents a more realistic experimental condition since gold POS tags are often unavailable in practice  Previous work that has trained from words alone often requires additional linguistic constraints (like sentence internal boundaries) BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , acoustic cues BIBREF33 , additional training data BIBREF4 , or annotated data from related languages BIBREF34   Our approach is naturally designed to train on word embeddings directly, thus we attempt to induce dependencies without using gold POS tags or other extra linguistic information 
Like previous work we use sections 02-21 of WSJ corpus as training data and evaluate on section 23, we remove punctuations and train the models on sentences of length INLINEFORM0 , “head-percolation” rules BIBREF39 are applied to obtain gold dependencies for evaluation  We train basic DMV, extended DMV (E-DMV) BIBREF35 and Gaussian DMV (which treats POS tag as unknown latent variables and generates observed word embeddings directly conditioned on them following Gaussian distribution) as baselines  Basic DMV and E-DMV are trained with Viterbi EM BIBREF40 on unsupervised POS tags induced from our Markov-structured model described in sec:pos  Multinomial parameters of the syntax model in both Gaussian DMV and our model are initialized with the pre-trained DMV baseline  Other parameters are initialized in the same way as in the POS tagging experiment  The directed dependency accuracy (DDA) is used for evaluation and we report accuracy on sentences of length INLINEFORM1 and all lengths  We train the parser until training data likelihood converges, and report the mean and standard deviation over 20 random restarts 
Our model directly observes word embeddings and does not require gold POS tags during training  Thus, results from related work trained on gold tags are not directly comparable  However, to measure how these systems might perform without gold tags, we run three recent state-of-the-art systems in our experimental setting: UR-A E-DMV BIBREF36 , Neural E-DMV BIBREF11 , and CRF Autoencoder (CRFAE) BIBREF37   We use unsupervised POS tags (induced from our Markov-structured model) in place of gold tags  We also train basic DMV on gold tags and include several state-of-the-art results on gold tags as reference points 
As shown in Table TABREF39 , our approach is able to improve over the Gaussian DMV by 4 8 points on length INLINEFORM0 and 4 8 points on all lengths, which suggests the additional latent embedding layer and neural projector are helpful  The proposed approach yields, to the best of our knowledge, state-of-the-art performance without gold POS annotation and without sentence-internal boundary information  DMV, UR-A E-DMV, Neural E-DMV, and CRFAE suffer a large decrease in performance when trained on unsupervised tags – an effect also seen in previous work BIBREF29 , BIBREF34   Since our approach induces latent POS tags jointly with dependency trees, it may be able to learn POS clusters that are more amenable to grammar induction than the unsupervised tags  We observe that CRFAE underperforms its gold-tag counterpart substantially  This may largely be a result of the model's reliance on prior linguistic rules that become unavailable when gold POS tag types are unknown  Many extensions to DMV can be considered orthogonal to our approach – they essentially focus on improving the syntax model  It is possible that incorporating these more sophisticated syntax models into our approach may lead to further improvements 
Sensitivity Analysis
In the above experiments we initialize the structured syntax components with the pre-trained Gaussian or discrete baseline, which is shown as a useful technique to help train our deep models  We further study the results with fully random initialization  In the POS tagging experiment, we report the results in Table TABREF48   While the performance with 4 layers is comparable to the pre-trained Gaussian initialization, deeper projections (8 or 16 layers) result in a dramatic drop in performance  This suggests that the structured syntax model with very deep projections is difficult to train from scratch, and a simpler projection might be a good compromise in the random initialization setting 
Different from the Markov prior in POS tagging experiments, our parsing model seems to be quite sensitive to the initialization  For example, directed accuracy of our approach on sentences of length INLINEFORM0 is below 40 0 with random initialization  This is consistent with previous work that has noted the importance of careful initialization for DMV-based models such as the commonly used harmonic initializer BIBREF2   However, it is not straightforward to apply the harmonic initializer for DMV directly in our model without using some kind of pre-training since we do not observe gold POS 
We investigate the effect of the choice of pre-trained embedding on performance while using our approach  To this end, we additionally include results using fastText embeddings BIBREF41 – which, in contrast with skip-gram embeddings, include character-level information  We set the context windows size to 1 and the dimension size to 100 as in the skip-gram training, while keeping other parameters set to their defaults  These results are summarized in Table TABREF50 and Table TABREF51   While fastText embeddings lead to reduced performance with our model, our approach still yields an improvement over the Gaussian baseline with the new observed embeddings space 
Qualitative Analysis of Embeddings
We perform qualitative analysis to understand how the latent embeddings help induce syntactic structures  First we filter out low-frequency words and punctuations in WSJ, and visualize the rest words (10k) with t-SNE BIBREF42 under different embeddings  We assign each word with its most likely gold POS tags in WSJ and color them according to the gold POS tags 
For our Markov-structured model, we have displayed the embedding space in Figure SECREF5 , where the gold POS clusters are well-formed  Further, we present five example target words and their five nearest neighbors in terms of cosine similarity  As shown in Table TABREF53 , the skip-gram embedding captures both semantic and syntactic aspects to some degree, yet our embeddings are able to focus especially on the syntactic aspects of words, in an unsupervised fashion without using any extra morphological information 
In Figure FIGREF54 we depict the learned latent embeddings with the DMV-structured syntax model  Unlike the Markov structure, the DMV structure maps a large subset of singular and plural nouns to the same overlapping region  However, two clusters of singular and plural nouns are actually separated  We inspect the two clusters and the overlapping region in Figure FIGREF54 , it turns out that the nouns in the separated clusters are words that can appear as subjects and, therefore, for which verb agreement is important to model  In contrast, the nouns in the overlapping region are typically objects  This demonstrates that the latent embeddings are focusing on aspects of language that are specifically important for modeling dependency without ever having seen examples of dependency parses  Some previous work has deliberately created embeddings to capture different notions of similarity BIBREF43 , BIBREF44 , while they use extra morphology or dependency annotations to guide the embedding learning, our approach provides a potential alternative to create new embeddings that are guided by structured syntax model, only using unlabeled text corpora 
Related Work
Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18   This relevant work mostly adopts simple (e g  Gaussian) and fixed priors and does not attempt to learn interpretable latent structures  Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47   Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48   Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models  However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures 
Conclusion
In this work, we define a novel generative approach to leverage continuous word representations for unsupervised learning of syntactic structure  Experiments on both POS induction and unsupervised dependency parsing tasks demonstrate the effectiveness of our proposed approach  Future work might explore more sophisticated invertible projections, or recurrent projections that jointly transform the entire input sequence 
Context 3:  In selecting relevant datasets, we do not consider those that are considered “solved”, i e  where the state of the art performance surpasses human performance, as is the case with SQuAD BIBREF28, BIBREF7  Concretely, we selected gold standards that fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\ year) \times 20$ citations, and bucket them according to the answer selection styles as described in Section SECREF4 We randomly draw one from each bucket and add two randomly drawn datasets from the candidate pool  This leaves us with the datasets described in Table TABREF19  For a more detailed description, we refer to Appendix  
Application of the Framework ::: Annotation Task
We randomly select 50 distinct question, answer and passage triples from the publicly available development sets of the described datasets  Training, development and the (hidden) test set are drawn from the same distribution defined by the data collection method of the respective dataset  For those collections that contain multiple questions over a single passage, we ensure that we are sampling unique paragraphs in order to increase the variety of investigated texts 
The samples were annotated by the first author of this paper, using the proposed schema  In order to validate our findings, we further take 20% of the annotated samples and present them to a second annotator (second author)  Since at its core, the annotation is a multi-label task, we report the inter-annotator agreement by computing the (micro-averaged) F1 score, where we treat the first annotator's labels as gold  Table TABREF21 reports the agreement scores, the overall (micro) average F1 score of the annotations is 0 82, which means that on average, more than two thirds of the overall annotated labels were agreed on by both annotators  We deem this satisfactory, given the complexity of the annotation schema 
Application of the Framework ::: Qualitative Analysis
We present a concise view of the annotation results in Figure FIGREF23  The full annotation results can be found in Appendix   We centre our discussion around the following main points:
Application of the Framework ::: Qualitative Analysis ::: Linguistic Features
As observed in Figure FIGREF23 the gold standards feature a high degree of Redundancy, peaking at 76% of the annotated HotpotQA samples and synonyms and paraphrases (labelled Synonym), with ReCoRd samples containing 58% of them, likely to be attributed to the elaborating type of discourse of the dataset sources (encyclopedia and newswire)  This is, however, not surprising, as it is fairly well understood in the literature that current state-of-the-art models perform well on distinguishing relevant words and phrases from redundant ones BIBREF32  Additionally, the representational capability of synonym relationships of word embeddings has been investigated and is well known BIBREF33  Finally, we observe the presence of syntactic features, such as ambiguous relative clauses, appositions and adverbial phrases, (RelAdvApp 40% in HotpotQA and ReCoRd) and those introducing variance, concretely switching between verbal and nominal styles (e g  Nominalisation 10% in HotpotQA) and from passive to active voice (Voice, 8% in HotpotQA) 
Syntactic features contributing to variety and ambiguity that we did not observe in our samples are the exploitation of verb symmetry, the use of dative and genitive cases or ambiguous prepositions and coordination scope (respectively Symmetry, Dative, Genitive, Prepositions, Scope)  Therefore we cannot establish whether models are capable of dealing with those features by evaluating them on those gold standards 
Application of the Framework ::: Qualitative Analysis ::: Factual Correctness
We identify three common sources that surface in different problems regarding an answer's factual correctness, as reported in Figure FIGREF23 and illustrate their instantiations in Table TABREF31:
Design Constraints: Choosing the task design and the data collection method introduces some constraints that lead to factually debatable examples  For example, a span might have been arbitrarily selected from multiple spans that potentially answer a question, but only a single continuous answer span per question is allowed by design, as observed in the NewsQA and MsMarco samples (32% and 34% examples annotated as Debatable with 16% and 53% thereof exhibiting arbitrary selection, respectively)  Sometimes, when additional passages are added after the annotation step, they can by chance contain passages that answer the question more precisely than the original span, as seen in HotpotQA (16% Debatable samples, 25% of them due to arbitrary selection)  In the case of MultiRC it appears to be inconsistent, whether multiple correct answer choices are expected to be correct in isolation or in conjunction (28% Debatable with 29% of them exhibiting this problem)  This might provide an explanation to its relatively weak human baseline performance of 84% F1 score BIBREF31 
Weak Quality assurance: When the (typically crowd-sourced) annotations are not appropriately validated, incorrect examples will find their way into the gold standards  This typically results in factually wrong expected answers (i e  when a more correct answer is present in the context) or a question is expected to be Unanswerable, but is actually answerable from the provided context  The latter is observed in MsMarco (83% of examples annotated as Wrong) and NewsQA, where 60% of the examples annotated as Wrong are Unanswerable with an answer present 
Arbitrary Precision: There appears to be no clear guideline on how precise the answer is expected to be, when the passage expresses the answer in varying granularities  We annotated instances as Debatable when the expected answer was not the most precise given the context (44% and 29% of Debatable instances in NewsQA and MultiRC, respectively) 
Application of the Framework ::: Qualitative Analysis ::: Semantics-altering grammatical modifiers
We took interest in whether any of the benchmarks contain what we call distracting lexical features (or distractors): grammatical modifiers that alter the semantics of a sentence for the final task of answering the given question while preserving a similar lexical form  An example of such features are cues for (double) Negation (e g , “no”, “not”), which when introduced in a sentence, reverse its meaning  Other examples include modifiers denoting Restrictivity, Factivity and Reasoning (such as Monotonicity and Conditional cues)  Examples of question-answer pairs containing a distractor are shown in Table FIGREF37 
We posit that the presence of such distractors would allow for evaluating reading comprehension beyond potential simple word matching  However, we observe no presence of such features in the benchmarks (beyond Negation in DROP, ReCoRd and HotpotQA, with 4%, 4% and 2% respectively)  This results in gold standards that clearly express the evidence required to obtain the answer, lacking more challenging, i e , distracting, sentences that can assess whether a model can truly understand meaning 
Application of the Framework ::: Qualitative Analysis ::: Other
In the Figure FIGREF23 we observe that Operational and Arithmetic reasoning moderately (6% to 8% combined) appears “in the wild”, i e  when not enforced by the data design as is the case with HotpotQA (80% Operations combined) or DROP (68% Arithmetic combined)  Causal reasoning is (exclusively) present in MultiRC (32%), whereas Temporal and Spatial reasoning requirements seem to not naturally emerge in gold standards  In ReCoRd, a fraction of 38% questions can only be answered By Exclusion of every other candidate, due to the design choice of allowing questions where the required information to answer them is not fully expressed in the accompanying paragraph 
Therefore, it is also a little surprising to observe that ReCoRd requires external resources with regard to knowledge, as seen in Figure FIGREF23  MultiRC requires technical or more precisely basic scientific knowledge (6% Technical/Scientific), as a portion of paragraphs is extracted from elementary school science textbooks BIBREF31  Other benchmarks moderately probe for factual knowledge (0% to 4% across all categories), while Intuitive knowledge is required to derive answers in each gold standard 
It is also worth pointing out, as done in Figure FIGREF23, that although MultiRC and MsMarco are not modelled as a span selection problem, their samples still contain 50% and 66% of answers that are directly taken from the context  DROP contains the biggest fraction of generated answers (60%), due to the requirement of arithmetic operations 
To conclude our analysis, we observe similar distributions of linguistic features and reasoning patterns, except where there are constraints enforced by dataset design, annotation guidelines or source text choice  Furthermore, careful consideration of design choices (such as single-span answers) is required, to avoid impairing the factual correctness of datasets, as pure crowd-worker agreement seems not sufficient in multiple cases 
Application of the Framework ::: Quantitative Results ::: Lexical overlap
We used the scores assigned by our proposed set of metrics (discussed in Section SECREF11 Dimensions of Interest: Complexity) to predict the supporting facts in the gold standard samples (that we included in our manual annotation)  Concretely, we used the following five features capturing lexical overlap: (i) the number of words occurring in sentence and question, (ii) the length of the longest n-gram shared by sentence and question, whether a (iii) uni- and (iv) bigram from the question is unique to a sentence, and (v) the sentence index, as input to a logistic regression classifier  We optimised on each sample leaving one example for evaluation  We compute the average Precision, Recall and F1 score by means of leave-one-out validation with every sample entry  The averaged results after 5 runs are reported in Table TABREF41 
We observe that even by using only our five features based lexical overlap, the simple logistic regression baseline is able to separate out the supporting facts from the context to a varying degree  This is in line with the lack of semantics-altering grammatical modifiers discussed in the qualitative analysis section above  The classifier performs best on DROP (66% F1) and MultiRC (40% F1), which means that lexical cues can considerably facilitate the search for the answer in those gold standards  On MultiRC, yadav2019quick come to a similar conclusion, by using a more sophisticated approach based on overlap between question, sentence and answer choices 
Surprisingly, the classifier is able to pick up a signal from supporting facts even on data that has been pruned against lexical overlap heuristics by populating the context with additional documents that have high overlap scores with the question  This results in significantly higher scores than when guessing randomly (HotpotQA 26% F1, and MsMarco 11% F1)  We observe similar results in the case the length of the question leaves few candidates to compute overlap with $6 3$ and $7 3$ tokens on average for MsMarco and NewsQA (26% F1), compared to $16 9$ tokens on average for the remaining four dataset samples 
Finally, it is worth mentioning that although the queries in ReCoRd are explicitly independent from the passage, the linear classifier is still capable of achieving 34% F1 score in predicting the supporting facts 
However, neural networks perform significantly better than our admittedly crude baseline (e g  66% F1 for supporting facts classification on HotpotQA BIBREF14), albeit utilising more training examples, and a richer sentence representation  This facts implies that those neural models are capable of solving more challenging problems than simple “text matching” as performed by the logistic regression baseline  However, they still circumvent actual reading comprehension as the respective gold standards are of limited suitability to evaluate this BIBREF34, BIBREF35  This suggests an exciting future research direction, that is categorising the scale between text matching and reading comprehension more precisely and respectively positioning state-of-the-art models thereon 
Related Work
Although not as prominent as the research on novel architecture, there has been steady progress in critically investigating the data and evaluation aspects of NLP and machine learning in general and MRC in particular 
Related Work ::: Adversarial Evaluation
The authors of the AddSent algorithm BIBREF11 show that MRC models trained and evaluated on the SQuAD dataset pay too little attention to details that might change the semantics of a sentence, and propose a crowd-sourcing based method to generate adversary examples to exploit that weakness  This method was further adapted to be fully automated BIBREF36 and applied to different gold standards BIBREF35  Our proposed approach differs in that we aim to provide qualitative justifications for those quantitatively measured issues 
Related Work ::: Sanity Baselines
Another line of research establishes sane baselines to provide more meaningful context to the raw performance scores of evaluated models  When removing integral parts of the task formulation such as question, the textual passage or parts thereof BIBREF37 or restricting model complexity by design in order to suppress some required form of reasoning BIBREF38, models are still able to perform comparably to the state-of-the-art  This raises concerns about the perceived benchmark complexity and is related to our work in a broader sense as one of our goals is to estimate the complexity of benchmarks 
Related Work ::: Benchmark evaluation in NLP
Beyond MRC, efforts similar to ours that pursue the goal of analysing the evaluation of established datasets exist in Natural Language Inference BIBREF13, BIBREF12  Their analyses reveal the existence of biases in training and evaluation data that can be approximated with simple majority-based heuristics  Because of these biases, trained models fail to extract the semantics that are required for the correct inference  Furthermore, a fair share of work was done to reveal gender bias in coreference resolution datasets and models BIBREF39, BIBREF40, BIBREF41 
Related Work ::: Annotation Taxonomies
Finally, related to our framework are works that introduce annotation categories for gold standards evaluation  Concretely, we build our annotation framework around linguistic features that were introduced in the GLUE suite BIBREF42 and the reasoning categories introduced in the WorldTree dataset BIBREF19  A qualitative analysis complementary to ours, with focus on the unanswerability patterns in datasets that feature unanswerable questions was done by Yatskar2019 
Conclusion
In this paper, we introduce a novel framework to characterise machine reading comprehension gold standards  This framework has potential applications when comparing different gold standards, considering the design choices for a new gold standard and performing qualitative error analyses for a proposed approach 
Furthermore we applied the framework to analyse popular state-of-the-art gold standards for machine reading comprehension: We reveal issues with their factual correctness, show the presence of lexical cues and we observe that semantics-altering grammatical modifiers are missing in all of the investigated gold standards  Studying how to introduce those modifiers into gold standards and observing whether state-of-the-art MRC models are capable of performing reading comprehension on text containing them, is a future research goal 
A future line of research is to extend the framework to be able to identify the different types of exploitable cues such as question or entity typing and concrete overlap patterns  This will allow the framework to serve as an interpretable estimate of reading comprehension complexity of gold standards  Finally, investigating gold standards under this framework where MRC models outperform the human baseline (e g  SQuAD) will contribute to a deeper understanding of the seemingly superb performance of deep learning approaches on them 

ground_truth:  Wall Street Journal (WSJ) portion of the Penn Treebank

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. How does proposed qualitative annotation schema looks like?
Context 4:  From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case  Table TABREF19 shows number of trials for these setups 
For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used 
DeepMine Database Parts ::: Part3 - Speech Recognition
As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition  There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7  Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian  Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training 
We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers  For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set  The remaining utterances of the other speakers are included in the training set  The test-set, large-test-set and train-set contain 5 9, 28 5 and 450 hours of speech respectively 
There are about 8300 utterances in Part3 which contain only Persian full names (i e  first and family name pairs)  Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P)  These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model 
Experiments and Results
Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition 
Experiments and Results ::: Speaker Verification Experiments
We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in BIBREF9, BIBREF10 and applied it to the Persian portion of Part1  In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16 kHz signals using HTK BIBREF11 with 25 ms Hamming windowed frames with 15 ms overlap 
The reported results are obtained with a 400-dimensional gender independent i-vector based system  The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN) BIBREF10  Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores  For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used BIBREF10  We only model the phonemes which appear in the 5 Persian text-dependent phrases 
For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm {NDCF_{0 01}^{min}}$) and NIST SRE10 ($\mathrm {NDCF_{0 001}^{min}}$)  As shown in Table TABREF22, in text-dependent SV there are 4 types of trials: Target-Correct and Imposter-Correct refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, Target-Wrong and Imposter-Wrong refer to trials when speakers uttered a wrong pass-phrase  In this paper, only the correct trials (i e  Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV BIBREF8, BIBREF12 
Table TABREF23 shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup  For filtering trials, the respondents' mobile brand and model were used in this experiment  In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i e  right side of the colon) relate for non-target trials  For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker  The second Y letter means both recordings were done using exactly the same device model  Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i e  manufacturer)  The second Y means that, in addition to the same brand, both devices have the same model  So, the most difficult target trials are “NN”, where the speaker has used different a device at the test time  In the same manner, the most difficult non-target trials which should be rejected by the system are “YY” where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device)  Hence, the similarity in the recording channel makes rejection more difficult 
The first row in Table TABREF23 shows the results for all trials  By comparing the results with the best published results on RSR2015 and RedDots BIBREF10, BIBREF8, BIBREF12, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases  For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0 3% for both genders (Table VI in BIBREF10)  The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i e  without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1 5% (Table XI in BIBREF10)  It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2 1 and 3 7% for males and females respectively 
One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device  This is allows us to analyse the effects of channel compensation methods  The second row in Table TABREF23 corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models  It is clear that severe degradation was caused by this kind of channel effects (i e  decreasing within-speaker similarities while increasing between-speaker similarities), especially for females 
The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment  Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker 
The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system  So, in this case, there is no device mismatch in all trials  By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device 
The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model  Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties 
The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials  For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case 
Looking at the first two and the last row of Table TABREF23, one can notice the significantly worse performance obtained for the female trials as compared to males  Note that these three rows include target trials where the devices used for enrollment do not necessarily match the devices used for recording test utterances  On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable  This suggest that the degraded results for females are caused by some problematic trials with device mismatch  The exact reason for this degradation is so far unclear and needs a further investigation 
In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand  In this case, as was expected, the performance degradation is smaller than in the second row 
Experiments and Results ::: Speech Recognition Experiments
In addition to speaker verification, we present several speech recognition experiments on Part3  The experiments were performed with the Kaldi toolkit BIBREF13  For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used  The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes)  The network is shown in Table TABREF25: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256  The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization BIBREF14  Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words 
Table TABREF26 shows the results in terms of word error rate (WER) for different evaluated methods  As can be seen, the created database can be used to train well performing and practically usable Persian ASR models 
Conclusions
In this paper, we have described the final version of a large speech corpus, the DeepMine database  It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English  In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions  Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition 
We provided several evaluation protocols for each part of the database  The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance  We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method 
As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part  Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance  Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases 
Acknowledgments
The data collection project was mainly supported by Sharif DeepMine company  The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No  19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602" 

ground_truth: The resulting taxonomy of the framework is shown in Figure FIGREF10

FIGREF10