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 is the corpus used for the task?
Context 1: Introduction
Natural languages evolve and words have always been subject to semantic change over time BIBREF1  With the rise of large digitized text resources recent NLP technologies have made it possible to capture such change with vector space models BIBREF2, BIBREF3, BIBREF4, BIBREF5, topic models BIBREF6, BIBREF7, BIBREF8, and sense clustering models BIBREF9  However, many approaches for detecting LSC differ profoundly from each other and therefore drawing comparisons between them can be challenging BIBREF10  Not only do architectures for detecting LSC vary, their performance is also often evaluated without access to evaluation data or too sparse data sets  In cases where evaluation data is available, oftentimes LSCD systems are not evaluated on the same data set which hinders the research community to draw comparisons 
For this reason we report the results of the first shared task on unsupervised lexical semantic change detection in German that is based on an annotated data set to guarantee objective reasoning throughout different approaches  The task was organized as part of the seminar 'Lexical Semantic Change Detection' at the IMS Stuttgart in the summer term of 2019 
Task
The goal of the shared task was to create an architecture to detect semantic change and to rank words according to their degree of change between two different time periods  Given two corpora Ca and Cb, the target words had to be ranked according to their degree of lexical semantic change between Ca and Cb as annotated by human judges  A competition was set up on Codalab and teams mostly consisting of 2 people were formed to take part in the task  There was one group consisting of 3 team members and two individuals who entered the task on their own  In total there were 12 LSCD systems participating in the shared task 
The shared task was divided into three phases, i e , development, testing and analysis phase  In the development phase each team implemented a first version of their model based on a trial data set and submitted it subsequently  In the testing phase the testing data was made public and participants applied their models to the test data with a restriction of possible result uploads to 30  The leaderboard was public at all times  Eventually, the analysis phase was entered and the models of the testing phase were evaluated in terms of the predictions they made and parameters could be tuned further  The models and results will be discussed in detail in sections 7 and 8 
Corpora
The task, as framed above, requires to detect the semantic change between two corpora  The two corpora used in the shared task correspond to the diachronic corpus pair from BIBREF0: DTA18 and DTA19  They consist of subparts of DTA corpus BIBREF11 which is a freely available lemmatized, POS-tagged and spelling-normalized diachronic corpus of German containing texts from the 16th to the 20th century  DTA18 contains 26 million sentences published between 1750-1799 and DTA19 40 million between 1850-1899  The corpus version used in the task has the following format: "year [tab] lemma1 lemma2 lemma3    " 
Evaluation
The Diachronic Usage Relatedness (DURel) gold standard data set includes 22 target words and their varying degrees of semantic change BIBREF12  For each of these target words a random sample of use pairs from the DTA corpus was retrieved and annotated  The annotators were required to rate the pairs according to their semantic relatedness on a scale from 1 to 4 (unrelated - identical meanings) for two time periods  The average Spearman's $\rho $ between the five annotators was 0 66 for 1,320 use paris  The resulting word ranking of the DURel data set is determined by the mean usage relatedness across two time periods and is used as the benchmark to compare the models’ performances in the shared task 
Evaluation ::: Metric
The output of a system with the target words in the predicted order is compared to the gold ranking of the DURel data set  As the metric to assess how well the model's output fits the gold ranking Spearman's $\rho $ was used  The higher Spearman's rank-order correlation the better the system's performance 
Evaluation ::: Baselines
Models were compared to two baselines for the shared task:
log-transformed normalized frequency difference (FD)
count vectors with column intersection and cosine distance (CNT + CI + CD)
The window size for CNT + CI + CD was 10  Find more information on these models in BIBREF0 
Participating Systems
Participants mostly rely on the models compared in BIBREF0 and apply modifications to improve them  In particular, most teams make use of skip-gram with negative sampling (SGNS) based on BIBREF13 to learn the semantic spaces of the two time periods and orthogonal procrustes (OP) to align these vector spaces, similar to the approach by BIBREF14  Different meaning representations such as sense clusters are used as well  As measure to detect the degree of LSC all teams except one choose cosine distance (CD)  This team uses Jensen-Shannon distance (JSD) instead, which computes the distance between probability distributions BIBREF15 
The models of each team will be briefly introduced in this section 
Participating Systems ::: sorensbn
Team sorensbn makes use of SGNS + OP + CD to detect LSC  They use similar hyperparameters as in BIBREF0 to tune the SGNS model  They use an open-sourced noise-aware implementation to improve the OP alignment BIBREF16 
Participating Systems ::: tidoe
Team tidoe builds on SGNS + OP + CD, but they add a transformation step to receive binarized representations of matrices BIBREF17  This step is taken to counter the bias that can occur in vector-space models based on frequencies BIBREF18 
Participating Systems ::: in vain
The team applies a model based on SGNS with vector initialization alignment and cosine distance (SGNS + VI + CD)  Vector initialization is an alignment strategy where the vector space learning model for $t_2$ is initialized with the vectors from $t_1$ BIBREF19  Since SGNS + VI + OP does not perform as well as other models in BIBREF0, they alter the vector initialization process by initializing on the complete model instead of only the word matrix of $t_1$ to receive improved results 
Participating Systems ::: Evilly
In line with previous approaches, team Evilly builds upon SGNS + OP + CD  They alter the OP step by using only high-frequency words for alignment 
Participating Systems ::: DAF
Team DAF uses an architecture based on learning vectors with fastText, alignment with unsupervised and supervised variations of OP, and CD, using the MUSE package BIBREF20, BIBREF21  For the supervised alignment stop words are used  The underlying assumption is that stop words serve as functional units of language and their usage should be consistent over time 
Participating Systems ::: SnakesOnAPlane
The team learns vector spaces with count vectors, positive pointwise mutual information (PPMI), SGNS and uses column intersection (CI) and OP as alignment techniques where applicable  Then they compare two distance measures (CD and JSD) for the different models CNT + CI, PPMI + CI and SGNS + OP to identify which measure performs better for these models  They also experiment with different ways to remove negative values from SGNS vectors, which is needed for JSD 
Participating Systems ::: TeamKulkarni15
TeamKulkarni15 uses SGNS + OP + CD with the modification of local alignment with k nearest neighbors, since other models often use global alignment that can be prone to noise BIBREF22 
Participating Systems ::: Bashmaistori
They use word injection (WI) alignment on PPMI vectors with CD  This approach avoids the complex alignment procedure for embeddings and is applicable to embeddings and count-based methods  They compare two implementations of word injection BIBREF23, BIBREF0 as these showed different results on different data sets 
Participating Systems ::: giki
Team giki uses PPMI + CI + CD to detect LSC  They state that a word sense is determined by its context, but relevant context words can also be found outside a predefined window  Therefore, they use tf-idf to select relevant context BIBREF24 
Participating Systems ::: Edu-Phil
Similar to team DAF they also use fastText + OP + CD  Their hypothesis is that fastText may increase the performance for less frequent words in the corpus since generating word embeddings in fasttext is based on character n-grams 
Participating Systems ::: orangefoxes
They use the model by BIBREF5 which is based on SGNS, but avoids alignment by treating time as a vector that may be combined with word vectors to get time-specific word vectors 
Participating Systems ::: Loud Whisper
Loud Whisper base their approach on BIBREF9 which is a graph-based sense clustering model  They process the data set to receive bigrams, create a co-occurence graph representation and after clustering assess the type of change per word by comparing the results against an intersection table  Their motivation is not only to use a graph-based approach, but to extend the approach by enabling change detection for all parts of speech as opposed to the original model 
Results and Discussion
Table TABREF8 shows the results of the shared task  All teams receive better results than baseline 1 (FD), of which a total of 8 teams outperform baseline 2 (CNT + CI + CD)  The 4 top scores with $\rho $ $>$ 0 7 are either modified versions of SGNS + OP + CD or use SGNS + VI + CD  The following 4 scores in the range of 0 5 $<$ $\rho $ $<$ 0 6 are generated by the models fastText + OP + CD, SGNS + OP + CD/JSD, and PPMI + WI + CD 
Contrary to the results by BIBREF0 the modified version of vector initialization shows high performance similar to OP alignment, as previously reported by BIBREF14  Some modifications to the SGNS + OP + CD approach are able to yield better results than others, e g  noise-aware alignment and binarized matrices as compared to frequency-driven OP alignment or local alignment with KNN  Team SnakesOnAPlane compare two distance measures and their results show that JSD ($\rho $ $=$  561) performs minimally worse than CD ($\rho $ $=$  565) as the semantic change measure for their model 
The overall best-performing model is Skip-Gram with orthogonal alignment and cosine distance (SGNS + OP + CD) with similar hyperparameters as in the model architecture described previously BIBREF0  Said architecture was used as the basis for the two best performing models  Team tidoe reports that binarizing matrices leads to a generally worse performance ($\rho $ $=$  811) compared to the unmodified version of SGNS + OP + CD ($\rho $ $=$ 0 9)  The noise aware alignment approach applied by team sorensbn obtains a higher score ($\rho $ $=$  854) compared to the result reported by tidoe, but is unable to exceed the performance of the unmodified SNGS + OP + CD for the same set of hyperparameters (window size = 10, negative sampling = 1; subsampling = None)  Of the 8 scores above the second baseline, 5 use an architecture that builds upon SGNS + OP + CD  Whereas in the lower score segment $\rho $ $<$ 0 5 none of the models use SGNS + OP + CD  These findings are in line with the results reported by BIBREF0, however the overall best results are lower in this shared task, which is expected from the smaller number of parameter combinations explored  Additionally, in the shared task the objective was to report the best score and not to calculate the mean which makes it more difficult to compare the robustness of the models presented here 

ground_truth: DTA18, DTA19

Diachronic Usage Relatedness (DURel) gold standard data set

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 are the existing approaches?
Context 2: Introduction
Users of photo-sharing websites such as Flickr often provide short textual descriptions in the form of tags to help others find the images  With the availability of GPS systems in current electronic devices such as smartphones, latitude and longitude coordinates are nowadays commonly made available as well  The tags associated with such georeferenced photos often describe the location where these photos were taken, and Flickr can thus be regarded as a source of environmental information  The use of Flickr for modelling urban environments has already received considerable attention  For instance, various approaches have been proposed for modelling urban regions BIBREF0 , and for identifying points-of-interest BIBREF1 and itineraries BIBREF2 , BIBREF3   However, the usefulness of Flickr for characterizing the natural environment, which is the focus of this paper, is less well-understood 
Many recent studies have highlighted that Flickr tags capture valuable ecological information, which can be used as a complementary source to more traditional sources  To date, however, ecologists have mostly used social media to conduct manual evaluations of image content with little automated exploitation of the associated tags BIBREF4 , BIBREF5 , BIBREF6   One recent exception is BIBREF7 , where bag-of-words representations derived from Flickr tags were found to give promising result for predicting a range of different environemental phenomena 
Our main hypothesis in this paper is that by using vector space embeddings instead of bag-of-words representations, the ecological information which is implicitly captured by Flickr tags can be utilized in a more effective way  Vector space embeddings are representations in which the objects from a given domain are encoded using relatively low-dimensional vectors  They have proven useful in natural language processing, especially for encoding word meaning BIBREF8 , BIBREF9 , and in machine learning more generally  In this paper, we are interested in the use of such representations for modelling geographic locations  Our main motivation for using vector space embeddings is that they allow us to integrate the textual information we get from Flickr with available structured information in a very natural way  To this end, we rely on an adaptation of the GloVe word embedding model BIBREF9 , but rather than learning word vectors, we learn vectors representing locations  Similar to how the representation of a word in GloVe is determined by the context words surrounding it, the representation of a location in our model is determined by the tags of the photos that have been taken near that location  To incorporate numerical features from structured environmental datasets (e g  average temperature), we associate with each such feature a linear mapping that can be used to predict that feature from a given location vector  This is inspired by the fact that salient properties of a given domain can often be modelled as directions in vector space embeddings BIBREF10 , BIBREF11 , BIBREF12   Finally, evidence from categorical datasets (e g  land cover types) is taken into account by requiring that locations belonging to the same category are represented using similar vectors, similar to how semantic types are sometimes modelled in the context of knowledge graph embedding BIBREF13  
While our point-of-departure is a standard word embedding model, we found that the off-the-shelf GloVe model performed surprisingly poorly, meaning that a number of modifications are needed to achieve good results  Our main findings are as follows  First, given that the number of tags associated with a given location can be quite small, it is important to apply some kind of spatial smoothing, i e  the importance of a given tag for a given location should not only depend on the occurrences of the tag at that location, but also on its occurrences at nearby locations  To this end, we use a formulation which is based on spatially smoothed version of pointwise mutual information  Second, given the wide diversity in the kind of information that is covered by Flickr tags, we find that term selection is in some cases critical to obtain vector spaces that capture the relevant aspects of geographic locations  For instance, many tags on Flickr refer to photography related terms, which we would normally not want to affect the vector representation of a given location  Finally, even with these modifications, vector space embeddings learned from Flickr tags alone are sometimes outperformed by bag-of-words representations  However, our vector space embeddings lead to substantially better predictions in cases where structured (scientific) information is also taken into account  In this sense, the main value of using vector space embeddings in this context is not so much about abstracting away from specific tag usages, but rather about the fact that such representations allow us to integrate numerical and categorical features in a much more natural way than is possible with bag-of-words representations 
The remainder of this paper is organized as follows  In the next section, we provide a discussion of existing work  Section SECREF3 then presents our model for embedding geographic locations from Flickr tags and structured data  Next, in Section SECREF4 we provide a detailed discussion about the experimental results  Finally, Section SECREF5 summarizes our conclusions 
Vector space embeddings
The use of low-dimensional vector space embeddings for representing objects has already proven effective in a large number of applications, including natural language processing (NLP), image processing, and pattern recognition  In the context of NLP, the most prominent example is that of word embeddings, which represent word meaning using vectors of typically around 300 dimensions  A large number of different methods for learning such word embeddings have already been proposed, including Skip-gram and the Continuous Bag-of-Words (CBOW) model BIBREF8 , GloVe BIBREF9 , and fastText BIBREF14   They have been applied effectively in many downstream NLP tasks such as sentiment analysis BIBREF15 , part of speech tagging BIBREF16 , BIBREF17 , and text classification BIBREF18 , BIBREF19   The model we consider in this paper builds on GloVe, which was designed to capture linear regularities of word-word co-occurrence  In GloVe, there are two word vectors INLINEFORM0 and INLINEFORM1 for each word in the vocabulary, which are learned by minimizing the following objective: DISPLAYFORM0
where INLINEFORM0 is the number of times that word INLINEFORM1 appears in the context of word INLINEFORM2 , INLINEFORM3 is the vocabulary size, INLINEFORM4 is the target word bias, INLINEFORM5 is the context word bias  The weighting function INLINEFORM6 is used to limit the impact of rare terms  It is defined as 1 if INLINEFORM7 and as INLINEFORM8 otherwise, where INLINEFORM9 is usually fixed to 100 and INLINEFORM10 to 0 75  Intuitively, the target word vectors INLINEFORM11 correspond to the actual word representations which we would like to find, while the context word vectors INLINEFORM12 model how occurrences of INLINEFORM13 in the context of a given word INLINEFORM14 affect the representation of this latter word  In this paper we will use a similar model, which will however be aimed at learning location vectors instead of the target word vectors 
Beyond word embeddings, various methods have been proposed for learning vector space representations from structured data such as knowledge graphs BIBREF20 , BIBREF21 , BIBREF22 , social networks BIBREF23 , BIBREF24 and taxonomies BIBREF25 , BIBREF26   The idea of combining a word embedding model with structured information has also been explored by several authors, for example to improve the word embeddings based on information coming from knowledge graphs BIBREF27 , BIBREF28   Along similar lines, various lexicons have been used to obtain word embeddings that are better suited at modelling sentiment BIBREF15 and antonymy BIBREF29 , among others  The method proposed by BIBREF30 imposes the condition that words that belong to the same semantic category are closer together than words from different categories, which is somewhat similar in spirit to how we will model categorical datasets in our model 
Embeddings for geographic information
The problem of representing geographic locations using embeddings has also attracted some attention  An early example is BIBREF31 , which used principal component analysis and stacked autoencoders to learn low-dimensional vector representations of city neighbourhoods based on census data  They use these representations to predict attributes such as crime, which is not included in the given census data, and find that in most of the considered evaluation tasks, the low-dimensional vector representations lead to more faithful predictions than the original high-dimensional census data 
Some existing works combine word embedding models with geographic coordinates  For example, in BIBREF32 an approach is proposed to learn word embeddings based on the assumption that words which tend to be used in the same geographic locations are likely to be similar  Note that their aim is dual to our aim in this paper: while they use geographic location to learn word vectors, we use textual descriptions to learn vectors representing geographic locations 
Several methods also use word embedding models to learn representations of Points-of-Interest (POIs) that can be used for predicting user visits BIBREF33 , BIBREF34 , BIBREF35   These works use the machinery of existing word embedding models to learn POI representations, intuitively by letting sequences of POI visits by a user play the role of sequences of words in a sentence  In other words, despite the use of word embedding models, many of these approaches do not actually consider any textual information  For example, in BIBREF34 the Skip-gram model is utilized to create a global pattern of users' POIs  Each location was treated as a word and the other locations visited before or after were treated as context words  They then use a pair-wise ranking loss BIBREF36 which takes into account the user's location visit frequency to personalize the location recommendations  The methods of BIBREF34 were extended in BIBREF35 to use a temporal embedding and to take more account of geographic context, in particular the distances between preferred and non-preferred neighboring POIs, to create a “geographically hierarchical pairwise preference ranking model”  Similarly, in BIBREF37 the CBOW model was trained with POI data  They ordered POIs spatially within the traffic-based zones of urban areas  The ordering was used to generate characteristic vectors of POI types  Zone vectors represented by averaging the vectors of the POIs contained in them, were then used as features to predict land use types  In the CrossMap method BIBREF38 they learned embeddings for spatio-temporal hotspots obtained from social media data of locations, times and text  In one form of embedding, intended to enable reconstruction of records, neighbourhood relations in space and time were encoded by averaging hotspots in a target location's spatial and temporal neighborhoods  They also proposed a graph-based embedding method with nodes of location, time and text  The concatenation of the location, time and text vectors were then used as features to predict peoples' activities in urban environments  Finally, in BIBREF39 , a method is proposed that uses the Skip-gram model to represent POI types, based on the intuition that the vector representing a given POI type should be predictive of the POI types that found near places of that type 
Our work is different from these studies, as our focus is on representing locations based on a given text description of that location (in the form of Flickr tags), along with numerical and categorical features from scientific datasets 
Analyzing Flickr tags
Many studies have focused on analyzing Flickr tags to extract useful information in domains such as linguistics BIBREF40 , geography BIBREF0 , BIBREF41 , and ecology BIBREF42 , BIBREF7 , BIBREF43   Most closely related to our work, BIBREF7 found that the tags of georeferenced Flickr photos can effectively supplement traditional scientific environmental data in tasks such as predicting climate features, land cover, species occurrence, and human assessments of scenicness  To encode locations, they simply combine a bag-of-words representation of geographically nearby tags with a feature vector that encodes associated structured scientific data  They found that the predictive value of Flickr tags is roughly on a par with that of the scientific datasets, and that combining both types of information leads to significantly better results than using either of them alone  As we show in this paper, however, their straightforward way of combining both information sources, by concatenating the two types of feature vectors, is far from optimal 
Despite the proven importance of Flickr tags, the problem of embedding Flickr tags has so far received very limited attention  To the best of our knowledge, BIBREF44 is the only work that generated embeddings for Flickr tags  However, their focus was on learning embeddings that capture word meaning (being evaluated on word similarity tasks), whereas we use such embeddings as part of our method for representing locations 
Model Description
In this section, we introduce our embedding model, which combines Flickr tags and structured scientific information to represent a set of locations INLINEFORM0   The proposed model has the following form: DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 are parameters to control the importance of each component in the model  Component INLINEFORM2 will be used to constrain the representation of the locations based on their textual description (i e  Flickr tags), INLINEFORM3 will be used to constrain the representation of the locations based on their numerical features, and INLINEFORM4 will impose the constraint that locations belonging to the same category should be close together in the space  We will discuss each of these components in more detail in the following sections 
Tag Based Location Embedding
Many of the tags associated with Flickr photos describe characteristics of the places where these photos were taken BIBREF45 , BIBREF46 , BIBREF47   For example, tags may correspond to place names (e g  Brussels, England, Scandinavia), landmarks (e g  Eiffel Tower, Empire State Building) or land cover types (e g  mountain, forest, beach)  To allow us to build location models using such tags, we collected the tags and meta-data of 70 million Flickr photos with coordinates in Europe (which is the region our experiments will focus on), all of which were uploaded to Flickr before the end of September 2015  In this section we first explain how tags can be weighted to obtain bag-of-words representations of locations from Flickr  Subsequently we describe a tag selection method, which will allow us to specialize the embedding depending on which aspects of the considered locations are of interest, after which we discuss the actual embedding model 
Tag weighting  Let INLINEFORM0 be a set of geographic locations, each characterized by latitude and longitude coordinates  To generate a bag-of-words representation of a given location, we have to weight the relevance of each tag to that location  To this end, we have followed the weighting scheme from BIBREF7 , which combines a Gaussian kernel (to model spatial proximity) with Positive Pointwise Mutual Information (PPMI) BIBREF48 , BIBREF49  
Let us write INLINEFORM0 for the set of users who have assigned tag INLINEFORM1 to a photo with coordinates near INLINEFORM2   To assess how relevant INLINEFORM3 is to the location INLINEFORM4 , the number of times INLINEFORM5 occurs in photos near INLINEFORM6 is clearly an important criterion  However, rather than simply counting the number of occurrences within some fixed radius, we use a Gaussian kernel to weight the tag occurrences according to their distance from that location: INLINEFORM7
where the threshold INLINEFORM0 is assumed to be fixed, INLINEFORM1 is the location of a Flickr photo, INLINEFORM2 is the Haversine distance, and we will assume that the bandwidth parameter INLINEFORM3 is set to INLINEFORM4   A tag occurrence is counted only once for all photos by the same user at the same location, which is important to reduce the impact of bulk uploading  The value INLINEFORM5 reflects how frequent tag INLINEFORM6 is near location INLINEFORM7 , but it does not yet take into account the total number of tag occurrences near INLINEFORM8 , nor how popular the tag INLINEFORM9 is overall  To measure how strongly tag INLINEFORM10 is associated with location INLINEFORM11 , we use PPMI, which is a commonly used measure of association in natural language processing  However, rather than estimating PPMI scores from term frequencies, we will use the INLINEFORM12 values instead: INLINEFORM13
where: INLINEFORM0
with INLINEFORM0 the set of all tags, and INLINEFORM1 the set of locations 
Tag selection  Inspired by BIBREF50 , we use a term selection method in order to focus on the tags that are most important for the tasks that we want to consider and reduce the impact of tags that might relate only to a given individual or a group of users  In particular, we obtained good results with a method based on Kullback-Leibler (KL) divergence, which is based on BIBREF51   Let INLINEFORM0 be a set of (mutually exclusive) properties of locations in which we are interested (e g  land cover categories)  For the ease of presentation, we will identify INLINEFORM1 with the set of locations that have the corresponding property  Then, we select tags from INLINEFORM2 that maximize the following score: INLINEFORM3
where INLINEFORM0 is the probability that a photo with tag INLINEFORM1 has a location near INLINEFORM2 and INLINEFORM3 is the probability that an arbitrary tag occurrence is assigned to a photo near a location in INLINEFORM4   Since INLINEFORM5 often has to be estimated from a small number of tag occurrences, it is estimated using Bayesian smoothing: INLINEFORM6
where INLINEFORM0 is a parameter controlling the amount of smoothing, which will be tuned in the experiments  On the other hand, for INLINEFORM1 we can simply use a maximum likelihood estimation: INLINEFORM2
Location embedding  We now want to find a vector INLINEFORM0 for each location INLINEFORM1 such that similar locations are represented using similar vectors  To achieve this, we use a close variant of the GloVe model, where tag occurrences are treated as context words of geographic locations  In particular, with each location INLINEFORM2 we associate a vector INLINEFORM3 and with each tag INLINEFORM4 we associate a vector INLINEFORM5 and a bias term INLINEFORM6 , and consider the following objective (which in our full model ( EQREF7 ) will be combined with components that are derived from the structured information): INLINEFORM7
Note how tags play the role of the context words in the GloVe model, while instead of learning target word vectors we now learn location vectors  In contrast to GloVe, our objective does not directly refer to co-occurrence statistics, but instead uses the INLINEFORM0 scores  One important consequence of this is that we can also consider pairs INLINEFORM1 for which INLINEFORM2 does not occur in INLINEFORM3 at all; such pairs are usually called negative examples  While they cannot be used in the standard GloVe model, some authors have already reported that introducing negative examples in variants of GloVe can lead to an improvement BIBREF52   In practice, evaluating the full objective above would not be computationally feasible, as we may need to consider millions of locations and millions of tags  Therefore, rather than considering all tags in INLINEFORM4 for the inner summation, we only consider those tags that appear at least once near location INLINEFORM5 together with a sample of negative examples 
Structured Environmental Data
There is a wide variety of structured data that can be used to describe locations  In this work, we have restricted ourselves to the same datasets as BIBREF7   These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg  temperature, avg  precipitation, avg  solar radiation, avg  wind speed, and avg  water vapor pressure)  In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids)  Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type 
Numerical features  Numerical features can be treated similarly to the tag occurrences, i e  we will assume that the value of a given numerical feature can be predicted from the location vectors using a linear mapping  In particular, for each numerical feature INLINEFORM0 we consider a vector INLINEFORM1 and a bias term INLINEFORM2 , and the following objective: INLINEFORM3
where we write INLINEFORM0 for set of all numerical features and INLINEFORM1 is the value of feature INLINEFORM2 for location INLINEFORM3 , after z-score normalization 
Categorical features  To take into account the categorical features, we impose the constraint that locations belonging to the same category should be close together in the space  To formalize this, we represent each category type INLINEFORM0 as a vector INLINEFORM1 , and consider the following objective: INLINEFORM2
Evaluation Tasks
We will use the method from BIBREF7 as our main baseline  This will allow us to directly evaluate the effectiveness of embeddings for the considered problem, since we have used the same structured datasets and same tag weighting scheme  For this reason, we will also follow their evaluation methodology  In particular, we will consider three evaluation tasks:
Predicting the distribution of 100 species across Europe, using the European network of nature protected sites Natura 2000 dataset as ground truth  For each of these species, a binary classification problem is considered  The set of locations INLINEFORM0 is defined as the 26,425 distinct sites occurring in the dataset 
Predicting soil type, again each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments  For these experiments, none of the soil type features are used for generating the embeddings 
Predicting CORINE land cover classes at levels 1, 2 and level 3, each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments  For these experiments, none of the CORINE features are used for generating the embeddings 
In addition, we will also consider the following regression tasks:
Predicting 5 climate related features: the average precipitation, temperature, solar radiation, water vapor pressure, and wind speed  We again use the same set of locations INLINEFORM0 as for species distribution in this experiment  None of the climate features is used for constructing the embeddings for this experiment 
Predicting people's subjective opinions of landscape beauty in Britain, using the crowdsourced dataset from the ScenicOrNot website as ground truth  The set INLINEFORM0 is chosen as the set of locations of 191 605 rated locations from the ScenicOrNot dataset for which at least one georeferenced Flickr photo exists within a 1 km radius 
Experimental Setup
In all experiments, we use Support Vector Machines (SVMs) for classification problems and Support Vector Regression (SVR) for regression problems to make predictions from our representations of geographic locations  In both cases, we used the SVM INLINEFORM0 implementation BIBREF53   For each experiment, the set of locations INLINEFORM1 was split into two-thirds for training, one-sixth for testing, and one-sixth for tuning the parameters  All embedding models are learned with Adagrad using 30 iterations  The number of dimensions is chosen for each experiment from INLINEFORM2 based on the tuning data  For the parameters of our model in Equation EQREF7 , we considered values of INLINEFORM3 from {0 1, 0 01, 0 001, 0 0001} and values of INLINEFORM4 from {1, 10, 100, 1000, 10 000, 100 000}  To compute KL divergence, we need to determine a set of classes INLINEFORM5 for each experiment  For classification problems, we can simply consider the given categories, but for the regression problems we need to define such classes by discretizing the numerical values  For the scenicness experiments, we considered scores 3 and 7 as cut-off points, leading to three classes (i e  less than 3, between 3 and 7, and above 7)  Similarly, for each climate related features, we consider two cut-off values for discretization: 5 and 15 for average temperature, 50 and 100 for average precipitation, 10 000 and 17 000 for average solar radiation, 0 7 and 1 for average water vapor pressure, and 3 and 5 for wind speed  The smoothing parameter INLINEFORM6 was selected among INLINEFORM7 based on the tuning data  In all experiments where term selection is used, we select the top 100 000 tags  We fixed the radius INLINEFORM8 at 1km when counting the number of tag occurrences  Finally, we set the number of negative examples as 10 times the number of positive examples for each location, but with a cap at 1000 negative examples in each region for computational reasons  We tune all parameters with respect to the F1 score for the classification tasks, and Spearman INLINEFORM9 for the regression tasks 
Variants and Baseline Methods
We will refer to our model as EGEL (Embedding GEographic Locations), and will consider the following variants  EGEL-Tags only uses the information from the Flickr tags (i e  component INLINEFORM0 ), without using any negative examples and without feature selection  EGEL-Tags+NS is similar to EGEL-Tags but with the addition of negative examples  EGEL-KL(Tags+NS) additionally considers term selection  EGEL-All is our full method, i e  it additionally uses the structured information  We also consider the following baselines  BOW-Tags represents locations using a bag-of-words representation, using the same tag weighting as the embedding model  BOW-KL(Tags) uses the same representation but after term selection, using the same KL-based method as the embedding model  BOW-All combines the bag-of-words representation with the structured information, encoded as proposed in BIBREF7   GloVe uses the objective from the original GloVe model for learning location vectors, i e  this variant differs from EGEL-Tags in that instead of INLINEFORM1 we use the number of co-occurrences of tag INLINEFORM2 near location INLINEFORM3 , measured as INLINEFORM4  
Results and Discussion
We present our results for the binary classification tasks in Tables TABREF23 – TABREF24 in terms of average precision, average recall and macro average F1 score  The results of the regression tasks are reported in Tables TABREF25 and TABREF29 in terms of the mean absolute error between the predicted and actual scores, as well as the Spearman INLINEFORM0 correlation between the rankings induced by both sets of scores  It can be clearly seen from the results that our proposed method (EGEL-All) can effectively integrate Flickr tags with the available structured information  It outperforms the baselines for all the considered tasks  Furthermore, note that the PPMI-based weighting in EGEL-Tags consistently outperforms GloVe and that both the addition of negative examples and term selection lead to further improvements  The use of term selection leads to particularly substantial improvements for the regression problems 
While our experimental results confirm the usefulness of embeddings for predicting environmental features, this is only consistently the case for the variants that use both the tags and the structured datasets  In particular, comparing BOW-Tags with EGEL-Tags, we sometimes see that the former achieves the best results  While this might seem surprising, it is in accordance with the findings in BIBREF54 , BIBREF38 , among others, where it was also found that bag-of-words representations can sometimes lead to surprisingly effective baselines  Interestingly, we note that in all cases where EGEL-KL(Tags+NS) performs worse than BOW-Tags, we also find that BOW-KL(Tags) performs worse than BOW-Tags  This suggests that for these tasks there is a very large variation in the kind of tags that can inform the prediction model, possibly including e g  user-specific tags  Some of the information captured by such highly specific but rare tags is likely to be lost in the embedding 
To further analyze the difference in performance between BoW representations and embeddings, Figure TABREF29 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations  What is clearly noticeable in Figure TABREF29 is that GloVe performs better than the bag-of-words model for large corpora and worse for smaller corpora  This issue has been alleviated in our embedding method by the addition of negative examples 
 Conclusions
In this paper, we have proposed a model to learn geographic location embeddings using Flickr tags, numerical environmental features, and categorical information  The experimental results show that our model can integrate Flickr tags with structured information in a more effective way than existing methods, leading to substantial improvements over baseline methods on various prediction tasks about the natural environment 
Acknowledgments
Shelan Jeawak has been sponsored by HCED Iraq  Steven Schockaert has been supported by ERC Starting Grant 637277 
Context 3: Introduction
In recent years, gender has become a hot topic within the political, societal and research spheres  Numerous studies have been conducted in order to evaluate the presence of women in media, often revealing their under-representation, such as the Global Media Monitoring Project BIBREF0  In the French context, the CSA BIBREF1 produces a report on gender representation in media on a yearly basis  The 2017 report shows that women represent 40% of French media speakers, with a significant drop during high-audience hours (6:00-8:00pm) reaching a value of only 29%  Another large scale study confirmed this trend with an automatic analysis of gender in French audiovisuals streams, highlighting a huge variation across type of shows BIBREF2 
Besides the social impact of gender representation, broadcast recordings are also a valuable source of data for the speech processing community  Indeed, automatic speech recognition (ASR) systems require large amount of annotated speech data to be efficiently trained, which leaves us facing the emerging concern about the fact that "AI artifacts tend to reflect the goals, knowledge and experience of their creators" BIBREF3  Since we know that women are under-represented in media and that the AI discipline has retained a male-oriented focus BIBREF4, we can legitimately wonder about the impact of using such data as a training set for ASR technologies  This concern is strengthened by the recent works uncovering gender bias in several natural language processing (NLP) tools such as BIBREF5, BIBREF6, BIBREF7, BIBREF8 
In this paper, we first highlight the importance of TV and radio broadcast as a source of data for ASR, and the potential impact it can have  We then perform a statistical analysis of gender representation in a data set composed of four state-of-the-art corpora of French broadcast, widely used within the speech community  Finally we question the impact of such a representation on the systems developed on this data, through the perspective of an ASR system 
From gender representation in data to gender bias in AI ::: On the importance of data
The ever growing use of machine learning in science has been enabled by several progresses among which the exponential growth of data available  The quality of a system now depends mostly on the quality and quantity of the data it has been trained on  If it does not discard the importance of an appropriate architecture, it reaffirms the fact that rich and large corpora are a valuable resource  Corpora are research contributions which do not only allow to save and observe certain phenomena or validate a hypothesis or model, but are also a mandatory part of the technology development  This trend is notably observable within the NLP field, where industrial technologies, such as Apple, Amazon or Google vocal assistants now reach high performance level partly due to the amount of data possessed by these companies BIBREF9 
Surprisingly, as data is said to be “the new oil", few data sets are available for ASR systems  The best known are corpora like TIMIT BIBREF10, Switchboard BIBREF11 or Fisher BIBREF12 which date back to the early 1990s  The scarceness of available corpora is justified by the fact that gathering and annotating audio data is costly both in terms of money and time  Telephone conversations and broadcast recordings have been the primary source of spontaneous speech used  Out of all the 130 audio resources proposed by LDC to train automatic speech recognition systems in English, approximately 14% of them are based on broadcast news and conversation  For French speech technologies, four corpora containing radio and TV broadcast are the most widely used: ESTER1 BIBREF13, ESTER2 BIBREF14, ETAPE BIBREF15 and REPERE BIBREF16  These four corpora have been built alongside evaluation campaigns and are still, to our knowledge, the largest French ones of their type available to date 
From gender representation in data to gender bias in AI ::: From data to bias
The gender issue has returned to the forefront of the media scene in recent years and with the emergence of AI technologies in our daily lives, gender bias has become a scientific topic that researchers are just beginning to address  Several studies revealed the existence of gender bias in AI technologies such as face recognition (GenderShades BIBREF17), NLP (word embeddings BIBREF5 and semantics BIBREF6) and machine translation (BIBREF18, BIBREF7)  The impact of the training data used within these deep-learning algorithms is therefore questioned 
Bias can be found at different levels as pointed out by BIBREF19  BIBREF20 defines bias as a skew that produces a type of harm  She distinguishes two types of harms that are allocation harm and representation harm  The allocation harm occurs when a system is performing better or worse for a certain group while representational harm contributes to the perpetuation of stereotypes  Both types of harm are the results of bias in machine learning that often comes from the data systems are trained on  Disparities in representation in our social structures is captured and reflected by the training data, through statistical patterns  The GenderShades study is a striking example of what data disparity and lack of representation can produce: the authors tested several gender recognition modules used by facial recognition tools and found difference in error-rate as high as 34 percentage points between recognition of white male and black female faces  The scarce presence of women and colored people in training set resulted in bias in performance towards these two categories, with a strong intersectional bias  As written by BIBREF21 "A data set may have many millions of pieces of data, but this does not mean it is random or representative  To make statistical claims about a data set, we need to know where data is coming from; it is similarly important to know and account for the weaknesses in that data " (p 668) 
Regarding ASR technology, little work has explored the presence of gender bias within the systems and no consensus has been reached  BIBREF22 found that speech recognizers perform better on female voice on a broadcast news and telephone corpus  They proposed several explanations to this observation, such as the larger presence of non-professional male speech in the broadcast data, implying a less prepared speech for these speakers or a more normative language and standard pronunciation for women linked to the traditional role of women in language acquisition and education  The same trend was observed by BIBREF23  More recently, BIBREF24 discovered a gender bias within YouTube's automatic captioning system but this bias was not observed in a second study evaluating Bing Speech system and YouTube Automatic Captions on a larger data set BIBREF8  However race and dialect bias were found  General American speakers and white speakers had the lowest error rate for both systems  If the better performance on General American speakers could be explained by the fact that they are all voice professionals, producing clear and articulated speech, but no explanation is provided for biases towards non-white speakers 
Gender bias in ASR technology is still an open research question as no clear answer has been reached so far  It seems that many parameters are to take into account to achieve a general agreement  As we established the importance of TV and radio broadcast as a source of data for ASR, and the potential impact it can have, the following content of this paper is structured as this: we first describe statistically the gender representation of a data set composed of four state-of-the-art corpora of French broadcast, widely used within the speech community, introducing the notion of speaker's role to refine our analysis in terms of voice professionalism  We then question the impact of such a representation on a ASR system trained on these data  BIBREF25
Methodology
This section is organized as follows: we first present the data we are working on  In a second time we explain how we proceed to describe the gender representation in our corpus and introduce the notion of speaker's role  The third subsection introduces the ASR system and metrics used to evaluate gender bias in performance 
Methodology ::: Data presentation
Our data consists of two sets used to train and evaluate our automatic speech recognition system  Four major evaluation campaigns have enabled the creation of wide corpora of French broadcast speech: ESTER1 BIBREF13, ESTER2 BIBREF14, ETAPE BIBREF15 and REPERE BIBREF16  These four collections contain radio and/or TV broadcasts aired between 1998 and 2013 which are used by most academic researchers in ASR  Show duration varies between 10min and an hour  As years went by and speech processing research was progressing, the difficulty of the tasks augmented and the content of these evaluation corpora changed  ESTER1 and ESTER2 mainly contain prepared speech such as broadcast news, whereas ETAPE and REPERE consists also of debates and entertainment shows, spontaneous speech introducing more difficulty in its recognition 
Our training set contains 27,085 speech utterances produced by 2,506 speakers, accounting for approximately 100 hours of speech  Our evaluation set contains 74,064 speech utterances produced by 1,268 speakers for a total of 70 hours of speech  Training data by show, medium and speech type is summarized in Table and evaluation data in Table   Evaluation data has a higher variety of shows with both prepared (P) and spontaneous (S) speech type (accented speech from African radio broadcast is also included in the evaluation set) 
Methodology ::: Methodology for descriptive analysis of gender representation in training data
We first describe the gender representation in training data  Gender representation is measured in terms of number of speakers, number of utterances (or speech turns), and turn lengths (descriptive statistics are given in Section SECREF16)  Each speech turn was mapped to its speaker in order to associate it with a gender 
As pointed out by the CSA report BIBREF1, women presence tends to be marginal within the high-audience hours, showing that women are represented but less than men and within certain given conditions  It is clear that a small number of speakers is responsible for a large number of speech turns  Most of these speakers are journalists, politicians, presenters and such, who are representative of a show  Therefore, we introduce the notion of speaker's role to refine our exploration of gender disparity, following studies which quantified women's presence in terms of role  Within our work, we define the notion of speaker role by two criteria specifying the speaker's on-air presence, namely the number of speech turns and the cumulative duration of his or her speaking time in a show  Based on the available speech transcriptions and meta-data, we compute for each speaker the number of speech turns uttered as well as their total length  We then use the following criteria to define speaker's role: a speaker is considered as speaking often (respectively seldom) if he/she accumulates a total of turns higher (respectively lower) than 1% of the total number of speech turns in a given show  The same process is applied to identify speakers talking for a long period from those who do not  We end up with two salient roles called Anchors and Punctual speakers:
the Anchor speakers (A) are above the threshold of 1% for both criteria, meaning they are intervening often and for a long time thus holding an important place in interaction;
the Punctual speakers (PS) on the contrary are below the threshold of 1% for both the total number of turns and the total speech time 
These roles are defined at the show level  They could be roughly assimilated to the categorization “host/guest” in radio and TV shows  Anchors could be described as professional speakers, producing mostly prepared speech, whereas Punctual speakers are more likely to be “everyday people"  The concept of speaker's role makes sense at both sociological and technical levels  An Anchor speaker is more likely to be known from the audience (society), but he or she will also likely have a professional (clear) way of speaking (as mentioned by BIBREF22 and BIBREF8), as well as a high number of utterances, augmenting the amount of data available for a given gender category 
Methodology ::: Gender bias evaluation procedure of an ASR system performance ::: ASR system
The ASR system used in this work is described in BIBREF25  It uses the KALDI toolkit BIBREF26, following a standard Kaldi recipe  The acoustic model is based on a hybrid HMM-DNN architecture and trained on the data summarized in Table   Acoustic training data correspond to 100h of non-spontaneous speech type (mostly broadcast news) coming from both radio and TV shows  A 5-gram language model is trained from several French corpora (3,323M words in total) using SRILM toolkit BIBREF27  The pronunciation model is developed using the lexical resource BDLEX BIBREF28 as well as automatic grapheme-to-phoneme (G2P) transcription to find pronunciation variants of our vocabulary (limited to 80K)  It is important to re-specify here, for further analysis, that our Kaldi pipeline follows speaker adaptive training (SAT) where we train and decode using speaker adapted features (fMLLR-adapted features) in per-speaker mode  It is well known that speaker adaptation acts as an effective procedure to reduce mismatch between training and evaluation conditions BIBREF29, BIBREF26 
Methodology ::: Gender bias evaluation procedure of an ASR system performance ::: Evaluation
Word Error Rate (WER) is a common metric to evaluate ASR performance  It is measured as the sum of errors (insertions, deletions and substitutions) divided by the total number of words in the reference transcription  As we are investigating the impact on performance of speaker's gender and role, we computed the WER for each speaker at the episode (show occurrence) level  Analyzing at such granularity allows us to avoid large WER variation that could be observed at utterance level (especially for short speech turns) but also makes possible to get several WER values for a given speaker, one for each occurrence of a show in which he/she appears on  Speaker's gender was provided by the meta-data and role was obtained using the criteria from Section SECREF6 computed for each show

ground_truth: BOW-Tags, BOW-KL(Tags), BOW-All, GloVe

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

Please answer the questions based on the text provided below. How big is imbalance in analyzed corpora?
Context 3:  This enables us to analyze our results across gender and role categories which was done using Wilcoxon rank sum tests also called Mann-Whitney U test (with $\alpha $= 0 001) BIBREF30  The choice of a Wilcoxon rank sum test and not the commonly used t-test is motivated by the non-normality of our data 
Results ::: Descriptive analysis of gender representation in training data ::: Gender representation
As expected, we observe a disparity in terms of gender representation in our data (see Table )  Women represent 33 16% of the speakers, confirming the figures given by the GMMP report BIBREF0  However, it is worth noticing that women account for only 22 57% of the total speech time, which leads us to conclude that women also speak less than men 
Results ::: Descriptive analysis of gender representation in training data ::: Speaker's role representation
Table presents roles' representation in training data and shows that despite the small number of Anchor speakers in our data (3 79%), they nevertheless concentrate 35 71 % of the total speech time 
Results ::: Descriptive analysis of gender representation in training data ::: Role and gender interaction
When crossing both parameters, we can observe that the gender distribution is not constant throughout roles  Women represent 29 47% of the speakers within the Anchor category, even less than among the Punctual speakers  Their percentage of speech is also smaller  When calculating the average speech time uttered by a female Anchor, we obtain a value of 15 9 min against 25 2 min for a male Anchor, which suggests that even within the Anchor category men tend to speak more  This confirms the existence of gender disparities within French media  It corroborates with the analysis of the CSA BIBREF1, which shows that women were less present during high-audience hours  Our study shows that they are also less present in important roles  These results legitimate our initial questioning on the impact of gender balance on ASR performance trained on broadcast recordings 
Results ::: Performance (WER) analysis on evaluation data ::: Impact of gender on WER
As explained in Section SECREF13, WER is the sum of errors divided by the number of words in the transcription reference  The higher the WER, the poorer the system performance  Our 70h evaluation data contains a large amount of spontaneous speech and is very challenging for the ASR system trained on prepared speech: we observe an overall average WER of 42 9% for women and 34 3% for men  This difference of WER between men and women is statistically significant (med(M) = 25%; med(F) = 29%; U = 709040; p-value < 0 001) 
However, when observing gender differences across shows, no clear trend can be identified, as shown in Figure FIGREF21  For shows like Africa1 Infos or La Place du Village, we find an average WER lower for women than for men, while the trend is reversed for shows such as Un Temps de Pauchon or Le Masque et la Plume  The disparity of the results depending on the show leads us to believe that other factors may be entangled within the observed phenomenon 
Results ::: Performance (WER) analysis on evaluation data ::: Impact of role on WER
Speaker's role seems to have an impact on WER: we obtain an average WER of 30 8% for the Anchor speakers and 42 23% for the Punctual speakers  This difference is statistically significant with a p-value smaller than $10^{-14}$ (med(A) = 21%; med(P) = 31%; U = 540,430; p-value < 0 001)  
Results ::: Performance (WER) analysis on evaluation data ::: Role and gender interaction
Figure FIGREF25 presents the WER distribution (WER being obtained for each speaker in a show occurrence) according to the speaker's role and gender  It is worth noticing that the gender difference is only significant within the Punctual speakers group  The average WER is of 49 04% for the women and 38 56% for the men with a p-value smaller than $10^{-6}$ (med(F) = 39%; med(M) = 29%; U = 251,450; p-value < 0 001), whereas it is just a trend between male and female Anchors (med(F) = 21%; med(M) = 21%; U = 116,230; p-value = 0 173)  This could be explained by the quantity of data available per speaker 
Results ::: Performance (WER) analysis on evaluation data ::: Speech type as a third entangled factor?
In order to try to explain the observed variation in our results depending on shows and gender (Figure FIGREF21), we add the notion of speech type to shed some light on our results  BIBREF22 and BIBREF24 suggested that the speaker professionalism, associated with clear and hyper-articulated speech could be an explaining factor for better performance 
Based on our categorization in prepared speech (mostly news reports) and spontaneous speech (mostly debates and entertainment shows), we cross this parameter in our performance analysis  As shown on Figure FIGREF26, these results confirm the inherent challenge of spontaneous speech compared to prepared speech  WER scores are similar between men and women when considering prepared speech (med(F) = 18%; med(M) = 21%; U = 217,160; p-value = 0 005) whereas they are worse for women (61 29%) than for men (46 51%) with p-value smaller than $10^{-14}$ for the spontaneous speech type (med(F) = 61%; med(M) = 37%; U = 153,580; p-value < 0 001) 
Discussion
We find a clear disparity in terms of women presence and speech quantity in French media  Our data being recorded between 1998 and 2013, we can expect this disparity to be smaller on more recent broadcast recordings, especially since the French government displays efforts toward parity in media representation  One can also argue that even if our analysis was conducted on a large amount of data it does not reach the exhaustiveness of large-scale studies such as the one of BIBREF2  Nonetheless it does not affect the relevance of our findings, because if real-world gender representation might be more balanced today, these corpora are still used as training data for AI systems 
The performance difference across gender we observed corroborates (on a larger quantity and variety of language data produced by more than 2400 speakers) the results obtained by BIBREF24 on isolated words recognition  However the following study on read speech does not replicate these results  Yet a performance degradation is observed across dialect and race BIBREF8  BIBREF22 found lower WER for women than men on broadcast news and conversational telephone speech for both English and French  The authors suggest that gender stereotypes associated with women role in education and language acquisition induce a more normative elocution  We observed that the higher the degree of normativity of speech the smaller the gender difference  No significant gender bias is observed for prepared speech nor within the Anchor category  Even if we do not find similar results with lower WER for women than men, we obtained a median WER smaller for women on prepared speech and equal to the male median WER for the Anchor speakers 
Another explanation could be the use of adaptation within the pipeline  Most broadcast programs transcription systems have a speaker adaptation step within their decoding pipeline, which is the case for our system  An Anchor speaker intervening more often would have a larger quantity of data to realize such adaptation of the acoustic model  On the contrary, Punctual speakers who appear scarcely in the data are not provided with the same amount of adaptation data  Hence we can hypothesize that gender performance difference observed for Punctual speakers is due to the fact that female speech is further from the (initial non-adapted) acoustic model as it was trained on unbalanced data (as shown in Table )  Considering that Punctual speakers represent 92 78% of the speakers, this explains why gender difference is significant over our entire data set  A way to confirm our hypothesis would be to reproduce our analysis on WER values obtained without using speaker adapted features at the decoding step 
When decoding prepared speech (hence similar to the training data), no significant difference is found in WER between men and women, revealing that the speaker adaptation step could be sufficient to reach same performance for both genders  But when decoding more spontaneous speech, there is a mismatch with the initial acoustic model (trained on prepared speech)  Consequently, the speaker adaptation step might not be enough to recover good ASR performance, especially for women for whom less adaptation data is available (see Section 4 2 3) 
Conclusion
This paper has investigated gender bias in ASR performance through the following research questions: i) what is the proportion of men and women in French radio and TV media data ? ii) what is the impact of the observed disparity on ASR performance ? iii) is this as simple as a problem of gender proportion in the training data or are other factors entangled ? Our contributions are the following:
Descriptive analysis of the broadcast data used to train our ASR system confirms the already known disparity, where 65% of the speakers are men, speaking more than 75% of the time 
When investigating WER scores according to gender, speaker's role and speech type, huge variations are observed  We conclude that gender is clearly a factor of variation in ASR performance, with a WER increase of 24% for women compared to men, exhibiting a clear gender bias 
Gender bias varies across speaker's role and speech spontaneity level  Performance for Punctual speakers respectively spontaneous speech seems to reinforce this gender bias with a WER increase of 27 2% respectively 31 8% between male and female speakers 
We found that an ASR system trained on unbalanced data regarding gender produces gender bias performance  Therefore, in order to create fair systems it is necessary to take into account the representation problems in society that are going to be encapsulated in the data  Understanding how women under-representation in broadcast data can lead to bias in ASR performances is the key to prevent re-implementing and reinforcing discrimination already existing in our societies  This is in line with the concept of “Fairness by Design" proposed by BIBREF31 
Gender, race, religion, nationality are all characteristics that we deem unfair to classify on, and these ethical standpoints needs to be taken into account in systems' design  Characteristics that are not considered as relevant in a given task can be encapsulated in data nonetheless, and lead to bias performance  Being aware of the demographic skews our data set might contain is a first step to track the life cycle of a training data set and a necessary step to control the tools we develop 

ground_truth: Women represent 33.16% of the speakers