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. By how much does their model outperform the state of the art results?
Context 1: Introduction
Recently, deep learning algorithms have successfully addressed problems in various fields, such as image classification, machine translation, speech recognition, text-to-speech generation and other machine learning related areas BIBREF0 , BIBREF1 , BIBREF2   Similarly, substantial improvements in performance have been obtained when deep learning algorithms have been applied to statistical speech processing BIBREF3   These fundamental improvements have led researchers to investigate additional topics related to human nature, which have long been objects of study  One such topic involves understanding human emotions and reflecting it through machine intelligence, such as emotional dialogue models BIBREF4 , BIBREF5  
In developing emotionally aware intelligence, the very first step is building robust emotion classifiers that display good performance regardless of the application; this outcome is considered to be one of the fundamental research goals in affective computing BIBREF6   In particular, the speech emotion recognition task is one of the most important problems in the field of paralinguistics  This field has recently broadened its applications, as it is a crucial factor in optimal human-computer interactions, including dialog systems  The goal of speech emotion recognition is to predict the emotional content of speech and to classify speech according to one of several labels (i e , happy, sad, neutral, and angry)  Various types of deep learning methods have been applied to increase the performance of emotion classifiers; however, this task is still considered to be challenging for several reasons  First, insufficient data for training complex neural network-based models are available, due to the costs associated with human involvement  Second, the characteristics of emotions must be learned from low-level speech signals  Feature-based models display limited skills when applied to this problem 
To overcome these limitations, we propose a model that uses high-level text transcription, as well as low-level audio signals, to utilize the information contained within low-resource datasets to a greater degree  Given recent improvements in automatic speech recognition (ASR) technology BIBREF7 , BIBREF2 , BIBREF8 , BIBREF9 , speech transcription can be carried out using audio signals with considerable skill  The emotional content of speech is clearly indicated by the emotion words contained in a sentence BIBREF10 , such as “lovely” and “awesome,” which carry strong emotions compared to generic (non-emotion) words, such as “person” and “day ” Thus, we hypothesize that the speech emotion recognition model will be benefit from the incorporation of high-level textual input 
In this paper, we propose a novel deep dual recurrent encoder model that simultaneously utilizes audio and text data in recognizing emotions from speech  Extensive experiments are conducted to investigate the efficacy and properties of the proposed model  Our proposed model outperforms previous state-of-the-art methods by 68 8% to 71 8% when applied to the IEMOCAP dataset, which is one of the most well-studied datasets  Based on an error analysis of the models, we show that our proposed model accurately identifies emotion classes  Moreover, the neutral class misclassification bias frequently exhibited by previous models, which focus on audio features, is less pronounced in our model 
Related work
Classical machine learning algorithms, such as hidden Markov models (HMMs), support vector machines (SVMs), and decision tree-based methods, have been employed in speech emotion recognition problems BIBREF11 , BIBREF12 , BIBREF13   Recently, researchers have proposed various neural network-based architectures to improve the performance of speech emotion recognition  An initial study utilized deep neural networks (DNNs) to extract high-level features from raw audio data and demonstrated its effectiveness in speech emotion recognition BIBREF14   With the advancement of deep learning methods, more complex neural-based architectures have been proposed  Convolutional neural network (CNN)-based models have been trained on information derived from raw audio signals using spectrograms or audio features such as Mel-frequency cepstral coefficients (MFCCs) and low-level descriptors (LLDs) BIBREF15 , BIBREF16 , BIBREF17   These neural network-based models are combined to produce higher-complexity models BIBREF18 , BIBREF19 , and these models achieved the best-recorded performance when applied to the IEMOCAP dataset 
Another line of research has focused on adopting variant machine learning techniques combined with neural network-based models  One researcher utilized the multiobject learning approach and used gender and naturalness as auxiliary tasks so that the neural network-based model learned more features from a given dataset BIBREF20   Another researcher investigated transfer learning methods, leveraging external data from related domains BIBREF21  
As emotional dialogue is composed of sound and spoken content, researchers have also investigated the combination of acoustic features and language information, built belief network-based methods of identifying emotional key phrases, and assessed the emotional salience of verbal cues from both phoneme sequences and words BIBREF22 , BIBREF23   However, none of these studies have utilized information from speech signals and text sequences simultaneously in an end-to-end learning neural network-based model to classify emotions 
Model
This section describes the methodologies that are applied to the speech emotion recognition task  We start by introducing the recurrent encoder model for the audio and text modalities individually  We then propose a multimodal approach that encodes both audio and textual information simultaneously via a dual recurrent encoder 
Audio Recurrent Encoder (ARE)
Motivated by the architecture used in BIBREF24 , BIBREF25 , we build an audio recurrent encoder (ARE) to predict the class of a given audio signal  Once MFCC features have been extracted from an audio signal, a subset of the sequential features is fed into the RNN (i e , gated recurrent units (GRUs)), which leads to the formation of the network's internal hidden state INLINEFORM0 to model the time series patterns  This internal hidden state is updated at each time step with the input data INLINEFORM1 and the hidden state of the previous time step INLINEFORM2 as follows: DISPLAYFORM0
where INLINEFORM0 is the RNN function with weight parameter INLINEFORM1 , INLINEFORM2 represents the hidden state at t- INLINEFORM3 time step, and INLINEFORM4 represents the t- INLINEFORM5 MFCC features in INLINEFORM6   After encoding the audio signal INLINEFORM7 with the RNN, the last hidden state of the RNN, INLINEFORM8 , is considered to be the representative vector that contains all of the sequential audio data  This vector is then concatenated with another prosodic feature vector, INLINEFORM9 , to generate a more informative vector representation of the signal, INLINEFORM10   The MFCC and the prosodic features are extracted from the audio signal using the openSMILE toolkit BIBREF26 , INLINEFORM11 , respectively  Finally, the emotion class is predicted by applying the softmax function to the vector INLINEFORM12   For a given audio sample INLINEFORM13 , we assume that INLINEFORM14 is the true label vector, which contains all zeros but contains a one at the correct class, and INLINEFORM15 is the predicted probability distribution from the softmax layer  The training objective then takes the following form: DISPLAYFORM0
where INLINEFORM0 is the calculated representative vector of the audio signal with dimensionality INLINEFORM1   The INLINEFORM2 and the bias INLINEFORM3 are learned model parameters  C is the total number of classes, and N is the total number of samples used in training  The upper part of Figure shows the architecture of the ARE model 
Text Recurrent Encoder (TRE)
We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies BIBREF7   We attempt to use the processed textual information as another modality in predicting the emotion class of a given signal  To use textual information, a speech transcript is tokenized and indexed into a sequence of tokens using the Natural Language Toolkit (NLTK) BIBREF27   Each token is then passed through a word-embedding layer that converts a word index to a corresponding 300-dimensional vector that contains additional contextual meaning between words  The sequence of embedded tokens is fed into a text recurrent encoder (TRE) in such a way that the audio MFCC features are encoded using the ARE represented by equation EQREF2   In this case, INLINEFORM0 is the t- INLINEFORM1 embedded token from the text input  Finally, the emotion class is predicted from the last hidden state of the text-RNN using the softmax function 
We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is last hidden state of the text-RNN, INLINEFORM1 , and the INLINEFORM2 and bias INLINEFORM3 are learned model parameters  The lower part of Figure indicates the architecture of the TRE model 
Multimodal Dual Recurrent Encoder (MDRE)
We present a novel architecture called the multimodal dual recurrent encoder (MDRE) to overcome the limitations of existing approaches  In this study, we consider multiple modalities, such as MFCC features, prosodic features and transcripts, which contain sequential audio information, statistical audio information and textual information, respectively  These types of data are the same as those used in the ARE and TRE cases  The MDRE model employs two RNNs to encode data from the audio signal and textual inputs independently  The audio-RNN encodes MFCC features from the audio signal using equation EQREF2   The last hidden state of the audio-RNN is concatenated with the prosodic features to form the final vector representation INLINEFORM0 , and this vector is then passed through a fully connected neural network layer to form the audio encoding vector A  On the other hand, the text-RNN encodes the word sequence of the transcript using equation EQREF2   The final hidden states of the text-RNN are also passed through another fully connected neural network layer to form a textual encoding vector T  Finally, the emotion class is predicted by applying the softmax function to the concatenation of the vectors A and T  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 is the feed-forward neural network with weight parameter INLINEFORM1 , and INLINEFORM2 , INLINEFORM3 are final encoding vectors from the audio-RNN and text-RNN, respectively  INLINEFORM4 and the bias INLINEFORM5 are learned model parameters 
Multimodal Dual Recurrent Encoder with Attention (MDREA)
Inspired by the concept of the attention mechanism used in neural machine translation BIBREF28 , we propose a novel multimodal attention method to focus on the specific parts of a transcript that contain strong emotional information, conditioning on the audio information  Figure shows the architecture of the MDREA model  First, the audio data and text data are encoded with the audio-RNN and text-RNN using equation EQREF2   We then consider the final audio encoding vector INLINEFORM0 as a context vector  As seen in equation EQREF9 , during each time step t, the dot product between the context vector e and the hidden state of the text-RNN at each t-th sequence INLINEFORM1 is evaluated to calculate a similarity score INLINEFORM2   Using this score INLINEFORM3 as a weight parameter, the weighted sum of the sequences of the hidden state of the text-RNN, INLINEFORM4 , is calculated to generate an attention-application vector Z  This attention-application vector is concatenated with the final encoding vector of the audio-RNN INLINEFORM5 (equation EQREF7 ), which will be passed through the softmax function to predict the emotion class  We use the same training objective as the ARE model, and the predicted probability distribution for the target class is as follows: DISPLAYFORM0
where INLINEFORM0 and the bias INLINEFORM1 are learned model parameters 
Dataset
We evaluate our model using the Interactive Emotional Dyadic Motion Capture (IEMOCAP) BIBREF18 dataset  This dataset was collected following theatrical theory in order to simulate natural dyadic interactions between actors  We use categorical evaluations with majority agreement  We use only four emotional categories happy, sad, angry, and neutral to compare the performance of our model with other research using the same categories  The IEMOCAP dataset includes five sessions, and each session contains utterances from two speakers (one male and one female)  This data collection process resulted in 10 unique speakers  For consistent comparison with previous work, we merge the excitement dataset with the happiness dataset  The final dataset contains a total of 5531 utterances (1636 happy, 1084 sad, 1103 angry, 1708 neutral) 
Feature extraction
To extract speech information from audio signals, we use MFCC values, which are widely used in analyzing audio signals  The MFCC feature set contains a total of 39 features, which include 12 MFCC parameters (1-12) from the 26 Mel-frequency bands and log-energy parameters, 13 delta and 13 acceleration coefficients The frame size is set to 25 ms at a rate of 10 ms with the Hamming function  According to the length of each wave file, the sequential step of the MFCC features is varied  To extract additional information from the data, we also use prosodic features, which show effectiveness in affective computing  The prosodic features are composed of 35 features, which include the F0 frequency, the voicing probability, and the loudness contours  All of these MFCC and prosodic features are extracted from the data using the OpenSMILE toolkit BIBREF26  
Implementation details
Among the variants of the RNN function, we use GRUs as they yield comparable performance to that of the LSTM and include a smaller number of weight parameters BIBREF29   We use a max encoder step of 750 for the audio input, based on the implementation choices presented in BIBREF30 and 128 for the text input because it covers the maximum length of the transcripts  The vocabulary size of the dataset is 3,747, including the “_UNK_" token, which represents unknown words, and the “_PAD_" token, which is used to indicate padding information added while preparing mini-batch data  The number of hidden units and the number of layers in the RNN for each model (ARE, TRE, MDRE and MDREA) are selected based on extensive hyperparameter search experiments  The weights of the hidden units are initialized using orthogonal weights BIBREF31 ], and the text embedding layer is initialized from pretrained word-embedding vectors BIBREF32  
In preparing the textual dataset, we first use the released transcripts of the IEMOCAP dataset for simplicity  To investigate the practical performance, we then process all of the IEMOCAP audio data using an ASR system (the Google Cloud Speech API) and retrieve the transcripts  The performance of the Google ASR system is reflected by its word error rate (WER) of 5 53% 
Performance evaluation
As the dataset is not explicitly split beforehand into training, development, and testing sets, we perform 5-fold cross validation to determine the overall performance of the model  The data in each fold are split into training, development, and testing datasets (8:0 5:1 5, respectively)  After training the model, we measure the weighted average precision (WAP) over the 5-fold dataset  We train and evaluate the model 10 times per fold, and the model performance is assessed in terms of the mean score and standard deviation 
We examine the WAP values, which are shown in Table 1  First, our ARE model shows the baseline performance because we use minimal audio features, such as the MFCC and prosodic features with simple architectures  On the other hand, the TRE model shows higher performance gain compared to the ARE  From this result, we note that textual data are informative in emotion prediction tasks, and the recurrent encoder model is effective in understanding these types of sequential data  Second, the newly proposed model, MDRE, shows a substantial performance gain  It thus achieves the state-of-the-art performance with a WAP value of 0 718  This result shows that multimodal information is a key factor in affective computing  Lastly, the attention model, MDREA, also outperforms the best existing research results (WAP 0 690 to 0 688) BIBREF19   However, the MDREA model does not match the performance of the MDRE model, even though it utilizes a more complex architecture  We believe that this result arises because insufficient data are available to properly determine the complex model parameters in the MDREA model  Moreover, we presume that this model will show better performance when the audio signals are aligned with the textual sequence while applying the attention mechanism  We leave the implementation of this point as a future research direction 
To investigate the practical performance of the proposed models, we conduct further experiments with the ASR-processed transcript data (see “-ASR” models in Table )  The label accuracy of the processed transcripts is 5 53% WER  The TRE-ASR, MDRE-ASR and MDREA-ASR models reflect degraded performance compared to that of the TRE, MDRE and MDREA models  However, the performance of these models is still competitive; in particular, the MDRE-ASR model outperforms the previous best-performing model, 3CNN-LSTM10H (WAP 0 691 to 0 688) 
Error analysis
We analyze the predictions of the ARE, TRE, and MDRE models  Figure shows the confusion matrix of each model  The ARE model (Fig  ) incorrectly classifies most instances of happy as neutral (43 51%); thus, it shows reduced accuracy (35 15%) in predicting the the happy class  Overall, most of the emotion classes are frequently confused with the neutral class  This observation is in line with the findings of BIBREF30 , who noted that the neutral class is located in the center of the activation-valence space, complicating its discrimination from the other classes 
Interestingly, the TRE model (Fig  ) shows greater prediction gains in predicting the happy class when compared to the ARE model (35 15% to 75 73%)  This result seems plausible because the model can benefit from the differences among the distributions of words in happy and neutral expressions, which gives more emotional information to the model than that of the audio signal data  On the other hand, it is striking that the TRE model incorrectly predicts instances of the sad class as the happy class 16 20% of the time, even though these emotional states are opposites of one another 
The MDRE model (Fig  ) compensates for the weaknesses of the previous two models (ARE and TRE) and benefits from their strengths to a surprising degree  The values arranged along the diagonal axis show that all of the accuracies of the correctly predicted class have increased  Furthermore, the occurrence of the incorrect “sad-to-happy" cases in the TRE model is reduced from 16 20% to 9 15% 
Conclusions
In this paper, we propose a novel multimodal dual recurrent encoder model that simultaneously utilizes text data, as well as audio signals, to permit the better understanding of speech data  Our model encodes the information from audio and text sequences using dual RNNs and then combines the information from these sources using a feed-forward neural model to predict the emotion class  Extensive experiments show that our proposed model outperforms other state-of-the-art methods in classifying the four emotion categories, and accuracies ranging from 68 8% to 71 8% are obtained when the model is applied to the IEMOCAP dataset  In particular, it resolves the issue in which predictions frequently incorrectly yield the neutral class, as occurs in previous models that focus on audio features 
In the future work, we aim to extend the modalities to audio, text and video inputs  Furthermore, we plan to investigate the application of the attention mechanism to data derived from multiple modalities  This approach seems likely to uncover enhanced learning schemes that will increase performance in both speech emotion recognition and other multimodal classification tasks 
Acknowledgments
K  Jung is with the Department of Electrical and Computer Engineering, ASRI, Seoul National University, Seoul, Korea  This work was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (No 10073144) 
Context 2: Introduction
Part-of-speech tagging is now a classic task in natural language processing, for which many systems have been developed or adapted for a large variety of languages  Its aim is to associate each “word” with a morphosyntactic tag, whose granularity can range from a simple morphosyntactic category, or part-of-speech (hereafter PoS), to finer categories enriched with morphological features (gender, number, case, tense, mood, etc ) 
The use of machine learning algorithms trained on manually annotated corpora has long become the standard way to develop PoS taggers  A large variety of algorithms have been used, such as (in approximative chronological order) bigram and trigram hidden Markov models BIBREF0 , BIBREF1 , BIBREF2 , decision trees BIBREF3 , BIBREF4 , maximum entropy Markov models (MEMMs) BIBREF5 and Conditional Random Fields (CRFs) BIBREF6 , BIBREF7   With such machine learning algorithms, it is possible to build PoS taggers for any language, provided adequate training data is available 
As a complement to annotated corpora, it has previously been shown that external lexicons are valuable sources of information, in particular morphosyntactic lexicons, which provide a large inventory of (word, PoS) pairs  Such lexical information can be used in the form of constraints at tagging time BIBREF8 , BIBREF9 or during the training process as additional features combined with standard features extracted from the training corpus BIBREF10 , BIBREF11 , BIBREF12  
In recent years, a different approach to modelling lexical information and integrating it into natural language processing systems has emerged, namely the use of vector representations for words or word sequences BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18   Such representations, which are generally extracted from large amounts of raw text, have proved very useful for numerous tasks including PoS tagging, in particular when used in recurrent neural networks (RNNs) and more specifically in mono- or bi-directional, word-level and/or character-level long short-term memory networks (LSTMs) BIBREF19 , BIBREF16 , BIBREF17 , BIBREF20  
Both approaches to representing lexical properties and to integrating them into a PoS tagger improve tagging results  Yet they rely on resources of different natures  The main advantage of word vectors is that they are built in an unsupervised way, only requiring large amounts of raw textual data  They also encode finer-grained information than usual morphosyntactic lexicons, most of which do not include any quantitative data, not even simple frequency information  Conversely, lexical resources often provide information about scarcely attested words, for which corpus-based approaches such as word vector representations are of limited relevance  Moreover, morphological or morphosyntactic lexicons already exist for a number of languages, including less-resourced langauges for which it might be difficult to obtain the large amounts of raw data necessary to extract word vector representations 
Our main goal is therefore to compare the respective impact of external lexicons and word vector representations on the accuracy of PoS models  This question has already been investigated for 6 languages by BIBREF18 using the state-of-the-art CRF-based tagging system MarMoT  The authors found that their best-performing word-vector-based PoS tagging models outperform their models that rely on morphosyntactic resources (lexicons or morphological analysers)  In this paper, we report on larger comparison, carried out in a larger multilingual setting and comparing different tagging models  Using different 16 datasets, we compare the performances of two feature-based models enriched with external lexicons and of two LSTM-based models enriched with word vector representations  A secondary goal of our work is to compare the relative improvements linked to the use of external lexical information in the two feature-based models, which use different models (MEMM vs  CRF) and feature sets 
More specifically, our starting point is the MElt system BIBREF12 , an MEMM tagging system  We first briefly describe this system and the way we adapted it by integrating our own set of corpus-based and lexical features  We then introduce the tagging models we have trained for 16 different languages using our adapted version of MElt  These models are trained on the Universal Dependencies (v1 2) corpus set BIBREF21 , complemented by morphosyntactic lexicons  We compare the accuracy of our models with the scores obtained by the CRF-based system MarMoT BIBREF22 , BIBREF18 , retrained on the same corpora and the same external morphosyntactic lexicons  We also compare our results to those obtained by the best bidirectional LSTM models described by BIBREF20 , which both make use of Polyglot word vector representations published by BIBREF23   We will show that an optimised enrichment of feature-based models with morphosyntactic lexicon results in significant accuracy gains  The macro-averaged accuracy of our enriched MElt models is above that of enriched MarMoT models and virtually identical to that of LSTMs enriched with word vector representations  More precisely, per-language results indicate that lexicons provide more useful information for languages with a high lexical variability (such as morphologically rich languages), whereas word vectors are more informative for languages with a lower lexical variability (such as English) 
MElt
MElt BIBREF12 is a tagging system based on maximum entropy Markov models (MEMM) BIBREF5 , a class of discriminative models that are suitable for sequence labelling BIBREF5   The basic set of features used by MElt is given in BIBREF12   It is a superset of the feature sets used by BIBREF5 and BIBREF24 and includes both local standard features (for example the current word itself and its prefixes and suffixes of length 1 to 4) and contextual standard features (for example the tag just assigned to the preceding word)  In particular, with respect to Ratnaparkhi's feature set, MElt's basic feature set lifts the restriction that local standard features used to analyse the internal composition of the current word should only apply to rare words 
One of the advantages of feature-based models such as MEMMs and CRFs is that complementary information can be easily added in the form of additional features  This was investigated for instance by BIBREF25 , whose best-performing model for PoS tagging dialogues was obtained with a version of MElt extended with dialogue-specific features  Yet the motivation of MElt's developers was first and foremost to investigate the best way to integrate lexical information extracted from large-scale morphosyntactic lexical resources into their models, on top of the training data BIBREF12   They showed that performances are better when this external lexical information is integrated in the form of additional lexical features than when the external lexicon is used as constraints at tagging time  These lexical features can also be divided into local lexical features (for example the list of possible tags known to the external lexicon for the current word) and contextual lexical features (for example the list of possible tags known to the external lexicon for surrounding words)  In particular, lexical contextual features provide a means to model the right context of the current word, made of words that have not yet been tagged by the system but for which the lexicon often provides a list of possible tags  Moreover, tagging accuracy for out-of-vocabulary (OOV) words is improved, as a result of the fact that words unknown to the training corpus might be known to the external lexicon 
Despite a few experiments published with MElt on languages other than French BIBREF12 , BIBREF40 , BIBREF41 , the original feature set used by MElt (standard and lexical features) was designed and tested mostly on this language, by building and evaluating tagging models on a variant of the French TreeBank  Since our goal was to carry out experiments in a multilingual setting, we have decided to design our own set of features, using the standard MElt features as a starting point  With respect to the original MElt feature set, we have added new ones, such as prefixes and suffixes of the following word, as well as a hybrid contextual feature obtained by concatenating the tag predicted for the preceding word and the tag(s) provided by the external lexicon for the following word 
In order to select the best performing feature set, we carried out a series of experiments using the multilingual dataset provided during the SPMRL parsing shared task BIBREF42   This included discarding useless or harmful features and selecting the maximal length of the prefixes and suffixes to be used as features, both for the current word and for the following word 
We incorporated in MElt the best performing feature set, described in Table TABREF1   All models discussed in this paper are based on this feature set 
Corpora
We carried out our experiments on the Universal Dependencies v1 2 treebanks BIBREF21 , hereafter UD1 2, from which morphosyntactically annotated corpora can be trivially extracted  All UD1 2 corpora use a common tag set, the 17 universal PoS tags, which is an extension of the tagset proposed by BIBREF43  
As our goal is to study the impact of lexical information for PoS tagging, we have restricted our experiments to UD1 2 corpora that cover languages for which we have morphosyntactic lexicons at our disposal, and for which BIBREF20 provide results  We considered UD1 2 corpora for the following 16 languages: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish  Although this language list contains only one non-Indo-European (Indonesian), four major Indo-European sub-families are represented (Germanic, Romance, Slavic, Indo-Iranian)
Context 3: Introduction
The Flickr30K dataset BIBREF0 is a collection of over 30,000 images with 5 crowdsourced descriptions each  It is commonly used to train and evaluate neural network models that generate image descriptions (e g  BIBREF2 )  An untested assumption behind the dataset is that the descriptions are based on the images, and nothing else  Here are the authors (about the Flickr8K dataset, a subset of Flickr30K):
“By asking people to describe the people, objects, scenes and activities that are shown in a picture without giving them any further information about the context in which the picture was taken, we were able to obtain conceptual descriptions that focus only on the information that can be obtained from the image alone ” BIBREF1
What this assumption overlooks is the amount of interpretation or recontextualization carried out by the annotators  Let us take a concrete example  Figure FIGREF1 shows an image from the Flickr30K dataset 
This image comes with the five descriptions below  All but the first one contain information that cannot come from the image alone  Relevant parts are highlighted in bold:
We need to understand that the descriptions in the Flickr30K dataset are subjective descriptions of events  This can be a good thing: the descriptions tell us what are the salient parts of each image to the average human annotator  So the two humans in Figure FIGREF1 are relevant, but the two soap dispensers are not  But subjectivity can also result in stereotypical descriptions, in this case suggesting that the male is more likely to be the manager, and the female is more likely to be the subordinate  rashtchian2010collecting do note that some descriptions are speculative in nature, which they say hurts the accuracy and the consistency of the descriptions  But the problem is not with the lack of consistency here  Quite the contrary: the problem is that stereotypes may be pervasive enough for the data to be consistently biased  And so language models trained on this data may propagate harmful stereotypes, such as the idea that women are less suited for leadership positions 
This paper aims to give an overview of linguistic bias and unwarranted inferences resulting from stereotypes and prejudices  I will build on earlier work on linguistic bias in general BIBREF3 , providing examples from the Flickr30K data, and present a taxonomy of unwarranted inferences  Finally, I will discuss several methods to analyze the data in order to detect biases 
Stereotype-driven descriptions
Stereotypes are ideas about how other (groups of) people commonly behave and what they are likely to do  These ideas guide the way we talk about the world  I distinguish two kinds of verbal behavior that result from stereotypes: (i) linguistic bias, and (ii) unwarranted inferences  The former is discussed in more detail by beukeboom2014mechanisms, who defines linguistic bias as “a systematic asymmetry in word choice as a function of the social category to which the target belongs ” So this bias becomes visible through the distribution of terms used to describe entities in a particular category  Unwarranted inferences are the result of speculation about the image; here, the annotator goes beyond what can be glanced from the image and makes use of their knowledge and expectations about the world to provide an overly specific description  Such descriptions are directly identifiable as such, and in fact we have already seen four of them (descriptions 2–5) discussed earlier 
Linguistic bias
Generally speaking, people tend to use more concrete or specific language when they have to describe a person that does not meet their expectations  beukeboom2014mechanisms lists several linguistic `tools' that people use to mark individuals who deviate from the norm  I will mention two of them 
[leftmargin=0cm]
One well-studied example BIBREF4 , BIBREF5 is sexist language, where the sex of a person tends to be mentioned more frequently if their role or occupation is inconsistent with `traditional' gender roles (e g  female surgeon, male nurse)  Beukeboom also notes that adjectives are used to create “more narrow labels [or subtypes] for individuals who do not fit with general social category expectations” (p  3)  E g  tough woman makes an exception to the `rule' that women aren't considered to be tough

ground_truth: the attention model, MDREA, also outperforms the best existing research results (WAP 0.690 to 0.688)

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

Please answer the questions based on the text provided below. which languages are explored?
Context 2:  Overall, the 16 languages considered in our experiments are typologically, morphologically and syntactically fairly diverse 
Lexicons
We generate our external lexicons using the set of source lexicons listed in Table TABREF3   Since external lexical information is exploited via features, there is no need for the external lexicons and the annotated corpora to use the same PoS inventory  Therefore, for each language, we simply extracted from the corresponding lexicon the PoS of each word based on its morphological tags, by removing all information provided except for its coarsest-level category  We also added entries for punctuations when the source lexicons did not contain any 
We also performed experiments in which we retained the full original tags provided by the lexicons, with all morphological features included  On average, results were slightly better than those presented in the paper, although not statistically significantly  Moreover, the granularity of tag inventories in the lexicons is diverse, which makes it difficult to draw general conclusions about results based on full tags  This is why we only report results based on (coarse) PoS extracted from the original lexicons 
Baseline models
In order to assess the respective contributions of external lexicons and word vector representations, we first compared the results of the three above-mentioned systems when trained without such additional lexical information  Table TABREF11 provides the results of MElt and MarMoT retrained on UD1 2 corpora, together with the results publised on the same corpora by BIBREF20 , using their best model not enhanced by external word vector representations —i e  the model they call INLINEFORM0 , which is a bidirectional LSTM that combines both word and character embeddings 
These results show that Plank et al 's (2016) bi-LSTM performs extremely well, surpassed by MarMoT on only 3 out of 16 datasets (Czech, French and Italian), and by MElt only once (Indonesian) 
Models enriched with external lexical information
Table TABREF13 provides the results of four systems enriched with lexical information  The feature-based systems MElt and MarMoT, respectively based on MEMMs and CRFs, are extended with the lexical information provided by our morphosyntactic lexicons  This extension takes the form of additional features, as described in Section SECREF2 for MElt  The results reported by BIBREF20 for their bidirectional LSTM when initialised with Polyglot embeddings trained on full wikipedias are also included, together with their new system FREQBIN, also initialised with Polyglot embeddings  FREQBIN trains bi-LSTMs to predict for each input word both a PoS and a label that represents its log frequency in the training data  As they word it, “the idea behind this model is to make the representation predictive for frequency, which encourages the model not to share representations between common and rare words, thus benefiting the handling of rare tokens ”
The results, which are also displayed in Figures FIGREF14 and FIGREF15 , show that all systems reach very similar results on average, although discrepancies can be observed from one dataset to another, on which we shall comment shortly  The best performing system in terms of macro-average is MElt (96 60%)  Both bi-LSTM systems reach the same score (96 58%), the difference with MElt's results being non significant, whereas MarMoT is only 0 14% behind (96 46%)  Given the better baseline scores of the neural approaches, these results show that the benefit of using external lexicons in the feature-based models MElt and MarMoT are much higher than those using Polyglot word vector representations as initialisations for bi-LSTMs 
Yet these very similar overall results reflect a different picture when focusing on OOV tagging accuracy  The best models for OOV tagging accuracy are, by far, FREQBIN models, which are beaten by MarMoT and by MElt only once each (on English and Danish respectively)  The comparison on OOV tagging between MElt and MarMoT shows that MElt performs better on average than MarMoT, despite the fact that MarMoT's baseline results were better than those reached by MElt  This shows that the information provided by external morphosyntactic lexicons is better exploited by MElt's lexical features than by those used by MarMoT  On the other hand, the comparison of both bi-LSTM-based approaches confirm that the FREQBIN models is better by over 10% absolute on OOV tagging accuracy (94 28% vs  83 59%), with 65% lower error rate 
One of the important differences between the lexical information provided by an external lexicon and word vectors built from raw corpora, apart from the very nature of the lexical information provided, is the coverage and accuracy of this lexical information on rare words  All words in a morphosyntactic lexicon are associated with information of a same granularity and quality, which is not the case with word representations such as provided by Polyglot  Models that take advantage of external lexicons should therefore perform comparatively better on datasets containing a higher proportion of rarer words, provided the lexicons' coverage is high  In order to confirm this intuition, we have used a lexical richness metric based on the type/token ratio  Since this ratio is well-known for being sensitive to corpus length, we normalised it by computing it over the 60,000 first tokens of each training set  When this normalised type/token ratio is plotted against the difference between the results of MElt and both bi-LSTM-based models, the expected correlation is clearly visible (see Figure FIGREF16 )  This explains why MElt obtains better results on the morphologically richer Slavic datasets (average normalised type/token ratio: 0 28, average accuracy difference: 0 32 compared to both bi-LSTM+Polyglot and FREQBIN+Polyglot) and, at the other end of the spectrum, significantly worse results on the English dataset (normalised type/token ratio: 0 15, average accuracy difference: -0 56 compared to bi-LSTM+Polyglot, -0 57 compared to FREQBIN+Polyglot) 
Conclusion
Two main conclusions can be drawn from our comparative results  First, feature-based tagging models adequately enriched with external morphosyntactic lexicons perform, on average, as well as bi-LSTMs enriched with word embeddings  Per-language results show that the best accuracy levels are reached by feature-based models, and in particular by our improved version of the MEMM-based system MElt, on datasets with high lexical variability (in short, for morphologically rich languages), whereas neural-based results perform better on datatsets with lower lexical variability (e g  for English) 
We have only compared the contribution of morphosyntactic lexicons to feature-based models (MEMMs, CRFs) and that of word vector representations to bi-LSTM-based models as reported by BIBREF20   As mentioned above, work on the contribution of word vector representations to feature-based approaches has been carried out by BIBREF18   However, the exploitation of existing morphosyntactic or morphological lexicons in neural models is a less studied question  Improvements over the state of the art might be achieved by integrating lexical information both from an external lexicon and from word vector representations into tagging models 
In that regard, further work will be required to understand which class of models perform the best  An option would be to integrate feature-based models such as a CRF with an LSTM-based layer, following recent proposals such as the one proposed by BIBREF45 for named entity recognition 
Context 3: 
can be used when prior beliefs about a particular social category are violated, e g  The garbage man was not stupid  See also BIBREF6  
These examples are similar in that the speaker has to put in additional effort to mark the subject for being unusual  But they differ in what we can conclude about the speaker, especially in the context of the Flickr30K data  Negations are much more overtly displaying the annotator's prior beliefs  When one annotator writes that A little boy is eating pie without utensils (image 2659046789), this immediately reveals the annotator's normative beliefs about the world: pie should be eaten with utensils  But when another annotator talks about a girls basketball game (image 8245366095), this cannot be taken as an indication that the annotator is biased about the gender of basketball players; they might just be helpful by providing a detailed description  In section 3 I will discuss how to establish whether or not there is any bias in the data regarding the use of adjectives 
Unwarranted inferences
Unwarranted inferences are statements about the subject(s) of an image that go beyond what the visual data alone can tell us  They are based on additional assumptions about the world  After inspecting a subset of the Flickr30K data, I have grouped these inferences into six categories (image examples between parentheses):
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We've seen an example of this in the introduction, where the `manager' was said to be talking about job performance and scolding [a worker] in a stern lecture (8063007) 
Many dark-skinned individuals are called African-American regardless of whether the picture has been taken in the USA or not (4280272)  And people who look Asian are called Chinese (1434151732) or Japanese (4834664666) 
In image 4183120 (Figure FIGREF16 ), people sitting at a gym are said to be watching a game, even though there could be any sort of event going on  But since the location is so strongly associated with sports, crowdworkers readily make the assumption 
Quite a few annotations focus on explaining the why of the situation  For example, in image 3963038375 a man is fastening his climbing harness in order to have some fun  And in an extreme case, one annotator writes about a picture of a dancing woman that the school is having a special event in order to show the american culture on how other cultures are dealt with in parties (3636329461)  This is reminiscent of the Stereotypic Explanatory Bias BIBREF7 , which refers to “the tendency to provide relatively more explanations in descriptions of stereotype inconsistent, compared to consistent behavior” BIBREF6   So in theory, odd or surprising situations should receive more explanations, since a description alone may not make enough sense in those cases, but it is beyond the scope of this paper to test whether or not the Flickr30K data suffers from the SEB 
Older people with children around them are commonly seen as parents (5287405), small children as siblings (205842), men and women as lovers (4429660), groups of young people as friends (36979) 
Annotators will often guess the status or occupation of people in an image  Sometimes these guesses are relatively general (e g  college-aged people being called students in image 36979), but other times these are very specific (e g  a man in a workshop being called a graphics designer, 5867606) 
Detecting stereotype-driven descriptions
In order to get an idea of the kinds of stereotype-driven descriptions that are in the Flickr30K dataset, I made a browser-based annotation tool that shows both the images and their associated descriptions  You can simply leaf through the images by clicking `Next' or `Random' until you find an interesting pattern 
Ethnicity/race
One interesting pattern is that the ethnicity/race of babies doesn't seem to be mentioned unless the baby is black or asian  In other words: white seems to be the default, and others seem to be marked  How can we tell whether or not the data is actually biased?
We don't know whether or not an entity belongs to a particular social class (in this case: ethnic group) until it is marked as such  But we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker  This gives us an upper bound that tells us how often ethnicity is indicated by the annotators  Note that this upper bound lies somewhere between 20% (one description) and 100% (5 descriptions)  Figure TABREF22 presents count data for the ethnic marking of babies  It includes two false positives (talking about a white baby stroller rather than a white baby)  In the Asian group there is an additional complication: sometimes the mother gets marked rather than the baby

ground_truth: Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish and Swedish

Bulgarian, Croatian, Czech, Danish, English, French, German, Indonesian, Italian, Norwegian, Persian, Polish, Portuguese, Slovenian, Spanish , Swedish

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

Please answer the questions based on the text provided below. Which methods are considered to find examples of biases and unwarranted inferences??
Context 3:  E g  An Asian woman holds a baby girl  I have counted these occurrences as well 
The numbers in Table TABREF22 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups  We can take one step further and look at all the 697 pictures with the word `baby' in it  If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased 
I have manually categorized each of the baby images  There are 504 white, 66 asian, and 36 black babies  73 images do not contain a baby, and 18 images do not fall into any of the other categories  While this does bring down the average number of times each category was marked, it also increases the contrast between white babies (who get marked in less than 1% of the images) and asian/black babies (who get marked much more often)  A next step would be to see whether these observations also hold for other age groups, i e  children and adults  INLINEFORM0
Other methods
It may be difficult to spot patterns by just looking at a collection of images  Another method is to tag all descriptions with part-of-speech information, so that it becomes possible to see e g  which adjectives are most commonly used for particular nouns  One method readers may find particularly useful is to leverage the structure of Flickr30K Entities BIBREF8   This dataset enriches Flickr30K by adding coreference annotations, i e  which phrase in each description refers to the same entity in the corresponding image  I have used this data to create a coreference graph by linking all phrases that refer to the same entity  Following this, I applied Louvain clustering BIBREF9 to the coreference graph, resulting in clusters of expressions that refer to similar entities  Looking at those clusters helps to get a sense of the enormous variation in referring expressions  To get an idea of the richness of this data, here is a small sample of the phrases used to describe beards (cluster 268): a scruffy beard; a thick beard; large white beard; a bubble beard; red facial hair; a braided beard; a flaming red beard  In this case, `red facial hair' really stands out as a description; why not choose the simpler `beard' instead?
Discussion
In the previous section, I have outlined several methods to manually detect stereotypes, biases, and odd phrases  Because there are many ways in which a phrase can be biased, it is difficult to automatically detect bias from the data  So how should we deal with stereotype-driven descriptions?
Conclusion
This paper provided a taxonomy of stereotype-driven descriptions in the Flickr30K dataset  I have divided these descriptions into two classes: linguistic bias and unwarranted inferences  The former corresponds to the annotators' choice of words when confronted with an image that may or may not match their stereotypical expectancies  The latter corresponds to the tendency of annotators to go beyond what the physical data can tell us, and expand their descriptions based on their past experiences and knowledge of the world  Acknowledging these phenomena is important, because on the one hand it helps us think about what is learnable from the data, and on the other hand it serves as a warning: if we train and evaluate language models on this data, we are effectively teaching them to be biased 
I have also looked at methods to detect stereotype-driven descriptions, but due to the richness of language it is difficult to find an automated measure  Depending on whether your goal is production or interpretation, it may either be useful to suppress or to emphasize biases in human language  Finally, I have discussed stereotyping behavior as the addition of a contextual layer on top of a more basic description  This raises the question what kind of descriptions we would like our models to produce 
Acknowledgments
Thanks to Piek Vossen and Antske Fokkens for discussion, and to Desmond Elliott and an anonymous reviewer for comments on an earlier version of this paper  This research was supported by the Netherlands Organization for Scientific Research (NWO) via the Spinoza-prize awarded to Piek Vossen (SPI 30-673, 2014-2019) 

ground_truth: spot patterns by just looking at a collection of images, tag all descriptions with part-of-speech information, I applied Louvain clustering

Looking for adjectives marking the noun "baby" and also looking for most-common adjectives related to certain nouns using POS-tagging