EGAN: Generatives Adversarial Networks for Text Generation with
Andres Pautrat-Lertora, Renzo Perez-Lozano and Willy Ugarte
Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru
GAN, Text Generation, NAO.
In these last years, communication with computers has made enormous steps, like the robot Sophia that sur-
prised many people with their human interactions, behind this kind of robot, there is a machine learning model
for text generation to interact with others, but in terms of text generation with sentiments not many investi-
gations have been done. A model like GAN has opportunities to become an excellent option to attack this
new problem because of their discriminator and generator competing for search the optimal solution. In this
paper, a GAN model is presented that can generate text with different emotions based on a dataset recompiled
from tweets labeled with emotions and then deployed in an NAO robot to speak the text in short phrases using
voice commands. The model is evaluated with different methods popular in text generation like BLLEU and
additionally, a human experiment is done to prove the quality and sentiment accuracy.
Text generation is a stringent computational task
that last years have has many utilities like improve-
ments in virtual assistants and Human-robot interac-
tion(HRI) with more elaborate dialogues. However,
the text generated is not totally accurate and presents
not realistic phrases as (Huszar, 2015) says, this in-
cludes that if the text generated is more extensive the
problems will be more frequent. Even though the ob-
jective is the human interaction, the sentiments are
not usually considered which in a real conversation
is usually a really important topic. Nowadays there
are many good models with the main function of text
generation, some of these can be transformers, that
don’t consider a sentiment but can be modified, or the
best known model GPT3 that have different uses.
Many people have incorporated voice assistants
as a tool in their daily lives to control appliances,
play multimedia products, create notes or reminders,
etc. The relevance can be noticed in (Newman, 2019)
where indicates that on 2018 in the United States 14%
of adults regularly use one of these devices, while in
the United Kingdom it is 10%. Also, the addition of
sentiments will bring a better experience to the user,
can be more personal and have benefits as explained
like recognizing the feeling and answering some-
thing depending on the user emotion.
The generation of coherent text is always a hard
task, all languages have a structure, grammatic and
correct order to be understood, furthermore, inside
every sentence, many topics can be mentioned and it
needs logic to don’t jump from one to other without
context. For that, a good text generation model needs
to catch many attributes of the already existing sen-
tence and then process them to generate more content.
Adding an emotion to the generation makes this work
even more problematic, there will be more attributes
to process and each word has to consider the senti-
ment of the previous text to continue the task. Origi-
nally the GAN architecture just have 2 models inside
the generator and the discriminator to evaluate how
realistic the samples of the generation are, but in our
model, there is another parameter, the sentiment, that
needs to be evaluated, for this, we use a third model
for the task and specialize that model on sentiment
analysis and leave the first one for its original task.
There are different solutions made for the text
generation that uses a model based on LSTM (Long
short-term memory), like (Cai et al., 2021), which is a
variation of an RNN (Recurrent neural network), this
is used because this kind of network work with a se-
Emotion AI will personalize interactions - Gart-
ner -
Pautrat-Lertora, A., Perez-Lozano, R. and Ugarte, W.
EGAN: Generatives Adversarial Networks for Text Generation with Sentiments.
DOI: 10.5220/0011548100003335
In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR, pages 249-256
ISBN: 978-989-758-614-9; ISSN: 2184-3228
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
quence of data instead of single data values, making
this perfect to generate new content from a context
like a sentence, some models that use this method
are (Li et al., 2018). But the model mentioned be-
fore only can work if they have a big amount of data
which is not always the scenario, so the GAN (gener-
ative adversarial networks) are used because can have
a good performance with a smaller dataset which can
be seen in models like (Rizzo and Van, 2020). On the
other hand, some investigators have tried to go further
away to make a generative model and add a sentiment
label (Li et al., 2018), with the time many works are
done to improve the text generation to make it indis-
tinguishable from a real text from a human.
We develop a GAN based model, which is com-
posed for two discriminators that are based in a con-
volutions network, one to recognize if the text evalu-
ated is true or fake and one that determines the emo-
tion of the text by extract each characteristic of the
sentence. Finally, a generator model based on LSMT
trains with the output of the other two models trying
to make them fail with the text generated.
Our contributions are as follows:
We develop a multilabel text generation with sen-
We build a implementation of a GAN model for
text generation with sentiments.
We make a analysis and comparison of our model
with other similar.
In Section 2, solutions with similar subjects will
be compared to ours. In Section 3 we explain the
different architectures and algorithms related to GAN
models and text generation and then we present the
structure of the algorithm developed and the contribu-
tions made. In Section 4, the experiments and results
will be explained and detailed. Finally, the conclusion
will be presented in section 5.
Many projects have proposed different solutions us-
ing their own implementation of GAN with details
that make unique models. We use some models as
inspiration for our implementation some of these can
be found next.
The model proposed on (Wu and Wang, 2020)
named TG-SeqGAN is based on SeqGAN(Yu et al.,
2017) with an addition of Truth-Guilded method to
make it closer to the real data. This model includes
an initial state where a transfer model and a cost func-
tion, defined as the distance between the generated
text and the real text, are used to find the next value
quicker with a CNN model for extract all character-
istics and context of words. In our model, we use a
CNN model as the discriminator, and an additional
classificator model is added to evaluate the sentiment
and generate sentences with a specific input.
In (Li et al., 2018) a novel framework is proposed,
where the author train a GAN model for text gen-
eration with categories based on SeqGAN(Yu et al.,
2017), with a LSTM as generator and a RNN model
for the discriminator for veracity and clasificator for
the category. Inspired by this work, we have imple-
mented an LSTM model as our generators and sep-
arated the discriminator and classificator but for the
last two models, we have used a CNN to obtain more
accurate predictions.
The model CD-GAN for text generation proposed
on (Yan et al., 2021) use an LSTM generator and a
CNN as a discriminator-augmented, this means that
their discriminator evaluates each word of the sen-
tence individually and founding the incoherence in
the sentences with the objective of avoiding the pre-
training. This is a novel technique, the CNN used has
a great performance on sequence classification but we
consider that the individual word analysis is too com-
plicated and doesn’t have enough benefits so we use
only one classification for the whole sentence.
EmoSen (Firdaus et al., 2020) is a framework end-
to-end with the job of generating dialogs, and con-
trolling the sentiment as happy, sad, angry, etc, and
the feeling as positive, negative, and neutral, this is
approached by analyzing reference text, audio, and
visual helpers. This model can manage a lot of sen-
timents, feeling,s and contexts because of the used
train dataset Sentiment and Emotion aware Multi-
modal dataset (SEMD), for this the dialogue system
is robust. Compared with our proposed model we
use the controlled training for sentiments on the text,
but the feeling as positive, negative, and neutral is
not considered like this project instead of these, we
present six feelings, enjoyment, sadness, love, anger,
surprise and fear..
Finally, on the work made on (Chen et al., 2020),
CTGAN model is proposed based on SeqGAN, it has
the addition of being able to generate text with vari-
able length and a label of sentiment as positive, nega-
tive, and neutral. Also, an algorithm of word replace-
ment is used to guarantee the quality of the generated
text in a specific context. In this project, the discrimi-
nator is used to evaluate if the text is real or generated
and if the sentiment is accurate, on our project these
functions are separated into two models to make each
one specialized on evaluate if the text is real or fake
and if the sentiment is accurate, since that if use a one
model that describe if is real or fake and add the sen-
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval
timents will have many parameters that It will loss in
the training.
In this section we present the main concepts and ar-
chitecture for our proposal.
3.1 Preliminary Concepts
The text generation with GAN models for human and
robot interaction has many challenges in the devel-
opment process. The adaptation and modification of
GAN models to generate text classified with a senti-
ment label is the main problems, but this brings other
problems like how to generate the text, how to iden-
tify the sentiment, and make the interaction with the
user. In this section, the necessary background will be
3.1.1 Text Generation
Text generation is a subarea of natural language pro-
cessing, so it acquires knowledge of the computa-
tional area of language and artificial intelligence, the
objective is to generate coherent and readable text.
To make this task easier for the computer the words
use to be tokenized giving each word on a dictionary
a corresponding number and transforming the inputs
sentences into a new format like one hot where an ar-
ray of the size of the dictionary is replaced with ones
and zeros depending on the words on the sentence.
There are many models that can be used for this objec-
tive, one that performs well in many projects like (Cai
et al., 2021), are the long short-term memory (LSTM)
which can process many data at the same time, which
means that can manage multiple words at the same
time. One of the main characteristics of LSTM is the
states it remembers over time and uses this informa-
tion for the followings generations, this is done by im-
plementing loops and gates within the model, as can
be seen in Fig. 1.
3.1.2 Sentiment Analysis
According to EmoShape
, the sentiment analysis
refers to the fact that the machine can understand the
feeling that the user wants to express, this can be by
image recognition, speech recognition, or text analy-
sis. When it comes to a text format, the task becomes
a natural language processing job, whereas machine
EmoShape: Emotion Synthesis for Metaverse - https:
Figure 1: LSTM model (Zia and Zahid, 2019).
Figure 2: Sentiment analysis in training own elaboration.
learning or deep learning techniques are needed. As
mentioned when text has to be processed tokenization
is really important and LSTM models make a good
job like paper (Hochreiter and Schmidhuber, 1997),
but other options like gated recurrent unit (GRU) and
convolutional neuronal networks (CNN) are good too
like (Liu et al., 2020) where a CNN model is used to
identify the sentiment on a text other model that for
use this criterion is SentiGAN (Wang and Wan, 2018)
in where use multiple generators and one multi-class
discriminator, to address the above problems. Since,
yours multiple generators are trained simultaneously,
aiming at generating texts of different sentiment la-
bels without supervision.
The generator model passes the words in integers
and the classifier identifies which of the sentiments
they belong to. Then the words considering the labels
re-enter the generator until they can enter the genera-
tor and the classifier consecutively Fig. 2.
3.1.3 Generative Adversarial Networks(GAN)
GAN models were proposed on (Goodfellow et al.,
2014) and are based on having two models that com-
pete with each other, a generator of content, and a
discriminator that is responsible for checking if the
content evaluated is generated by the first model or is
EGAN: Generatives Adversarial Networks for Text Generation with Sentiments
Figure 3: GAN arquitecture (Goodfellow et al., 2014).
real data from the dataset used, the structure of this
first architecture is presented on the Fig. 3. Goodfel-
low develops the GANs to generate images, but be-
cause of their good results these models gain a lot of
popularity and other researchers start using this model
for the same purpose or adapting it to other investiga-
tion fields. One of the first investigations published
that approached the GAN model for the text gener-
ation was (Firdaus et al., 2020) where an adaptation
of the GAN model for images is used to generate se-
quences instead of images, making possible to gener-
ate text or music composition. Other paper have use
gan and is DGSAN (Montahaei et al., 2021) in where
is a model that upgrade of before model since that fix
the gradient step problem and used 2 model in same
network and subsequently generate text, obtaining re-
sults greater than 90 percent in BLEU 3.
3.1.4 Human Robot Interaction (HRI)
This refers to the study of any interaction between hu-
mans and robots. In the last years, the interactions
with robots have been improved, like the example of
Sophia where the objective is o make a robot simi-
lar to the human appearance to interact with humans.
One area of the HRI is verbal communication that can
be translated into text, here artificial intelligence is
needed to create coherent and understandable inter-
actions. Interaction between machines and humans
a better experience for the user can be reached by
adding emotional analysis and proper response to his
feeling (see Fig. 4).
3.2 Main Contribution
In this work of text generation based on sentiments,
we proposed a GAN model with a discriminator
model to evaluate the veracity of the text and clas-
sifier model to evaluate the sentiment of the sentence
with the objective of training a text generator model
Figure 4: NAO robot interaction.
that can receive a sentiment and generate a coherent
sentence corresponding to it.
3.2.1 GAN for Text Generation
In this section, we explain, the structure and func-
tion of the different models used inside the GAN
which are the generator, discriminator and classifica-
tion. This GAN model is based on different models
as CatGAN (Liu et al., 2020) and SeqGAN (Firdaus
et al., 2020), adding a second discriminator for sen-
timent analysis in the first and tuning the model pa-
rameters and structures to obtain better results in both
cases. For other hand, this structure uses thresholds
to train the discriminator with sentences with noise to
make a better discriminator and classificator
Before working with a sentence, each one is trans-
formed with a dictionary from words to integers and
are codified with one hot method to be easier to man-
age them inside the models.
Discriminator. The objective of this model is to be
able to differentiate between generated sentence and
a original ones, then use the results a make a train
step on the generator. Once this is done, the generator
will be able to generate better sentences to make the
discriminator wrong.
This model is based on a CNN, the model struc-
ture is presented in the Fig. 5. The first layer is an
embedding layer as usually made on natural language
processing(NLP), this help with the large input vector
before the one-hot codification to be easier to manage
for the model. Next, we use four 2D convolutional
layers separately with one input channel and 300 out-
put channels to extract features from the sentence and
then make a max pooling to each. The result is con-
catenated and applied on a linear layer of input and
output size of 1200 to evaluate the features extracted
by the convolutional layers. Then an activation func-
tion is applied where x is the result of the previous
linear layer and the function f (x) can be expressed as
f (x) =
× max(0, x)(1
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval
Figure 5: Discriminator Structure own elaboration.
Figure 6: Generator Structure own elaboration.
Classifier. The purpose of this model is to evaluate
sentences and classify the sentiment present in them,
and this information is used to train the generator too.
This model has a similar function to the discriminator
but with more labels, although both tasks of the dis-
criminator and classifier can be done by one model,
separating these tasks into single specialized models
ensures the efficiency of each one on its work and re-
sult in a better train for the generator model.
Because of the similar objective between the dis-
criminator and the classification the structure used is
the same, just variation the result depending on the
number of sentiments that are being evaluated.
Generator. The generator is the main model to
train, the discriminator and classificator work is done
to help with the training on the generator. This model
has the job of receiving a sentiment and part of the
sentence if it already exists and generate the next word
on the sentence or a dot to finish it. The objective of
the training is that this model can generate coherent
sentences that convey the feeling given.
The model is based on an LSTM model because
of its efficiency in generating text, architecture devel-
oped can be found on Fig. 6. Like the previous mod-
els, the first layer is an embedding model with the in-
put size equal to the dictionary size and an output of
32. Then the label of sentiment is added and the in-
formation is passed to the LSTM layer with an input
and output size of 32. Finally a linear layer an array
with the size of the dictionary and a softmax function
determinates the next word of the sentence.
Training. For the training, on each step of the train-
ing the sentences are transformed from words to inte-
gers with a dictionary of words as X = [x
, x
, ...] and
a one-hot encoding transforms it to an array of the
size of the dictionary with ones and zeros depending
on the words on the sentence. Before starting with the
GAN training each model is pre-trained with only the
real data, this gives the models an initial state to not
start generating and evaluating sentences randomly.
The final GAN train has 4 steps, first, a sentence is
generated, second, the discriminator and classifica-
tor evaluate the sentence, third the fit of the 3 mod-
els is done and finally the discriminator and generator
train with a real sentence. For the construction of the
model, Pytorch is used, with a learning rate of 0.01
for the pre-training, GAN train of 0.0001, and a batch
size of 8. The training was performed with 150 pre-
train epochs and 2000 GAN train epochs, that was
done on 3 separate trains of 4 hours each with a Tesla
3.2.2 Connection Nao Robot
For the connection with the NAO robot we needed
to use its specific IP on the local red to send it com-
mands with the library Naoqi. This library contains
all function that can be used with the NAO robot
and is only available in Python 2, but our model was
developed on Python 3, so several connected scrips
were made. In the first step, a subscription to AL-
TextToSpeech was necessary for the robot to say the
instructions, and ALSpeechRecognition to recognize
the user voice of what sentiment is indicated. In the
second step, the sentiment is sent to Python 3 to gen-
erate the sentence in English with the correct label,
in this part, it is necessary to clarify that we have to
present the sentences in Spanish due to academic is-
sues of our institution, so we used a model to trans-
late but for security, the red where the NAO robot is
connected has no internet so the use of translation
with APIs was not possible, for this reason, We use
the Argos model for offline translate from English to
Spanish, this model used help of OpenNMT toolkit,
SentencePiece for tokenization, Stanza for sentence
boundary detection
. In the final step, the translated
text is sent to a script on Python 2 and uses ALText-
ToSpeech to interpret the phrase generate.
4.1 Experimental Protocol
To recreate the experimentation process we will men-
tion the hardware, dataset, parameters, and the valida-
tion of the results of our project.
Open Tech -
EGAN: Generatives Adversarial Networks for Text Generation with Sentiments
4.1.1 Development Environment
The model training environment used as the main
platform was Google Colab with the Pro subscription,
this tier of the platform was mainly needed for the ex-
tended runtimes compared to the Free version, this
service offers us a Tesla T4 or a Tesla P100 GPU and
25GB of RAM.
4.1.2 Dataset
The used dataset was the emotions dataset for NLP
found on Kaggle, this one has a long recompilation of
sentences labeled with one of 6 emotions and a total
of 16000 sentences. The dataset was pre-processed,
symbols were deleted, sentiments were separated into
groups of 700 sentences to make the same number
on each sentiment and the number of sentences was
reduced due to the resource given by Google Colab
Pro were not enough to make the training with all the
4.1.3 Models Training
The model was developed using Pytorch and the train-
ing was realized on Google Colab Pro GPU, this one
has a maximum runtime of 24 hours and sometimes
less, to approach this issue every 20 epochs the state
of the model was saved, and if the runtime ends a new
one was generated manually, load the last state and
continue with the training, finally the train was done
on around 48 hours. For the parameters of the model,
we train 500 epochs with a batch size of 8, vocabu-
lary size of 15213, and generator, discriminator, and
classificator learning rate of 0.0001.
4.1.4 Testing Environment
We have developed two environments for testing and
validation, the first is a user interface (UI), and the
second is an interaction with de NAO robot. The UI
is a simple environment where the user can select a
sentiment and generate a sentence based on it, then a
button can translate the English text to Spanish or vice
versa. The NAO robot interaction is the main testing
environment where it makes a presentation of the in-
teraction and tells the user to say a sentiment, next the
robot sends this sentiment to the model to generate 10
sentences, these sentences are classified with the text
discriminator from the GAN which tell us which one
is the more realistic one, finally, the selected sentence
returns to the robot to say the phrase.
Emotions dataset for NLP -
4.2 Results
We have used BLEU(Papineni et al., 2002) to val-
idate the text quality of the sentences generated by
our model, comparing 48 example sentences with the
16000 of the dataset we obtain the quality of the text
generated. This metric use the number of words to
compare sentences, the metric will be more demand-
ing if this amount of words is higher. We evaluate our
models with BLEU-1, BLEU-2, BLEU-3, and BLEU-
4, where the number means the number of words
used, the result of this metric can be found in the ta-
ble 1.
Another metric that we have used is Jaccard which
evaluates 2 groups of data to compare the similarity of
these ones, we compare the 48 examples generated to
the 16000 sentences of the dataset as on BLEU and
obtain 0.0966 as shown in table 3, this means that the
similarity is pretty low to the dataset. This metric is
usually presented with BLEU because an overfitted
model can generate the same sentences as the dataset,
which means BLEU will be really high, so Jaccard
help to discard that a good score is the result of over-
Other metrics were considered to be used but
many were discarded because they considers parame-
ters that our model was not supposed to accomplish.
One of these metrics was METEOR (Banerjee and
Lavie, 2005), this is an improvement of BLEU fo-
cused on evaluating text translation, its bases on eval-
uating the matching unigrams considering the surface
forms of the sentence. This makes the similarity of
the generated and evaluation text important, but in our
model, we don’t want a big similarity with the dataset
so our results are bad on this metric because is focused
on novelty, not similarity. Another metric we con-
sider was ROUGE (Lin, 2004) which is used to eval-
uate text summarization and translation and is based
on counting the overlapped n-grams or sequences of
words, this makes the similarity of the texts evaluated
really important and this is not relevant for our model
as explained before.
4.3 Discussion
As shown in table 2 our model presents better perfor-
mance on BLEU metric than some similar implemen-
tations but worse in other cases, for this is important
Table 1: BLEU Metrics.
Metrics BLEU-3 BLEU-4 BLEU-5
EGAN .8127 .6138 .4574
KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval
too to compare the Jaccard score too in table 3, so
we can notice that despite the higher score on BLEU
the text of their model is more similar to the dataset
used by them. Our model presents a great alternative
to other project implementations despite can presents
lower quality text these is more novelty.
On the found results we have the DGSAN
model (Montahaei et al., 2021) that is one of the mod-
els with the best results on the benchmarking shown
on table 2, it has better results than us on BLEU-3 and
BLEU-5, and this quality difference can be noticed on
the table 3
Next, on table 4 text generated by different mod-
els are presented. Comparing the text shown we can
notice some of them are not too coherent. For exam-
ple, in the case of the model WRGAN they present a
good text coherence in the reading, our text has less
coherence but it’s longer than WRGAN examples.
Also, we have used the NAO robot to make test-
ing and validation, as a proposition of making bet-
ter the interaction for the user we make the robot say
an introduction and then received a voice command
telling a sentiment so the model can generate sen-
tences with it and finally answer with that sentence.
We make some surveys where with a video the re-
spondents evaluate from 0 to 5 the quality of the in-
teraction, from this the average score was 3.67 so we
can conclude that the interaction with the NAO robot
with the implementation of our model was good.
Also, continue with the survey we have the qual-
ified results on the feelings of the generated from the
model shown on table 5 where the sentences of Sad-
ness and Surprises are the two feelings that users per-
ceive. On the other hand, analyzing the coherence of
Table 2: BLEU Metrics.
Metrics BLEU-3 BLEU-5
EGAN(ours) .813 .457
SeqGAN .807 .419
DGSAN .945 .728
DoubAN-Full .095 .056
WRGAN .634 .303
Table 3: Jaccard Metrics.
Metrics Jaccard
EGAN(ours) .097
SeqGAN .140
DGSAN .254
Table 4: Text Comparation of differents models.
Model Text Generate
I go through the time i had too
much more and feel that she
asked why you feel is
I dont feel apprehensive and ap-
prehensive among my feelings
that i m feeling reluctant to post
I dont feel extremely worthless
feeling so apprehensive among
a bit
I was driving my van to work
one day
She bought some new books
He saw some band members
What did the appletalk system
What is the immune system of?
Where did the grand canal oc-
Could use a little more human-
ity and delight
So boring and meandering
A pleasant, but it’s also ex-
tremely effective
Table 5: Sentiment accuracy by survey.
Anger Sadness Love Surprise
22.22 66.67 38.89 66.67
sentences with users indicates that 36 percent is bad,
other 25 percent think that is normal and finally the
38.89 percent think it’s excellent how to shown on ta-
ble 6.
In conclusion, the diversity and quality of the text
its really important to validate the results o text gen-
eration, metrics like BLEU and Jaccard can be really
useful in these cases.
EGAN: Generatives Adversarial Networks for Text Generation with Sentiments
Table 6: Coherence accuracy by survey.
Bad Normal Excellent
36.11 25 38.89
Through the development of the project, the analysis
of the metrics, and the results found in the other mod-
els, we concluded that our model has good text gener-
ation results, but it needs a high processing power to
be trained. For this limitation, the model parameters
were not the desired ones and that can be the cause
for some incoherence in the generated text.
The CNN and LSTM models have provided a
good performance on the GAN architecture for the
text generation with sentiments. A benefit of using
convolutional networks is that they are capable of fea-
ture extracting, this help to be more precise on the
discriminator and classificator work. In the case of
the LSTM generator, due to the information saved on
each interaction on the generation, the text result has
good coherence and quality.
A good upgrade to this work that can be done in
the future, is the exchange of the internal models, sim-
ilarly to GPT3 based models (de Rivero et al., 2021).
Despite the good performance it presented, this can be
improved, for example, by replacing the LSTM gen-
erator with a transformed-based generator or transfer
learning from a CNN (Rodr
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