MCCD: Generating Human Natural Language Conversational Datasets
Matheus F. Sanches
, Jader M. C. de S
, Allan M. de Souza
, Diego A. Silva
, Rafael R. de Souza
Julio C. dos Reis
and Leandro A. Villas
Institute of Computing, University of Campinas, S
ao Paulo, Brazil
Natural Language Processing, Data Wrangling, Data Acquisition, Human Conversation, Model Learning,
In recent years, state-of-the-art problems related to Natural Language Processing (NLP) have been extensively
explored. This includes better models for text generation and text understanding. These solutions depend
highly on data to training models, such as dialogues. The limitations imposed by the lack of data in a specific
language significantly limit the available datasets. This becomes worse as intensive data is required to achieve
specific solutions for a particular domain. This investigation proposes MCCD, a methodology to extract human
conversational datasets based on several data sources. MCCD identifies different answers to the same message
differentiating various conversation flows. This enables the resulting dataset to be used in more applications.
Datasets generated by MCCD can train models for different purposes, such as Questions & Answers (QA) and
open-domain conversational agents. We developed a complete software tool to implement and evaluate our
proposal. We applied our solution to extract human conversations from two datasets in Portuguese language.
Pre-trained models are considered the backbone of
several modern NLP systems, as they are one of the
most prominent models at the moment (Qiu et al.,
2020). These models often rely on large amounts of
data to be trained (Qiu et al., 2020). Furthermore, data
used during the training stage directly affects the qual-
ity of models and the relations and biases learned. In
this sense, high-quality datasets are essential to devel-
oping human-like NLP systems (Bansal et al., 1993)
(Mehrabi et al., 2021).
Some of the largest datasets are available pri-
marily in English. They tend to be less moderated
and may present negative biases as they grow. The
lack of moderation occurs due to high costs and the
time needed to be performed. The use of moderated
data sources may significantly increase the quality of
the final dataset. In this context, it lacks adequate
methodologies to create conversational datasets to al-
low standardization of several stages performed dur-
ing the identification of conversations and generation
of a novel dataset.
State of the art language models led to remarkable
progress in NLP tasks (Brown et al., 2020). However,
given the challenges of obtaining massive datasets of
unlabeled text from the Web, there is still a problem in
creating conversational applications relying on such
models. Despite the importance of data for models, it
is hard to find complete datasets in multiple languages
simultaneously. The Web is an essential source of
texts as several websites are available in multiple lan-
guages, where there are many sites in different lan-
guages. However, in several languages, such as Por-
tuguese, the total volume of data ready to be used is
limited. If we add more constraints, such as informal
speech or conversations with at least three turns, there
is no available conversational dataset of such nature.
Creating conversation datasets from Web sources
is a challenge because usually there is more than one
way to answer each message, regardless of its lan-
guage. We observed a gap in the literature regard-
ing solutions to create conversational datasets. It af-
fects consumers of pre-trained language models be-
cause it is unknown the influences of pre-training data
on their systems. In this context, our investigation
addresses the following research question: how to
create conversational datasets using existing Web
data in place? This includes data from dialogues
with different language aspects, i.e., formal, informal,
slang, and internet abbreviations. These are essential
to training models with different ways to understand
Sanches, M., C. de Sá, J., M. de Souza, A., Silva, D., R. de Souza, R., Reis, J. and Villas, L.
MCCD: Generating Human Natural Language Conversational Datasets.
DOI: 10.5220/0011077400003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 2, pages 247-255
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
messages and communicate with distinct audiences.
This article proposes a new source-agnostic
methodology for generating conversational datasets,
called Methodology for Creating Conversational
Datasets (MCCD). The results obtained through our
methodology allow the creation of a unique dataset.
On this basis, it is possible to train models with lan-
guage modeling techniques or even segment the inter-
action between two or more humans to train models
with different answers to the same questions.
In our solution, we implemented a software tool
that instantiates the methodology. The software is
fed with data sources from online Web forums and
automatically generates conversational datasets. We
conducted a case study in which the software tool
was used to acquire, anonymize, clean and identify
conversations from data obtained from Web forums.
Our study implies three main contributions: A novel
methodology to generate conversation datasets in an
automatic way; a developed completed software tool
for mining data from online forums (implementing
the methodology); and two completed datasets in
Portuguese language generated from the application
of our tool. We centralized the available tool and
datasets into a single GitHub repository
The remaining of this article is organized as fol-
lows: Section 2 presents the background, includ-
ing relevant concepts and related studies. Section
3 presents the proposed methodology. Section 4
presents the tool implemented using the proposed
methodology. Section 5 presents two datasets gener-
ated with the tool presented. Section 6 discusses the
achievements, contributions, and limitations of this
work. Section 7 concludes this work.
Several natural language applications rely on textual
data for training and execution. We consider three
main applications to use our generated datasets from
the application of our proposal: language modeling,
word embedding, and dialogue agents.
Language modeling is the process of predicting
the chance of a specific sequence of words appear-
ing in a determined sentence. Natural Language (NL)
models that generate text as output perform language
modeling as part of the training phase. Some state-of-
the-art NLP models were trained using only language
modeling techniques, such as Bidirectional Encoder
Representations from Transformers (BERT) (Devlin
et al., 2018) and Generative Pre-Training Transformer
2 (GPT-2) (Radford et al., 2019). Although the pro-
cess was slightly different between these two mod-
els, the goal of predicting a word was similar. BERT
was fed with a sequence of words, with 15% of the
words masked, and it should output the correct se-
quence without masked words. GPT-2 was fed with a
sequence of words and predicted the next word to that
Word embedding represents words and phrases
digitally, as a list of numbers. There are different tech-
niques to create this representation, each with advan-
tages and disadvantages. One of the most important is
ELMo (Peters et al., 2018), where the representation
depends on the context, which means that the same
word may have a different representation according to
the context being used. ELMo achieves state-of-the-
art results in different tasks, such as sentiment classi-
fication and QA.
Dialogue agents are applications designed to in-
teract with real users, having conversations with natu-
ral language. This application faces a variety of chal-
lenges while interacting with real users. For instance,
during conversations, humans may refer to out-of-
domain concepts. They can also use metaphors, irony,
or rely on interlocutors, common sense, or general
knowledge. However, in a dialogue, both parties
need to be active in many turns, and it is context-
dependent: concepts introduced at the beginning of a
conversation may be referred to in different moments
(Traum, 1999).
The data used has a crucial role in the three NL ap-
plications presented. Data quality directly influences
the results as these applications require data to train
and refine their underlying classification models. In
the following, we present a list of a few problems that
may occur in this context.
Language Modeling: Trained models using data
with problems can insert bias into the model (Wolf
et al., 2017);
Word Embedding: This technique may create
similar representations for words that do not have
any common characteristics;
Dialogue Agents: If this application is trained on
low-quality data, agents may not be able to deal
with different situations or can incorporate prob-
lems related to language modeling or even word
A large amount of the available written text, such
as the articles at Wikipedia, can be used to perform
Language Modeling and Word Embedding. How-
ever, this text may not be enough to train a Dialogue
Agent. In order to tackle this, we create conversation
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Conversational datasets are composed of data
about text exchanged between two or more agents,
where ideally, these agents are humans. Conversa-
tions are composed of turns, where one agent re-
sponds to the other. Each response considers the lat-
est messages, the conversation context, and even the
main topic related to the data source.
We conducted an exploratory literature review to
reach key studies correlated to our investigation. One
of the central related studies found was the dataset
Brazilian Portuguese Web as Corpus (BrWaC) (Wag-
ner Filho et al., 2018). This dataset was constructed
with a crawler acquiring different web pages in Por-
tuguese. Although this is one of the largest datasets
in Portuguese, there are important characteristics to
be taken into account to use this dataset, such as its
data varying from highly informal to highly formal
and opinion pages, which may create bias to mod-
els using it. The BrWaC dataset lacks conversational
data, as most of its pages are only informative pages,
such as news and sales. Similar, there is Common
Crawl (Smith et al., 2013), which is a dataset about
web pages that is constantly updated. Due to the sim-
ilarity with the BrWaC, the Common Crawl faces the
same challenges and limitations.
The ubuntu dialogue corpus(Lowe et al., 2015) is
an English dataset based on conversations obtained
through a crawler used on operating systems forums.
This dataset was used to construct a multi-turn con-
versational dataset. To prove their hypotheses about
using the dataset, they used an Recurrent Neural Net-
work (RNN) to select the best response for each turn.
This dataset was complete enough to present good re-
sults in the test scenarios. Nevertheless, it presents
a strong bias about technology theme questions and
may need a large fine-tune processing or a comple-
mentary dataset during training to be used in other
domains. This dataset is a Human-to-Human (H2H)
The MultiWOZ dataset (Budzianowski et al.,
2018) is a popular English dataset that imitates a con-
versation between a user talking to a virtual assis-
tant, where the user requests information about places
and services, and requests the virtual assistant to book
reservations at different places and times. This dataset
follows the Wizard-of-Oz approach (Kelley, 1984)
and uses crowd workers to construct the task-oriented
dialog. Although this dataset is only 10k dialogues,
it is fully annotated, significantly increasing its rele-
vance. This dataset is a Human-to-Machine (H2M)
synthetic dataset.
Corporations are maintaining datasets and compe-
titions to promote the development of specific areas.
Microsoft is currently maintaining a competition on
Task-Oriented Dialog Systems, which periodically re-
leases machine-generated dialogues (Williams et al.,
2016). The main goal of this challenge is to keep track
dialogue state at each turn, such as the user’s goal.
The data used in this challenge is also H2M.
Li et al. (Li et al., 2017) created a multi-turn dia-
log about daily life for everyday conversations in En-
glish. This dataset was created artificially by humans
writing the conversations; the conversations cover a
large variety of areas, such as sports, weather, mood,
transportation, and more. This dataset simulated a
conversation H2M and was artificially created. Simi-
larly, Byrne et al. (Byrne et al., 2019) created a simi-
lar dataset, but for six specific domains.
Although there are different datasets available,
we found limitations to where each dataset can be
used. This is due to different limitations, such as lan-
guage, variables included, size, processing aspects,
and more. In our study, we tackle specifically the lack
of conversational H2H datasets, in which the ubuntu
dialogue corpus is the closest one. The main limi-
tation of this dataset is how the message references
work in the data source, which results in the lack of
different answers to the same question. Our MCCD
methodology (cf. Section 3) defines how to select
data sources with proper message references to create
complete conversation datasets with multiple answers
to the same message. Table 1 presents an example of
multiple messages replying to the same message.
Table 1: Example of multiple answers to the same message.
ID Answering Content
1 -
What is the best route to go
from A to B?
2 1
The fastest way is through a
straight line.
3 1
You can go straight for a few
blocks and turn right.
4 2
Than you! I will follow your
We present our proposed methodology with the de-
signed stages to create conversational datasets with
all possible conversation flows. The identification of
conversation flow is the process of identifying all ram-
ifications an online conversation may have based on
the message references. Different conversation flows
may start and finish with the same messages, but the
messages exchanged are different. This allows us to
create a QA dataset.
MCCD: Generating Human Natural Language Conversational Datasets
MCCD was designed to use any data source dur-
ing the data acquisition stage. As a result of following
each stage in our methodology, the final output con-
templates a specific data structured, organized, and
cleaned in a specific way to allow future researchers
to use it for training NLP models.
The main goal of our methodology is the gener-
ation of conversational datasets. The results gener-
ated include a clear, structured, and chronologically
ordered sequence of text messages exchanged among
users who wrote online forum messages.
We defined how to structure messages among
users from the data source (Web forum) under pro-
cessing. We described recommendations to be exe-
cuted during the cleaning process of messages, sug-
gesting replacements for specific situations presented
in texts. This includes identifying images or emojis
and overwriting them with pre-defined tags.
Our methodology with the proposed stages applies
to any language, regardless of the used character set,
as long as the storage method chosen during imple-
mentation allows it. To this end, requirements need to
be fulfilled for the processing stage to reach very rich
We designed the methodology to have its require-
ments as simple as possible. Their primary purpose is
to ensure that the resulting dataset contains the writ-
ing marks from a data source, such as formal or infor-
mal writing. The solution must be suited to identify a
conversation’s possible flows. We present the require-
ments in the following:
Human Requirement: The textual data obtained
must have been written by humans talking to each
other or answering a specific topic (open issue).
References between Messages: The step of con-
versation identification in the processing stage re-
quires the messages in the data source to reference
zero or more messages as replying.
The human-to-human textual data ensures that the
generated dataset contains specific characteristics that
humans insert in writing texts by using Web systems,
such as emojis while using smartphone apps or slangs
in online forums. This kind of element allows the re-
sulting dataset to contain informal language model-
As the cleaning process changes the final results,
the generation of the final dataset must be accompa-
nied by documentation that details how the data clean-
ing was performed. In this document, the replaced
tags, removed elements, or any other transformation
done in the data must be described, as already defined
in the literature. (Gebru et al., 2021).
Figure 1 presents our methodology’s designed
flow of tasks organized into three major stages. Our
methodology was designed to be as straightforward as
possible, considering the complexity of the acquired
and generated data.
Data Acquiring Anonymization
Cleaning Conversation
Figure 1: Designed stages in MCCD.
The first stage is data acquiring, in which the data
source is accessed to retrieve the needed data (cf.
Subsection 3.1).
The second stage is the Anonymization stage (cf.
Subsection 3.2). At this stage, all data used to identify
a user is removed.
The last stage is the processing (cf. Subsection
3.3). This stage is separated into two steps: cleaning
and conversation identification. The cleaning step is
responsible for removing or replacing elements from
the data preparing it to be used. The conversation
identification step identifies and saves the conversa-
tions and conversation flows. The conversation iden-
tification step may generate a large amount of data to
be stored.
Once the three stages are completed, the generated
files are cleaned, structured, and ready for use.
3.1 Data Acquisition Stage
Our methodology is source agnostic, which allows it
to be used in further scenarios, as long as data fulfill
the basic requirements mentioned.
The only required information for the data acqui-
sition is a parameter indicating where the data is lo-
cated. This stage is responsible for understanding
how to access the data and how they are structured
in the source. It may require the information of ad-
ditional parameters, indicating whether data may be
accessed using threads to speed up this stage or even
credentials to access specific data sources.
Figure 2 presents the steps performed inside this
Data Acquiring
Figure 2: Steps in the Data Acquiring stage.
The first step is to access the data being. This
methodology does not define how this access is made
as long as the requirements are satisfied. The second
step is the identification of categories, sub-categories,
and meta-data from the data being acquired, such as
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
timestamps and structure.
The third step aims to get the topics from each
sub-category. If the data is being acquired directly
from a database, a single access for each category may
already gather all information needed.
The fourth step gets all messages from a specific
topic. This may encounter the same requirements as
the previous step. The last step is responsible for per-
sist the data acquired. Although this step is the last
in the flow, it can save partial results while the data is
acquired for each stage. Each file saves information
about a specific item, such as a single topic and its
message or a single sub-category and its topics.
This stage validates if the output directory already
has a valid dataset from the same source. If there are
old files from a previous acquisition, this step loads
the old dataset and only searches for new data in the
data source.
3.2 Anonymization Stage
This stage is responsible for removing all meta-data
that may identify a user in the acquired dataset. The
location and the amount of data that need to be
anonymized may vary according to the data source
and which data is saved in the acquisition stage.
This stage overwrites the original files, replacing
the values with an empty value or setting an id to
keep track of which user created messages, but with-
out identifying this user outside the dataset. It is im-
portant to note that this stage is mandatory if the final
dataset is released publicly.
3.3 Processing Stage
The processing stage encompasses the key steps in
the methodology. Figure 3 shows the steps to com-
plete the entire processing stage. As a result of this
stage, files are generated, one for each topic and one
for each conversation between two or more persons
inside each topic.
Figure 3: Steps in the processing stage.
Each stage depends on previous stages (cf. Fig-
ure 3), as it is impossible to identify the message ex-
change if the references were not identified. The final
result can be severely degraded if the data cleaning is
not completed correctly. The input for the processing
stage is either the output from the acquisition stage or
the output from the anonymization stage.
The step of data cleaning is responsible for identi-
fying elements that should not be presented in a con-
versation dataset, such as images and videos. Once
one of these elements is identified, it is removed or
replaced with proper tags to indicate what was in that
place before. This step must be utilized only on text
The quality of this step directly impacts the final
result because poorly cleaned texts may decrease the
quality of the models using it. One example of this
situation is to use a collection of PDFs as the data
source. The images from the PDFs can be replaced
with a particular tag indicating the presence of an im-
age. It is also possible to replace tables with the text
separated by a colon character.
The elements replaced or removed are changed ac-
cording to the data source and must be explicit at the
final documentation of the dataset generated. The last
step creates the messages for a specific topic with the
messages in chronological order.
Each conversation in the generated dataset from
our solution is duly identified, and they are organized
so that each different response to a message gener-
ates different conversation flows. This process allows
identifying different responses to the same message;
this happens in the real world, where there are many
ways to answer the same question.
Figure 4 presents how the identifications of mul-
tiple conversation flows happens. The messages are
organized in chronological order, wherein the instant
t0 the message 1 was created; and the instant t4
the message 5 was created. Each message may refer-
ence 0 or more messages. In our example, message 4
references messages 3, 2, and 1.
2 3 4 5
Figure 4: References between messages.
Table 2 shows the references in the example pre-
sented in Figure 4.
The process starts backward from the last mes-
sage. In our example from Figure 4, the message
number 5. For each reference, a recursive call occurs
until it finds a message that does not reference any
other message. Once the process finds this message,
the solution returns to the initial message, creating
one possible flow starting from message 5. This pro-
MCCD: Generating Human Natural Language Conversational Datasets
Table 2: References between messages.
Message References to
1 None
2 1
3 2, 1
4 3, 2, 1
5 1
cess is similar to building a tree data structure, where
the root node is the most recent message, and each
way to a leaf node is a conversation flow. Algorithm
1 shows a pseudo implementation of how of our con-
versation flow identification solution.
Algorithm 1: Conversation flow identification.
procedure CONVERSATION FLOWS(message)
re f erences get re f erences(message)
for re f re f erences do
f low Conversation Flows(re f )
end for
if re f erences is empty then
res []
while message. parent is not empty do
message message. parent
end while
return res
end if
end procedure
3.4 Final Output
This section describes the final output generated by
the methodology and the data that need to be per-
sisted. The methodology MCCD does not define that
the data must be saved with individual files, the char-
acter encoding, or extension. In order words, our
methodology only explicitly defines the relevant be
Category data: All data relating to the category
and sub-category must be stored, such as category and
sub-category list, names, creation time, and the num-
ber of topics.
Topic data: All topics identified and information
about it must be stored. The content of each topic is
saved separated.
Messages/User Texts: The raw content of all
messages inside a topic must be stored. This includes
at least the written text of the message, references, and
creation time
Clear texts: The cleaned messages must be saved
separated. This data allows the training of novel mod-
Conversations: Each conversation flow identified
must be saved, cleaned, and chronologically.
We implemented a complete software tool called
Miner-XenForo that implements our proposed
methodology. In addition, it specifies and implements
functionalities to a specific domain. The Miner-
XenForo used as data source Web forums that are
constructed using XenForo
, a platform for managing
online forums. Our software tool gathers data from
websites of online forums such that the acquisition
step working refers to a web scraper.
Figure 5 shows the modules and execution flow of
the software tool. The label R indicates the pres-
ence of requests to websites. The label “OS Threads
indicates the use of operating system threads. The
Data Acquisition stage is performed with four steps
such that each one is responsible for a specific task
defined in the MCCD methodology.
The Anonymization stage is performed by a single
module without using system threads but prepared to
work with it. The Processing stage utilizes system
threads. According to the dataset under treatment,
this stage can be the slowest one (in terms of pro-
cessing time). Four modules were created to complete
the Processing stage. These modules run sequentially
while the processing for each topic runs in parallel.
4.1 Miner-XenForo: Data Acquisiton
The acquisition stage is designed to acquire data from
a website built using XenForo. The only required in-
put regarding how to access the data source is the
URL where the forum is hosted. The List below
presents the structure of such forums.
1. Main Page
(a) List with categories. (Reference to 2)
(b) List with sub-categories. (Reference to 3)
2. Category
(a) List with sub-categories. (Reference to 3)
3. Sub-category
(a) List with topics. (Reference to 4)
4. Topic (Also named as ”thread” by some forums).
(a) List with messages.
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
Get Metadata and
Get Topics Get Messages Save Data
Cleaning Step
Identify Message
Identify Conversation
Save Dataset
OS Threads OS Threads
OS Threads
Figure 5: Miner-XenForo Software tool Modules.
In addition, we prepared the implementation to
deal with the following optional parameters:
reload-threads: Iterate each page inside each
sub-category to search for new topics.
reload-posts: Iterate over each topic page,
searching for new messages.
max-request: Set how many simultaneous re-
quests the software can do to the website. Caution
is required as the website can prevent multiple re-
quests quickly.
cache-pages: Cache mechanism to debug and
adapt the acquisition process. Must not be used
in production as the amount of data generated in-
creases rapidly
In the acquisition, the software performs a single
request to the main page to obtain a list of categories
and sub-categories available. Afterward, the Miner-
XenForo checks the local files to determine if a new
dataset is being created or updating an existing one
while acquiring information about the available topics
and messages. During this process, our software tool
checks the local files to update older versions of the
same dataset.
4.2 Miner-XenForo: Anonymization
This module was implemented following all the re-
quirements from the methodology. If this module is
executed right after the acquisition stage, all results
generated from examining or using the dataset are al-
ready anonymized. This is possible due to the over-
writing of the original un-anonymized files with the
ones generated by this module.
Due to the structure generated from the ac-
quisition module, two types of files need to be
anonymized: data about topics and data about the
messages. These files contain the user who created
each topic and each message in the dataset.
The JSON files containing unprocessed versions
of topics and sub-categories may contain fields that
the values identify a user. Due to this, the JSON
files about sub-categories have the fields “user href
and “user name” replaced with an empty string. The
JSON files with information about topics, the field
“member href” is replaced with an empty string, and
the field “member name” is replaced with a unique id
for this user. This unique id used allows identifying
all messages that this user has created without letting
information about who the user is.
4.3 Miner-XenForo: Processing
The processing stage was specified to work with data
acquired from the data source used during the acqui-
sition stage. In this sense, in particular, the clean-
ing process is prepared to deal with HTML elements
and specific elements presented in Web forums made
using XenForo. All the elements being replaced, re-
moved, or changed during the cleaning process can be
viewed at the Mine-XenForo documentation.
The identification of conversation flows as close
as possible from the original methodology specifica-
tion. It differs only at ignoring references to messages
in the future. This situation may happen as XenForo
allows users to edit and cite messages created after
theirs. This action needs to be done to keep the gener-
ated results consistent, as answering not already cre-
ated messages may be a problem. This situation cre-
ates loops during processing that would require a spe-
cific approach.
MCCD: Generating Human Natural Language Conversational Datasets
Our Software tool Miner-XenForo was applied and
used to create two huge conversational datasets in
Portuguese language.
The first one, called “Adrenaline Dataset”, used
a website
about technology, hardware, and games.
Our generated dataset based on the execution of
Miner-XenForo reached a compressed file with about
2.0Gb of data; and a compressed file with the pro-
cessed files is more than 70Gb. This dataset has more
than 356K topics and 9.5M messages.
The second generated dataset was the “Out-
erSpace Dataset”. We applied Miner-XenForo to
a Web forum
with content about games, generic
themes, and a buy/sell category. As a result, the com-
pressed version is 4.6Gb, and the compressed file with
processed files is 5.4Gb. This dataset has more than
570K topics with more than 24M messages.
Figure 6 compares the number of messages per
topic in the Adrenaline dataset and the Out-
erSpace dataset”.
Figure 6: Topic size in Adrenaline and OuterSpace datasets.
We observed the same behavior regarding the size
of topics in both datasets. We found most topics
with less than ten messages and few topics with more
than one hundred. About 9% of all topics in the
Adrenaline Dataset” have one message; this is 7%
in the “OuterSpace dataset”. Furthermore, the major-
ity of the topics in both datasets have less than 100
messages. In both datasets, 99.9% of the topics have
at most 1000 messages, but the largest topic in the
Adrenaline dataset” has more than 134K messages;
and in the “OuterSpace dataset” has more than 324K
Table 3: Messages density distribution.
Max of # Messages Adrenaline OuterSpace
1 9.2% 7.0%
10 67.7% 39.5%
100 98.6% 94.1%
1000 99.7% 99.7%
Both data sources utilize the same web forum plat-
form, and they were acquired and processed with
the Miner-XenForo software. However, the interac-
tion among the users occurring on them was differ-
ent enough to generate 13 times more data on the
Adrenaline dataset” compared to the “OuterSpace
This investigation contributed in the definition and
evaluation of a methodology capable of structur-
ing the requirements, stages, and steps as a way
to develop software tools to extract conversational
datasets. Furthermore, we implemented a complete
software tool following our developed methodology
MCCD, called Miner-XenForo. This tool was used
to create two different conversational datasets already
available publicly.
MCCD was created to be used in any data source
that fulfills the defined requirements. In particular,
our use case software tool, Miner-XenForo, was im-
plemented to acquire data from forums built on top of
XenForo. Despite this limitation, our obtained soft-
ware tool covers many forums in different languages
and MCCD may be applied to any language.
The contribution obtained in this investigation
paves the way for the creation of novel datasets that
can be used to train NLP models in different lan-
The generation of adequate conversational datasets
from available Web data sources for model learn-
ing is still an open research challenge. This is even
true and required for languages in addition to En-
glish, such as Portuguese. This study proposed a
novel source agnostic methodology to generate con-
versational datasets automatically. On this basis, we
implemented a complete software tool to acquire,
anonymize, clean and identify conversations from
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
forum-like online data sources. This study applied our
software tool to generate two conversational datasets
in Portuguese language. The obtained datasets from
our study were made available for sharing and reuse
purposes. We found that our designed methodol-
ogy and software tool enables the creation of rele-
vant datasets. This must leverage the training of lan-
guage models. Indeed, our subsequent research steps
involve using the generated conversational datasets to
train new models and refine existing language mod-
els. We aim to apply and evaluate such models in
constructing chatbots for customer services.
Bansal, A., Kauffman, R. J., and Weitz, R. R. (1993). Com-
paring the modeling performance of regression and
neural networks as data quality varies: A business
value approach. Journal of Management Information
Systems, 10(1):11–32.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.,
Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G.,
Askell, A., Agarwal, S., Herbert-Voss, A., Krueger,
G., Henighan, T. J., Child, R., Ramesh, A., Ziegler,
D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler,
E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner,
C., McCandlish, S., Radford, A., Sutskever, I., and
Amodei, D. (2020). Language models are few-shot
learners. ArXiv, abs/2005.14165.
Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I.,
Ultes, S., Ramadan, O., and Ga
c, M. (2018). Multi-
woz - a large-scale multi-domain wizard-of-oz dataset
for task-oriented dialogue modelling. Proceedings of
the 2018 Conference on Empirical Methods in Natural
Language Processing.
Byrne, B., Krishnamoorthi, K., Sankar, C., Neelakantan,
A., Goodrich, B., Duckworth, D., Yavuz, S., Dubey,
A., Kim, K.-Y., and Cedilnik, A. (2019). Taskmaster-
1: Toward a realistic and diverse dialog dataset. Pro-
ceedings of the 2019 Conference on Empirical Meth-
ods in Natural Language Processing and the 9th Inter-
national Joint Conference on Natural Language Pro-
cessing (EMNLP-IJCNLP).
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.
(2018). Bert: Pre-training of deep bidirectional trans-
formers for language understanding. arXiv preprint
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W.,
Wallach, H., Iii, H. D., and Crawford, K. (2021).
Datasheets for datasets. Communications of the ACM,
Kelley, J. F. (1984). An iterative design methodology for
user-friendly natural language office information ap-
plications. ACM Trans. Inf. Syst., 2(1):26–41.
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., and Niu, S.
(2017). DailyDialog: A manually labelled multi-turn
dialogue dataset. In Proceedings of the Eighth Inter-
national Joint Conference on Natural Language Pro-
cessing (Volume 1: Long Papers), pages 986–995,
Taipei, Taiwan. Asian Federation of Natural Language
Lowe, R., Pow, N., Serban, I., and Pineau, J. (2015). The
ubuntu dialogue corpus: A large dataset for research
in unstructured multi-turn dialogue systems. arXiv
preprint arXiv:1506.08909.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and
Galstyan, A. (2021). A survey on bias and fairness in
machine learning. ACM Comput. Surv., 54(6).
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark,
C., Lee, K., and Zettlemoyer, L. (2018). Deep contex-
tualized word representations. In Proc. of NAACL.
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., and Huang,
X. (2020). Pre-trained models for natural language
processing: A survey. Science China Technological
Sciences, 63(10):1872–1897.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and
Sutskever, I. (2019). Language models are unsuper-
vised multitask learners. In OpenAI.
Smith, J. R., Saint-Amand, H., Plamada, M., Koehn, P.,
Callison-Burch, C., and Lopez, A. (2013). Dirt cheap
web-scale parallel text from the Common Crawl. In
Proceedings of the 51st Annual Meeting of the Associ-
ation for Computational Linguistics (Volume 1: Long
Papers), pages 1374–1383, Sofia, Bulgaria. Associa-
tion for Computational Linguistics.
Traum, D. R. (1999). Speech Acts for Dialogue Agents,
pages 169–201. Springer Netherlands, Dordrecht.
Wagner Filho, J. A., Wilkens, R., Idiart, M., and Villav-
icencio, A. (2018). The brWaC corpus: A new
open resource for Brazilian Portuguese. In Pro-
ceedings of the Eleventh International Conference on
Language Resources and Evaluation (LREC 2018),
Miyazaki, Japan. European Language Resources As-
sociation (ELRA).
Williams, J. D., Raux, A., and Henderson, M. (2016). The
dialog state tracking challenge series: A review. Dia-
logue & Discourse, 7(3):4–33.
Wolf, M., Miller, K., and Grodzinsky, F. (2017). Why
we should have seen that coming: Comments on mi-
crosoft’s tay “experiment, and wider implications.
The ORBIT Journal, 1(2):1–12.
MCCD: Generating Human Natural Language Conversational Datasets