Assessing the Effectiveness of Multilingual Transformer-based Text
Embeddings for Named Entity Recognition in Portuguese
Diego Bernardes de Lima Santos
, Frederico Giffoni de Carvalho Dutra
2 a
Fernando Silva Parreiras
3 b
and Wladmir Cardoso Brand
1 c
Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas), Belo Horizonte, Brazil
Companhia Energ
etica de Minas Gerais (CEMIG), Belo Horizonte, Brazil
Laboratory for Advanced Information Systems, FUMEC University, Belo Horizonte, Brazil
Named Entity Recognition, Text Embedding, Neural Network, Transformer, Multilingual, Portuguese.
Recent state of the art named entity recognition approaches are based on deep neural networks that use an
attention mechanism to learn how to perform the extraction of named entities from relevant fragments of text.
Usually, training models in a specific language leads to effective recognition, but it requires a lot of time and
computational resources. However, fine-tuning a pre-trained multilingual model can be simpler and faster, but
there is a question on how effective that recognition model can be. This article exploits multilingual models for
named entity recognition by adapting and training tranformer-based architectures for Portuguese, a challenging
complex language. Experimental results show that multilingual trasformer-based text embeddings approaches
fine tuned with a large dataset outperforms state of the art trasformer-based models trained specifically for
Portuguese. In particular, we build a comprehensive dataset from different versions of HAREM to train our
multilingual transformer-based text embedding approach, which achieves 88.0% of precision and 87.8% in F1
in named entity recognition for Portuguese, with gains of up to 9.89% of precision and 11.60% in F1 compared
to the state of the art single-lingual approach trained specifically for Portuguese.
Natural Language Processing (NLP) is a computer
science research field with several practical applica-
tions, such as automatic text reading and question an-
swering, audio content interpretation, document clas-
sification, and predictive text analysis. Usually, NLP
systems perform a set of basic preprocessing tasks on
input text, such as parsing, tokenization, stop-words
removal, stemming and tagging. Particularly, Named
Entity Recognition (NER) is a NLP tagging task that
extracts important information by marking up it on
text, such as names of people, places and currency
values (Borthwick, 1999). The extracted elements are
relevant entities in the textual content that make sense
within a context. For instance, the recognition of the
entity “New York” as a location in a sentence can be
important to detect where a particular event occurred
or even to relate that location to other locations, deal-
ing with similar entities or with entities with the same
semantic value.
NER is strongly dependent on the context, i.e.,
words or expressions can be recognized as different
types of entity in different contexts. For instance, in
the sentence “Mary prays to Saint Paul for health”, the
expression “Saint Paul” refers to a person (religious
entity), but in the sentence “We will move to Saint
Paul next year”, the expression “Saint Paul” refers
to a place (location entity). Even if the spelling of
a word or expression cited in different sentences is
identical, the meaning can be distinct given differ-
ent contexts. Additionally, sentences are formulated
in distinct ways in different languages, and the lan-
guages differ from each other in structure, form and
complexity, which impose even more challenging is-
sues for NER.
Traditional NER approaches use hand-crafted lin-
guistic grammar-based strategies or statistic models
that requires a large amount of manually annotated
training data to recognize entities in text (Marsh and
Perzanowski, 1998). For years, Conditional Random
Fields (CRF) has been the state of the art strategy for
Santos, D., Dutra, F., Parreiras, F. and Brandão, W.
Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese.
DOI: 10.5220/0010443204730483
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 473-483
ISBN: 978-989-758-509-8
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
NER, taking context into account in a learning model
that support sequential dependencies between predic-
tions (Lafferty et al., 2001). Recently, deep neural
networks based approaches have achieved even more
effective results than CRF for NER (Goldberg, 2016).
They learn distributed text representations (text em-
beddings) from a huge amount of text to build a lan-
guage model that can be effectively used in several
NLP tasks, including NER.
Deep neural single-lingual models (training NLP
models in a specific language) usually leads to effec-
tive entity recognition, requiring a lot of time and
computational resources for training. In addition,
such single-lingual approaches require a large amount
of data in each specific language for training, some-
times not available or easily obtained for certain lan-
guages. However, fine-tuning a pre-trained multilin-
gual model can be cheaper, simpler and faster, re-
quiring no specific single-language training dataset
and less time and computational resources for train-
ing. But how effective multilingual NER models can
be compared to single-lingual models, particularly for
complex languages, such as Portuguese?
In this article, we exploit multilingual models
for NER by adapting and training transformer-based
text embeddings for named entity recognition in Por-
tuguese. Particularly, we propose a NER approach
by training and fine tuning a multilingual transformer-
based NLP model using a comprehensive dataset we
created by combining different versions of HAREM.
Additionally, we evaluate our proposed approach by
contrasting it with the state-of-the-art (SOTA) single-
lingual approach for NER in Portuguese.
Experimental results show that our multilingual
approach for NER in Portuguese outperforms the
SOTA single-lingual approach with gains of up 9.89%
of precision and 11.60% in F1, achieving 88.00% of
precision and 87.80% in F1 in named entity recogni-
tion. The main contributions of this article are:
We propose a comprehensive dataset to improve
the training of NER models for Portuguese by
combining different versions of the HAREM
We propose a multilingual NER approach for
Portuguese by adapting and training different
transformer-based neural networks for multilin-
gual NER in English.
We provide a throughout evaluation of our pro-
posed approach by contrasting them with the
SOTA single-lingual approach for NER in Por-
tuguese reported in literature.
The present article is organized as follows: Section 2
presents the theoretical background in named entity
recognition, word embeddings and transformer-based
architectures of neural networks. Section 3 presents
related work reported in literature for NER, including
the state-of-the-art approach for NER in Portuguese.
Section 4 presents our multilingual NER approach for
Portuguese, as well as the comprehensive dataset we
create to improve the training of our approach. Sec-
tion 5 presents the experimental setup and the results
of the experiments we carry out to evaluate our pro-
posed approach. Finally, Section 6 concludes this ar-
ticle, suggesting directions for future work.
Named Entity Recognition (NER) is a NLP task that
identifies people, location, currency, and other rel-
evant information within a text (Borthwick, 1999).
While traditional NER approaches use hand-crafted
linguistic grammar-based strategies or statistic mod-
els that require a large amount of manually anno-
tated training data to recognize entities in text (Marsh
and Perzanowski, 1998), recent NER approaches use
deep neural networks to learn an effective recognition
model (Goldberg, 2016). In particular, they learn text
embeddings from a huge amount of text to build a lan-
guage model that can be effectively used for NER.
2.1 Word Embeddings
Recently, different ways to represent text have
emerged, allowing more accurate analyzes of textual
information, e.g., the analysis of similarity between
two words. A distributed text representation, or text
embeddings, can be generated by deep neural net-
work (NN) approaches that learn language models
from a huge amount of natural language corpus. In
particular, word embeddings take the form of a con-
tinuous vector representation describing the meaning
of terms (Levy and Goldberg, 2014). Usually, this
distributed representation is a not mutually exclusive
continuous real-valued vector of fixed length learned
by a NN, typically much smaller than the size of the
vocabulary (Bengio et al., 2003).
The continuous vectors representation are capable
of syntactically representing words, but also allow the
learning of semantic values of terms, that is, word em-
beddings can capture similarity between words with
similar meaning, even if their spelling is quite differ-
ent among them (Mikolov et al., 2013b). Figure 1
presents groups of words with similar context mea-
sured by cosine similarity between word embeddings.
In recent years, different frameworks and algo-
rithms for word embeddings generation have been
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: Correlation among words represented as contin-
uous vectors measured by cosine similarity. Source: (Xun
et al., 2017).
proposed, particularly WORD2VEC, GLOVE, and
FASTTEXT. WORD2VEC (Mikolov et al., 2013a;
Mikolov et al., 2013b) is a framework composed of
the first efficient word embeddings models, particu-
larly the continuous BoW (CBOW) and the continu-
ous skip-gram (SKIP-GRAM), to learn distributed rep-
resentations of words from large amount of unstruc-
tured text with billions of words. Training such mod-
els does not require dense matrix multiplications and
can be done in one day on a hundred billion words
dataset with a single machine. Particularly, the CBOW
model is a simplification of the first practical neural
language model approach proposed in literature (Ben-
gio et al., 2003) that uses a fully connected feedfor-
ward neural network to learn simultaneously a dis-
tributed representation for words and the joint prob-
ability distribution function for these word represen-
tations from a huge corpus of natural language text
with millions of words.
In the CBOW model the non-linear hidden layer is
removed, the projection layer is shared for all words,
and word context is captured by a log-linear classi-
fier trained to predict a target word given its two pre-
vious and two next neighboring words. The SKIP-
GRAM model is similar to CBOW but the log-linear
classifier is trained to predict the two previous and two
next neighboring words given a target word (Mikolov
et al., 2013a). Additionally, the models perform
subsampling of frequent words, resulting in faster
training and improved representations of uncommon
words. Moreover, they use two replacement training
methods for full softmax resulting in speedup and ac-
curate distributed representations especially for fre-
quent words (Mikolov et al., 2013b). The replacement
training methods are hierarchical softmax (Morin and
Bengio, 2005) and negative sampling, a simplified
NCE (Gutmann and Hyv
arinen, 2012).
An interesting property of the word embeddings
learned by WORD2VEC models is that simple vector
operations can often produce meaningful results. For
instance, the sum operation between the vector(usa)
and the vector(capital) results in a vector close to the
vector(washington). Additionally, word embeddings
can be combined using simple operations to represent
longer pieces of text, such as sentences, paragraphs
and documents. For instance, the vector(boston) and
the vector(globe) can be combined to get the vec-
tor(boston globe). However, the resulting word em-
beddings is often unable to represent idiomatic sen-
tences that are not compositions of the individual
words, such as boston globe. Moreover, the word em-
beddings learned by WORD2VEC models exhibit lin-
ear structure that makes precise analogical reasoning
possible. For instance, the vector(queen) being the
nearest representation of the vector(king) minus the
vector(man) plus the vector(woman) provide a way to
test the analogy pair man:king::woman:queen.
GLOVE incorporates global statistics of words
occurrences typically captured by count-based lan-
guage models in a log-bilinear model for unsuper-
vised learning of word embeddings (Pennington
et al., 2014). The intuition is that shallow window
models, such as SKIP-GRAM, poorly utilize statistics
of the corpus since they train on local context window
instead of on global co-occurrence counts. Therefore,
training a NN model simultaneously on local context
and on global word-word co-occurrence counts, mak-
ing efficient use of statistics, produces word embed-
dings with meaningful substructure.
FASTTEXT is another simple and unsupervised
approach that learns distributed representations by
considering subword units and representing words by
a sum of their character n-grams (Bojanowski et al.,
2017). It is an extension of the continuous skip-
gram model (Kiros et al., 2015) that incorporates n-
grams, taking into account the internal structure of
words, which is important for morphologically rich
languages where many word formations follow rules.
For instance, in Latin languages most verbs have more
than dozens different inflected forms. These lan-
guages contain many word forms that occur rarely
(or not at all) in the training corpus, making it diffi-
cult to learn good word representations. Additionally,
FASTTEXT is capable of building word vectors for
words that do not appear in the training set. Exper-
imental results on word similarity and word analogies
tasks show that FASTTEXT outperforms WORD2VEC
models that do not take into account subword infor-
mation, as well as methods relying on morphological
analysis in different languages (Joulin et al., 2016).
2.2 Transformers
Transformers are sequence transduction models based
exclusively on attention, replacing the recurrent lay-
Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese
ers most commonly used in encoder-decoder architec-
tures with multi-headed self-attention, consequently
allowing more parallelization (Vaswani et al., 2017).
In particular, it follows an encoder-decoder structure
using stacked self-attention and point-wise, fully con-
nected layers for both the encoder and decoder, where
the encoder maps an input sequence of symbol rep-
resentations to a sequence of continuous representa-
tions feeding the decoder that generates an output se-
quence of symbols one element at a time. At each
step the model is auto-regressive, consuming the pre-
viously generated symbols as additional input when
generating the next. Experimental results on machine
translation and English constituency parsing show
that Transformers outperform baseline discriminative
models at a fraction of the training cost.
Figure 2: The attention mechanism’s mapping. Source:
(Vaswani et al., 2017).
The attention mechanism is a strong differentiation
between Transformers and other NN architectures, al-
lowing the estimation of the correlation between ele-
ments in a bidirectional way. Typically, there are two
attention mechanism:
Self-attention: intra-analysis of a sentence em-
beddings vectors, performing the similarity cal-
culation between different words within the same
sentence. In this analysis the mechanism extracts
the correlation between words in the sentence.
The sense of the vectors represents whether the
words have similar or distinct semantic values.
Multi-head-attention: divides the sentences into
smaller parts to perform the similarity calculation
between the matrices. It is similar to the self-
attention mechanism, but between different por-
tions of the sentences, identifying the relationship
between words using text segments (sub-spaces).
Figure 2 presents the attention mechanism that esti-
mates the correlation between words with similar se-
mantic values, in a bidirectional way. From Figure 2
we can observe that the word “making” has a close
relationship with the words “2009” and “laws” for in-
stance, i.e., the word “making” appears in the same
expressions than “2009” and “laws”. This relation-
ship allows the prediction of the next terms in sen-
tences with words with similar meanings.
2.2.1 BERT
BERT (Bidirectional Encoder Representations from
Transformers) is a language representation approach
designed to pre-train deep bidirectional representa-
tions from unlabeled text by jointly conditioning on
both left and right context in all layers (Devlin et al.,
2018). In particular, a deep bidirectional TRANS-
FORMER is pre-trained in a masked language model
and next sentence prediction objectives, enabling the
representation to fuse the left and the right context,
thus reducing the need for many heavily-engineered
task-specific architectures. BERT is the first fine-
tuning based representation model that achieves state-
of-the-art performance on a large suite of sentence-
level and token-level tasks, outperforming many task-
specific architectures.
Figure 3 presents the NN layers of the BERT
architecture. Particularly, we can observe the pre-
training and the fine-tuning steps. During pre-training
the input data set is used without labels, thus perform-
ing unsupervised training of the data. There are two
main tasks during this stage:
Token masking: randomly selecting a percentage
of about 15 % of the tokens of input and apply-
ing a mask to them so that the training makes the
prediction of these tokens.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 3: The BERT layers. Source: (Devlin et al., 2018).
Next sentence prediction (NSP): training for ques-
tion answering, predicting which sentences are
subsequent to previous sentences.
After the pre-training step, the output data can be used
as input to another NLP tasks. From Figure 3 we
can observe that the pre-training output data is used
for natural language inference (MNLI), named entity
recognition (NER) and question answering (SQuAD).
ROBERTA (Robustly Optimized BERT Approach) is
a BERT-based framework for language model pre-
training that extends BERT by training the model
with bigger batches, over more data, and on longer
sequences, also removing the next sentence predic-
tion objective and dynamically changing the masking
pattern applied to the training data (Liu et al., 2019).
Experimental results on downstream tasks using the
GLUE, RACE and SQuAD benchmarks show that
ROBERTA achieves state-of-the-art results outper-
forming BERT and XLNET, an autoregressive learn-
ing approach (Yang et al., 2019). Table 1 presents
the experimental parameters and results (performance
measured by precision) comparing ROBERTA and
BERT in three different tasks: question answering
(SQuAD), natural language inference (MNLI), and
sentence classification (SST).
Table 1: BERT/ROBERTA parameters and performance.
Source: (Liu et al., 2019).
Data 13GB 16GB
Batches 256 8K
Steps 1M 100K
SQuAD v1.1 90,9 93,6
SQuAD v2.0 81,8 87,3
MNLI-m 86,6 89.0
SST-2 93,7 95.3
From Table 1 we can observe that there are signifi-
cant differences in training, with changes in the size of
batches and in the number of steps in training. While
ROBERTA uses a larger dataset than BERT to carry
out its training, vigorously larger batches for its pro-
cessing, however the processing occurs in a smaller
number of steps. ROBERTA is a robust approach,
however, as can be seen in theSQuAD, MNLI and
SST tasks, RoBERTa presents similar and even bet-
ter results than in the BERT approach.
DISTILBERT (Distilled BERT) is a general-purpose
smaller and faster pre-trained version of BERT, that
retains almost the same language understanding ca-
pabilities (Sanh et al., 2019). In particular it uses lan-
guage models pre-trained with knowledge distillation,
a compression technique in which a compact model is
trained to reproduce the behaviour of a larger model
or an ensemble of models, resulting in models that
are lighter and faster at inference time, while also re-
quiring smaller computational training. Particularly,
it keeps 97% of language comprehension in its model
with approximately 60% reduction in the model size,
running 60% faster. DISTILBERT can be fine-tuned
on several downstream tasks, keeping the flexibility
of larger models while it is small enough to run on
the edge, e.g. on mobile devices.
The distillation technique (Hinton et al., 2015)
consists of training a distilled (student) model to re-
produce the behavior of a larger (teacher) model.
Thus, DISTILBERT is a leaner model based on
the behavior of the original BERT model. Table 2
presents a comparison of precision performance in
different NLP tasks among BERT, DISTILBERT, and
ELMO, a deep contextualized word representation
approach that models complex syntactic and semantic
characteristics of word uses and how these uses vary
across different linguistic contexts (polysemy) (Peters
et al., 2018).
Table 2: BERT, DISTILBERT and ELMO performance.
Source: (Sanh et al., 2019).
ELMO 68.7 44.1 68.6 76.6
BERT-BASE 79.5 56.3 86.7 88.6
DISTILBERT 77.0 51.3 82.2 87.5
From Table 2 we observe that DISTILBERT perfor-
mance is close to BERT, even providing a reduced
Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese
2.2.4 ALBERT
ALBERT is another BERT based efficient architec-
ture with significantly fewer parameters than a tradi-
tional BERT architecture (Lan et al., 2019). In par-
ticular, ALBERT incorporates parameter reduction
techniques that lift the major obstacles in scaling pre-
trained models, also acting as a form of regulariza-
tion that stabilizes the training and helps with gener-
alization. First, it incorporates factorized embedding
parametrization, i.e., decompose the large vocabulary
embedding matrix into two small matrices, thus sep-
arating the size of the hidden layers from the size of
vocabulary embedding, making it easier to grow the
hidden size without significantly increasing the pa-
rameter size of the vocabulary embeddings. Second it
incorporates cross-layer parameter sharing, prevent-
ing the parameter from growing with the depth of the
network. Additionally, ALBERT replaces the next
sentence prediction proposed in the original BERT by
a self-supervised loss for sentence-order prediction.
Experiments with GLUE, RACE and SQuAD bench-
marks show that ALBERT achieves state-of-the-art
performance on natural language understanding tasks
outperforming BERT, XLNET and ROBERTA.
In particular, ALBERT address the scalability
problem of BERT derived from memory consump-
tion issues. The growth in the number of param-
eters of BERT has become an important challenge
due to the high memory consumption. Few works
reported in literature address this problem, by using
parallelism (Shazeer et al., 2018) or effectively man-
aging memory consumption through a cleaning mech-
anism to minimize performance impact (Gomez et al.,
2017). However, the obstacle created by the commu-
nication overhead of the BERT architecture is not ad-
dressed by these reported works.
Thus, BERT was extended by ALBERT in order
to reduce around 89% of the number of parameters,
improving performance in NLP tasks. Table 3 shows
a comparison between the hyperparameters of BERT
and ALBERT. Even using less hyperparameters than
BERT, ALBERT provide improved results in differ-
ent NLP tasks, such as SQuAD v1.1 (+1.9%), SQuAD
v2.0 (+3.1%), MNLI (+1.4%), SST-2 (+2.2%), and
RACE (+8.4%) using relatively less resources and
with a faster training phase (Lan et al., 2019).
The emergence of approaches that use Transformers
to improve performance in NLP tasks has grown in
recent years. Particularly for NER in complex lan-
guages, a recent work reported in literature (Arkhipov
et al., 2019) uses Transformers for named entity
recognition in Slavic languages, achieving up to 93%
of performance in F1 measure when applied to the
Czech language.
Recently, different NN architectures were pro-
posed to perform NER in Portuguese (Souza et al.,
2019). In addition to the comparative analysis be-
tween the architectures, the authors proposed an ef-
fective approach for both word embbedings gener-
ation and named entity recognition in Portuguese.
The proposed approach uses BERT to first gener-
ate the word embeddings for Portuguese and finally
use this word embeddings for NER. The authors also
evaluate different NN architectures, such as LSTM
(Long-Short Term Memory) and BiLSTM (Bidirec-
tional LSTM) for named entity recognition in Por-
tuguese. They also combine these different architec-
tures with CRF (Conditional Random Fields) (Laf-
ferty et al., 2001) to improve performance. Table 4
summarizes the experimental results of the proposed
architectures for multilingual (ML) and Portuguese
(PT) in two scenarios: a full scenario using all the
HAREM dataset with 10 classes, and a selective sce-
nario using a subset of 5 classes of HAREM where
the proposed approach performs better.
From Table 4 we observe that the BERT-LARGE
approach outperforms BERT-BASE. Additionally,
the LSTM architecture does not provide any gain,
however combining CRF brings outstanding perfor-
mance. Moreover, single-lingual models outperforms
multi-lingual models for NER in Portuguese. Thus,
the best results were obtained with a single-lingual
model trained specifically for Portuguese. Although
the single-lingual approach performs better, the com-
putational cost of training the model in Portuguese
is much higher than using a pre-trained multilingual
Although the authors provide a single-lingual
SOTA approach for NER in Portuguese, a question
remains: is it possible that multilingual NER models
can outperform single-lingual models, particularly for
complex languages, such as Portuguese?
In this section we present our multilingual
transformer-based text embeddings approach for
NER in Portuguese. First, we present a comprehen-
sive dataset we propose to improve the training of
NER models for Portuguese. Second, we present the
architecture of our proposed approach.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Table 3: BERT and ALBERT hyperparameters. Source: (Lan et al., 2019).
Parameters Layer Embedding
# Sharing # Hidden Size
BERT-BASE 108M No 12 768 768
BERT-LARGE 334M No 24 1024 1024
ALBERT-BASE 12M Yes 12 128 768
ALBERT-LARGE 18M Yes 24 128 1024
ALBERT-XLARGE 60M Yes 24 128 2048
ALBERT-XXLARGE 235M Yes 12 128 4096
Table 4: Performance in precision, recall and F1 of the SOTA single-lingual approach for NER trained specifically for Por-
tuguese in two experimental scenarios. Source: (Souza et al., 2019).
Full Scenario Selective Scenario
Precision Recall F1 Precision Recall F1
4.1 Training Dataset
To improve the training of multilingual NER mod-
els for Portuguese, we build a comprehensive
dataset from HAREM (Santos and Cardoso, 2007).
is a manually annotated dataset used to
assess the performance of information systems for
named entity recognition in Portuguese. HAREM
is widely used by several NLP approaches reported
in literature (Souza et al., 2019; de Castro et al.,
2018; Gonc¸alo Oliveira and Cardoso, 2009; Fernan-
des et al., 2018; Consoli and Vieira, 2019; Pires,
2017). In particular, the HAREM dataset has the fol-
lowing divisions:
“CD Primeiro HAREM”: 129 documents and
80,060 words.
“CD Segundo HAREM”: 129 documents and
147,991 words.
“Mini-HAREM CD”: 128 documents and 54,074
Available at
All HAREM divisions were joined into a single uni-
fied training dataset. Originally, some expressions in
HAREM are ambiguous, i.e., some of them have two
entity labels with different meanings. To build the
unified training dataset we choose the first classifi-
cation described in the HAREM dataset, discarding
the second one. Thus, all expressions were classified
in a single entity label. Additionally, the paragraph
structure was converted into smaller sentences so that
the BERT-based algorithm can receive input data in
an appropriate format. Paragraphs of up to 256 to-
kens were automatically converted to sentences and
the paragraphs were divided with entity labels also
been incorporated into the unified training dataset.
4.2 Architecture
The proposed multilingual approach for NER in Por-
tuguese can use multiple transformer-based text em-
beddings. In particular, we implement and evaluate
BERT (Devlin et al., 2018), ROBERTA (Liu et al.,
2019) and DISTILBERT (Sanh et al., 2019). Fig-
ure 4 presents the architecture of our proposed ap-
Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese
Figure 4: The architecture of the proposed multilingual transformer-based text embeddings approach for NER in Portuguese.
proach. Particularly, there are four processing steps:
i) Dataset preprocessing; ii) Multilingual transformer-
based NER; iii) Fine-tuning; iv) NER prediction.
In the dataset preprocessing step our approach
builds the training dataset as described in Section 4.1,
removing the original ambiguities in HAREM, stan-
dardizing the data in sentences within the BERT
standard and consolidation in a single data file. In
the second step our approach selects the multilin-
gual transformers-based model for NER, instantiat-
ing them in the processing engine and loading the
pre-trained multilingual models for the generation of
the text embeddings. In the fine tuning step our ap-
proach sets the model hyperparameters for the NER
task, generating the final NER model by training the
model using Portuguese training data. Finally, in the
prediction step our approach loads the trained model,
receives all the sentences to be evaluated and gener-
ates a final output with the named entities recognized
from the input sentences.
The pipeline works in a flexible way so that if a
new version of the HAREM dataset is published it is
possible to incorporate it in the training dataset, pre-
serving the original content and expanding the vol-
ume of data available for training and testing models.
Similarly, although three Transformers approaches
have been initially used in our experiments, it is
also possible to plug in new Transformers-based ap-
proaches with no impact to the processing workflow.
In this section we present the experiments we carried
out to evaluate our proposed approach, including ex-
perimental setup, procedures and results. In particu-
lar, the experimental evaluation answer the following
research questions:
1. How effective is each one of the multilingual
BERT-based algorithm for NER in Portuguese?
2. How does our multilingual approach performs
compared to the SOTA single-lingual approach
for NER in Portuguese?
In our evaluation we consider for distinct training sce-
narios: i) 70% of data for training and 30% of data
for testing; ii) 80% of data for training and 20% of
data for testing; iii) 90% of data for training and
10% of data for testing; iv) 95% of data for training
and 5% of data for testing. In each of the scenar-
ios, we evaluate BERT (Devlin et al., 2018), XLM-
ROBERTA (Lample and Conneau, 2019) and DIS-
TILBERT (Sanh et al., 2019), also performing fine-
tuning for NER task. For fair comparison, the same
training dataset and setup parameters were used for
each BERT-based algorithm.
The large number of batch sizes implies in reduc-
ing the number of examples sent for the input of the
BERT-based algorithm, consequently negatively im-
pacting in performance. Thus, batches of 128 and
256 have become more suitable for our experiments.
Batches smaller than 128 could cause truncation is-
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Table 5: Performance of our multilingual approach using different BERT-based algorithms with multiple training set variations
and 3 epochs of training.
Approach Train (%) SEQ SIZE Precision (%) Recall (%) F1 (%)
BERT-BASE 95 128 85.00 86.80 85.90
BERT-BASE 95 256 85.70 86.30 86.00
DISTILBERT 95 128 77.10 82.90 79.90
DISTILBERT 95 256 78.50 83.00 80.70
XML-ROBERTA 95 128 88.00 87.60 87.80
XML-ROBERTA 95 256 86.30 88.40 87.30
BERT-BASE 90 128 67.00 74.20 70.40
BERT-BASE 90 256 68.60 75.40 71.80
DISTILBERT 90 128 62.30 68.20 65.10
DISTILBERT 90 256 62.60 69.30 65.80
XML-ROBERTA 90 128 73.00 78.60 75.70
XML-ROBERTA 90 256 74.60 79.80 77.10
BERT-BASE 80 128 66.40 69.90 68.10
BERT-BASE 80 256 68.30 71.20 69.70
DISTILBERT 80 128 59.10 64.60 61.70
DISTILBERT 80 256 60.80 64.70 62.70
XML-ROBERTA 80 128 67.90 70.90 69.40
XML-ROBERTA 80 256 67.90 71.50 69.70
BERT-BASE 70 128 61.40 62.30 61.80
BERT-BASE 70 256 62.50 64.40 63.40
DISTILBERT 70 128 58.00 59.50 58.80
DISTILBERT 70 256 59.30 61.10 60.20
XML-ROBERTA 70 128 64.40 64.80 64.60
XML-ROBERTA 70 256 64.10 64.80 64.40
sues, that is, the sentences would be truncated, gener-
ating more data loss.
Transformer-based approaches, particularly
BERT, usually require few interactions to converge in
a model able to provide efficient results (Wolf et al.,
2019). We test different epochs to finally set this
parameter to 3, for better balancing between training
time and model performance. Table 5 presents the
performance of our multilingual approach using dif-
ferent BERT-based algorithms with multiple training
set variations and 3 epochs of training.
From Table 5 we observe that XML-ROBERTA
outperforms BERT and DISTILBERT in different
scenarios. Particularly, the volume of training data
impacts the performance of all BERT-based algo-
rithms, with XML-ROBERTA outperforming BERT-
BASE in 2.68% in precision, 1.84% in recall and
2.09% in F1, also outperforming DISTILBERT in
12.10% in precision, 6.50% in recall and 8.79% in
F1, considering the 95% of training scenario. Addi-
tionally, we can observe that the differences in XML-
ROBERTA performance with batches of 128 and 256
are negligible (0.57% in F1). Recalling our first
research question, these experimental results attest
the effectiveness of our multilingual ROBERTA ap-
proach for NER in Portuguese.
Table 6 presents the performance of the SOTA
single-lingual transformer-based text embeddings ap-
proach reported in literature (Souza et al., 2019) in
comparison to our proposed multilingual transformer-
based text embeddings approach for NER in Por-
tuguese. From Table 6 we observe that our mul-
tilingual approach (XML-ROBERTA) outperforms
the best single-lingual approach (PT-BERT-LARGE-
CRF) in the full scenario with gains of 9.89% in
precision, 13.31% in recall, and in 11.60% in F1.
Even considering the selective (best) scenario for the
single-lingual approach, the gains are still significant
of 3.74% in precision, 7.19% in recall, and 5.47% in
F1. Recalling our second research question, these ex-
perimental results show that multilingual trasformer-
based text embeddings approaches fine tuned with
a large dataset outperforms SOTA trasformer-based
models trained specifically for Portuguese.
Multilingual transformer-based approaches for
NER becomes particularly interesting in scenarios
where the amount of computational resources is lim-
ited to train single-lingual approaches but the amount
Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese
of training data is abundant for fine tuning. In ad-
dition, the fine tuning step can be generalized for
any multilingual approach based on BERT. Therefore,
ALBERT (Lan et al., 2019) and BART (Lewis et al.,
2019) for instance, can be easily implemented in our
proposed transformed-based approach, similarly we
implemented DISTILBERT (Sanh et al., 2019) and
ROBERTA (Liu et al., 2019).
Table 6: Performance of the SOTA single-lingual and the
proposed multilingual transformed-based text embeddings
approaches for NER in Portuguese.
Approach Prec. Rec. F1
Single-lingual (Full Scenario)
ML-BERT-BASE-CRF 74.82 73.49 74.15
PT-BERT-BASE-CRF 78.60 76.89 77.73
PT-BERT-LARGE-CRF 80.08 77.31 78.67
Single-lingual (Selective Scenario)
ML-BERT-BASE-CRF 80.10 78.78 79.44
PT-BERT-BASE-CRF 83.89 81.50 82.68
PT-BERT-LARGE-CRF 84.82 81.72 83.24
Multilingual (Full Scenario)
In this article, we assessed the effectiveness of multi-
lingual transformer-based text embeddings for named
entity recognition in Portuguese. Particularly, we
fine-tuned our approach using a large Portuguese
dataset, and we carried out experiments comparing
our approach with the state of the art single-lingual
approach trained specifically for Portuguese.
Experimental results showed that our multilingual
trasformer-based approach outperformed the state of
the art approach, achieving 88.0% of precision and
87.8% in F1 in named entity recognition for Por-
tuguese, with gains of up to 9.89% of precision and
11.60% in F1. Additionally, even considering a se-
lective scenario, where the state of the art approach
performed better, our approach outperformed it by
3.74% of precision and 5.47% in F1. Thus, our exper-
iments showed that pre-trained multilingual generic
language models based on BERT and fine-tuned with
a larger dataset can outperforms single-lingual spe-
cific language models that requires a lot of time and
computational resources to be trained.
In future work, we intent to evaluate the impact of
the size of the unified dataset over the effectiveness of
the NER model, as well to improve the transformer-
based algorithms so that it is possible to adjust the
batches to smaller sizes, such as 64, allowing to in-
crease the number of sentences analyzed and possibly
get outstanding results. In addition, similarly to the
state of the art single-lingual approach, we intent to
add a CRF layer to our multilingual approach, which
can further improve the precision.
The present work was carried out with the support
of the Coordenac¸
ao de Aperfeic¸oamento de Pessoal
de N
ıvel Superior - Brazil (CAPES) - Financing
Code 001. The authors thank the partial support of
the CNPq (Brazilian National Council for Scientific
and Technological Development), FAPEMIG (Foun-
dation for Research and Scientific and Technological
Development of Minas Gerais), CEMIG, FUMEC,
LIAISE and PUC Minas.
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Assessing the Effectiveness of Multilingual Transformer-based Text Embeddings for Named Entity Recognition in Portuguese