=Paper=
{{Paper
|id=Vol-2936/paper-163
|storemode=property
|title=Style Change Detection on Real-World Data using an LSTM-powered Attribution Algorithm
|pdfUrl=https://ceur-ws.org/Vol-2936/paper-163.pdf
|volume=Vol-2936
|authors=Robert Deibel,Denise Loefflad
|dblpUrl=https://dblp.org/rec/conf/clef/DeibelL21
}}
==Style Change Detection on Real-World Data using an LSTM-powered Attribution Algorithm==
https://ceur-ws.org/Vol-2936/paper-163.pdf
Style Change Detection on Real-World Data using
an LSTM-powered Attribution Algorithm
Notebook for PAN at CLEF 2021
Robert Deibel1 , Denise Löfflad1
1
Eberhard Karls University Tübingen, Geschwister-Scholl-Platz, 72074 Tübingen, Germany
Abstract
The task of Style Change Detection (SCD) aims at detecting author switches within one document based
on their individual writing style. In this notebook, this task is divided into three sub-tasks: detecting
multi-authored documents, finding style change positions, and attributing each paragraph to a unique
author [1, 2]. We chose different machine learning approaches for the first task of multi-author detection,
and the second task of style change detection. Our approach to the third task of SCD-based authorship
attribution is a hybrid method building upon the prediction of the style change detection and extended by
an attribution algorithm. The data was given by the PAN’21 challenge and is a data set collected from an
English written Q&A forum [1, 2]. While the approach to task three showed to be very computationally
expensive, we found good results for task one and two with F1-scores of 86% for task one and 78% for
task two on the validation set.
Keywords
Style Change Detection, Stylometry, Word Embeddings, NLP, Machine Learning, LSTM, MLP
1. Introduction
Due to the ever increasing interconnection and collaborations resulting in multi-authored texts,
identifying authors within texts becomes an interesting effort. The steadily increasing amount
of readily available data gathered form online text forums or short message boards allows for
construction of methods and models for text analysis based on machine learning approaches.
Possible applications for such models can relate to e.g. plagiarism detection or style analysis.
Style analysis opens the possibility for authors to adjust their writing styles to that of other
collaborators and consequently to write more coherent texts.
The task of Style Change Detection (SCD) aims at analysing texts by detecting whether or
not a document is multi-authored, and if so, where a Style Change occurs in order to find author
changes and predict authorship in the context of one document [3]. This year’s PAN Shared Task
builds up on competitions from previous years [3]. The PAN’21 SCD task [1, 2] is threefold:
1. Determining whether a document is single or multi-authored
2. Determining whether a Style Change occurs between paragraphs
3. Assigning each paragraph to one author
CLEF 2021 – Conference and Labs of the Evaluation Forum, September 21–24, 2021, Bucharest, Romania
" deibel.robert@gmail.com (R. Deibel); denise.loefflad@student.uni-tuebingen.de (D. Löfflad)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
Especially the last task, which is a type of author attribution task, adds additional complexity in
comparison to the previous years. Traditionally in author attribution, the setting contains a fixed
and known set of authors. The data set for this year’s PAN Shared Task represents real-world
data [1, 2] and therefore, we decided to treat the exact number of authors of the texts as unknown.
Additionally there is no closed set of authors thus we have to assume that the sets of authors
for any two texts are pairwise disjoint. Therefore, we can only use a single text to identify the
authors, and cannot use a global author profile, that is one spanning all texts.
The following notebook focuses on our efforts to construct machine learning based methods to
solve the three given tasks. After this introduction we will give a brief overview of related works
in Section 2, describe our methods in Section 3, state our results and their evaluation in Section 4,
and lastly give a short discussion and conclusion in Section 5.
2. Related Works
As mentioned before, the goal of the PAN’21 Shared Task is to detect the exact positions of
authorship changes. In order to lead up to this task, it is split into three sub-tasks: deciding whether
a document is multi-author, finding style changes between paragraphs, and lastly attributing the
paragraphs uniquely to one of the authors.
Regarding the first task of detecting multi-authored documents, several different solutions have
been proposed over the last decades [4, 5, 6]. Zuo et al. [6] used a multi-layer perceptron (MLP)
with a single layer for the binary classification task and represent each document by a TFIDF-
weighted word vector. Iyer and Vosoughi [5] generated sentence vectors using embeddings and
compared Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and
Naive Bayes classifiers. They reported to yield the best results with a Random Forest classifier.
Similar to the first task, the second task received attention by researchers over the last years.
Especially in the PAN Shared Task series, the detection of style changes between paragraphs
has been addressed repeatedly and various approaches have been proposed. Iyer and Vosoughi
(2020) [5] yielded the best results in 2020 [3], using Google’s BERT language model and word
embeddings [7]. Another approach included computing nearly 200 textual features and using
𝐵0 -maximal clustering [8], however this approach yielded less promising results in the PAN Task.
Moreover, Hosseinia et al. (2018) [4] implemented a complex Parallel Hierarchical Attention
Network using LSTM layers and achieved promising results.
Finally, the third task, namely assigning paragraphs to specific and unknown authors, is a more
recent problem and has consequently been addressed less frequently. It is similar to the problem
of authorship identification of multi-author documents (AIMD) [9]. Sarwar et al. [9] tackled this
problem by using character n-grams and a multilingual feature space to identify co-authors from
a set of known authors.
In all three tasks, individual writing style plays a role and can be addressed more or less
specifically. Castro-Castro et al. (2020) [8] for example computed nearly 200 textual features for
their pipeline. Stylometry is defined as statistical analysis of written texts [10] and assumes that
textual features are quantifiable and represent one’s individual and distinct writing style reliably.
Stylometry has application in and has been shown to work effectively for Authorship Attribution
[11, 12, 13, 10], and for authorship attribution of single author documents [9].
In the next section, we will focus on our contribution to the research in Style Change Detection
and Author Attribution, while taking into account past research and results.
3. Methods
For the three tasks tackled in this notebook we chose different machine learning approaches
for the first task of multi-author detection, and the second task of style change detection. We
attempted to solve the first task using per-document embeddings and an MLP, while we utilized
per-paragraph embeggins and textual features as input for an LSTM for the second task. Our
approach for the third task of SCD-based authorship attribution is a hybrid method building upon
the prediction of the style change detection and extended by an attribution algorithm. The training
of the models was performed on an Intel®Core™i7-7500U CPU at 2.70GHz without hardware
acceleration. Additionally, a server environment on TIRA [14] was provided by the PAN’21
team.
In the following we will describe our approaches in more detail and state other important
considerations for our application.
3.1. Data Set
The data set provided by the PAN’21 SCD challenge consisted of one training and one validation
set with 11200 and 2400 problems, respectively, as well as solutions for these problems. The
problems consist of uniquely English posts scraped from the StackExchange network that where
put together in one to four author documents of total length between 1000 and 3000 characters.
Additionally to the training and validation sets a test set was generated by the PAN’21 team but
not provided during development. The test set is similar to the validation in terms of size and
content [1, 2]. Overall the data set is similar to the one used in Zangerle et al. [3]. Since the data
sets provided additional metadata e.g. the total number of authors in a text, it would have been
possible to construct a model that uses this additional data for its prediction. To simulate the
real world application of SCD we chose not to consider the maximum number of authors in our
application but rather keep this value variable.
3.2. Textual Features
We manually extracted a small set of traditional features that are often used in Authorship
Attribution tasks [15], and one additional feature. We computed these measures for every
paragraph. The measures are described in more detail as follows:
• Corrected Type-Token Ratio (CTTR): The total number of unique words (types) divided by
the total number of words (tokens) in a paragraph. In contrast to the traditional TTR which
is negatively influenced by texts longer than 100 words, the CTTR takes into account the
varying lengths of the paragraphs and is therefore not affected by text length.
• Mean Sentence Length in Words: The average number of words in a sentence. Considering
the creation of the data set - an online Q&A, where presumably each author is less likely to
stick to a writing standard - we expected this measure to be adequate for this dataset and
task.
• Mean Word Length: The average word length in syllables. It has been shown that the use
of shorter compared to longer words is a good indicator of an author’s style [16].
• Function Word Frequency: It is unlikely that the frequency of function word use is con-
sciously controlled [17]. We decided to add this feature, as it is not expected that their
frequency would vary much with the topic of the text.
• Linsear Write Formula: A readability formula to score the difficulty of English text. The
standard Linsear Write metric runs on a 100-word sample. For calculating it, we used the
following method: For the first 100 words of the text, for each simple word, defined as
words with two syllables or less, we added one point to the result 𝑟. For each complex
word, defined as words with three syllables or more, we added three points to 𝑟. Then, we
divide the points by the number of sentences in the 100-word sample, and adjust the result
𝑟:
{︃
𝑟
, if 𝑟 < 20
𝐿𝑊 𝐹 = 2 𝑟
2−1 , otherwise
We chose to manually implement those rather basic features as they have been shown to be good
predictors of style. However, we did not expect the model trained on this set of five features
to outperform the model trained on word embeddings, simply because of the seemingly small
amount of information that such a set would achieve.
3.3. Embeddings
The word embeddings used for our model are the pretrained fastText word vectors provided by
Facebook’s AI Research lab [18]. They were trained on Common Crawl and Wikipedia data,
using continuous bag of words with position-weights, in dimension 300, with character n-grams
of length five, a window of size five and ten negatives. Unlike Word2Vec, fastText uses character
n-grams in order to create an inherent association between words that share the same stem, thus it
not only encodes semantic and syntactic information, but morphological information as well.
For the task of multi-author prediction we chose to calculate the embeddings on a per-document
basis. We anticipated that multi-author documents would be separate from single-author docu-
ments in embedding space.
For the task of style-change detection we use a per-paragraph embedding. Since each paragraph
is guaranteed to have been written by a single author, we computed the average of the word
embeddings for each paragraph, thus generating a paragraph embedding. As pointed out by
Kenter et al. [19], simply averaging word embeddings of all words in a text has proven to be a
surprisingly successful and efficient way of obtaining features across a multitude of tasks. The
final vectors of both tasks were then padded and fed into the model.
3.4. Model Single vs. Multiple
Due to the promissing results reported by Hosseinia et al. (2018) [4], we decided to opt for a
MLP to tackle the first task of the challenge. Our machine learning models across all the tasks are
built using the Keras API [20] with the TensorFlow [21] backend. For this approach specifically
we used a standard MLP pipeline with three hidden, fully connected, and feed forward layers. We
assumed that the set of multi-authored documents and the set of single authored documents could
be well separated in the space of per-document embeddings. As a per-document approach makes
more sense for this task, we decided not to use textual features. Computing textual features on a
per-document basis would distort the feature values and lead to poorer results.
The number of neurons and the learning rate were determined using a grid search approach in
the range of 32 to 512 with increments of 32 for the number of neurons and the options of 10−2 ,
10−3 and 10−4 for the learning rate. For the hidden layers this resulted in 480, 480, 288 neurons,
respectively. The optimal learning rate was found to be 10−3 .
The activation of hidden layers was set as the ReLU function as this has proven to be successful
in MLP applications [22]. The output layer activation was set as the sigmoid function as we want
to predict a binary decision. Again for all of our models the Adam optimizer [23] and the binary
cross entropy loss was used as the optimization loss function.
As input data we used only the per-document embeddings as described in Section 3.3 as we
wanted to analyze the discriminative abilities of MLPs on per-document level. We believe that
calculating per-document complexity measures would not yield a satisfying separation since the
documents are constructed to be similar in their global structure.
3.5. Model Style Change Basic
We trained a two-layered Bidirectional LSTM model with 128 hidden units per layer, adding a
Masking layer, and a Time-Distributed layer as the output layer with a sigmoid activation function.
We used binary cross-entropy as the loss function. In order to prevent overfitting, early stopping
was used.A threshold of 0.5 was established in order to decide whether there is a style change.
We applied normalization. We found that a batch size of one achieved the best results, compared
to batch sizes five and ten, which yielded results worse by 60% points.
Our anticipation for using an LSTM was that the model could learn similarities in writing style
on a per-paragraph basis by using the paragraphs as time steps in its input.
3.6. Model Style Change Real-World
We implemented different approaches for this task, which turned out to be more difficult than
the previous two tasks. Our first approach was to train a simple LSTM model similar to that of
task two, but because there are no consistent classes across the data, the model achieved bad
results. The second approach was a two-fold pipeline containing a k-means clustering algorithm
and a classification model. As the data for the Shared Task should represent real-world data the
number of authors of every text is unknown and may differ from text to text. Therefore, it was
not straightforward to build a clustering system that would predict the number of authors.
The third approach showed to yield the best result. For this approach, we used an LSTM-
powered Attribution Algorithm, visualized in Figure 1 and represented as pseudo-code in Al-
gorithm 1. The algorithm functions as follows. For every paragraph the authorship attribution
decision is made. We utilize the style change detection prediction that is generated by our
LSTM-model for the whole document. The first paragraph is always attributed to author A1 , we
assume that the paragraphs are written by the same author, A1 , as long as no style change occurs.
After a style change was detected in the predictions we construct a new prediction problem for
our LSTM to solve. This problem consists of the current paragraph up for authorship attribution
and preceded by a previously attributed paragraph p𝑖 . p𝑖 is iterated until either, no style change
is detected or p𝑖 is equal to the current paragraph. The first case implies that the author of p𝑖 is
the same in the current paragraph while the second case implies that the author was not detected
before and a new author should be selected.
Algorithm 1 LSTM-powered Attribution Algorithm
1: 𝑇 := 𝑠𝑒𝑡𝑂𝑓 𝑇 𝑒𝑥𝑡𝑠()
2: 𝐴 ← {𝑛𝑒𝑤𝐴𝑢𝑡ℎ𝑜𝑟()}
3: for all 𝑡 ∈ 𝑇 do
4: 𝑃 := 𝑠𝑒𝑡𝑂𝑓 𝑃 𝑎𝑟𝑎𝑔𝑟𝑎𝑝ℎ𝑠(𝑡)
5: 𝑝1 := 𝑓 𝑖𝑟𝑠𝑡𝑃 𝑟𝑎𝑔𝑟𝑎𝑝ℎ(𝑃 )
6: 𝑠𝑒𝑡𝐴𝑢𝑡ℎ𝑜𝑟(𝑝1 , 𝐴1 )
7: 𝑃 ′ ← {𝑝1 }
8: for all 𝑝 ∈ 𝑃 do
9: if No Style Change then
10: 𝑠𝑒𝑡𝐴𝑢𝑡ℎ𝑜𝑟(𝑝, 𝐴𝑝−1 )
11: else
12: for all 𝑝′ ∈ 𝑃 ′ do
13: if Style Change then
14: continue
15: else
16: 𝑠𝑒𝑡𝐴𝑢𝑡ℎ𝑜𝑟(𝑝, 𝐴𝑝′ )
17: break
18: end if
19: end for
20: if Author Not Set then
21: 𝑠𝑒𝑡𝐴𝑢𝑡ℎ𝑜𝑟(𝑝, 𝑛𝑒𝑤𝐴𝑢𝑡ℎ𝑜𝑟())
22: end if
23: end if
24: 𝑃 ′ ← 𝑃 ′ ∪ {𝑝}
25: end for
26: end for
Figure 1: Visualization of one iteration of the attribution of our proposed algorithm. A𝑗 represents
the attributed author. p𝑖 represents 𝑖-th paragraph. The white box represents the prediction of the
trained model, the * represent padding. First the style change prediction of the whole document
is analyzed. Every paragraph that is classified as no style change (0) is considered to have the
same author as before. For a detected style the previous paragraphs are iterated and a style
change problem is constructed with the iterated paragraph p𝑖 as the first entry and the paragraph
that the author is to be detected. no style change leads us to assume that we have found the
author. A style change leads us to repeat the process until we find an author or the algorithm
ends up at the current paragraph. In the last case a new author is chosen.
4. Evaluation
In this section, we present the results we achieved by testing the models on the validation set that
was made available by the PAN’21 committee [1, 2], followed by the results yielded on the test
set. For the test set, only the F1-scores are reported by the PAN team.
Table 1
F1, Precision, Recall, Accuracy Scores for our Predictions for Task 1 on Validation and Test Set
using Embeddings
Validation Set Test Set
F1 86.86 62.08
Precision 91.88 -
Recall 82.37 -
Accuracy 79.16 -
Table 2
F1, Precision, Recall, Accuracy Scores for our Predictions for Task 2 on Validation and Test Set
Combined Model
Feature Model Embeddings Model Combined Model
Test Set
F1 74.61 78.00 79.18 66.90
Precision 85.34 82.51 87.26 -
Recall 66.27 74.07 72.47 -
Accuracy 94.87 95.91 95.95 -
4.1. Single vs. Multiple
In this section we present our results for the task of detecting multi-authored documents, shown
in Table 1. It can be seen that the model yields good results across all measures. The small
drop in recall and accuracy suggest that the model classifies some multi-authored documents as
single-authored documents. When evaluated on the test set, the F1-score drops noticeably, this
can indicate that the model might not generalize well.
Nevertheless, the F1-score achieved by our model outperforms the winning’s team model from
last year’s PAN Task [3, 5] and therefore suggests that this method is a good approach to the task
of detecting multi-authored documents. Of course, the data set differs somewhat from that of Iyer
and Vosoughi (2020) [3, 5], but we still believe that the results are comparable. The generation of
the data was similar, and since both data sets are retrieved from the same English Q&A forum,
the data sets are also comparable.
4.2. Style Change Detection Basic
In this section we present our results for the task of detecting style change positions. As can be
seen in Table 2 the three categories yielded similar results. It is interesting to see that the model
trained with the textual features only achieved high accuracy. Nevertheless, the comparatively
low recall score of 66.27% suggests that this model tend to predict no style change, the reason
for the high accuracy being the imbalance in the data. The other two models trained with the
embeddings and the combination of the embeddings and features achieved recall scores of 74%
and 72.5%, respectively. This suggests that these models are more robust to imbalance in the data
and therefore more suitable for real-world data.
Compared to the self-reported results from the winning team of last year’s PAN Shared Task,
our model does not outperform Iyer and Vosoughi’s model [5]. Similar to Task 1, a drop in the
F1-score evaluated on the test is observed.
Nevertheless, all three models yield good results and despite the drop in recall, the feature
model achieves satisfying results.
4.3. Style Change Real-World
The implementation of the algorithm showed to be very computationally expensive and time
consuming. Due to our limited access to computational power we were not able to run this code
on the validation set in order to get results. Nevertheless, when running it on a smaller corpus we
yielded promising outcomes. However, considering the small amount of data we used we decided
to refrain from reviewing the results here.
The PAN team reported a F1-score of 26.25 %. Considering this result, we assume that a
sequence of two paragraphs might not be enough information for the model to make a legitimate
prediction.
5. Conclusion and Outlook
In this notebook, we presented our approaches to solve the PAN’21 Shared Task of Style Change
Detection [1, 2]. For each sub-task, we implemented different algorithms and built a hybrid
algorithm for the task of assigning paragraphs to authors. This task showed to be the most
complex out of the three.
With regards to the stylometric aspect of the problems, we implemented only five different and
rather traditional features. Surprisingly, it was possible to achieve good results with only those
features, which shows that for this data set, the individual style of the authors was accurately
representable by mean word length, mean sentence length, function word frequency, CTTR, and
the LWF.
Our approaches to the first and second task yielded good results on the validation set, which
shows that simple machine learning models can be good solutions for the tasks. Future work could
try to implement attention based models [24], convolutional layers combining and compressing
paragraphs, or an autoencoder approach [25]. Expecially autoencoders are often used in style
transformation [26, 27] and could be a potential candidate for style change tasks.
The third task is a newer problem and more complicated to solve, especially on real-world
data with an unknown number of unknown authors. For future projects, we plan on attempting
to implement a clustering/classification model for this problem. Furthermore, it is possible to
build up on our model by increasing the number of paragraphs used to make predictions and
parallelizing the loop of the attribution algorithm to increase computation speed. We therefore
consider our model to be a promising approach despite achieving low results. It is also important
to note that we challenged ourselves to a more real-world-like problem by not taking into account
the maximum number of authors, as it would certainly lower the complexity of the task if this
information is used.
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