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EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

 | 
Valerio Basile
, 
Danilo Croce
, 
Maria Maro
, 
et al.

SardiStance: Stance Detection

DeepReading @ SardiStance: Combining Textual, Social and Emotional Features

María S. Espinosa, Rodrigo Agerri, Alvaro Rodrigo et Roberto Centeno

Résumé

In this paper we describe our participation to the SardiStance shared task held at EVALITA 2020. We developed a set of classifiers that combined text features, such as the best performing systems based on large pre-trained language models, together with user profile features, such as psychological traits and social media user interactions. The classification algorithms chosen for our models were various monolingual and multilingual Transformer models for text only classification, and XGBoost for the non-textual features. The combination of the textual and contextual models was performed by a weighted voting ensemble learning system. Our approach obtained the best score for Task B, on Contextual Stance Detection.

Note de l’éditeur

This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities (DeepReading RTI2018-096846-B-C21, MCIU/AEI/FEDER, UE), and DeepText (KK-2020/00088), funded by the Basque Government. Rodrigo Agerri is additionally funded by the RYC-2017-23647 fellowship and acknowledges the donation of a Titan V GPU by the NVIDIA Corporation. Maria S. Espinosa is also funded by the European Social Fund through the Youth Employment Initiative (YEI 2019).

Texte intégral

1. Introduction

1One of the most important research topics in the field of Natural Language Processing (NLP) is automatic information extraction from textual data. The recent rise of social media has completely changed the way in which people communicate their ideas and has thus led to the emergence of new research problems regarding the automatic analysis of online contents, such as sentiment analysis, emotion recognition, or fake news detection. Stance detection (usually considered as a subproblem of sentiment analysis) is part of the aforementioned family of research problems (Küçük and Can 2020). While there are various formulation of the stance detection task, for SardiStance 2020 the aim is to detect the stance (AGAINST, FAVOR or NEUTRAL) conveyed by a given tweet with respect to a specific, previously given topic (Mohammad et al. 2016), namely, about the Sardines movement in Italy.

2Thus, we address the problem of automatic stance detection in tweets written in Italian language for the SardiStance 2020 shared task (Cignarella et al. 2020), organized within EVALITA 2020 (Basile et al. 2020). In this paper we include the participation of three teams within the framework of the DeepReading project:1 (1) Ixa Group, (2) UNED group, and (3) DeepReading Group. While Ixa focused on developing text classifiers based on textual information only (Task A), UNED was more interested in exploring how to use contextual information available (Task B). Likewise, DeepReading is the product of combining both Ixa and UNED systems into one.

3In this sense, the main idea behind our model is to exploit textual information, based on fine-tuning large pre-trained language models for text classification, together with contextual information using several feature categories, such as psychological traits of the user, social media data, and network based features. As a result of our joint effort, we submitted 4 and 5 runs, respectively, to tasks A and B. The official results show that our systems obtained the 3rd position among the constrained runs submitted to Task A, which considered only textual information for prediction, and 1st position from 13 participants for Task B, which considered textual and contextual information.

2. Systems Description

4In this section we first describe the text classification systems developed for Task A and then the contextual features used to train XGBoost classifiers for Task B. We also include a description of the strategies used to combine the classifiers from both tasks, which resulted in the winner system for Task B.

2.1 Task A: Textual Stance Detection

5The main objective of our participation in Task A was to benchmark the performance, on the stance detection task for Italian, of large pre-trained language models based on the transformer architecture (Vaswani et al. 2017). This would help us to identify the best performing models which will be leveraged to generate features for Task B (Contextual Stance Detection).

6As for many other Natural Language Processing (NLP) tasks, current best performing systems for text classification are based on large pre-trained language models which allow to build rich representations of text based on contextual word embeddings. Deep learning methods in NLP represent words as continuous vectors on a low dimensional space, called word embeddings. The first approaches generated static word embeddings (Mikolov et al. 2013; Bojanowski et al. 2017), namely, they provided a unique vector-based representation for a given word independently of the context in which the word occurs. This means that polysemy cannot be represented.

7In order to address this problem, contextual word embeddings were proposed. The idea is to be able to generate word representations according to the context in which the word occurs. Currently there are many approaches to generate such contextual word representations, but we will focus on publicly available multilingual and monolingual pre-trained models for Italian.

8There are several multilingual versions of these models. Thus, the multilingual version of BERT (Devlin et al. 2019) was trained for the top 100 languages with the largest Wikipedias. More recently, XLM-RoBERTa (Conneau et al. 2019) distributes a multilingual model which contains 104 languages trained on 2.5 TB of Common Crawl data. Italian is included in both multilingual models.

9These multilingual models perform very well in tasks involving high-resourced languages such as English or Spanish, but their performance drops when applied to languages not so well represented in the language model (Agerri et al. 2020). Although this is still an open issue, a number of reasons can be found in the literature. First, each language has to share the quota of substrings and parameters with the rest of the languages represented in the pre-trained multilingual model. As the quota of substrings partially depends on corpus size, this means that larger languages such as English or Spanish are better represented than other languages such as Italian. Moreover, multilingual models also seem to behave better for structurally similar languages (Karthikeyan et al. 2020).

10We have benchmarked four monolingual pre-trained language models for Italian: AlBERTo, GilBERTo, UmBERTo and Italian BERT XXL with the aim of comparing them with respect to the multilingual pre-trained models previosly mentioned, namely, mBERT and XLM-RoBERTa.

11AlBERTo is a BERT base pre-trained lowercased model containing a vocabulary of 128k terms from 200M of Italian tweets (Polignano et al. 2019).

12The Italian BERT XXL models2 are also based on the BERT base architecture. The training data contains the Italian Wikipedia, various parts of the OPUS corpus and the OSCAR corpus for Italian (Ortiz Suárez, Sagot, and Romary 2019), for a total of 81GB of Italian text.

13GilBERTo3 is based on the RoBERTa base (Liu et al. 2019) architecture, an improved, optimized version of BERT which discards the next sentence prediction task. The model was trained using the Italian Oscar (Ortiz Suárez, Sagot, and Romary 2019), which contains 71GB of text. The vocabulary used consisted of 32k BPE subwords tokenized by the SentencePiece tokenizer4.

14UmBERTo5 also leverages the RoBERTa base architecture, the OSCAR corpus for Italian and the SentencePiece tokenizer, but it adds Whole Word Masking to the training process. The idea is to mask an entire word, instead of subwords, if at least one of all (sub-)tokens generated by SentencePiece was originally selected as mask.

2.2 Task B: Contextual Stance Detection

15In this task, we use several sets of features with the purpose of trying to model user’s behaviour when writing a tweet. We obtain such features from both the text and the social network. Our hypothesis is that the stance of a user regarding a particular tweet is highly correlated with the way of writing of the own user extracted in terms of psychological and emotional features. On the other hand, we focus on exploring how the concept of “homopohily”, namely, the tendency of individuals to associate and bond with similar individuals, previously studied in DellaPosta et al. (2015). In order to test this hypothesis, we have tested different models that are explained below.

16In this task, we use several sets of features with the purpose of trying to model user’s behaviour when writing a tweet. We obtain such features from text and the network.

17The complete set of features extracted from the data is depicted in Table 1. The set of features used in the model can be divided into five main types: psychological, emotional, Twitter-based, network-based, and language model features.

Table 1: Complete set of features extracted from the data

Category

Feature name

Description

Psychological features

pers_pred

personality prediction

self_pred

self-revealing prediction

info_pred

information-seeking prediction

action_pred

action-seeking prediction

fact_pred

fact-oriented prediction

Emotion freatures

arousal

mean arousal value

valence

mean valence value

russell

emotion value on Russell’s model

Twitter features

statuses_count

number of tweets posted by user

friends_count

number of following users

followers_count

number of follower users

created_at

account creation date

Network features

d_favor

mean distance to users in favor

d_against

mean distance to users against

d_none

mean distance to neutral users

Language model features

p_favor

prob. of tweet being in favor

p_against

prob. of tweet being against

p_none

prob. of tweet being neutral

Table 2: Official Results for Task A

Team

Model

Rank

F1avg

F1Against

F1Favour

F1None

DeepReading

Italian BERT XXL

3

66.21

75.80

56.63

42.13

Ixa

UmBERTo

4

64.73

76.16

53.30

38.88

Ixa

GilBERTo

6

61.71

75.43

48.00

36.75

DeepReading

XML-RoBERTa

8

60.04

69.66

50.42

39.16

-

baseline

12-13

57.84

71.58

44.09

27.64

18Psychological features. These features were extracted using a third-party API developed by Symanto6. Each tweet was sent to the API in order to retrieve the personality traits and communication styles obtained from the analysis of the tweet contents.

19The personality traits value would be either “emotional” or “rational” depending on the analysis of the user’s text. The value returned by the API when the communication styles are requested is a collection of traits, such as self-revealing, which means sharing one’s own experience and opinion; fact-oriented, which implies focusing on factual information, objective observations or statements; information-seeking, that is, posing questions; and action-seeking or aiming to trigger someone’s action by giving recommendation, requests or advice.

20Emotional features. In order to retrieve the emotion values from the tweets, we used Russell’s circumplex model of affect (Russell 1980). Russell argues that emotions can be conceptualized in a two-dimensional continuous space where the axes correspond to the degree of arousal and valence (or pleasure). These two dimensions form a Cartesian space that can be configured in a circular order in which the different combinations of valence and arousal correspond to one of four discrete emotion regions: tired, tense, excited, and pleased.

21The values for the degree of arousal and valence of the tweets were obtained using an adaptation to Italian language of the Affective Norms for English Words (ANEW) (Bradley and Lang 1999). This database was developed from translations of the 1,034 English words present in the ANEW dictionary and from words taken from Italian semantic norms (Montefinese et al. 2014).

22Twitter features. Exploring how the users behave in the social network could offer some insights on the stance tendency of the users. The collection of Twitter data of each user contained four features: the number of statuses published by the user, the number of users followed by the user, the number of users following the user, and the creation date of the Twitter account of the user.

23Network features. Using the FRIEND.csv data provided, we built a network consisting of 669817 nodes (or users) and 2847197 edges (or relationships) in order to represent the following network of the users. From that network, we extracted a sub-graph containing the users of known stance from the training data and the users involved in testing in order to calculate the mean distances of each user to the rest of known stance users using the following formula:

Image 100000000000009400000037846C9FD42F5BB25F.jpg

where |T| is the total number of users of a determined stance (AGAINST, FAVOR, NONE) and Image 100000000000002200000016F6D34D563304C629.jpgcorresponds to the square distance in users from node n to node i. From this calculation we obtained 3 values per user: mean distance to users against (dagainst), mean distance to users in favor (dfavor), and mean distance to neutral users (dnone)

24Language model features. In order to incorporate the language model results into the rest of the features of the system we choose the best performing, at the development phase, of the models described in Section 2.1, which was UmBERTo. Since this kind of language models use a great amount of features for learning and training, the strategy used in order to incorporate the language model without having a great imbalance in the number of features representing each category, consisted in extracting the probabilities assigned by the model to each class for each tweet. In this way, the language model would be present in 3 of the 18 features of the model, and it would therefore have a balanced size with regards to the rest of features of the model.

3. Results

3.1 Task A

25As we use the base version of every transformer model we can fine-tune them in a basic GPU of 12GB RAM. Hyperparameter tuning (batch size, maximum sequence length, learning rate and number of epochs) was performed on the development set. For mBERT, AlBERTo, Italian BERT XXL and UmBERTo the best configuration was: maximum sequence length 256, batch 32, learning rate 5e-5, and 5 epochs. For GilBERTo we used the same values except the number of epochs, which was increased to 10. Finally, the best performing hyperparameters for XLM-RoBERTa was the following: maximum sequence length 256, batch 16, learning rate 2e-5, and 10 epochs.

26While the monolingual models clearly outperformed both mBERT and XLM-RoBERTa on the development data, we decided to submit the three best monolingual runs and the best multilingual one. Table 2 reports the official results obtained by each of the models and their position with respect to the ranking of constrained runs for Task A released by the task organizers. Our submission based on Italian BERT XXL was clearly the best of our four runs, although its performance was around 1.5 scores in F1 lower than the winner system for Task A. Furthermore, the ranking obtained in the test does not correspond with the results obtained during the development phase, where UmBERTo outperformed the other monolingual models by more than 3 points in F1 score.

3.2 Task B

Table 3: Ranking results of model 1 to 5 in task B of the competition

Team

Model

Rank

F1avg

F1Against

F1Favour

F1None

Ixa

Model 5

1

74.45

85.62

63.29

42.14

DeepReading

Model 3

3

72.30

83.68

60.93

33.64

DeepReading

Model 4

4

72.22

83.00

61.43

42.51

UNED

Model 2

7

68.88

81.75

56.00

24.55

-

baseline

10-11

62.84

76.72

48.95

30.09

UNED

Model 1

13

53.13

73.99

32.26

20.00

27We presented a total of five models to Task B, which consisted of different combinations of the features listed in Table 1.

28Models 1, 2, and 3. During the training and development phases of the models, several configurations were tested on models 1, 2, and 3, including training with different classifiers, such as Random Forest Classifier, Decision Tree Classifier and XGBoost Classifier. The best performing classifier was XGBoost configured for multi-class classification and taking into account class weights in order to deal with the imbalance present in the data. XGBoost is an efficient and scalable implementation of gradient boosting framework by (Friedman 2001). With regards to the set of features, the first approach to the task considered only psychological, emotion, and Twitter features. For the second model, network features were added to the feature set. Finally, model 3 considered the probabilities of each class (AGAINST, FAVOR, NONE) predicted by the UmBERTo language model as three additional features for training.

29Models 4 and 5. These two models were constructed using voting based ensemble learning. The voting system for model 4 considered predictions of models 1, 2, and 3 as well as predictions by the best performing language models on the development data: UmBERTo, GilBERTo, and Italian BERT XXL, described in Section 2.1. The most common predicted value among the 6 systems was chosen as the final prediction of model 4. In case of having two or more values with the same counts, the final value is randomly selected. On the other hand, model 5 used a weighted voting ensemble learning in which each of the systems considered had as weight the F1 value obtained on the development data. Therefore, the model considered the weighted predictions of each system in order to choose the final prediction.

30Table 3 shows the official results obtained by each model and their position with respect to the ranking for Task B on Contextual Stance Detection. As it can be noted, model 5 ranked first in this task, obtaining an average F1 of 0.7445. Models 3 and 4 also had promising results in the official test set, ranking third and fourth, respectively, and just 0.0079 below the system which obtained the second best result. Model 2 had a slightly worse performance, ranking seventh from a total of 13, but still 0.0604 above the baseline. Finally, model 1 had the lowest performance, ranking last for the task.

4. Discussion

31Figure 1 shows the confusion matrices obtained from the released gold test data for each of the five runs submitted to task B. As it can be noticed, the performance of each model is increasingly better from the first to the fifth, as new features are added to them. The biggest increase, especially with respect to false positives in the AGAINST class, takes place from model 1 to model 2, that is, with the inclusion of network features into the model. This indicates that considering contextual information for stance detection tasks, such as the stance of those who are part of the friendship network of the user, can help determine their stance more accurately.

32Furthermore, we can see that predictions from model 3 also experimented a great increase in true positives of each of the classes. This increase is related to the inclusion of the language model into the features of model 2, which demonstrates the importance of textual data in stance detection tasks.

Figure 1

Image 10000000000001CD0000010A2F99C25A64356BFB.jpg

Confusion matrices for models 1 to 5 on test data.

33Finally, models 4 and 5 shows the adequacy of combining several complementary systems in order to improve results. Since each single model can detect the stance for different instances, a proper combination of them could outperform single models.

5. Conclusions and Future Work

34In this paper we have shown the benefits of exploiting information from different and heterogeneous sources. For our participation to the SardiStance 2020 shared task we have experimented with classifiers trained with the textual content of the tweets as well as with features based on social networks. This combination of features has allowed us to obtain the best overall results in the task.

35As future work, we plan to further explore the contribution of network information. Besides, we want to develop new divergent models and study how to combine them.

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Auteurs

NLP & IR Group, UNED, Spain – mespinosa@lsi.uned.es

HiTZ Center – Ixa, University of the Basque Country UPV/EHU – rodrigo.agerri@ehu.eus

NLP & IR Group, UNED, Spain – alvarory@lsi.uned.es

NLP & IR Group, UNED, Spain – rcenteno@lsi.uned.es

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