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Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

 | 
Felice Dell'Orletta
, 
Johanna Monti
, 
Fabio Tamburini

Contributed Papers

Italian Transformers Under the Linguistic Lens

Alessio Miaschi, Gabriele Sarti, Dominique Brunato, Felice Dell’Orletta et Giulia Venturi

Résumé

In this paper we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language. In particular, we investigate whether and how using different architectures of probing models affects the performance of Italian transformers in encoding a wide spectrum of linguistic features. Moreover, we explore how this implicit knowledge varies according to different textual genres.

Texte intégral

1. Introduction and Background

1In the last few years, the study of Neural Language Models (NLMs) and their representations has become a key research area in the NLP community. Several methods have been devised to obtain meaningful explanations regarding the linguistic information encoded in NLMs (Belinkov and Glass 2019). The most common approach is based on the development of probes, i.e. supervised models trained to predict a variety of language properties using the contextual word/sentence embeddings of a pre-trained model (Conneau et al. 2018; Zhang and Bowman 2018; Miaschi and Dell’Orletta 2020). This approach demonstrated that NLMs representations encode linguistic knowledge in a hierarchical manner (Belinkov et al. 2017; Blevins, Levy, and Zettlemoyer 2018; Tenney et al. 2019), and can even support the extraction of dependency parse trees (Hewitt and Manning 2019). investigated the representations learned by BERT (Devlin et al. 2019), one of the most prominent NLM, across its layers, showing that lower ones are usually better for capturing surface features, while embeddings from higher layers are better for syntactic and semantic properties. Using a suite of probing tasks, Tenney et al. (2019a) deeply explore this behavior showing that the linguistic knowledge encoded by BERT through its 12/24 layers follows the traditional NLP pipeline.

2While the vast majority of this research focused on English contextual representations, relatively little work has been done to understand the inner workings of non-English models. The study by de Vries et al. (2020) represents an exception in this context: authors apply the probing task approach to compare the linguistic competence encoded by a Dutch BERT-based model and multilingual BERT (mBERT), showing that earlier layers of mBERT are consistently more informative that earlier layers of the monolingual model. The survey by Nozza et al. (2020) also provides a comparative study of mBERT and language-specific BERT models but focused on the performance that each model obtains after training on several specific downstream tasks.

3In this paper, we adopt a task-agnostic perspective to carry out an in-depth investigation of the linguistic knowledge implicitly encoded by 6 Italian monolingual models and multilingual BERT. We define a broad set of probing tasks, each corresponding to a specific property of sentence structure. We then compare the average performance reached by each model in predicting the feature value, evaluating the results obtained by models using their layer-wise sentence-level representations. A further comparative perspective, which to our knowledge is still rather under-investigated, concerns the study of how the architecture of the probing model itself influences probing scores. To address this point, for each model, we perform the same suite of probing tasks using both a linear SVR and a multilayer perceptron (MLP), and compare whether and how each probing task’s resolution is affected by the two architectures.

4Since all experiments were carried out on different sections of Italian Universal Dependency Treebank (Nivre et al. 2016), we were also able to investigate how linguistic knowledge of NLMs varies according to different textual genres.

Contributions

5To the best of our knowledge, this is the first study aimed at comparing the linguistic knowledge encoded in the representations of multiple non-English pre-trained transformer models. In particular: (i) we compare the probing performances of 6 Italian NLMs spanning three models over multiple linguistic feature categories; (ii) we investigate whether and how using different architectures of probing models affects the performance of transformers in encoding specific features; and (iii) we show how the implicit knowledge learned by these models differs across textual genres.

2. Approach

6To inspect the inner knowledge of language encoded by Italian Transformers, we relied on a suite of 82 probing tasks, each of which corresponds to predicting the value of a corresponding feature modeling a specific property of the sentence. We designed two sets of experiments. The first one consists in comparing the linguistic knowledge encoded by the Italian Transformers and evaluating the best probing model for inferring such knowledge from the NLMs. We compared the results obtained with two simple probing models, a linear SVR and a multilayer perceptron (MLP), which take as input layer-wise sentence-level representations extracted from the Italian models. These representations are produced for each sentence of different sections of the Italian Universal Dependency Treebank (IUDT), version 2.5 (Zeman et al. 2019), and used to predict the actual value of each probing feature. In the second set of experiments, we evaluated how the Italian models’ linguistic knowledge differs across textual genres and varieties, considering different IUDT sections.

2.1 Models and Data

Table 1

Name

Training data

BERT Architecture

Multilingual-BERT

Wikipedia

BERT-base-italian1

Wikipedia + OPUS (13GB) (Tiedemann and Nygaard 2004)

AlBERTo2

TWITA (191GB) (Basile, Lai, and Sanguinetti 2018)

RoBERTa Architecture

GilBERTo3

OSCAR (71GB) (Suárez, Sagot, and Romary 2019)

UmBERTo-Commoncrawl

OSCAR (69GB)

UmBERTo-Wikipedia4

Wikipedia (7GB)

GPT-2 Architecture

GePpeTto5

Wikipedia + ItWAC (14GB) (Baroni et al. 2009)

NLMs used in the experiments

7We relied on 7 pre-trained Italian Transformers models. Models statistics are reported in Table 1. Sentence level representations were computed performing a Mean-pooling operation over the word embeddings provided by the models.

8NLM’s linguistic competences are probed against five IUDT sections including texts representative of different textual varieties and genres. As shown in the overview in Table 2, we also distinguish the whole ISDT into different sub–corpora according to the specific language variety they represent, e.g. transcription of spontaneous speech (ISDT_europarl), questions (ISDT_quest) or simplified language (ISDT_2parole).

Table 2

Short Name

Types of texts

# sent

ParTUT (Sanguinetti and Bosco 2015)

Multi-genre

2,090

VIT (Delmonte, Bristot, and Tonelli 2007)

Multi-genre

10,087

ISDT (Bosco, Montemagni, and Simi 2013)

Multi-genre

14,167

ISDT_tanl

Newswire

4,043

ISDT_tut

Legal/Newswire/Wiki

3,802

ISDT_quest

Interrogative sentences

2,162

ISDT_2parole

Simplified Italian news

1,421

ISDT_europarl

EU Parliament acts

497

PoSTWITA (Sanguinetti et al. 2018)

Tweets

6,713

TWITTIRÒ (Cignarella, Bosco, and Rosso 2019)

Ironic Tweets

1,424

Total

35,481

Sections of the Italian Universal Dependency Treebank (IUDT)

2.2 Probing features

Table 3

Linguistic Feature

Raw Text Properties

Sentence Length

Word Length

Vocabulary Richness

Type/Token Ratio for words and lemmas

Morphosyntactic information

Distibution of UD and language–specific POS

Lexical density

Inflectional morphology

Inflectional morphology of lexical verbs and auxiliaries

Verbal Predicate Structure

Distribution of verbal heads and verbal roots

Verb arity and distribution of verbs by arity

Global and Local Parsed Tree Structures

Depth of the whole syntactic tree

Average length of dependency links and of the longest link

Average length of prepositional chains and distribution by depth

Clause length

Relative order of elements

Order of subject and object

Syntactic Relations

Distribution of dependency relations

Use of Subordination

Distribution of subordinate and principal clauses

Average length of subordination chains and distribution by depth

Relative order of subordinate clauses

Probing Features used in the experiments

9The set of probing tasks consists of predicting the value of a specific linguistic feature automatically extracted from each POS tagged and dependency parsed sentence of the IUTD datasets.

10The set of features is based on the ones described in and are acquired from raw, morpho-syntactic and syntactic levels of annotation and can be categorised in 9 groups corresponding to different linguistic phenomena. As shown in Table 3, these features model linguistic phenomena ranging from raw text one , to morpho–syntactic information and inflectional properties of verbs, to more complex aspects of sentence structure capturing global and local properties of the whole parsed tree and of specific subtrees, such as the order of subjects and objects with respect to the verb, the distribution of UD syntactic relations, also including features referring to the use of subordination and to the structure of verbal predicates.

11All these features have been shown to play a highly predictive role when leveraged by traditional learning models on a variety of classification problems, covering different aspects of stylometric and complexity analysis. In addition, in their recent work, showed that these features can be effectively used to profile the knowledge encoded in the language representations of a pretrained NLM, specifically the English Bert, and how it changes across layers. Since these features are based on the UD formalism, which guarantees the comparative encoding of language phenomena between the two languages (Nivre 2015), we focused on the same set to investigate the linguistic knowledge of Italian transformers.

3. Results

12Average R2 scores for all the NLMs obtained with the LinearSVR and the MLP probing models. Baseline scores are also reported.

Groups

LinearSVR

MLP

Baseline

RawText

0.84

0.80

0.50

Vocabulary

0.70

0.34

0.19

POS

0.69

0.68

0.03

VerbInflection

0.50

0.61

0.03

VerbPredicate

0.32

0.43

0.08

TreeStructure

0.61

0.64

0.40

Order

0.46

0.55

0.06

SyntacticDep

0.65

0.74

0.04

Subord

0.49

0.60

0.16

AllFeatures

0.60

0.64

0.10

  • 6 The Coefficient of determination (R2) is a statistical measure of how close the data are to the fit (...)

13We first investigate which is the best architecture for probing the linguistic knowledge encoded by the Italian Transformers. Since many of our probing features are strongly related to sentence length, we compared the two probing models’ results with the ones obtained by a baseline corresponding to a LinearSVR model trained using only sentence length as input feature. Table 4 reports average R^2 results6 for all the 7 NLMs obtained with the LinearSVR and the MLP probing models, along with baseline scores. The MLP probe is a three-layer feedforward network with ReLU activations and was selected to investigate the presence of nonlinear relations in representations, which could hamper the probing performance of the LinearSVM probe, but would be highlighted by a sharp difference between MLP and LinearSVM performances. As a first remark, we notice that both probing models outperform the baseline. This proves that all NLMs encode a spectrum of phenomena that, although related to sentence length, require a more sophisticated linguistic knowledge to be accurately predicted. Best scores are obtained with the MLP model, which achieved higher R^2 scores especially for features grouping more complex syntactic phenomena (e.g. TreeStructure, SyntacticDep). Interestingly enough, the LinearSVR model outperforms the MLP by more than .30 R^2 points when predicting features related to vocabulary richness (Vocabulary).

Figure 1

Figure 1

Average R2 scores obtained by each NLM with the two probing models.

Figure 2

Figure 2

Average layerwise R2 scores obtained with the LinearSVR (top) and the MLP (bottom) using the internal representations of the 7 NLMs.

14In order to ensure that our probes are actually showing the linguistic generalization abilities of the NLMs rather than learning the linguistic tasks, we also tested the probing models using the control task approach devised in Hewitt and Liang (2019). We produced a control version of the IUDT corpus by randomly shuffling the linguistic features assigned to each sentence and performed the same probing tasks with the two probing classifiers for all NLMs representations. The correlation and R2 scores between regressors’ predictions and shuffled scores were low (< 0.05) and comparable for both the SVR and the MLP. These results support the claim that NLMs representations encode information closely related to linguistic competence and that our probing models are not relying on spurious signals unrelated to our linguistic properties to solve the regression task.

Figure 3

Figure 3

Average LinearSVM $R2$ score considering all the UD Italian sentences (all) and according to the 10 treebanks previously described.

15To investigate how each transformer encodes the linguistic knowledge, we report in Figure 1 average R2 scores obtained with the two probing models for all the 7 NLMs. As we can notice, the seven transformers achieve quite similar results when considering all features as a whole, although BERT-base-italian has the best overall performance (0.65 for all features). The same did not hold when we analyzed their performances in terms of R2 scores for the different previously described groups of features. For instance, we can notice that, for both the probing models, features related to the distribution of syntactic relations (SyntacticDep) are better predicted by GePpeTto, while GilBERTo and UmBERTo-Commoncrawl are the best ones in the prediction of tree structure properites. Differences hold for what regards competencies related to vocabulary richness (Vocabulary): while UmBERTo-Wikipedia extensively outperforms all the other transformers using the MLP model, the best transformer is BERT-base-italian when these competences are probed with the LinearSVR model.

16Similar trends can be observed in Figure 2, where we report how the linguistic knowledge encoded by the 7 NLMs evolves across layers according to the two probing models. Regardless of the architectures, for all transformers, raw text features (RawText) are mainly encoded in the first layers, while the knowledge about the order of subject/object (Order) and the use of subordination (Subord) increases consistently across layers and specifically in the first ones. Contrarily to what was observed by de Vries et al. (2020), mBERT’s linguistic knowledge is not encoded systematically earlier than in monolingual transformers. This perspective of analysis also reveals other differences among the considered transformers: e.g.  even though GePpeTto has a lower average competence on verb inflection (see Figure 1), it achieves the highest scores in the middle layers. Focusing instead on differences between layerwise scores obtained by the two probing models, we can clearly notice that the encoding of linguistic knowledge shows a quite rough trend for what concerns the results obtained with the MLP. This is particularly the case of features belonging to the vocabulary, POS and tree structure groups.

17Finally, we inspected whether the overall linguistic competence encoded in the contextual representations of each model changes according to the type of texts in the different IUDT sections we considered. As we could expect, the results reported in Figure 3 show that all transformers achieve lower performance when they have to predict the value of features extracted from treebanks representative of social media language (PoSTWITA and TWITTIRÒ). Quite surprisingly, it is also the case of AlBERTo which is trained on Twitter data. A possible explanation is that, although PoSTWITA and TWITTIRÒ contain sentences representative of Twitter language, these sentences are still quite close to the Italian standard language, in order to be compliant with the UD morpho-syntactic and syntactic annotation schema. On the contrary, AlBERTo’s training set is derived from Twitter’s official streaming API that included all possible typologies of sentences. However, bert-base italian is slightly less affected by the non-standard linguistic peculiarities of this genre. Similarly to what is observed for the whole Italian dataset (see Figure 1), this model also reaches the highest performance in almost all different IUDT sections, except for the one containing interrogative sentences (isdt_quest). Interestingly, this type of sentence is hardly mastered by all models. This is possible due to the fact that interrogative sentences are more likely to display a less canonical distribution of morphosyntactic and syntactic phenomena, hence being more difficult to encode effectively.

4. Conclusion

18In this paper we presented an in-depth comparative investigation of the linguistic knowledge encoded in the Italian transformer models. Relying on a suite of more than 80 probing features and testing our approach with two different probing models, we showed that MLP is the best model for inferring the amount of information implicitly encoded in the NLMs representations. We also observed that BERT-base-italian achieved best scores in average, but the linguistic generalization abilities of the examined transformers vary according to specific groups of linguistic phenomena and across layers. Finally, we examined how the linguistic knowledge learned by the NLMs is affected by the distinct textual varieties available in Italian treebanks showing, for instance, that social media language represents a harder domain for all models.

19We are currently investigating if the linguistic knowledge encoded by a NLM positively affects the resolution of downstream tasks, as already suggested by the recent work by Miaschi et al. (2020) for English. This connection, which is still rather investigated, can improve our understanding of how such models make their decisions.

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Notes

1 https://github.com/dbmdz/berts

2 Polignano et al. (2019)

3 https://github.com/idb-ita/GilBERTo

4 https://github.com/musixmatchresearch/umberto

5 De Mattei et al. (2020)

6 The Coefficient of determination (R2) is a statistical measure of how close the data are to the fitted regression line and corresponds to the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

Table des illustrations

Titre Figure 1
Légende Average R2 scores obtained by each NLM with the two probing models.
URL http://books.openedition.org/aaccademia/docannexe/image/8745/img-1.jpg
Fichier image/jpeg, 164k
Titre Figure 2
Légende Average layerwise R2 scores obtained with the LinearSVR (top) and the MLP (bottom) using the internal representations of the 7 NLMs.
URL http://books.openedition.org/aaccademia/docannexe/image/8745/img-2.jpg
Fichier image/jpeg, 603k
Titre Figure 3
Légende Average LinearSVM $R2$ score considering all the UD Italian sentences (all) and according to the 10 treebanks previously described.
URL http://books.openedition.org/aaccademia/docannexe/image/8745/img-3.jpg
Fichier image/jpeg, 237k

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