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

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

KIPoS: Part-of-speech Tagging on Spoken Language

UniBO @ KIPoS: Fine-tuning the Italian “BERTology" for PoS-tagging Spoken Data

Fabio Tamburini

Résumé

The use of contextualised word embeddings allowed for a relevant performance increase for almost all Natural Language Processing (NLP) applications. Recently some new models especially developed for Italian became available to scholars. This work aims at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al. 2020).

Texte intégral

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

1. Introduction

  • 1 Copyright ©️2020 for this paper by its authors. Use permitted under Creative Commons License Attrib (...)

1The introduction of contextualised word embeddings, starting with ELMo (Peters et al. 2018) and in particular with BERT (Devlin et al. 2019) and the subsequent BERT-inspired transformer models (Liu et al. 2019; Martin et al. 2020; Sanh et al. 2019), marked a strong revolution in Natural Language Processing (NLP), boosting the performance of almost all applications and especially those based on statistical analysis and Deep Neural Networks (DNN).1

2This work heavily refers to an upcoming work of the same author (Tamburini 2020) experimenting various contextualised word embeddings for Italian to a number of different tasks and it is aimed at applying simple fine-tuning methods for producing high-performance solutions at the EVALITA KIPOS PoS-tagging task (Bosco et al. 2020; Basile et al. 2020).

2. Italian “BERTology”

3The availability of various powerful computational solutions for the community allowed for the development of some BERT-derived models trained specifically on big Italian corpora of various textual types. All these models have been taken into account for our evaluation. In particular we considered those models that, at the time of writing, are the only one available for Italian:

  • Multilingual BERT2: with the first BERT release Google developed also a multilingual model (‘bert-base-multilingual-cased’ – bertMC) that can be applied also for processing Italian texts.

  • AlBERTo3: last year a research group from the University of Bari developed a brand new model for Italian especially devoted to Twitter texts and social media (‘m-polignano-uniba/bert_uncased_L-12_H-768_A-12_italian_alb3rt0’ – alUC) (Polignano et al. 2019). Only the uncased model is available to the community. Due to the specific training of alUC, it requires a particular pre-processing step for replacing hashtags, urls, etc. that alter the official tokenisation, rendering it not really applicable to word-based classification tasks in general texts; thus, it will be used only for working on twitter or social media data. In any case we tested it in all considered tasks and, whenever results were reasonable, we reported them.

  • GilBERTo4: it is a rather new CamemBERT Italian model (‘idb-ita/gilberto-uncased-from-camembert’ – giUC) trained by using the huge Italian Web corpus section of the OSCAR (Ortis Suárez, Sagot, and Romary 2019) project. Also for GilBERTo it is available only the uncased model.

  • UmBERTo5: the more recent model developed explicitly for Italian, as far as we know, is UmBERTo (‘Musixmatch/umberto-commoncrawl-cased-v1’ – umC). As well as GilBERTo, it has been trained by using OSCAR, but the produced model, differently from GilBERTo, is cased.

3. KIPOS 2020 PoS-tagging Task

4Part-of-speech tagging is a very basic task in NLP and a lot of applications rely on precise PoS-tag assignments. Spoken data present further challenges for PoS-taggers: small datasets for system training, short training sentences, less constrained language, the massive presence of interjections, etc. are all examples of phenomena that increase the difficulties for building reliable automatic systems.

5The PoS-tagging system used for our experiments is very simple and consist of a slight modification to the fine tuning script ‘run_ner.py’ available with the version 2.7.0 of the Huggingface/Transformers package6. We did not employ any hyperparameter tuning, and, as the stopping criterion, we fixed the number of epoch to 10 and chose the UmBERTo model on the basis of the previous experience (Tamburini 2020). After the challenge, we evaluated all the BERT-derived models in order to propose a complete overview of the available resources.

Table 1: PoS-tagging Accuracy for the EVALITA KIPOS 2020 benchmark for the Main Task. The Fine-TuningumC has been submitted for the challenge as the system “UniBO”

System

Main Task Accuracy

Form.

Inform.

Both

Fine-TuningumC

93.49

91.13

92.26

Fine-TuninggiUC

92.96

89.92

91.38

Fine-TuningalUC

90.02

89.82

89.92

Fine-TuningbertMC

91.67

88.05

89.79

2nd ranked system

87.56

88.24

87.91

3rd ranked system

81.58

79.37

80.43

6Table 1 shows the results obtained by fine tuning all the considered BERT-derived models for the Main Task. A very relevant increase in performance w.r.t. the other participants is evident looking at the results and UmBERTo is consistently the best system.

7We did not participate at the official challenge for the two subtasks, but we included the results of our best system also for these tasks into this report. Tables 2 and 3 show the results compared with the other two participating systems.

Table 2: PoS-tagging Accuracy for the EVALITA KIPOS 2020 benchmark for the Sub-Task A

System

Sub-Task A Accuracy

Form.

Inform.

Both

Other Participant 1

87.37

87.58

87.48

Fine-TuningumC

86.47

83.16

84.75

Other participant 2

78.73

75.79

77.20

Table 3: PoS-tagging Accuracy for the EVALITA KIPOS 2020 benchmark for the Sub-Task B.

System

Sub-Task B Accuracy

Form.

Inform.

Both

Fine-TuningumC

89.74

89.52

89.63

Other participant 1

87.81

88.10

87.96

Other Participant 2

77.11

77.50

77.31

8Again, the simple fine tuning of a BERT-derived model, namely UnBERTo, exhibits the best performance on Sub-task B. The small amount of data could probably affect the results on Sub-task A.

9We collected the most frequent errors produced by the proposed system: Table 4 shows that, unexpectedly, the most frequent misclassifications involve grammatical words. The typical behaviour of the classical PoS-taggers tend to wrongly classify lexical words, namely nouns, verbs and adjectives, intermixing their classes. Apparently, on this dataset, grammatical words appear to be more complex to classify than lexical words. This behaviour should be investigated more appropriately by using bigger datasets and better consistency checks on the annotated data.

Table 4: Error Analysis

Formal

#mistakes

Gold tag

System tag

19

ADP_A

ADP

16

CCONJ

ADV

12

PROPN

X

10

NOUN.LIN

X

10

ADJ

VERB

Informal

#mistakes

Gold tag

System tag

59

PRON

SCONJ

38

ADP_A

ADP

22

ADV

CCONJ

15

NUM

DET

15

INTJ

PARA

15

CCONJ

ADV

12

NOUN

PROPN

10

VERB_PRON

VERB

4. Discussion and Conclusions

10The starting idea of this work was to design the simplest DNN model for Italian PoS-tagging after the ‘BERT-revolution’ thanks to the recent availability of Italian BERT-derived models. Looking at the results presented in previous sections, we can certainly conclude that BERT-derived models, specifically trained on Italian texts, allow for a relevant increase in performance also when applied to spoken language by simple fine-tuning procedures. The multilingual BERT model developed by Google was not able to produce good results and should not be used when are available specific models for the studied language.

11A side, and sad, consideration that emerges from this study regards the complexity of the models. All the DNN models used in this work involved very simple fine-tuning processes of some BERT-derived model. Machine learning and Deep learning changed completely the approaches to NLP solutions, but never before we were in a situation in which a single methodological approach can solve different NLP problems always establishing the state-of-the-art for that problem. Moreover, we did not apply any parameter tuning at all and fixed the early stopping criterion on 10 epochs without any optimisation. By tuning all the hyperparameters, it is reasonable we can further increase the overall performance.

Bibliographie

Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. 2020. “EVALITA 2020: Overview of the 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian.” In Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (Evalita 2020), edited by Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. Online: CEUR.org.

C. Bosco, S. Ballarè, M. Cerruti, E. Goria, and C. Mauri. 2020. “KIPoS@EVALITA2020: Overview of the Task on KIParla Part of Speech tagging.” In Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020), edited by Valerio Basile, Danilo Croce, Maria Di Maro, and Lucia C. Passaro. Online: CEUR.org.

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M. Polignano, P. Basile, M. de Gemmis, G. Semeraro, and V. Basile. 2019. “ALBERTO: Italian BERT Language Understanding Model for NLP Challenging Tasks Based on Tweets.” In Proceedings of the Sixth Italian Conference on Computational Linguistics (CLiC-it 2019). Bari, Italy.

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Notes

1 Copyright ©️2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

2 https://github.com/google-research/bert

3 https://github.com/marcopoli/AlBERTo-it

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

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

6 https://github.com/huggingface/transformers

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