A Portuguese Native Language Identification Dataset

In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency parses. NLI-PT can be used not only in NLI but also in research on several topics in the field of Second Language Acquisition and educational NLP. We discuss possible applications of this dataset and present the results obtained for the first lexical baseline system for Portuguese NLI.

section_name: Introduction
paragraphs: Several learner corpora have been compiled for English, such as the International Corpus of Learner English BIBREF0 . The importance of such resources has been increasingly recognized across a variety of research areas, from Second Language Acquisition to Natural Language Processing. Recently, we have seen substantial growth in this area and new corpora for languages other than English have appeared. For Romance languages, there are a several corpora and resources for French, Spanish BIBREF1 , and Italian BIBREF2 .
Portuguese has also received attention in the compilation of learner corpora. There are two corpora compiled at the School of Arts and Humanities of the University of Lisbon: the corpus Recolha de dados de Aprendizagem do Português Língua Estrangeira (hereafter, Leiria corpus), with 470 texts and 70,500 tokens, and the Learner Corpus of Portuguese as Second/Foreign Language, COPLE2 BIBREF3 , with 1,058 texts and 201,921 tokens. The Corpus de Produções Escritas de Aprendentes de PL2, PEAPL2 compiled at the University of Coimbra, contains 516 texts and 119,381 tokens. Finally, the Corpus de Aquisição de L2, CAL2, compiled at the New University of Lisbon, contains 1,380 texts and 281,301 words, and it includes texts produced by adults and children, as well as a spoken subset.
The aforementioned Portuguese learner corpora contain very useful data for research, particularly for Native Language Identification (NLI), a task that has received much attention in recent years. NLI is the task of determining the native language (L1) of an author based on their second language (L2) linguistic productions BIBREF4 . NLI works by identifying language use patterns that are common to groups of speakers of the same native language. This process is underpinned by the presupposition that an author’s L1 disposes them towards certain language production patterns in their L2, as influenced by their mother tongue. A major motivation for NLI is studying second language acquisition. NLI models can enable analysis of inter-L1 linguistic differences, allowing us to study the language learning process and develop L1-specific pedagogical methods and materials.
However, there are limitations to using existing Portuguese data for NLI. An important issue is that the different corpora each contain data collected from different L1 backgrounds in varying amounts; they would need to be combined to have sufficient data for an NLI study. Another challenge concerns the annotations as only two of the corpora (PEAPL2 and COPLE2) are linguistically annotated, and this is limited to POS tags. The different data formats used by each corpus presents yet another challenge to their usage.
In this paper we present NLI-PT, a dataset collected for Portuguese NLI. The dataset is made freely available for research purposes. With the goal of unifying learner data collected from various sources, listed in Section "Collection methodology" , we applied a methodology which has been previously used for the compilation of language variety corpora BIBREF5 . The data was converted to a unified data format and uniformly annotated at different linguistic levels as described in Section "Preprocessing and annotation of texts" . To the best of our knowledge, NLI-PT is the only Portuguese dataset developed specifically for NLI, this will open avenues for research in this area.
section_name: Related Work
paragraphs: NLI has attracted a lot of attention in recent years. Due to the availability of suitable data, as discussed earlier, this attention has been particularly focused on English. The most notable examples are the two editions of the NLI shared task organized in 2013 BIBREF6 and 2017 BIBREF7 .
Even though most NLI research has been carried out on English data, an important research trend in recent years has been the application of NLI methods to other languages, as discussed in multilingual-nli. Recent NLI studies on languages other than English include Arabic BIBREF8 and Chinese BIBREF9 , BIBREF10 . To the best of our knowledge, no study has been published on Portuguese and the NLI-PT dataset opens new possibilities of research for Portuguese. In Section "A Baseline for Portuguese NLI" we present the first simple baseline results for this task.
Finally, as NLI-PT can be used in other applications besides NLI, it is important to point out that a number of studies have been published on educational NLP applications for Portuguese and on the compilation of learner language resources for Portuguese. Examples of such studies include grammatical error correction BIBREF11 , automated essay scoring BIBREF12 , academic word lists BIBREF13 , and the learner corpora presented in the previous section.
section_name: Collection methodology
paragraphs: The data was collected from three different learner corpora of Portuguese: (i) COPLE2; (ii) Leiria corpus, and (iii) PEAPL2 as presented in Table 1 .
The three corpora contain written productions from learners of Portuguese with different proficiency levels and native languages (L1s). In the dataset we included all the data in COPLE2 and sections of PEAPL2 and Leiria corpus.
The main variable we used for text selection was the presence of specific L1s. Since the three corpora consider different L1s, we decided to use the L1s present in the largest corpus, COPLE2, as the reference. Therefore, we included in the dataset texts corresponding to the following 15 L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. It was the case that some of the L1s present in COPLE2 were not documented in the other corpora. The number of texts from each L1 is presented in Table 2 .
Concerning the corpus design, there is some variability among the sources we used. Leiria corpus and PEAPL2 followed a similar approach for data collection and show a close design. They consider a close list of topics, called “stimulus”, which belong to three general areas: (i) the individual; (ii) the society; (iii) the environment. Those topics are presented to the students in order to produce a written text. As a whole, texts from PEAPL2 and Leiria represent 36 different stimuli or topics in the dataset. In COPLE2 corpus the written texts correspond to written exercises done during Portuguese lessons, or to official Portuguese proficiency tests. For this reason, the topics considered in COPLE2 corpus are different from the topics in Leiria and PEAPL2. The number of topics is also larger in COPLE2 corpus: 149 different topics. There is some overlap between the different topics considered in COPLE2, that is, some topics deal with the same subject. This overlap allowed us to reorganize COPLE2 topics in our dataset, reducing them to 112.
Due to the different distribution of topics in the source corpora, the 148 topics in the dataset are not represented uniformly. Three topics account for a 48.7% of the total texts and, on the other hand, a 72% of the topics are represented by 1-10 texts (Figure 1 ). This variability affects also text length. The longest text has 787 tokens and the shortest has only 16 tokens. Most texts, however, range roughly from 150 to 250 tokens. To better understand the distribution of texts in terms of word length we plot a histogram of all texts with their word length in bins of 10 (1-10 tokens, 11-20 tokens, 21-30 tokens and so on) (Figure 2 ).
The three corpora use the proficiency levels defined in the Common European Framework of Reference for Languages (CEFR), but they show differences in the number of levels they consider. There are five proficiency levels in COPLE2 and PEAPL2: A1, A2, B1, B2, and C1. But there are 3 levels in Leiria corpus: A, B, and C. The number of texts included from each proficiency level is presented in Table 4 .
section_name: Preprocessing and annotation of texts
paragraphs: As demonstrated earlier, these learner corpora use different formats. COPLE2 is mainly codified in XML, although it gives the possibility of getting the student version of the essay in TXT format. PEAPL2 and Leiria corpus are compiled in TXT format. In both corpora, the TXT files contain the student version with special annotations from the transcription. For the NLI experiments we were interested in a clean txt version of the students' text, together with versions annotated at different linguistics levels. Therefore, as a first step, we removed all the annotations corresponding to the transcription process in PEAPL2 and Leiria files. As a second step, we proceeded to the linguistic annotation of the texts using different NLP tools.
We annotated the dataset at two levels: Part of Speech (POS) and syntax. We performed the annotation with freely available tools for the Portuguese language. For POS we added a simple POS, that is, only type of word, and a fine-grained POS, which is the type of word plus its morphological features. We used the LX Parser BIBREF14 , for the simple POS and the Portuguese morphological module of Freeling BIBREF15 , for detailed POS. Concerning syntactic annotations, we included constituency and dependency annotations. For constituency parsing, we used the LX Parser, and for dependency, the DepPattern toolkit BIBREF16 .
section_name: Applications
paragraphs: NLI-PT was developed primarily for NLI, but it can be used for other research purposes ranging from second language acquisition to educational NLP applications. Here are a few examples of applications in which the dataset can be used:
section_name: A Baseline for Portuguese NLI
paragraphs: To demonstrate the usefulness of the dataset we present the first lexical baseline for Portuguese NLI using a sub-set of NLI-PT. To the best of our knowledge, no study has been published on Portuguese NLI and our work fills this gap.
In this experiment we included the five L1s in NLI-PT which contain the largest number of texts in this sub-set and run a simple linear SVM BIBREF21 classifier using a bag of words model to identify the L1 of each text. The languages included in this experiment were Chinese (355 texts), English (236 texts), German (214 texts), Italian (216 texts), and Spanish (271 texts).
We evaluated the model using stratified 10-fold cross-validation, achieving 70% accuracy. An important limitation of this experiment is that it does not account for topic bias, an important issue in NLI BIBREF22 . This is due to the fact that NLI-PT is not balanced by topic and the model could be learning topic associations instead. In future work we would like to carry out using syntactic features such as function words, syntactic relations and POS annotation.
section_name: Conclusion and Future Work
paragraphs: This paper presented NLI-PT, the first Portuguese dataset compiled for NLI. NLI-PT contains 1,868 texts written by speakers of 15 L1s amounting to over 380,000 tokens.
As discussed in Section "Applications" , NLI-PT opens several avenues for future research. It can be used for different research purposes beyond NLI such as grammatical error correction and CALL. An experiment with the texts written by the speakers of five L1s: Chinese, English, German, Italian, and Spanish using a bag of words model achieved 70% accuracy. We are currently experimenting with different features taking advantage of the annotation available in NLI-PT thus reducing topic bias in classification.
In future work we would like to include more texts in the dataset following the same methodology and annotation.
section_name: Acknowledgement
paragraphs: We want to thank the research teams that have made available the data we used in this work: Centro de Estudos de Linguística Geral e Aplicada at Universidade de Coimbra (specially Cristina Martins) and Centro de Linguística da Universidade de Lisboa (particularly Amália Mendes).
This work was partially supported by Fundação para a Ciência e a Tecnologia (postdoctoral research grant SFRH/BPD/109914/2015).

Questions and Answers:
question: Are the annotations automatic or manually created?
answers: answer: unanswerable: no
evidence: We annotated the dataset at two levels: Part of Speech (POS) and syntax. We performed the annotation with freely available tools for the Portuguese language. For POS we added a simple POS, that is, only type of word, and a fine-grained POS, which is the type of word plus its morphological features. We used the LX Parser BIBREF14 , for the simple POS and the Portuguese morphological module of Freeling BIBREF15 , for detailed POS. Concerning syntactic annotations, we included constituency and dependency annotations. For constituency parsing, we used the LX Parser, and for dependency, the DepPattern toolkit BIBREF16 .
free_form_answer: Automatic
answer: unanswerable: no
extractive_spans: We performed the annotation with freely available tools for the Portuguese language.
evidence: We annotated the dataset at two levels: Part of Speech (POS) and syntax. We performed the annotation with freely available tools for the Portuguese language. For POS we added a simple POS, that is, only type of word, and a fine-grained POS, which is the type of word plus its morphological features. We used the LX Parser BIBREF14 , for the simple POS and the Portuguese morphological module of Freeling BIBREF15 , for detailed POS. Concerning syntactic annotations, we included constituency and dependency annotations. For constituency parsing, we used the LX Parser, and for dependency, the DepPattern toolkit BIBREF16 .
question: Do the errors of the model reflect linguistic similarity between different L1s?
answers: answer: unanswerable: yes
question: Is the dataset balanced between speakers of different L1s?
answers: answer: unanswerable: no
yes_no: no
evidence: FLOAT SELECTED: Table 2: Distribution by L1s and source corpora.
answer: unanswerable: no
evidence: FLOAT SELECTED: Table 2: Distribution by L1s and source corpora.
yes_no: no
question: How long are the essays on average?
answers: answer: unanswerable: no
evidence: FLOAT SELECTED: Figure 2: Histogram of document lengths, as measured by the number of tokens. The mean value is 204 with standard deviation of 103.
free_form_answer: 204 tokens
answer: unanswerable: no
extractive_spans: Most texts, however, range roughly from 150 to 250 tokens.
evidence: Due to the different distribution of topics in the source corpora, the 148 topics in the dataset are not represented uniformly. Three topics account for a 48.7% of the total texts and, on the other hand, a 72% of the topics are represented by 1-10 texts (Figure 1 ). This variability affects also text length. The longest text has 787 tokens and the shortest has only 16 tokens. Most texts, however, range roughly from 150 to 250 tokens. To better understand the distribution of texts in terms of word length we plot a histogram of all texts with their word length in bins of 10 (1-10 tokens, 11-20 tokens, 21-30 tokens and so on) (Figure 2 ).

Figure and Table Captions:
caption: Table 1: Distribution of the dataset: Number of texts, tokens, types, and type/token ratio (TTER) per source corpus.
caption: Figure 1: Topic distribution by number of texts. Each bar represents one of the 148 topics.
caption: Table 2: Distribution by L1s and source corpora.
caption: Table 3: Number of different topics by source.
caption: Figure 2: Histogram of document lengths, as measured by the number of tokens. The mean value is 204 with standard deviation of 103.
caption: Table 4: Distribution by proficiency levels and by source corpus.