Abstract
This chapter presents research on the teaching-learning of Portuguese as a host language, based on the exploration of authentic informational and institutional texts targeting migrant and refugee people, and considering that successful host language teaching must correspond to the needs of its target audience. The chapter discusses methods of defining and identifying criteria and features to monitor official texts with regard to inclusiveness and bias. It provides insights on how to select real texts to be used in task-based language teaching approaches for inclusive host language teaching. Departing from a real corpus analysis, the potential and the limitations of existing guidelines to inclusiveness for the assessment of real texts are shown, as well as other still neglected issues. Furthermore, this chapter provides future research directions to an effective and reliable assessment of inclusive texts that can serve as inclusive host language teaching materials through NLP and machine learning approaches.
TopIntroduction
Gathering knowledge and expertise from several subareas of Linguistics, the work discussed in this chapter concerns preliminary research on the teaching-learning of Portuguese as a host language through Natural Language Processing (NLP) approaches to real texts, aiming at informing machine learning systems. Successful host language teaching must correspond to the needs of its target audience – migrant population and people on the move for the widest range of reasons. Also, the needs of the hosting community must be met, considering, in particular, the goals of a well-succeeded and inclusive integration for recently arrived people. This means providing relevant and pragmatic information regarding many aspects of daily life, on the one hand, and of institutional and legal constraints, on the other hand. These two aspects should enable an autonomous, free and unconstrained life and participation in society for those arriving in a host-country.
Language skills have been considered as a form of host-country specific human capital in Economics since the early 1980s (Carliner, 1981; McManus et al., 1983), and several studies clearly establish the negative consequences of language barriers in fields such as health care (Jaeger et al., 2019), labour market performance and income (Chiswick & Miller, 2014; Miranda & Zhu, 2013), demographic outcomes, children education and residential choice (Bleakley & Chin, 2010; Chen, 2013; Guven & Islam, 2015). Language learning is thus a key factor that promotes socio-economic integration, as well as access to rights and services (CoE, 2018).
Task-Based Language Teaching framework may contribute directly to the achievement of these goals by providing the means to train use-oriented language skills in an integrated way, thus being particularly adequate for inclusive host language teaching (Ellis, 2017; Long, 2014).
Having these two quite simple assumptions in mind, this chapter has as main objectives:
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To discuss methods and ways of defining and identifying criteria and features to monitor official (informational and institutional) texts targeting migrant and refugee people, with regard to inclusiveness and bias,
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To provide insights on how to select real texts to be used in task-based language teaching approaches for inclusive host language teaching.
It is organized in five main sections presenting: (1) the background and related work concerning the concepts and approaches to integration in general, and integration through language in particular; (2) Portuguese official texts targeting migrant and refugee people, with regard to the research issues pursued and methods used, and the set of data collected; (3) the linguistic cues and constructions expressing bias or inclusiveness, namely in what concerns the type of information extracted from the corpus and the results obtained; (4) the assessment and selection of materials for inclusive host language teaching, accounting for semantic values relevant for inclusiveness, and recommendations for selecting appropriate language teaching materials; and (5) final remarks and future research directions on featuring inclusiveness in texts.
Key Terms in this Chapter
Corpus Linguistics: Subfield of linguistics that studies language from large sets of electronically stored language data, collected and compiled from real world usage using specific criteria to assure representativeness, and analysed using dedicated NLP tools.
Host Language: The official and/or communication language spoken in a given geopolitical territory.
Aggregation: The act or process of bringing together individuals in a collective organization or a whole.
Task-Based Language Teaching: Evidence-based approach to language teaching, including curriculum design and the development of teaching materials, using actual and real communicative needs and focusing on the achievement of a specific goal.
Linguistic Bias: The set of linguistic expressions – words, phrases, or constructions – that denote social, religious or other type of bias, either intentionally or by cultural and historical reasons.
Machine Learning: Subfield of computer science that studies and develops computation algorithms that improve automatically through experience, based on sample data.