Towards Personalised Simplification based on L2 Learners’ Native Language
Authors: Alessio Palmero Aprosio, Stefano Menini, Sara Tonelli, Luca Ducceschi, Leonardo Herzog (Fondazione Bruno Kessler, Università di Trento, 2018)
Topic: Text Simplification

Abstract
We present an approach to imEnglish. We present an approach to improve the selection of complex words for automatic text simpliﬁcation, addressing the need of L2 learners to take into account their native language during simpliﬁcation. In particular, we develop a methodology that automatically identiﬁes ‘difﬁcult’ terms (i.e. false friends) for L2 learners in order to simplify them. We evaluate not only the quality of the detected false friends but also the impact of this methodology on text simpliﬁcation compared with a standard frequency-based approach.

Introduction
The task of automated text simpliﬁcation has been investigated within the NLP community for several years with a number of different approaches, from rule-based ones (Siddharthan, 2010; Barlacchi and Tonelli, 2013; Scarton et al., 2017) to supervised (Bingel and Søgaard, 2016; AlvaManchego et al., 2017) and unsupervised ones (Paetzold and Specia, 2016), including recent studies using deep learning (Zhang and Lapata, 2017; Nisioi et al., 2017). Nevertheless, only recently researchers have started to build simpliﬁcation systems that can adapt to users, based on the observation that the preceived simplicity of a document depends a lot on the user proﬁle, including not only speciﬁc disabilities but also language proﬁciency, age, profession, etc. Therefore in the last few months the ﬁrst approaches to personalised text simpliﬁcation have been proposed at major conferences, with the goal of simplifying a document for different language proﬁciency levels (Scarton and Specia, 2018; Bingel et al., 2018; Lee and Yeung, 2018). Along this research line, we present in this paper an approach to perform automated lexical simpliﬁcation for L2 learners, able to adapt to the user mother tongue. To our knowledge, this is the ﬁrst work taking into account this aspect and presenting a solution that, given an Italian document and the user’s mother tongue as input, selects only the words that the user may ﬁnd difﬁcult given his/her knowledge of another language. Speciﬁcally, we detect and simplify automatically the terms that may be misleading for the user because they are false friends, while we do not simplify those that have an orthographically and semantically similar translation in the user native language (so-called cognates). In multilingual settings, for instance while teaching, learning or translating a foreign language, these two phenomena have proven to be very relevant (Ringbom, 1986), because the lexical similarities between the two languages in contact have proven to create interferences, favouring or hindering the course of learning. We compare our approach to the selection of words to be simpliﬁed with a standard frequencybased one, in which only the terms that are not listed in De Mauro’s Dictionary of Basic Italian1 are simpliﬁed, regardless of the user native language. Our experiments are evaluated on the Italian-French pair, but the approach is generic.

Conclusion
In this work, we have presented an approach supporting personalized simpliﬁcation in that it enables to adapt the selection of difﬁcult words for lexical simpliﬁcation to the native language of L2 learners. To our knowledge, this is the ﬁrst attempt to deal with this kind of adaptation. The approach is relatively easy to apply to new languages provided that they have a similar alphabet, since multilingual embeddings are already available and lists of cognates and false friends, although of limited size, can be easily retrieved online.8 The work will be extended along different research directions: ﬁrst, we will evaluate the approach on other language pairs. Then, we will add a lexical simpliﬁcation module selecting only the words identiﬁed as complex by our approach. For this, we can rely on existing simpliﬁcation tools (Paetzold and Specia, 2015), which could be tuned to adapt also the simpliﬁcation choices to the user native language, for example by changing the candidate ranking algorithm. Finally, it would be interesting to involve L2 learners in the evaluation, with the goal to measure the effectiveness of different simpliﬁcation strategies in a real setting.