An Emergent Approach to Text Analysis Based on a Connectionist Model and the Web

In this paper, we present a method to provide proactive assistance in text checking, based on usage relationships between words structuralized on the Web. For a given sentence, the method builds a connectionist structure of relationships between word n-grams. Such structure is then parameterized by means of an unsupervised and language agnostic optimization process. Finally, the method provides a representation of the sentence that allows emerging the least prominent usage-based relational patterns, helping to easily find badly-written and unpopular text. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach and some experimental use

Multiple Admissibility in Language Learning: : Judging Grammaticality using Unlabeled Data

We present our work on the problem of detection Multiple Admissibility (MA) in language learning. Multiple Admissibility occurs when more than one grammatical form of a word fits syntactically and semantically in a given context. In second-language education—in particular, in intelligent tutoring systems/computer-aided language learning (ITS/CALL), systems generate exercises automatically. MA implies that multiple alternative answers are possible. We treat the problem as a grammaticality judgement task. We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a "simulated learner corpus": a dataset with correct text and with artificial errors, generated automatically. While MA occurs commonly in many languages, this paper focuses on learning Russian. We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by users of the Revita language learning system.Peer reviewe

Interactive correction and recommendation for computer language learning and training

Active learning and training is a particularly effective form of education. In various domains, skills are equally important to knowledge. We present an automated learning and skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides meaningful, knowledge-level feedback such as correction of student solutions and personalised guidance through recommendations. Specifically, we address automated synchronous feedback and recommendations based on personalised performance assessment. At the core of the tutoring system is a pattern-based error classification and correction component that analyses student input in order to provide immediate feedback and in order to diagnose student weaknesses and suggest further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions and comparison techniques based on equivalence rules and pattern matching are specific approaches

Empirically determined strategic input and gamification in mastering Russian word forms

Source at https://scholarsarchive.byu.edu/rlj/We propose two designs to gamify second language (L2) learning of Russian inflectional morphology: Treasure Hunt and Story Time. The goal of these designs is to focus learning on high-frequency word forms that are most strategic and effective for L2 acquisition in a way that stimulates engagement and builds lifelong learning skills. These two gamification designs emerged from a student focus group that was convened to propose implementations for the SMARTool (see Section 3). After an initial brainstorming session, the ideas were further developed by the instructor, honed by the students, and tested in class. Students have also contributed to and commented on the contents of this article. In Section 2 we briefly identify the problem, namely, the enormous number of paradigm forms potentially present in Russian paradigms and their skewed distribution. We cite research showing that inflectional morphology is a major hurdle for L2 learners but not for native speakers, who use only a fraction of the potential forms and can easily understand and produce forms that they have never encountered. Furthermore, evidence demonstrates that learning can be enhanced by strategically concentrating on the highest-frequency forms. Access to the highest-frequency forms of over 3,000 lexemes is provided by the SMARTool described in Section 3, but that resource is relatively static, meaning that more guidance is needed on how to implement this tool in the classroom and in self-study. Our two proposed designs are presented in Section 4 (Treasure Hunt) and Section 5 (Story Time). Conclusions are offered in Section 6

The use of neural machine translation in translating Finnish news articles:an error analysis of the NMT service DeepL

Abstract. In this thesis, a brief overview of the functionality of the neural machine translation system DeepL is provided. Machine translation is an expanding field in translation studies, and it continuously provides us with new technology and applications to translate texts as accurately as possible. The aim of this study was to examine the capabilities of DeepL in translating Finnish news articles with no available reference translations. The reason for this was to prevent DeepL from finding completed translations and possibly benefitting from them, as neural machine translation systems seek data from sources available on the internet. The articles were selected randomly from the free internet news providers Yle and Iltalehti. The errors have been listed, categorized and analyzed in the section “Analysis”. Conclusions along with general discussion about the performance of DeepL can be found in the last section of this thesis. Overall, this thesis shows DeepL’s promising capability when translating news text. Still, it must be kept in mind that the aim of this thesis was not to seek perfect translations, but rather successful message transmission. The idea of machine translation being a worthy competitor to human-made translations in more specific areas of translation, such as medical or legal translation, is still far away. Still, any conducted research is vital for the progression and development of machine translation services. The analysis of this study provides examples of areas where DeepL is not sufficient. These areas include for example, the translation of new words, translation of pronouns and culture-specific terms. Instances when DeepL succeeds to make acceptable translations in one of the listed categories, have been presented also in the analysis section.Neuroverkkokääntämisen käyttö käännettäessä suomalaisia uutisartikkeleita : virheanalyysi DeepL:n toiminnasta. Tiivistelmä. Kandidaatintyössäni käsitellään neuroverkkoihin perustuvan käännösohjelman, DeepL:n suoriutumista käännettäessä suomalaisia uutistekstejä. Käytettävät tekstit on poimittu Ylen ja Iltalehden ilmaisista nettiuutispalveluista. Työssä käytettyjä uutistekstejä ei ole käännetty englannin kielelle. Tämä oletettavasti välttää konekääntöohjelmien hyötymisen valmiista käännöksistä, joka on tärkeää ottaen huomioon neuroverkkoihin perustuvien kääntämisohjelmien toimintaperusteet. Konekääntäminen on aiheena trendikäs, ja kuuluu nykypäivänä sovellusten myötä osaksi lähes jokaisen elämää. Alan kehittymisen voi selvästi havaita tarkkailemalla koko ajan uudistuvia konekääntöohjelmia. Käännetyistä teksteistä havaitut virheet on analysoitu virheanalyysin muodossa, ja johtopäätökset esitetty tutkielman lopussa. Virheanalyysi on jaettu kategorioihin virhetyyppien perusteella. Yleisellä tasolla voinee todeta, että DeepL suoriutuu hyvin käännettäessä suomalaista uutistekstiä. Tämän tutkimuksen tavoitteena ei kuitenkaan ollut etsiä täydellistä kääntämistä, vaan onnistunutta viestinvälitystä. Eri kääntämisen alat, kuten lääketieteellinen kääntäminen ja lakitekstikääntäminen vaativat äärimmäistä tarkkuutta, ja tässä työssä esitettävien virhe-esimerkkien perusteella voinee todeta, että konekääntäminen ei vielä sovellu vaikkapa edellä mainittujen alojen tekstien kääntämiseen. Konekääntäminen on joka tapauksessa toimiva apuväline jokapäiväisiin kieleen liittyviin ongelmiin, ja sen tutkiminen on tärkeää sen kehittämiselle. Analyysini antaa pintaraapaisun siitä, missä DeepL:n kaltaiset konekääntöohjelmat eivät vielä suoriudu. Virheitä löytyi esimerkiksi pronominien käytössä, kulttuurille omien termien käännöksessä ja uudissanojen kääntämisessä. Analyysissä on esitelty myös esimerkkejä tapauksista, joissa DeepL suoriutuu kääntämään tiettyyn kategoriaan liittyvän tekstin osan onnistuneesti

Significant Relationships between EFL Teachers’ Practice and Knowledge in the Teaching of Grammar in Libyan Secondary Schools

Studies of teacher cognition and the teaching of grammar have attracted increasing research attention in recent years, yet relatively little has been published about how EFL teachers working in secondary schools teach grammar compared to what they know about their teaching. The present study considers this relationship by looking at eight teachers and investigating if their knowledge is consistent with their instructional practice. The value of this study is that it examines the current situation in grammar teaching by exploring how knowledge may influence performance in secondary school, teaching in the Libyan context. Observation and semi-structured interviews were employed to collect the necessary data. A factual questionnaire was used to collect background information and then to choose the most appropriate participants in a sample of eight who were more and less experienced teachers and both male and female. Purposive sampling was used to select the sample. Data were transcribed and encoded for analysis according to grounded theory principles, and a framework was designed to analyse the coded data in order to triangulate the findings gathered from observation and interviews. The findings revealed that grammar was taught using different approaches and techniques, but there was no single way of teaching that worked perfectly with all classes. What did not work for one teacher worked for another in certain cases. The teachers had different levels of knowledge which was not always reflected in their classroom practice. The more experienced teachers had better practical knowledge, although all had similar levels of theoretical knowledge about teaching and learning English grammar. This study offers a more profound understanding of the complex relationship between teachers’ practice and their knowledge about teaching grammar. Different patterns of incongruence and congruence between practice and knowledge are acknowledged, such as ‘teachers knew but did not do’; ‘teachers did but were not aware that they did’; and ‘teachers did and they knew’. Some of the most interesting findings in this study have not been reported before, and it is clear that not all relationships of congruence between practice and knowledge have positive pedagogical value, and not all incongruent relationships have negative value. The rationales behind of all of these relationships between practice and knowledge were related to the complex relationship between teachers’ practice and knowledge and contextual factors. Thus, the implications of this research should benefit future EFL teachers of grammar and open doors to further research