Cross-Lingual Lexico-Semantic Transfer in Language Learning

نویسندگان

  • Ekaterina Kochmar
  • Ekaterina Shutova
چکیده

Lexico-semantic knowledge of our native language provides an initial foundation for second language learning. In this paper, we investigate whether and to what extent the lexico-semantic models of the native language (L1) are transferred to the second language (L2). Specifically, we focus on the problem of lexical choice and investigate it in the context of three typologically diverse languages: Russian, Spanish and English. We show that a statistical semantic model learned from L1 data improves automatic error detection in L2 for the speakers of the respective L1. Finally, we investigate whether the semantic model learned from a particular L1 is portable to other, typologically related languages.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

English-Persian Plagiarism Detection based on a Semantic Approach

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-li...

متن کامل

Learning Monolingual Compositional Representations via Bilingual Supervision

Bilingual models that capture the semantics of sentences are typically only evaluated on cross-lingual transfer tasks such as cross-lingual document categorization or machine translation. In this work, we evaluate the quality of the monolingual representations learned with a variant of the bilingual compositional model of Hermann and Blunsom (2014), when viewing translations in a second languag...

متن کامل

Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources

Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables ...

متن کامل

Vector Disambiguation for Translation Extraction from Comparable Corpora

We present a new data-driven approach for enhancing the extraction of translation equivalents from comparable corpora which exploits bilingual lexico-semantic knowledge harvested from a parallel corpus. First, the bilingual lexicon obtained from word-aligning the parallel corpus replaces an external seed dictionary, making the approach knowledge-light and portable. Next, instead of using simple...

متن کامل

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation

Existing approaches to automatic VerbNetstyle verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word em...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016