Overview for the First Shared Task on Language Identification in Code-Switched Data

Abstract

We present an overview of the first shared task on language identification on codeswitched data.The shared task included code-switched data from four language pairs: Modern Standard ArabicDialectal Arabic (MSA-DA), MandarinEnglish (MAN-EN), Nepali-English (NEP-EN), and Spanish-English (SPA-EN). A total of seven teams participated in the task and submitted 42 system runs. The evaluation showed that language identification at the token level is more difficult when the languages present are closely related, as in the case of MSA-DA, where the prediction performance was the lowest among all language pairs. In contrast, the language pairs with the higest F-measure where SPA-EN and NEP-EN. The task made evident that language identification in code-switched data is still far from solved and warrants further research.

AIDA: Identifying Code Switching in Informal Arabic Text

“…The shared task for "Language Identification in Code-Switched Data" (Solorio et al, 2014) aims at allowing participants to perform wordlevel language identification in code-switched Spanish-English, MSA-DA, Chinese-English and Nepalese-English data. In this work, we only focus on MSA-DA data.…”

“…The system relies on Language Models and a tool for morphological analysis and disambiguation for Arabic to identify the class of each word in a given sentence. We evaluate the performance of our system on the test datasets of the shared task at the EMNLP workshop on Computational Approaches to Code Switching (Solorio et al, 2014). The system yields an average token-level F β=1 score of 93.6%, 77.7% and 80.1%, on the first, second, and surprise-genre test-sets, respectively, and a tweet-level F β=1 score of 4.4%, 36% and 27.7%, on the same test-sets.…”

DCU-UVT: Word-Level Language Classification with Code-Mixed Data

“…This paper describes DCU-UVT's participation in the shared task Language Identification in Code-Switched Data (Solorio et al, 2014) at the Workshop on Computational Approaches to Code Switching, EMNLP, 2014. The task is to make word-level predictions (six labels: lang1, lang2, ne, mixed, ambiguous and other) for mixedlanguage user generated content.…”

Incremental N-gram Approach for Language Identification in Code-Switched Text

“…The main aim of this paper is to describe my system submission to the Computational Approaches to Code Switching task (Solorio et al, 2014). The training dataset provided for the classification task were tweets composed of Spanish and English words or Nepali and English words.…”