Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text

TL;DR

This paper investigates various input representations and deep learning models for language identification in Hindi-English code-mixed text, finding sub-word embeddings with LSTM yield the highest accuracy.

Contribution

It systematically evaluates input representations and deep learning architectures for language identification in code-mixed text, highlighting the effectiveness of sub-word embeddings with LSTM.

Findings

Sub-word representations outperform character and word embeddings.

LSTM models achieve higher accuracy than CNN models.

Best accuracy of 94.52% on standard dataset.

Abstract

Natural language processing (NLP) techniques have become mainstream in the recent decade. Most of these advances are attributed to the processing of a single language. More recently, with the extensive growth of social media platforms focus has shifted to code-mixed text. The code-mixed text comprises text written in more than one language. People naturally tend to combine local language with global languages like English. To process such texts, current NLP techniques are not sufficient. As a first step, the text is processed to identify the language of the words in the text. In this work, we focus on language identification in code-mixed sentences for Hindi-English mixed text. The task of language identification is formulated as a token classification task. In the supervised setting, each word in the sentence has an associated language label. We evaluate different deep learning models and input representation combinations for this task. Mainly, character, sub-word, and word embeddings are considered in combination with CNN and LSTM based models. We show that sub-word representation along with the LSTM model gives the best results. In general sub-word representations perform significantly better than other input representations. We report the best accuracy of 94.52% using a single layer LSTM model on the standard SAIL ICON 2017 test set.