Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering
Existing research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, the rapid growth of multi-lingual web content has posed several challenges to the existing QA systems. Code-mixing is one such challenge that makes the task more complex. In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA). For evaluation, we manually create the code-mixed questions for Hindi-English language pair. In order to show the effectiveness of our neural network based CMQA technique, we utilize two benchmark datasets, SQuAD and MMQA. Experiments show that our proposed model achieves encouraging performance on CMQG and CMQA.
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringQuestion GenerationQuestion-GenerationSimilar Papers 제목 키워드 기반
Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text
This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocate…
Language ModelingLanguage ModellingPOSPOS Tagging+1Persona-aware Generative Model for Code-mixed Language
Code-mixing and script-mixing are prevalent across online social networks and multilingual societies. However, a user's preference toward code-mixing depends on the socioeconomic status, demographics of the user, and the…
DecodermodelvalidUncovering the Hidden Threat of Text Watermarking from Users with Cross-Lingual Knowledge
In this study, we delve into the hidden threats posed to text watermarking by users with cross-lingual knowledge. While most research focuses on watermarking methods for English, there is a significant gap in evaluating …
Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities
Amidst the rapid advances of large language models (LLMs), most LLMs still struggle with mixed-language inputs, limited Codeswitching (CSW) datasets, and evaluation biases, which hinder their deployment in multilingual s…
CalBERT - Code-mixed Adaptive Language representations using BERT
A code-mixed language is a type of language that involves the combination of two or more language varieties in its script or speech. Code-mixed language has become increasingly prevalent in recent times, especially on so…
Sentiment Analysis