Atmosphere kamaal ka tha (Was Wonderful): A Multilingual Joint Learning Framework for Aspect Category Detection and Sentiment Classification

Code-mixing refers to switching between two or more languages within the same utterance, which is very prevalent in multilingual societies. The amount of code-mixed content has increased due to the spike in multilingual users on review platforms. Analyzing these reviews can be beneficial for both consumers and service providers. Aspect category (AC) sentiment analysis (ACSA) provides a fine-grained analysis of reviews. ACSA identifies the AC and measures the sentiment expressed toward a given AC. The research in this direction has mostly focused on monolingual languages, which are insufficient for analyzing code-mixed reviews. To expedite research in this direction, we propose new tasks in the code-mixed language (ACSA-Mix). We develop a benchmark setup to create a code-mixed Hinglish (i.e., mixing of Hindi and English) dataset for ACSA-Mix, annotated with AC and sentiment values. To demonstrate the practical usage of the dataset, we solve ACSA-Mix tasks in the Seq2Seq framework, where natural language sentences are generated to represent the outputs that allow pretrained language models to be used effectively. Further, a multilingual multitask joint learning framework is proposed that transfers knowledge between Hinglish (ACSA-Mix) and English (ACSA) tasks. We consider ACSA-Mix tasks the primary tasks and enhance their performance by ACSA tasks (auxiliary) by sharing knowledge between them. We observe improvement over the single task ACSA-Mix models.
^1
1

The dataset has been made available on https://www.iitp.ac.in/ ai-nlp-ml/resources.html and at Github repository: https://github.com/20118/ACSA-Mix