This paper illustrates the overview of the shared task on automatic speech recognition in the Tamil language. In the shared task, spontaneous Tamil speech data gathered from elderly and transgender people was given for recognition and evaluation. These utterances were collected from people when they communicated in the public locations such as hospitals, markets, vegetable shop, etc. The speech corpus includes utterances of male, female, and transgender and was split into training and testing data. The given task was evaluated using WER (Word Error Rate). The participants used the transformer-based model for automatic speech recognition. Different results using different pre-trained transformer models are discussed in this overview paper.
This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.
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