CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations
This addresses the problem of limited datasets for discourse parsing in realistic, multi-modal, and code-mixed settings, which is incremental as it extends existing resources.
The authors tackled the lack of diverse datasets for discourse parsing in conversations by introducing CoMuMDR, a code-mixed Hindi-English corpus with audio and text across multiple domains, annotated with nine discourse relations, and found that state-of-the-art models performed poorly on it.
Discourse parsing is an important task useful for NLU applications such as summarization, machine comprehension, and emotion recognition. The current discourse parsing datasets based on conversations consists of written English dialogues restricted to a single domain. In this resource paper, we introduce CoMuMDR: Code-mixed Multi-modal Multi-domain corpus for Discourse paRsing in conversations. The corpus (code-mixed in Hindi and English) has both audio and transcribed text and is annotated with nine discourse relations. We experiment with various SoTA baseline models; the poor performance of SoTA models highlights the challenges of multi-domain code-mixed corpus, pointing towards the need for developing better models for such realistic settings.