Abstract
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. Our goal is to build Artificial Intelligence (AI) systems that can assist children in their learning process. Within such complex frameworks, Spoken Dialogue Systems (SDS) are crucial building blocks to handle efficient task-oriented communication with children in game-based learning settings. We are working towards a multimodal dialogue system for younger kids learning basic math concepts. Our focus is on improving the Natural Language Understanding (NLU) module of the task-oriented SDS pipeline with limited datasets. This work explores the potential benefits of data augmentation with paraphrase generation for the NLU models trained on small task-specific datasets. We also investigate the effects of extracting entities for conceivably further data expansion. We have shown that paraphrasing with model-in-the-loop (MITL) strategies using small seed data is a promising approach yielding improved performance results for the Intent Recognition task.- Anthology ID:
- 2022.lrec-1.437
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4114–4125
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.437
- DOI:
- Cite (ACL):
- Eda Okur, Saurav Sahay, and Lama Nachman. 2022. Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4114–4125, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System (Okur et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.lrec-1.437.pdf
- Data
- ConceptNet, PARANMT-50M, PAWS