Abstract
Learning-based slot filling - a key component of spoken language understanding systems - typically requires a large amount of in-domain hand-labeled data for training. In this paper, we propose a novel two-stage model architecture that can be trained with only a few in-domain hand-labeled examples. The first step is designed to remove non-slot tokens (i.e., O labeled tokens), as they introduce noise in the input of slot filling models. This step is domain-agnostic and therefore, can be trained by exploiting out-of-domain data. The second step identifies slot names only for slot tokens by using state-of-the-art pretrained contextual embeddings such as ELMO and BERT. We show that our approach outperforms other state-of-art systems on the SNIPS benchmark dataset.- Anthology ID:
- 2020.sustainlp-1.10
- Volume:
- Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- sustainlp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 73–82
- Language:
- URL:
- https://aclanthology.org/2020.sustainlp-1.10
- DOI:
- 10.18653/v1/2020.sustainlp-1.10
- Cite (ACL):
- Cennet Oguz and Ngoc Thang Vu. 2020. A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings. In Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, pages 73–82, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Two-stage Model for Slot Filling in Low-resource Settings: Domain-agnostic Non-slot Reduction and Pretrained Contextual Embeddings (Oguz & Vu, sustainlp 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.sustainlp-1.10.pdf
- Data
- SNIPS