# mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling

arXiv: https://arxiv.org/abs/2203.12940 · HTML: https://mlsanity.com/paper/2203.12940/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://mlsanity.com/paper/2109.14084/) (2021)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Towards Zero-Shot Frame Semantic Parsing for Domain Scaling](https://mlsanity.com/paper/1707.02363/) (2017)
- [Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://mlsanity.com/paper/1906.06870/) (2019)
- [Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling](https://mlsanity.com/paper/2004.11727/) (2020)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)

## Built on by (most cited first, 4 of 4)

- [Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling](https://mlsanity.com/paper/2310.15294/) (2023)
- [MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU](https://mlsanity.com/paper/2408.08144/) (2024)
- [Topics in Contextualised Attention Embeddings](https://mlsanity.com/paper/2301.04339/) (2023)
- [HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling](https://mlsanity.com/paper/2310.09135/) (2023)

## Abstract (arXiv)

Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain. One of the important factors in zero-shot learning is to make the model learn generalized and reliable representations. For this purpose, we present mcBERT, which stands for momentum contrastive learning with BERT, to develop a robust zero-shot slot filling model. mcBERT uses BERT to initialize the two encoders, the query encoder and key encoder, and is trained by applying momentum contrastive learning. Our experimental results on the SNIPS benchmark show that mcBERT substantially outperforms the previous models, recording a new state-of-the-art. Besides, we also show that each component composing mcBERT contributes to the performance improvement.