Ehi Nosakhare


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Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Samyadeep Basu | Amr Sharaf | Karine Ip Kiun Chong | Alex Fischer | Vishal Rohra | Michael Amoake | Hazem El-Hammamy | Ehi Nosakhare | Vijay Ramani | Benjamin Han
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems are not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and data augmentation methods can benefit these existing meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed approaches outperform standard meta-learning methods: contrastive losses as a regularizer in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin

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SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
Apurva Gandhi | Ryan Serrao | Biyi Fang | Gilbert Antonius | Jenna Hong | Tra My Nguyen | Sheng Yi | Ehi Nosakhare | Irene Shaffer | Soundararajan Srinivasan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or “inked”) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.