Summary of Transforming Nlu with Babylon: a Case Study in Development Of Real-time, Edge-efficient, Multi-intent Translation System For Automated Drive-thru Ordering, by Mostafa Varzaneh et al.
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering
by Mostafa Varzaneh, Pooja Voladoddi, Tanmay Bakshi, Uma Gunturi
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Babylon, a transformer-based architecture designed to handle Natural Language Understanding (NLU) tasks in dynamic outdoor environments, such as automated drive-thru systems. The proposed model tackles NLU as an intent translation task, converting natural language inputs into sequences of regular language units that encode both intents and slot information. This approach enables Babylon to manage multi-intent scenarios in a single dialogue turn. Additionally, the architecture incorporates an LSTM-based token pooling mechanism to preprocess phoneme sequences, reducing input length and optimizing for low-latency, low-memory edge deployment. The paper highlights the importance of robustness to errors from upstream Automatic Speech Recognition (ASR) outputs, which are often noisy in these environments. Experimental results show that Babylon achieves significantly better accuracy-latency-memory footprint trade-offs over typically employed NMT models like Flan-T5 and BART. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using a drive-thru ordering system where you can talk to the computer and it understands what you want. This paper introduces a new way for computers to understand natural language, called Babylon. It’s designed to work in noisy environments like drive-thrus, where there may be background noise or different accents. The model is good at handling multiple requests at once and can even correct mistakes made by the system that converts spoken words into text. This technology has potential applications in other areas, such as ticketing kiosks. |
Keywords
» Artificial intelligence » Language understanding » Lstm » T5 » Token » Transformer » Translation
