Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters
By Marc Velay, Fabrice Daniel. 2018
This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot. It presents two techniques, sequence to sequence learning and Multi-Task Learning, which might improve on the previous task.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Andrew M. Dai
- Jugal Kumar Kalita
- Quoc V. Le
- Kelvin Guu
- Adams Wei Yu
- Jason Wei
- Mohit Bansal
- Joseph Worsham
Citations in the corpus
- Finetuned Language Models Are Zero-Shot Learners (2021, background): “…formulation of multitask learning (MTL), which is an established area within deep learning (Collobert et al., 2011; Luong et al., 2016; Ruder, 2017; Velay & Daniel, 2018; Clark et al., 2019b; Liu et al., 2019b, inter alia)—see Worsham & Kalita (2020) for a recent survey on MTL for NLP. Differing…”
- Multi-task learning for natural language processing in the 2020s: where are we going? (2020, methodology): “This includes specific deep learning architectures such as MTL seq2seq models (Velay and Daniel, 2018) and MTL transformers (Liu et al., 2019a).”