MTL-SLT: Multi-Task Learning for Spoken Language Tasks

no code implementations • NLP4ConvAI (ACL) 2022 • Zhiqi Huang, Milind Rao, Anirudh Raju, Zhe Zhang, Bach Bui, Chul Lee

The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

On joint training with interfaces for spoken language understanding

no code implementations • 30 Jun 2021 • Anirudh Raju, Milind Rao, Gautam Tiwari, Pranav Dheram, Bryan Anderson, Zhe Zhang, Chul Lee, Bach Bui, Ariya Rastrow

Spoken language understanding (SLU) systems extract both text transcripts and semantics associated with intents and slots from input speech utterances.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces

no code implementations • 14 Aug 2020 • Milind Rao, Anirudh Raju, Pranav Dheram, Bach Bui, Ariya Rastrow

Finally, we contrast these methods to a jointly trained end-to-end joint SLU model, consisting of ASR and NLU subsystems which are connected by a neural network based interface instead of text, that produces transcripts as well as NLU interpretation.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4