Spoken Language Understanding
118 papers with code • 5 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Spoken Language Understanding
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Libraries
Use these libraries to find Spoken Language Understanding models and implementationsDatasets
SLURP
Dialogue State Tracking Challenge
Spoken-SQuAD
xSID
MEDIA
Timers and Such
Most implemented papers
Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding
MiuLab/Time-SLU
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However, the previous model only paid attention to the content in history utterances without considering their temporal information and speaker roles.
Towards end-to-end spoken language understanding
Spoken language understanding system is traditionally designed as a pipeline of a number of components.
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Reading comprehension has been widely studied.
How Time Matters: Learning Time-Decay Attention for Contextual Spoken Language Understanding in Dialogues
MiuLab/Time-Decay-SLU
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NAACL 2018
Spoken language understanding (SLU) is an essential component in conversational systems.
ISO-Standard Domain-Independent Dialogue Act Tagging for Conversational Agents
Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue system.
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance.
Fully Statistical Neural Belief Tracking
nmrksic/neural-belief-tracker
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ACL 2018
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).
Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
huagong-chung/PtrNet_SLU
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ACL 2018
We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data.
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
momohuang/FlowQA
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ICLR 2019
Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question.
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents
sxjscience/GluonNLP-Slot-Filling
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Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer.
