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Found 7 papers, 0 papers with code
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
The Frozen large LM is then prompted to predict a task output based on the rationale generated by the lightweight LM.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.
Contextual Domain Classification with Temporal Representations
In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities.
Exploring Transfer Learning For End-to-End Spoken Language Understanding
Since our model can process both speech and text input sequences and learn to predict a target sequence, it also allows us to do zero-shot E2E SLU by training on only text-hypothesis data (without any speech) from a new domain.
Ranked #3 on
Spoken Language Understanding
on Snips-SmartLights
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention
The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
A Re-ranker Scheme for Integrating Large Scale NLU models
An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated.
