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Found 5 papers, 3 papers with code
A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction
However, these generative methods output a whole sequence consisting of term-status pairs in one stage and ignore integrating prior knowledge, which demands a deeper understanding to model the relationship between terms and infer the status of each term.
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition
In this paper, we first propose ViLaS (Vision and Language into Automatic Speech Recognition), a novel multimodal ASR model based on the continuous integrate-and-fire (CIF) mechanism, which can integrate visual and textual context simultaneously or separately, to facilitate speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Mixture of personality improved Spiking actor network for efficient multi-agent cooperation
Inspired by this two-step psychology theory, we propose a biologically plausible mixture of personality (MoP) improved spiking actor network (SAN), whereby a determinantal point process is used to simulate the complex formation and integration of different types of personality in MoP, and dynamic and spiking neurons are incorporated into the SAN for the efficient reinforcement learning.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding
Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems.
