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Found 7 papers, 4 papers with code
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline
To effectively address this, it is imperative to consider both the speech-level, crucial for recognition accuracy, and the text-level, critical for user-friendliness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks
In this paper, we propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers.
You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona
The agent selects the proper knowledge and persona to use for generating the answers with our candidate scoring implemented with a poly-encoder.
KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks
As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential.
GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP).
Ranked #2 on
Dialog Relation Extraction
on DialogRE
Dialog Relation Extraction
Emotion Recognition in Conversation
+1
Learning source-aware representations of music in a discrete latent space
To address this issue, we propose a novel method to learn source-awarelatent representations of music through Vector-Quantized Variational Auto-Encoder(VQ-VAE). We train our VQ-VAE to encode an input mixture into a tensor of integers in a discrete latentspace, and design them to have a decomposed structure which allows humans to manipulatethe latent vector in a source-aware manner.
LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation
Conditioned source separations have attracted significant attention because of their flexibility, applicability and extensionality.
