Search Results for author:
Found 14 papers, 6 papers with code
A Closer Look at Few-Shot Out-of-Distribution Intent Detection
Comprehensive experiments on three real-world intent detection benchmark datasets demonstrate the high effectiveness of our proposed approach and its great potential in improving state-of-the-art methods for few-shot OOD intent detection.
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM
In this paper, we propose to streamline the semantic tokenization and generative recommendation process with a unified framework, dubbed STORE, which leverages a single large language model (LLM) for both tasks.
Do self-supervised speech and language models extract similar representations as human brain?
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception.
Leveraging Label Information for Multimodal Emotion Recognition
Finally, we devise a novel label-guided attentive fusion module to fuse the label-aware text and speech representations for emotion classification.
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
Neighborhood-based Hard Negative Mining for Sequential Recommendation
Motivated by this observation, we propose a Graph-based Negative sampling approach based on Neighborhood Overlap (GNNO) to exploit structural information hidden in user behaviors for negative mining.
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
With the availability of large-scale, comprehensive, and general-purpose vision-language (VL) datasets such as MSCOCO, vision-language pre-training (VLP) has become an active area of research and proven to be effective for various VL tasks such as visual-question answering.
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
UFO2: A unified pre-training framework for online and offline speech recognition
In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training
Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification
User intent classification plays a vital role in dialogue systems.
Reconstructing Capsule Networks for Zero-shot Intent Classification
With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network.
