Search Results for author:
Found 12 papers, 8 papers with code
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems
Neural machine translation (NMT) systems have demonstrated promising results in recent years.
Multi-Domain Dialogue State Tracking with Top-K Slot Self Attention
In the slot self-attention layers, we force each slot to involve information from the other k prominent slots and mask the rest out.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Streaming Target-Speaker ASR with Neural Transducer
We confirm in experiments that our TS-ASR achieves comparable recognition performance with conventional cascade systems in the offline setting, while reducing computation costs and realizing streaming TS-ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.
Ranked #1 on
Cross-Lingual ASR
on Common Voice
Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching
End-to-end automatic speech recognition (ASR) can achieve promising performance with large-scale training data.
Ranked #1 on
Cross-environment ASR
on Libri-Adapt
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning
In this paper, we report a large-scale end-to-end language-independent multilingual model for joint automatic speech recognition (ASR) and language identification (LID).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection
The key problem here is the cost of transcribing speech data.
