Search Results for author:
Found 11 papers, 5 papers with code
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
A unified multichannel far-field speech recognition system: combining neural beamforming with attention based end-to-end model
Far-field speech recognition is a challenging task that conventionally uses signal processing beamforming to attack noise and interference problem.
Improving Audio-Visual Speech Recognition by Lip-Subword Correlation Based Visual Pre-training and Cross-Modal Fusion Encoder
In this paper, we propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+2
Network Pruning Spaces
Statistically, the structure of a winning subnetwork guarantees an approximately optimal ratio in this regime.
CHMATCH: Contrastive Hierarchical Matching and Robust Adaptive Threshold Boosted Semi-Supervised Learning
In the meantime, we make full use of the structured information in the hierarchical labels to learn an accurate affinity graph for contrastive learning.
McQueen: a Benchmark for Multimodal Conversational Query Rewrite
In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting.
Bootstrap Latent Representations for Multi-modal Recommendation
Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e. g., user-user or item-item relation graph) to augment the learned representations of users and/or items.
iEmoTTS: Toward Robust Cross-Speaker Emotion Transfer and Control for Speech Synthesis based on Disentanglement between Prosody and Timbre
The emotion encoder extracts the identity of emotion type as well as the respective emotion intensity from the mel-spectrogram of input speech.
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging
Named entity recognition (NER) models generally perform poorly when large training datasets are unavailable for low-resource domains.
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions.
Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch
Semantic parsing which maps a natural language sentence into a formal machine-readable representation of its meaning, is highly constrained by the limited annotated training data.
