Search Results for author:
Found 12 papers, 3 papers with code
FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition
In this paper, we present FAC$^2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models.
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation
To solve above limitations, in this paper we propose an improved non-autoregressive (NAR) spelling correction model for contextual biasing in E2E neural transducer-based ASR systems to improve the previous CSC model from two perspectives: Firstly, we incorporate acoustics information with an external attention as well as text hypotheses into CSC to better distinguish target phrase from dissimilar or irrelevant phrases.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion
In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody.
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input context of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts.
Feeding What You Need by Understanding What You Learned
In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems
In this work, we introduce a novel approach to do contextual biasing by adding a contextual spelling correction model on top of the end-to-end ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.
A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems
We incorporate the context information into the spelling correction model with a shared context encoder and use a filtering algorithm to handle large-size context lists.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Ordering-Based Causal Discovery with Reinforcement Learning
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.
Quantum tensor singular value decomposition with applications to recommendation systems
In this paper, we present a quantum singular value decomposition algorithm for third-order tensors inspired by the classical algorithm of tensor singular value decomposition (t-svd) and then extend it to order-$p$ tensors.
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning
In this paper, a new MARL, called Cooperative double Q-learning (Co-DQL), is proposed, which has several prominent features.
