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Found 13 papers, 2 papers with code
RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates
Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest.
Parameter-Efficient Tuning on Layer Normalization for Pre-trained Language Models
Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research.
Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems
Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations.
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in a cloze-style machine reading comprehension setup.
Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation
This paper presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses.
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7.
Ranked #1 on
Conversation Disentanglement
on irc-disentanglement
Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
We present our work on Track 4 in the Dialogue System Technology Challenges 8 (DSTC8).
Condition-Transforming Variational AutoEncoder for Conversation Response Generation
This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs).
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.
Promoting Diversity for End-to-End Conversation Response Generation
At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.
Building Sequential Inference Models for End-to-End Response Selection
This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7).
Ranked #5 on
Conversational Response Selection
on DSTC7 Ubuntu
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences
The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs.
