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Found 11 papers, 3 papers with code
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction
Our framework can model complicated relations between emotions and causes while avoiding generating the pairing matrix (the leading cause of the label sparsity problem).
Hierarchical Image Classification with A Literally Toy Dataset
We define the classification task where classes have characteristics above and the flat classes and the base classes are organized hierarchically as hierarchical image classification.
Dynamic Multi-scale Convolution for Dialect Identification
To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling.
Modularized Interaction Network for Named Entity Recognition
Although the existing Named Entity Recognition (NER) models have achieved promising performance, they suffer from certain drawbacks.
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning
To develop machine with cognition-level visual understanding and reasoning abilities, the visual commonsense reasoning (VCR) task has been introduced.
Ranked #4 on
Visual Question Answering (VQA)
on VCR (Q-AR) test
Transformer with Bidirectional Decoder for Speech Recognition
To demonstrate our proposed speech transformer with a bidirectional decoder(STBD), we conduct extensive experiments on the AISHELL-1 dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods.
THUEE system description for NIST 2019 SRE CTS Challenge
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge.
Small-footprint Keyword Spotting with Graph Convolutional Network
Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices.
Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
