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Found 48 papers, 26 papers with code
基于迭代信息传递和滑动窗口注意力的问题生成模型研究(Question Generation Model Based on Iterative Message Passing and Sliding Windows Hierarchical Attention)
“知识图谱问题生成任务是从给定的知识图谱中生成与其相关的问题。目前, 知识图谱问题生成模型主要使用基于RNN或Transformer对知识图谱子图进行编码, 但这种方式丢失了显式的图结构化信息, 在解码器中忽视了局部信息对节点的重要性。本文提出迭代信息传递图编码器来编码子图, 获取子图显式的图结构化信息, 此外, 我们还使用滑动窗口注意力机制提高RNN解码器, 提升子图局部信息对节点的重要度。从WQ和PQ数据集上的实验结果看, 我们提出的模型比KTG模型在BLEU4指标上分别高出2. 16和15. 44, 证明了该模型的有效性。”
Non-autoregressive Translation with Dependency-Aware Decoder
In this paper, we propose a novel and general approach to enhance the target dependency within the NAT decoder from two perspectives: decoder input and decoder self-attention.
Weakly Supervised Object Localization as Domain Adaption
To avoid this problem, this work provides a novel perspective that models WSOL as a domain adaption (DA) task, where the score estimator trained on the source/image domain is tested on the target/pixel domain to locate objects.
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
Specifically, we first introduce a word-level prosody encoder, which quantizes the low-frequency band of the speech and compresses prosody attributes in the latent prosody vector (LPV).
Background-aware Classification Activation Map for Weakly Supervised Object Localization
In our B-CAM, two image-level features, aggregated by pixel-level features of potential background and object locations, are used to purify the object feature from the object-related background and to represent the feature of the pure-background sample, respectively.
Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network
To date, live-cell imaging at the nanometer scale remains challenging.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
We propose a novel Pooling Network (PoNet) for token mixing in long sequences with linear complexity.
BeamTransformer: Microphone Array-based Overlapping Speech Detection
We propose BeamTransformer, an efficient architecture to leverage beamformer's edge in spatial filtering and transformer's capability in context sequence modeling.
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
From Single to Multiple: Leveraging Multi-level Prediction Spaces for Video Forecasting
Despite video forecasting has been a widely explored topic in recent years, the mainstream of the existing work still limits their models with a single prediction space but completely neglects the way to leverage their model with multi-prediction spaces.
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
Evaluations are conducted on the English Wiki-727K document segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset.
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.
Learning Synergistic Attention for Light Field Salient Object Detection
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
Discriminative Self-training for Punctuation Prediction
Punctuation prediction for automatic speech recognition (ASR) output transcripts plays a crucial role for improving the readability of the ASR transcripts and for improving the performance of downstream natural language processing applications.
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning
In this paper, we propose a novel joint textual-phonetic pre-training approach for learning spoken language representations, aiming at exploring the full potentials of phonetic information to improve SLU robustness to ASR errors.
Automatic Speech Recognition
Natural Language Understanding
+3
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
To answer these questions, in this work we first theoretically analyze and outline sufficient conditions for adversarial transferability between models; then propose a practical algorithm to reduce the transferability between base models within an ensemble to improve its robustness.
RGB-D Salient Object Detection via 3D Convolutional Neural Networks
The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams.
Multi-feature driven active contour segmentation model for infrared image with intensity inhomogeneity
Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize the local or global single feature information of image to minimize the energy function, which is easy to cause false segmentations in IR images.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
EF-Net: A novel enhancement and fusion network for RGB-D saliency detection
Finally, we propose an effective layer-wise aggregation module to fuse the features extracted from the enhanced depth maps and RGB images for the accurate detection of salient objects.
Infrared target tracking based on proximal robust principal component analysis method
Infrared target tracking plays an important role in both civil and military fields.
Transfer Learning for Context-Aware Spoken Language Understanding
We explore different transfer learning approaches to reduce dependency on data collection and annotation.
Spoken Language Understanding
Task-Oriented Dialogue Systems
+2
Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection
With the increased applications of automatic speech recognition (ASR) in recent years, it is essential to automatically insert punctuation marks and remove disfluencies in transcripts, to improve the readability of the transcripts as well as the performance of subsequent applications, such as machine translation, dialogue systems, and so forth.
Sequential Neural Networks for Noetic End-to-End Response Selection
The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.
Dual Adversarial Domain Adaptation
Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.
Ranked #3 on
Domain Adaptation
on ImageCLEF-DA
Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously.
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation.
ColluEagle: Collusive review spammer detection using Markov random fields
Previous works use review network effects, i. e. the relationships among reviewers, reviews, and products, to detect fake reviews or review spammers, but ignore time effects, which are critical in characterizing group spamming.
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
We also observe that fine-tuned models after the proposed pre-training approach maintain comparable performance on other NLP tasks, such as sentence classification and natural language inference tasks, compared to the original BERT models.
Ranked #14 on
Common Sense Reasoning
on CommonsenseQA
Gift Contagion in Online Groups: Evidence From WeChat Red Packets
Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.
Several Experiments on Investigating Pretraining and Knowledge-Enhanced Models for Natural Language Inference
Natural language inference (NLI) is among the most challenging tasks in natural language understanding.
High-speed in vitro intensity diffraction tomography
We demonstrate a label-free, scan-free {\it intensity} diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume 3D refractive index distributions in vitro.
Optics Biological Physics
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
Ranked #2 on
Slot Filling
on ATIS
Sequential Attention-based Network for Noetic End-to-End Response Selection
The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.
Ranked #1 on
Conversational Response Selection
on Advising Corpus
Thermal Infrared Colorization via Conditional Generative Adversarial Network
Transforming a thermal infrared image into a realistic RGB image is a challenging task.
A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework
One-day-ahead VaR and ES forecasting results favor the proposed models, especially when incorporating the sub-sampled Realized Variance and the sub-sampled Realized Range in the model.
Enhancing Sentence Embedding with Generalized Pooling
This paper explores generalized pooling methods to enhance sentence embedding.
Ranked #10 on
Sentiment Analysis
on Yelp Fine-grained classification
Natural Language Inference with External Knowledge
Modeling informal inference in natural language is very challenging.
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.
Neural Natural Language Inference Models Enhanced with External Knowledge
With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.
Ranked #18 on
Natural Language Inference
on SNLI
Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector
The proposed multi-resolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and edges at successfully finer resolution sampled by Hadamard multiplexing along the wavelet trees.
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.
Ranked #68 on
Natural Language Inference
on SNLI
Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second
Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy.
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA).
Ranked #40 on
Question Answering
on SQuAD1.1 dev
Distraction-Based Neural Networks for Document Summarization
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.
Enhanced LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence.
Ranked #27 on
Natural Language Inference
on SNLI
