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Found 75 papers, 40 papers with code
Visualizing attention zones in machine reading comprehension models
The attention mechanism plays an important role in the machine reading comprehension (MRC) model.
A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation
To address this issue, in this paper, we proposed a static and dynamic attention-based approach to model the dialogue history and then generate open domain multi turn dialogue responses.
M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Instruction tuning has emerged as an effective strategy for achieving zero-shot generalization by finetuning pretrained models on diverse multimodal tasks.
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.
Efficient Deep Learning-based Cascaded Channel Feedback in RIS-Assisted Communications
In the realm of reconfigurable intelligent surface (RIS)-assisted communication systems, the connection between a base station (BS) and user equipment (UE) is formed by a cascaded channel, merging the BS-RIS and RIS-UE channels.
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner
To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts.
Deep Learning-based CSI Feedback in Wi-Fi Systems
In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations.
ProMotion: Prototypes As Motion Learners
We capitalize on a dual mechanism involving the feature denoiser and the prototypical learner to decipher the intricacies of motion.
SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving
Spatial global-local aggregation fuses the local information from the neighboring frames and global semantics from the current frame to eliminate the feature degradation; 3).
Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral
Mixtral, a representative sparse mixture of experts (SMoE) language model, has received significant attention due to its unique model design and superior performance.
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning.
CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation
The advancement of computer vision has pushed visual analysis tasks from still images to the video domain.
ClusterFormer: Clustering As A Universal Visual Learner
This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER.
E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Specifically, we introduce a set of learnable key-value prompts and visual prompts into self-attention and input layers, respectively, to improve the effectiveness of model fine-tuning.
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location and category of one specific object from each query.
IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
IDOL achieves state-of-the-art performance on ReClor and LogiQA, the two most representative benchmarks in logical reasoning MRC, and is proven to be capable of generalizing to different pre-trained models and other types of MRC benchmarks like RACE and SQuAD 2. 0 while keeping competitive general language understanding ability through testing on tasks in GLUE.
Ranked #1 on
Reading Comprehension
on ReClor
Cloud-RAIN: Point Cloud Analysis with Reflectional Invariance
We prove a theorem to explain why Cloud-RAIN can enjoy reflection symmetry.
Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca
While several large language models, such as LLaMA, have been open-sourced by the community, these predominantly focus on English corpora, limiting their usefulness for other languages.
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model
In natural language processing, pre-trained language models have become essential infrastructures.
Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback
Additionally, since the heterogeneity of CSI datasets in different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to improve feedback performance.
FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors
With Transformerbased object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video object detection.
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability.
Feature Aggregated Queries for Transformer-Based Video Object Detectors
With Transformer-based object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video object detection.
Gradient-based Intra-attention Pruning on Pre-trained Language Models
In this work, we propose a structured pruning method GRAIN (Gradient-based Intra-attention pruning), which performs task-specific pruning with knowledge distillation and yields highly effective models.
LERT: A Linguistically-motivated Pre-trained Language Model
We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy.
Ranked #6 on
Stock Market Prediction
on Astock
Lightweight Neural Network with Knowledge Distillation for CSI Feedback
Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder.
DFA: Dynamic Feature Aggregation for Efficient Video Object Detection
Video object detection is a fundamental yet challenging task in computer vision.
Dynamic Proposals for Efficient Object Detection
Object detection is a basic computer vision task to loccalize and categorize objects in a given image.
GL-RG: Global-Local Representation Granularity for Video Captioning
Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description.
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection
The same multi-word expressions may have different meanings in different sentences.
HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity
This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity.
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models
Pre-trained language models have been prevailed in natural language processing and become the backbones of many NLP tasks, but the demands for computational resources have limited their applications.
PERT: Pre-training BERT with Permuted Language Model
We permute a proportion of the input text, and the training objective is to predict the position of the original token.
Ranked #4 on
Stock Market Prediction
on Astock
CINO: A Chinese Minority Pre-trained Language Model
It covers Standard Chinese, Yue Chinese, and six other ethnic minority languages.
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training
In this paper, we aim to improve the multilingual model's supervised and zero-shot performance simultaneously only with the resources from supervised languages.
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study
By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world complexity for network training to address various driving settings; 2) the working pipeline annotation tool is under-studied in the literature to improve the quality of MOTS learning examples.
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
TF-Blender: Temporal Feature Blender for Video Object Detection
One of the popular solutions is to exploit the temporal information and enhance per-frame representation through aggregating features from neighboring frames.
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks.
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning.
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).
Ranked #1 on
Multi-Choice MRC
on ExpMRC - RACE+ (test)
SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation
Video instance segmentation (VIS) is a new and critical task in computer vision.
Hierarchical Attention Fusion for Geo-Localization
We extract the hierarchical feature maps from a convolutional neural network (CNN) and organically fuse the extracted features for image representations.
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them.
DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation
In this work, we introduce a Denser Feature Network (DenserNet) for visual localization.
Unsupervised Explanation Generation for Machine Reading Comprehension
With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances.
CharBERT: Character-aware Pre-trained Language Model
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable.
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze
It is a common practice for vehicles to use GPS to acquire location information.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
Ranked #13 on
Stock Market Prediction
on Astock
Benchmarking Robustness of Machine Reading Comprehension Models
To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of adversarial attacks, including our novel distractor extraction and generation attacks.
Conversational Word Embedding for Retrieval-Based Dialog System
Human conversations contain many types of information, e. g., knowledge, common sense, and language habits.
Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Deep pretrained language models have achieved great success in the way of pretraining first and then fine-tuning.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC).
Is Graph Structure Necessary for Multi-hop Question Answering?
We construct a strong baseline model to establish that, with the proper use of pre-trained models, graph structure may not be necessary for multi-hop question answering.
TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing
In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing.
Discriminative Sentence Modeling for Story Ending Prediction
Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge.
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension
We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.
Contextual Recurrent Units for Cloze-style Reading Comprehension
Recurrent Neural Networks (RNN) are known as powerful models for handling sequential data, and especially widely utilized in various natural language processing tasks.
Improving Machine Reading Comprehension via Adversarial Training
With virtual adversarial training (VAT), we explore the possibility of improving the RC models with semi-supervised learning and prove that examples from a different task are also beneficial.
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels.
Cross-Lingual Machine Reading Comprehension
In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English.
Pre-Training with Whole Word Masking for Chinese BERT
To demonstrate the effectiveness of these models, we create a series of Chinese pre-trained language models as our baselines, including BERT, RoBERTa, ELECTRA, RBT, etc.
Exploiting Persona Information for Diverse Generation of Conversational Responses
Giving conversational context with persona information to a chatbot, how to exploit the information to generate diverse and sustainable conversations is still a non-trivial task.
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates.
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Context-Sensitive Generation of Open-Domain Conversational Responses
Despite the success of existing works on single-turn conversation generation, taking the coherence in consideration, human conversing is actually a context-sensitive process.
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge.
Dataset for the First Evaluation on Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
Attention-over-Attention Neural Networks for Reading Comprehension
Cloze-style queries are representative problems in reading comprehension.
Ranked #3 on
Question Answering
on Children's Book Test
Consensus Attention-based Neural Networks for Chinese Reading Comprehension
Reading comprehension has embraced a booming in recent NLP research.
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers.
LSTM Neural Reordering Feature for Statistical Machine Translation
Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling.
Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT
Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist.
