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Found 196 papers, 96 papers with code
Position Offset Label Prediction for Grammatical Error Correction
We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task.
Rethinking Denoised Auto-Encoding in Language Pre-Training
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
The nature of no word delimiter or inflection that can indicate segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks.
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation?
Diffusion models have exhibited remarkable capabilities in text-to-image generation.
Towards Multimodal Video Paragraph Captioning Models Robust to Missing Modality
Video paragraph captioning (VPC) involves generating detailed narratives for long videos, utilizing supportive modalities such as speech and event boundaries.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents
We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis on the different forms of agent backdoor attacks.
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding.
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
The ability to perceive how objects change over time is a crucial ingredient in human intelligence.
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
The multi-aspect categorization of FETV enables fine-grained analysis of the metrics' reliability in different scenarios.
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
TESTA can reduce the number of visual tokens by 75% and thus accelerate video encoding.
Ranked #1 on
Video Retrieval
on Condensed Movies
(using extra training data)
Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction
Existing multimodal works that train models from scratch face the problem of lacking universal knowledge when modeling financial news.
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
To deal with the label shortage problem, we present a simple yet effective zero-shot approach MultiCapCLIP that can generate visual captions for different scenarios and languages without any labeled vision-caption pairs of downstream datasets.
Towards Codable Watermarking for Injecting Multi-bits Information to LLMs
As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs.
M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions.
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
Can Language Models Understand Physical Concepts?
Language models~(LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite.
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
However, as pre-trained language models (PLMs) continue to increase in size, the communication cost for transmitting parameters during synchronization has become a training speed bottleneck.
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment.
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias
To settle this issue, we propose the Fine-purifying approach, which utilizes the diffusion theory to study the dynamic process of fine-tuning for finding potentially poisonous dimensions.
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
This work proposes POMP, a prompt pre-training method for vision-language models.
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features
We find that: (1) no existing method behaves well in both settings; (2) fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be attributed to the distortion of task-agnostic features.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning
Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner.
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy.
A Survey on In-context Learning
With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples.
Aligning Source Visual and Target Language Domains for Unpaired Video Captioning
To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language.
Gradient Knowledge Distillation for Pre-trained Language Models
Knowledge distillation (KD) is an effective framework to transfer knowledge from a large-scale teacher to a compact yet well-performing student.
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format.
Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model
We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.
Prophet Attention: Predicting Attention with Future Attention for Image Captioning
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
In this work, we take the first step to exploit the pre-trained (unfine-tuned) weights to mitigate backdoors in fine-tuned language models.
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks
In this work, we take the first step to investigate the unconcealment of textual poisoned samples at the intermediate-feature level and propose a feature-based efficient online defense method.
Holistic Sentence Embeddings for Better Out-of-Distribution Detection
On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9. 33% FAR95 margin.
GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization
It demonstrates that flat minima tend to imply better generalization abilities.
Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation
NLP attacks tend to have small relative backdoor strengths, which may result in the failure of robust federated aggregation methods for NLP attacks.
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters.
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.
Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction
Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction.
Delving into the Openness of CLIP
However, evaluating the openness of CLIP-like models is challenging, as the models are open to arbitrary vocabulary in theory, but their accuracy varies in practice.
Hierarchical Inductive Transfer for Continual Dialogue Learning
However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of the dialogue system on the embedded devices but also causes knowledge forgetting on pre-trained models and knowledge interference among diverse dialogue tasks.
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture.
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge -- Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2).
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly reduce the retraining computational cost and the potential environmental side-effects.
KNAS: Green Neural Architecture Search
Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations.
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
Motivated by this observation, we construct a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models.
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary.
Topology-Imbalance Learning for Semi-Supervised Node Classification
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community.
Dynamic Knowledge Distillation for Pre-trained Language Models
Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models.
Adversarial Parameter Defense by Multi-Step Risk Minimization
To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions.
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors.
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations.
Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling
Trading volume movement prediction is the key in a variety of financial applications.
ASAT: Adaptively Scaled Adversarial Training in Time Series
Besides the security concerns of potential adversarial examples, adversarial training can also improve the generalization ability of neural networks, train robust neural networks, and provide interpretability for neural networks.
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Video captioning combines video understanding and language generation.
Rethinking Stealthiness of Backdoor Attack against NLP Models
In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users.
Contrastive Attention for Automatic Chest X-ray Report Generation
In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis.
Neural Network Surgery: Injecting Data Patterns into Pre-trained Models with Minimal Instance-wise Side Effects
Motivated by neuroscientific evidence and theoretical results, we demonstrate that side effects can be controlled by the number of changed parameters and thus, we propose to conduct \textit{neural network surgery} by only modifying a limited number of parameters.
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.
Learning Relation Alignment for Calibrated Cross-modal Retrieval
To bridge the semantic gap between the two modalities, previous studies mainly focus on word-region alignment at the object level, lacking the matching between the linguistic relation among the words and the visual relation among the regions.
Ranked #4 on
Image-to-Text Retrieval
on MS COCO
Rethinking Skip Connection with Layer Normalization in Transformers and ResNets
In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could be addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
The finding of general knowledge is further hindered by the unidirectional distillation, as the student should obey the teacher and may discard some knowledge that is truly general but refuted by the teacher.
High-Likelihood Area Matters --- Rewarding Near-Correct Predictions Under Imbalanced Distributions
We find in practice that the high-likelihood area contains correct predictions for tail classes and it plays a vital role in learning imbalanced class distributions.
A Gradient-based Kernel Approach for Efficient Network Architecture Search
Inspired by our new finding, we explore a simple yet effective network architecture search (NAS) approach that leverages gradient correlation and gradient values to find well-performing architectures.
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions.
Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network
Recent deep learning based methods focus on learning clustering oriented representations.
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC).
EQG-RACE: Examination-Type Question Generation
Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments.
Prophet Attention: Predicting Attention with Future Attention
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Rethinking Skip Connection with Layer Normalization
In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection.
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models
Conventional knowledge graph embedding (KGE) often suffers from limited knowledge representation, leading to performance degradation especially on the low-resource problem.
A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment
The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.
Graph-based Multi-hop Reasoning for Long Text Generation
The reasoning module is responsible for searching skeleton paths from a knowledge graph to imitate the imagination process in the human writing for semantic transfer.
Collaborative Group Learning
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima.
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective
In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.
How to Ask Good Questions? Try to Leverage Paraphrases
Given a sentence and its relevant answer, how to ask good questions is a challenging task, which has many real applications.
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter.
Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research
Finally, we discuss related issues when building such a database and our future directions in the context of BROOK.
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding
While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.
Parallel Data Augmentation for Formality Style Transfer
The main barrier to progress in the task of Formality Style Transfer is the inadequacy of training data.
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019.
Query-Variant Advertisement Text Generation with Association Knowledge
In this paper, we propose the query-variant advertisement text generation task that aims to generate candidate advertisement texts for different web search queries with various needs based on queries and item keywords.
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks
Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them.
Exploring and Distilling Cross-Modal Information for Image Captioning
Recently, attention-based encoder-decoder models have been used extensively in image captioning.
Mining Commonsense Facts from the Physical World
In this paper, we propose a new task of mining commonsense facts from the raw text that describes the physical world.
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Further analysis demonstrates that the proposed regularized training can effectively improve the agreement of attention on the image, leading to better use of visual information.
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.
Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion.
MUSE: Parallel Multi-Scale Attention for Sequence to Sequence Learning
In this work, we explore parallel multi-scale representation learning on sequence data, striving to capture both long-range and short-range language structures.
Ranked #8 on
Machine Translation
on WMT2014 English-French
Understanding and Improving Layer Normalization
Unlike them, we find that the derivatives of the mean and variance are more important than forward normalization by re-centering and re-scaling backward gradients.
Ranked #5 on
Machine Translation
on IWSLT2015 English-Vietnamese
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.
Specificity-Driven Cascading Approach for Unsupervised Sentiment Modification
The task of unsupervised sentiment modification aims to reverse the sentiment polarity of the input text while preserving its semantic content without any parallel data.
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks.
Asking Clarification Questions in Knowledge-Based Question Answering
We provide representative baselines for these tasks and further introduce a coarse-to-fine model for clarification question generation.
Blast-wave description of $Υ$ elliptic flow at energies available at the CERN Large Hadron Collider
A simultaneous blast-wave fit to particle yields and elliptic flow ($v_{2}$) measured as a function of transverse momentum in Pb-Pb collisions at LHC energies is presented.
High Energy Physics - Phenomenology Nuclear Experiment Nuclear Theory
An Adaptive and Momental Bound Method for Stochastic Learning
The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of deep neural networks.
Pun-GAN: Generative Adversarial Network for Pun Generation
It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.
Aligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction
In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement.
Sparse Transformer: Concentrated Attention Through Explicit Selection
Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance.
Recursive Graphical Neural Networks for Text Classification
Graph is a natural structure to describe the complicated relation between tokens.
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting.
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks.
Ranked #52 on
Node Classification
on Cora
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
We propose a novel model to separate the generation into two stages: key fact prediction and surface realization.
Towards Fine-grained Text Sentiment Transfer
In this paper, we focus on the task of fine-grained text sentiment transfer (FGST).
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction
The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages.
Enhancing Topic-to-Essay Generation with External Commonsense Knowledge
Experiments show that with external commonsense knowledge and adversarial training, the generated essays are more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels.
Learning to Control the Fine-grained Sentiment for Story Ending Generation
Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain.
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
Unsupervised text style transfer aims to alter text styles while preserving the content, without aligned data for supervision.
Imitation Learning for Non-Autoregressive Neural Machine Translation
Non-autoregressive translation models (NAT) have achieved impressive inference speedup.
Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Ranked #1 on
Unsupervised Text Style Transfer
on GYAFC
Memorized Sparse Backpropagation
Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers.
Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations
In vision-and-language grounding problems, fine-grained representations of the image are considered to be of paramount importance.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input.
Ranked #21 on
Visual Storytelling
on VIST
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy
In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation
Existing text generation methods tend to produce repeated and {''}boring{''} expressions.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.
Unsupervised Machine Commenting with Neural Variational Topic Model
To fully exploit the unpaired data, we completely remove the need for parallel data and propose a novel unsupervised approach to train an automatic article commenting model, relying on nothing but unpaired articles and comments.
LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
We introduce the task of automatic live commenting.
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet
To evaluate the effectiveness of the proposed model, we build a large-scale rationality evaluation dataset.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification
However, the Seq2Seq model is not suitable for the MLTC task in essence.
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
The encode-decoder framework has shown recent success in image captioning.
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
Different from conventional text generation tasks, the mapping between inputs and responses in conversations is more complicated, which highly demands the understanding of utterance-level semantic dependency, a relation between the whole meanings of inputs and outputs.
Ranked #1 on
Text Generation
on DailyDialog
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
We propose a novel model for multi-label text classification, which is based on sequence-to-sequence learning.
Identifying High-Quality Chinese News Comments Based on Multi-Target Text Matching Model
In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments.
Learning Sentiment Memories for Sentiment Modification without Parallel Data
The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content.
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine Translation
Most of the Neural Machine Translation (NMT) models are based on the sequence-to-sequence (Seq2Seq) model with an encoder-decoder framework equipped with the attention mechanism.
Ranked #7 on
Machine Translation
on IWSLT2015 English-Vietnamese
A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation
Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation.
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation.
Primal Meaning Recommendation via On-line Encyclopedia
We define the primal meaning of an expression to be a frequently used sense of that expression from which its other frequent senses can be deduced.
A Neural Question Answering Model Based on Semi-Structured Tables
Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer.
Question Condensing Networks for Answer Selection in Community Question Answering
Answer selection is an important subtask of community question answering (CQA).
SGM: Sequence Generation Model for Multi-label Classification
Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.
Deconvolution-Based Global Decoding for Neural Machine Translation
A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order.
Ranked #9 on
Machine Translation
on IWSLT2015 English-Vietnamese
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #6 on
Unsupervised Text Style Transfer
on Yelp
Bag-of-Words as Target for Neural Machine Translation
However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage.
Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
In this work, we supervise the learning of the representation of the source content with that of the summary.
Global Encoding for Abstractive Summarization
To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.
Ranked #29 on
Text Summarization
on GigaWord
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
As more and more academic papers are being submitted to conferences and journals, evaluating all these papers by professionals is time-consuming and can cause inequality due to the personal factors of the reviewers.
A Hierarchical End-to-End Model for Jointly Improving Text Summarization and Sentiment Classification
Text summarization and sentiment classification both aim to capture the main ideas of the text but at different levels.
Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
In this paper, we propose the task of relation classification for Chinese literature text.
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words.
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
General Classification
Implicit Discourse Relation Classification
+3
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation
ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration.
DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text
Existing text generation methods tend to produce repeated and "boring" expressions.
Exploration on Generating Traditional Chinese Medicine Prescription from Symptoms with an End-to-End method
Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas.
Building an Ellipsis-aware Chinese Dependency Treebank for Web Text
Web 2. 0 has brought with it numerous user-produced data revealing one's thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base construction.
A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction
However, due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include those full form expressions that do not have valid abbreviations, namely the negative full forms (NFFs).
Hybrid Oracle: Making Use of Ambiguity in Transition-based Chinese Dependency Parsing
In the training of transition-based dependency parsers, an oracle is used to predict a transition sequence for a sentence and its gold tree.
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
The decoding of the complex structure model is regularized by the additionally trained simple structure model.
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
In our work, we first design a new model called "high order LSTM" to predict multiple tags for the current token which contains not only the current tag but also the previous several tags.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method.
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.
Deep Stacking Networks for Low-Resource Chinese Word Segmentation with Transfer Learning
In this paper, we propose a deep stacking framework to improve the performance on word segmentation tasks with insufficient data by integrating datasets from diverse domains.
Cascading Multiway Attentions for Document-level Sentiment Classification
Document-level sentiment classification aims to assign the user reviews a sentiment polarity.
Ranked #5 on
Sentiment Analysis
on User and product information
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure
Boundary features are widely used in traditional Chinese Word Segmentation (CWS) methods as they can utilize unlabeled data to help improve the Out-of-Vocabulary (OOV) word recognition performance.
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks.
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
In this work, our goal is to improve semantic relevance between source texts and simplified texts for text summarization and text simplification.
Minimal Effort Back Propagation for Convolutional Neural Networks
As traditional neural network consumes a significant amount of computing resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet effective technique to alleviate this problem.
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
In back propagation, only a small subset of the full gradient is computed to update the model parameters.
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization.
Lock-Free Parallel Perceptron for Graph-based Dependency Parsing
To deal with this problem, we propose a parallel algorithm called parallel perceptron.
A Generic Online Parallel Learning Framework for Large Margin Models
To speed up the training process, many existing systems use parallel technology for online learning algorithms.
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network
First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model.
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features
Existing asynchronous parallel learning methods are only for the sparse feature models, and they face new challenges for the dense feature models like neural networks (e. g., LSTM, RNN).
A New Recurrent Neural CRF for Learning Non-linear Edge Features
Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction.
F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media
We focus on named entity recognition (NER) for Chinese social media.
Towards Easier and Faster Sequence Labeling for Natural Language Processing: A Search-based Probabilistic Online Learning Framework (SAPO)
We show that this method with fast training and theoretical guarantee of convergence, which is easy to implement, can support search-based optimization and obtain top accuracy.
Structure Regularization for Structured Prediction
Many existing systems on structured prediction focus on increasing the level of structural dependencies within the model.
Structure Regularization for Structured Prediction: Theories and Experiments
Many existing systems on structured prediction focus on increasing the level of structural dependencies within the model.
Exact Decoding on Latent Variable Conditional Models is NP-Hard
The computational complexity of the exact decoding/inference in latent conditional random fields is unclear.
