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Found 59 papers, 17 papers with code
Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization
It thereby takes advantage of prior copying distributions and, at each time step, explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Ranked #9 on
Abstractive Text Summarization
on CNN / Daily Mail
(using extra training data)
You Don’t Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers’ Private Personas
To this end, we propose effective defense objectives to protect persona leakage from hidden states.
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary.
CgT-GAN: CLIP-guided Text GAN for Image Captioning
Particularly, we use adversarial training to teach CgT-GAN to mimic the phrases of an external text corpus and CLIP-based reward to provide semantic guidance.
Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber
Through numerical evaluations, it shows that the nonconvex method offers an optimum efficiency of focusing with less running time or sampling rate.
Multi-modal Learning based Prediction for Disease
From the dataset, we quantitatively analyze and select clinical metadata that most contribute to NAFLD prediction.
Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence
In this work, we further investigate the information leakage issue and propose a generative embedding inversion attack (GEIA) that aims to reconstruct input sequences based only on their sentence embeddings.
Multi-step Jailbreaking Privacy Attacks on ChatGPT
With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts.
Fixed Design Analysis of Regularization-Based Continual Learning
We consider a continual learning (CL) problem with two linear regression tasks in the fixed design setting, where the feature vectors are assumed fixed and the labels are assumed to be random variables.
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Video prediction is a complex time-series forecasting task with great potential in many use cases.
Cross-domain Random Pre-training with Prototypes for Reinforcement Learning
Moreover, prototypical representation learning with a novel intrinsic loss is proposed to pre-train an effective and generic encoder across different domains.
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks.
Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries.
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
In this work, we propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.
ED-FAITH: Evaluating Dialogue Summarization on Faithfulness
Abstractive summarization models typically generate content unfaithful to the input, thus highlighting the significance of evaluating the faithfulness of generated summaries.
Prompt-driven efficient Open-set Semi-supervised Learning
In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters.
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.
CoNSoLe: Convex Neural Symbolic Learning
In the first step of searching for right symbols, we convexify the deep Q-learning.
You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas
To this end, we propose effective defense objectives to protect persona leakage from hidden states.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks
Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns.
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
Using human evaluation and automatic faithfulness metrics, we show that our model significantly reduces all kinds of factual errors on the dialogue summarization, SAMSum corpus.
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization
One goal of answer summarization is to produce a summary that reflects the range of answer perspectives.
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting
To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model.
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss.
WHAT TO DO IF SPARSE REPRESENTATION LEARNING FAILS UNEXPECTEDLY?
RISE is based on a complete analysis for the generalizability of data properties for physical systems.
Adversarial twin neural networks: maximizing physics recovery for physical system
Thus, we propose (1) sparsity regularization for the physical model and (2) physical superiority over the virtual model.
The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service
We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.
Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
Current pre-trained models applied to summarization are prone to factual inconsistencies which either misrepresent the source text or introduce extraneous information.
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images.
Mixed Cross Entropy Loss for Neural Machine Translation
In neural machine translation, cross entropy (CE) is the standard loss function in two training methods of auto-regressive models, i. e., teacher forcing and scheduled sampling.
Syntax-augmented Multilingual BERT for Cross-lingual Transfer
In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning.
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.
Differentially Private Federated Knowledge Graphs Embedding
However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data.
EASE: Extractive-Abstractive Summarization with Explanations
Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability.
Lifelong Learning with Sketched Structural Regularization
In practice and due to computational constraints, most SR methods crudely approximate the importance matrix by its diagonal.
K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION
K-PLUG achieves new state-of-the-art results on a suite of domain-specific NLP tasks, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue, significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks.
On the Faithfulness for E-commerce Product Summarization
In this work, we present a model to generate e-commerce product summaries.
Multimodal Sentence Summarization via Multimodal Selective Encoding
Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.
Dynamic radiomics: a new methodology to extract quantitative time-related features from tomographic images
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static characteristics.
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks.
Conversational Semantic Parsing
In this paper, we propose a semantic representation for such task-oriented conversational systems that can represent concepts such as co-reference and context carryover, enabling comprehensive understanding of queries in a session.
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
We annotate a multimodal product attribute value dataset that contains 87, 194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task.
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets.
Self-Attention Guided Copy Mechanism for Abstractive Summarization
Copy module has been widely equipped in the recent abstractive summarization models, which facilitates the decoder to extract words from the source into the summary.
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference
The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.
BiFNet: Bidirectional Fusion Network for Road Segmentation
Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud.
Multi-modal Datasets for Super-resolution
However, due to the variety of image degradation types in the real world, models trained on single-modal simulation datasets do not always have good robustness and generalization ability in different degradation scenarios.
Music-oriented Dance Video Synthesis with Pose Perceptual Loss
We present a learning-based approach with pose perceptual loss for automatic music video generation.
Predictive Multi-level Patient Representations from Electronic Health Records
Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction.
Emerging Cross-lingual Structure in Pretrained Language Models
We study the problem of multilingual masked language modeling, i. e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer.
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
MSMO: Multimodal Summarization with Multimodal Output
In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO).
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence.
Ranked #7 on
Text Summarization
on DUC 2004 Task 1
Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification
In this paper we continue experiments where neural machine translation training is used to produce joint cross-lingual fixed-dimensional sentence embeddings.
Cross-Lingual Document Classification
Cross-Lingual Transfer
+6
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video
In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.
Automatic Speech Recognition (ASR)
Document Summarization
+1
