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Found 64 papers, 26 papers with code
Automatic Document Sketching: Generating Drafts from Analogous Texts
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document.
Joint Retrieval and Generation Training for Grounded Text Generation
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.
An Adversarially-Learned Turing Test for Dialog Generation Models
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.
InverseForm: A Loss Function for Structured Boundary-Aware Segmentation
We present a novel boundary-aware loss term for semantic segmentation using an inverse-transformation network, which efficiently learns the degree of parametric transformations between estimated and target boundaries.
Finetuning Pretrained Transformers into RNNs
Specifically, we propose a swap-then-finetune procedure: in an off-the-shelf pretrained transformer, we replace the softmax attention with its linear-complexity recurrent alternative and then finetune.
Ranked #1 on
Machine Translation
on WMT2017 Chinese-English
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.
What Makes Good In-Context Examples for GPT-$3$?
Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.
SDA: Improving Text Generation with Self Data Augmentation
Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.
Towards Robust and Efficient Contrastive Textual Representation Learning
There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.
Narrative Incoherence Detection
We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation
Second, we devise an effective data selection policy for judiciously sampling the generated images: (1) to make the generated training set better cover the dataset, the clusters that are underrepresented in the original training set are covered more; (2) to make the training process more effective, we identify and oversample the images of "hard cases" in the data for which annotated training data may be scarce.
Improving Text Generation with Student-Forcing Optimal Transport
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
Contextualized Perturbation for Textual Adversarial Attack
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
Weakly supervised cross-domain alignment with optimal transport
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.
Structured Convolutions for Efficient Neural Network Design
We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
Contextual Text Style Transfer
To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space
We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.
INSET: Sentence Infilling with INter-SEntential Transformer
Missing sentence generation (or sentence infilling) fosters a wide range of applications in natural language generation, such as document auto-completion and meeting note expansion.
Contrastive Multi-document Question Generation
In the SL stage, a single-document question generator is trained.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order to learn a better alignment between the two domains.
Structuring Latent Spaces for Stylized Response Generation
This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.
Unsupervised Dialogue Spectrum Generation for Log Dialogue Ranking
Although the data-driven approaches of some recent bot building platforms make it possible for a wide range of users to easily create dialogue systems, those platforms don{'}t offer tools for quickly identifying which log dialogues contain problems.
Second-order Non-local Attention Networks for Person Re-identification
Recent efforts have shown promising results for person re-identification by designing part-based architectures to allow a neural network to learn discriminative representations from semantically coherent parts.
Domain Adaptive Text Style Transfer
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository.
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
Ablation study confirms the effectiveness of our proposed learning scheme for medical images.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool for network analysis.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation
To better mimic human visual perception, we think it is desirable for the deep learning model to be able to perceive not only raw images but also SP images.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables.
Improving Sequence-to-Sequence Learning via Optimal Transport
Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE).
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
The cascade decoder structure aims to conduct more effective decoding of hierarchically encoded features and is more compatible with common encoders than the known decoders.
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models.
Ranked #1 on
Cardiovascular MR Segmentaiton
on HVSMR 2016
Towards Coherent and Cohesive Long-form Text Generation
Generating coherent and cohesive long-form texts is a challenging task.
Adversarial Text Generation via Feature-Mover's Distance
However, the discrete nature of text hinders the application of GAN to text-generation tasks.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack informativeness and diversity.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).
BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation
In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation.
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on
Named Entity Recognition
on CoNLL 2000
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on
Text Classification
on DBpedia
A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences.
On the Use of Word Embeddings Alone to Represent Natural Language Sequences
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
Zero-Shot Learning via Class-Conditioned Deep Generative Models
We present a deep generative model for learning to predict classes not seen at training time.
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.
Triangle Generative Adversarial Networks
The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.
Image-to-Image Translation
Semi-Supervised Image Classification
+1
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important first step in many natural language processing applications.
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
Image segmentation is a fundamental problem in biomedical image analysis.
Adversarial Feature Matching for Text Generation
We propose a framework for generating realistic text via adversarial training.
Stochastic Gradient Monomial Gamma Sampler
A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo.
Stochastic Gradient MCMC with Stale Gradients
In this paper we develop theory to show that while the bias and MSE of an SG-MCMC algorithm depend on the staleness of stochastic gradients, its estimation variance (relative to the expected estimate, based on a prescribed number of samples) is independent of it.
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Segmentation of 3D images is a fundamental problem in biomedical image analysis.
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
Learning a Hybrid Architecture for Sequence Regression and Annotation
When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states.