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Found 17 papers, 2 papers with code
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Text classification is a critical research topic with broad applications in natural language processing.
Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process
Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Recent neural network models have significantly advanced the task of coreference resolution.
Ranked #13 on
Coreference Resolution
on CoNLL 2012
Context-aware Active Multi-Step Reinforcement Learning
Then given the selected samples, we propose the adaptive multi-step TD, which generalizes TD($\lambda$), but adaptively switch on/off the backups from future returns of different steps.
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation
To this end, we introduce a cooperative training paradigm, where a language model is cooperatively trained with the generator and we utilize the language model to efficiently shape the data distribution of the generator against mode collapse.
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection
The thriving of deep models and generative models provides approaches to model high dimensional distributions.
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation
Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains.
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems
Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e. g. users’ implicit feedback).
Variational Flow Graphical Model
VFGs learn the representation of high dimensional data via a message-passing scheme by integrating flow-based functions through variational inference.
PromptGen: Automatically Generate Prompts using Generative Models
Recently, prompt learning has received significant attention, where the downstream tasks are reformulated to the mask-filling task with the help of a textual prompt.
Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning
In this paper, we propose a new weakly supervised paraphrase generation approach that extends the success of a recent work that leverages reinforcement learning for effective model training with data selection.
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation
Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models
In particular, we focus on few-shot image recognition tasks on pretrained vision-language models (PVLMs) and develop a method of prompting through prototype (PTP), where we define $K$ image prototypes and $K$ prompt prototypes.
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator.
Word Embedding with Neural Probabilistic Prior
To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models.
