An Empirical Study on Neural Keyphrase Generation

• URL: http://arxiv.org/abs/2009.10229v3
• Date: Thu, 15 Apr 2021 15:33:40 GMT
• Title: An Empirical Study on Neural Keyphrase Generation
• Authors: Rui Meng, Xingdi Yuan, Tong Wang, Sanqiang Zhao, Adam Trischler, Daqing He
• Abstract summary: Recent years have seen a flourishing of neural keyphrase generation (KPG) works. Model performance on KPG tasks has increased significantly with evolving deep learning research.
• Score: 32.98420137439619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic.

Related papers

• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)
• Towards Causal Classification: A Comprehensive Study on Graph Neural Networks [9.360596957822471]
  Graph Neural Networks (GNNs) for processing graph-structured data have expanded their potential for causal analysis. Our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields.
  arXiv  Detail & Related papers  (2024-01-27T15:35:05Z)
• Data-Centric Long-Tailed Image Recognition [49.90107582624604]
  Long-tail models exhibit a strong demand for high-quality data. Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance. There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
  arXiv  Detail & Related papers  (2023-11-03T06:34:37Z)
• On the Joint Interaction of Models, Data, and Features [82.60073661644435]
  We introduce a new tool, the interaction tensor, for empirically analyzing the interaction between data and model through features. Based on these observations, we propose a conceptual framework for feature learning. Under this framework, the expected accuracy for a single hypothesis and agreement for a pair of hypotheses can both be derived in closed-form.
  arXiv  Detail & Related papers  (2023-06-07T21:35:26Z)
• Theory on Forgetting and Generalization of Continual Learning [41.85538120246877]
  Continual learning (CL) aims to learn a sequence of tasks. There is a lack of understanding on what factors are important and how they affect "catastrophic forgetting" and generalization performance. We show that our results not only explain some interesting empirical observations in recent studies, but also motivate better practical algorithm designs of CL.
  arXiv  Detail & Related papers  (2023-02-12T02:14:14Z)
• Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery [14.839673015887275]
  Knowledge Graphs (KGs) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models.
  arXiv  Detail & Related papers  (2021-05-17T11:39:54Z)
• Understanding Overparameterization in Generative Adversarial Networks [56.57403335510056]
  Generative Adversarial Networks (GANs) are used to train non- concave mini-max optimization problems. A theory has shown the importance of the gradient descent (GD) to globally optimal solutions. We show that in an overized GAN with a $1$-layer neural network generator and a linear discriminator, the GDA converges to a global saddle point of the underlying non- concave min-max problem.
  arXiv  Detail & Related papers  (2021-04-12T16:23:37Z)
• Multi-task Causal Learning with Gaussian Processes [17.205106391379026]
  This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model. We propose the first multi-task causal Gaussian process (GP) model, which allows for information sharing across continuous interventions and experiments on different variables.
  arXiv  Detail & Related papers  (2020-09-27T11:33:40Z)
• Causal Inference using Gaussian Processes with Structured Latent Confounders [9.8164690355257]
  This paper shows how to semiparametrically model latent confounders that have this structure and thereby improve estimates of causal effects. The key innovations are a hierarchical Bayesian model, Gaussian processes with structured latent confounders (GP-SLC), and a Monte Carlo inference algorithm for this model based on elliptical slice sampling.
  arXiv  Detail & Related papers  (2020-07-14T15:45:28Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.