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Found 31 papers, 14 papers with code
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text
Numerical reasoning skills are essential for complex question answering (CQA) over text.
Semi-supervised Network Embedding with Differentiable Deep Quantisation
Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many downstream network analytics tasks.
Unsupervised Domain-adaptive Hash for Networks
Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms.
Exploiting Scene Graphs for Human-Object Interaction Detection
Human-Object Interaction (HOI) detection is a fundamental visual task aiming at localizing and recognizing interactions between humans and objects.
Semantic Compositional Learning for Low-shot Scene Graph Generation
Scene graphs provide valuable information to many downstream tasks.
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training.
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
Temporal Cascade and Structural Modelling of EHRs for Granular Readmission Prediction
Although a point process (e. g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation
Question generation (QG) has recently attracted considerable attention.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Knowledge Base Question Answering
Meta Reinforcement Learning
+1
Less is More: Data-Efficient Complex Question Answering over Knowledge Bases
Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer.
Understanding Unnatural Questions Improves Reasoning over Text
A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions which is then executed on the raw text by the interpreter.
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
Boosting House Price Predictions using Geo-Spatial Network Embedding
Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.
Learning from the Scene and Borrowing from the Rich: Tackling the Long Tail in Scene Graph Generation
Despite the huge progress in scene graph generation in recent years, its long-tail distribution in object relationships remains a challenging and pestering issue.
Generating Question Titles for Stack Overflow from Mined Code Snippets
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet.
Software Engineering
$\mathtt{MedGraph:}$ Structural and Temporal Representation Learning of Electronic Medical Records
To address these limitations, we present $\mathtt{MedGraph}$, a supervised EMR embedding method that captures two types of information: (1) the visit-code associations in an attributed bipartite graph, and (2) the temporal sequencing of visits through a point process.
Gaussian Embedding of Large-scale Attributed Graphs
Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations.
Ranked #1 on
Link Prediction
on Cora (nonstandard variant)
Question Generation from Paragraphs: A Tale of Two Hierarchical Models
Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text
The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.
ParaQG: A System for Generating Questions and Answers from Paragraphs
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.
OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies
Ontologies are formal representations of concepts and complex relationships among them.
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Network
We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.
Vector and Line Quantization for Billion-scale Similarity Search on GPUs
The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs.
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text.
CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations.