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Found 20 papers, 12 papers with code
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8, 068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets.
Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends
Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field.
TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification
We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks.
Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling
Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc.
Meta-Learning Siamese Network for Few-Shot Text Classification
Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO).
Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
In addition, we propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples to further improve node representation.
Understanding Long Programming Languages with Structure-Aware Sparse Attention
With top-$k$ sparse attention, the most crucial attention relation can be obtained with a lower computational cost.
GypSum: Learning Hybrid Representations for Code Summarization
Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets.
Programming Knowledge Tracing: A Comprehensive Dataset and A New Model
More specifically, we construct a bipartite graph for programming problem embedding, and design an improved pre-training model PLCodeBERT for code embedding, as well as a double-sequence RNN model with exponential decay attention for effective feature fusion.
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance.
On Disambiguating Authors: Collaboration Network Reconstruction in a Bottom-up Manner
In this paper, we model the author disambiguation as a collaboration network reconstruction problem, and propose an incremental and unsupervised author disambiguation method, namely IUAD, which performs in a bottom-up manner.
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
In this paper, we propose a general approach to learn relation prototypesfrom unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient trainingdata.
EDSL: An Encoder-Decoder Architecture with Symbol-Level Features for Printed Mathematical Expression Recognition
The symbol-level image encoder of EDSL consists of segmentation module and reconstruction module.
Improving Neural Relation Extraction with Implicit Mutual Relations
In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings
Lexical relations describe how meanings of terms relate to each other.
Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wider range of industrial areas, how to execute smart contracts efficiently becomes a big challenge nowadays.
Databases Distributed, Parallel, and Cluster Computing
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network representation learning (NRL).
Learning Fine-grained Relations from Chinese User Generated Categories
User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly.
A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances
A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations.
Transductive Non-linear Learning for Chinese Hypernym Prediction
Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc.
