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Found 14 papers, 2 papers with code
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations
There has been great progress in unifying various table-to-text tasks using a single encoder-decoder model trained via multi-task learning (Xie et al., 2022).
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner
Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises.
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification
Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.
Multi-Task Neural Model for Agglutinative Language Translation
Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora.
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in the 5th generation cellular networks, base stations need real-time optimization of radio resources in time-varying network conditions.
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods.
Morphological Word Segmentation on Agglutinative Languages for Neural Machine Translation
Experimental results show that our morphologically motivated word segmentation method is better suitable for the NMT model, which achieves significant improvements on Turkish-English and Uyghur-Chinese machine translation tasks on account of reducing data sparseness and language complexity.
Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin
We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server.
Noisy Neural Language Modeling for Typing Prediction in BCI Communication
Language models have broad adoption in predictive typing tasks.
Multi-Input Attention for Unsupervised OCR Correction
We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding.
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach.
