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Found 15 papers, 9 papers with code
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation
We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search.
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
Additionally, a separate model must be trained for each inference scenario with its distinct computational budget.
SSMix: Saliency-Based Span Mixup for Text Classification
Data augmentation with mixup has shown to be effective on various computer vision tasks.
Consistency Training with Virtual Adversarial Discrete Perturbation
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.
Two-stage Textual Knowledge Distillation for End-to-End Spoken Language Understanding
End-to-end approaches open a new way for more accurate and efficient spoken language understanding (SLU) systems by alleviating the drawbacks of traditional pipeline systems.
Ranked #3 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding
Language model pre-training has shown promising results in various downstream tasks.
Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search
We then conduct a multi-objective evolutionary search to find a length configuration that maximizes the accuracy and minimizes the efficiency metric under any given computational budget.
Large Product Key Memory for Pretrained Language Models
Product key memory (PKM) proposed by Lample et al. (2019) enables to improve prediction accuracy by increasing model capacity efficiently with insignificant computational overhead.
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Because of the scale invariance, this modification only alters the effective step sizes without changing the effective update directions, thus enjoying the original convergence properties of GD optimizers.
Efficient Dialogue State Tracking by Selectively Overwriting Memory
This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations.
Ranked #10 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Subword Language Model for Query Auto-Completion
We present how to utilize subword language models for the fast and accurate generation of query completion candidates.
Training IBM Watson using Automatically Generated Question-Answer Pairs
Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy.
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems
In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems.
NASCUP: Nucleic Acid Sequence Classification by Universal Probability
Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.
Genomics Information Theory Information Theory
