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Found 18 papers, 8 papers with code
LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea
We train our model with CoKEC-text and significantly improve the accuracy of readability assessment for texts in the Korean ELT curriculum.
Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation
Given a continuous latent variable model for machine translation (Shu et al., 2020), we train an inference network to approximate the gradient of the marginal log probability of the target sentence, using only the latent variable as input.
On the Discrepancy between Density Estimation and Sequence Generation
In this paper, by comparing several density estimators on five machine translation tasks, we find that the correlation between rankings of models based on log-likelihood and BLEU varies significantly depending on the range of the model families being compared.
Countering Language Drift via Visual Grounding
Emergent multi-agent communication protocols are very different from natural language and not easily interpretable by humans.
Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference Using a Delta Posterior
By decoding multiple initial latent variables in parallel and rescore using a teacher model, the proposed model further brings the gap down to 1. 0 BLEU point on WMT'14 En-De task with 6. 8x speedup.
Kernel and Rich Regimes in Overparametrized Models
A recent line of work studies overparametrized neural networks in the "kernel regime," i. e. when the network behaves during training as a kernelized linear predictor, and thus training with gradient descent has the effect of finding the minimum RKHS norm solution.
Multi-Turn Beam Search for Neural Dialogue Modeling
We propose a novel approach for conversation-level inference by explicitly modeling the dialogue partner and running beam search across multiple conversation turns.
On the Margin Theory of Feedforward Neural Networks
We establish: 1) for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for deep networks.
Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging
Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification.
Countering Language Drift via Grounding
While reinforcement learning (RL) shows a lot of promise for natural language processing—e. g.
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
We provide new theoretical insights on why over-parametrization is effective in learning neural networks.
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems.
Implicit Bias of Gradient Descent on Linear Convolutional Networks
We show that gradient descent on full-width linear convolutional networks of depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge penalty in the frequency domain.
Characterizing Implicit Bias in Terms of Optimization Geometry
We study the implicit bias of generic optimization methods, such as mirror descent, natural gradient descent, and steepest descent with respect to different potentials and norms, when optimizing underdetermined linear regression or separable linear classification problems.
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
We propose a conditional non-autoregressive neural sequence model based on iterative refinement.
Ranked #5 on
Machine Translation
on IWSLT2015 German-English
Emergent Translation in Multi-Agent Communication
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans.
Fully Character-Level Neural Machine Translation without Explicit Segmentation
We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of BLEU score and human judgment.
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares
The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition.
