Search Results for author:
Found 38 papers, 8 papers with code
Structural Supervision for Word Alignment and Machine Translation
Syntactic structure has long been argued to be potentially useful for enforcing accurate word alignment and improving generalization performance of machine translation.
Alibaba Speech Translation Systems for IWSLT 2018
This work describes the En→De Alibaba speech translation system developed for the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2018.
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT
We also apply an imitation learning strategy to augment a reasonable amount of pseudo APE training data, potentially preventing the model to overfit on the limited real training data and boosting the performance on held-out data.
MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkit
Large language models (LLMs) have been explored in a variety of reasoning tasks including solving of mathematical problems.
MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities.
Adaptive Policy with Wait-$k$ Model for Simultaneous Translation
Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model.
Neural Machine Translation with Dynamic Graph Convolutional Decoder
Existing wisdom demonstrates the significance of syntactic knowledge for the improvement of neural machine translation models.
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process.
Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference
A popular approach to streaming speech translation is to employ a single offline model with a wait-k policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints.
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
The lack of competency awareness makes NMT untrustworthy.
A scalable pipeline for COVID-19: the case study of Germany, Czechia and Poland
Here we report the design of a scalable pipeline which serves as a data synchronization to support inter-country top-down spatiotemporal observations and forecasting models of COVID-19, named the where2test, for Germany, Czechia and Poland.
Efficient Cluster-Based k-Nearest-Neighbor Machine Translation
k-Nearest-Neighbor Machine Translation (kNN-MT) has been recently proposed as a non-parametric solution for domain adaptation in neural machine translation (NMT).
StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks
Scene text detection is still a challenging task, as there may be extremely small or low-resolution strokes, and close or arbitrary-shaped texts.
Unifying Cross-lingual Summarization and Machine Translation with Compression Rate
Through introducing compression rate, the information ratio between the source and the target text, we regard the MT task as a special CLS task with a compression rate of 100%.
Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation
In this paper, we research extensively the pros and cons of the standard transformer in document-level translation, and find that the auto-regressive property can simultaneously bring both the advantage of the consistency and the disadvantage of error accumulation.
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results.
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss
One transition is basically parameterized by a non-linear transformation between hidden layers that implicitly represents the conversion between the true and noisy labels, and it can be readily optimized together with other model parameters.
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System
The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are, however, expensively available in the real scenario.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Lattice Transformer for Speech Translation
Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Multi-modal Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language.
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector
Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations.
Alibaba Submission for WMT18 Quality Estimation Task
The goal of WMT 2018 Shared Task on Translation Quality Estimation is to investigate automatic methods for estimating the quality of machine translation results without reference translations.
"Bilingual Expert" Can Find Translation Errors
Recent advances in statistical machine translation via the adoption of neural sequence-to-sequence models empower the end-to-end system to achieve state-of-the-art in many WMT benchmarks.
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the inverse problems.
Zero-Shot Learning via Class-Conditioned Deep Generative Models
We present a deep generative model for learning to predict classes not seen at training time.
An inner-loop free solution to inverse problems using deep neural networks
For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i. e., the inverse mapping is learned by feeding the input through the learned forward network.
Adversarial Feature Matching for Text Generation
We propose a framework for generating realistic text via adversarial training.
Unifying the Stochastic Spectral Descent for Restricted Boltzmann Machines with Bernoulli or Gaussian Inputs
Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models.
Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
Stochastic gradient MCMC algorithms (SG-MCMC) are a family of diffusion-based sampling methods for large-scale Bayesian learning.
Fast Second Order Stochastic Backpropagation for Variational Inference
We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well.
$k$-means: Fighting against Degeneracy in Sequential Monte Carlo with an Application to Tracking
Specifically, we propose a Stochastic SMC algorithm which initializes the set of $k$ means, providing the initial centers chosen from the collapsed particles.
Fast Second-Order Stochastic Backpropagation for Variational Inference
We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well.
Efficient Algorithm for Privately Releasing Smooth Queries
Outputting the summary runs in time $O(n^{1+\frac{d}{2d+K}})$, and the evaluation algorithm for answering a query runs in time $\tilde O (n^{\frac{d+2+\frac{2d}{K}}{2d+K}} )$.
