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Found 26 papers, 7 papers with code
Neural Machine Translation with Soft Prototype
Neural machine translation models usually use the encoder-decoder framework and generate translation from left to right (or right to left) without fully utilizing the target-side global information.
Microsoft Research Asia's Systems for WMT19
We Microsoft Research Asia made submissions to 11 language directions in the WMT19 news translation tasks.
Hint-Based Training for Non-Autoregressive Machine Translation
Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency.
Depth Growing for Neural Machine Translation
While very deep neural networks have shown effectiveness for computer vision and text classification applications, how to increase the network depth of neural machine translation (NMT) models for better translation quality remains a challenging problem.
Ranked #11 on
Machine Translation
on WMT2014 English-French
Multi-Agent Dual Learning
Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities.
Ranked #1 on
Machine Translation
on WMT2016 English-German
Hint-based Training for Non-Autoregressive Translation
To improve the accuracy of NART models, in this paper, we propose to leverage the hints from a well-trained ART model to train the NART model.
Non-Autoregressive Machine Translation with Auxiliary Regularization
However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states).
Learning to Teach with Dynamic Loss Functions
Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher).
Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter
Many previous works have discussed the relationship between error propagation and the \emph{accuracy drop} (i. e., the left part of the translated sentence is often better than its right part in left-to-right decoding models) problem.
A Study of Reinforcement Learning for Neural Machine Translation
Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system.
Neural Architecture Optimization
The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.
Model-Level Dual Learning
Many artificial intelligence tasks appear in dual forms like English$\leftrightarrow$French translation and speech$\leftrightarrow$text transformation.
Towards Binary-Valued Gates for Robust LSTM Training
Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling.
Learning to Teach
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations.
Achieving Human Parity on Automatic Chinese to English News Translation
Machine translation has made rapid advances in recent years.
Ranked #3 on
Machine Translation
on WMT 2017 English-Chinese
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation.
Adversarial Neural Machine Translation
The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human.
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence.
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries.
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces.
Generalization Analysis for Game-Theoretic Machine Learning
For Internet applications like sponsored search, cautions need to be taken when using machine learning to optimize their mechanisms (e. g., auction) since self-interested agents in these applications may change their behaviors (and thus the data distribution) in response to the mechanisms.
Agent Behavior Prediction and Its Generalization Analysis
Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model.
