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Found 43 papers, 7 papers with code
The DeepMind Chinese–English Document Translation System at WMT2020
This paper describes the DeepMind submission to the Chinese\rightarrowEnglish constrained data track of the WMT2020 Shared Task on News Translation.
MAD for Robust Reinforcement Learning in Machine Translation
We introduce a new distributed policy gradient algorithm and show that it outperforms existing reward-aware training procedures such as REINFORCE, minimum risk training (MRT) and proximal policy optimization (PPO) in terms of training stability and generalization performance when optimizing machine translation models.
Enabling arbitrary translation objectives with Adaptive Tree Search
When applied to autoregressive models, our algorithm has different biases than beam search has, which enables a new analysis of the role of decoding bias in autoregressive models.
A Mutual Information Maximization Perspective of Language Representation Learning
We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i. e., a sentence).
Better Document-Level Machine Translation with Bayes' Rule
We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents are not always available.
Putting Machine Translation in Context with the Noisy Channel Model
We show that Bayes' rule provides a compelling mechanism for controlling unconditional document language models, using the long-standing challenge of effectively leveraging document context in machine translation.
Relative Pixel Prediction For Autoregressive Image Generation
In natural images, transitions between adjacent pixels tend to be smooth and gradual, a fact that has long been exploited in image compression models based on predictive coding.
Learning and Evaluating General Linguistic Intelligence
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.
Variational Smoothing in Recurrent Neural Network Language Models
We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017).
Sentence Encoding with Tree-constrained Relation Networks
The meaning of a sentence is a function of the relations that hold between its words.
Memory Architectures in Recurrent Neural Network Language Models
We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.
Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems
Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer.
Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Solving algebraic word problems requires executing a series of arithmetic operations---a program---to obtain a final answer.
Generative and Discriminative Text Classification with Recurrent Neural Networks
We empirically characterize the performance of discriminative and generative LSTM models for text classification.
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
This allows us to expand lexicons describing multiple semantic properties.
Learning to Compose Words into Sentences with Reinforcement Learning
We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences.
Reference-Aware Language Models
We propose a general class of language models that treat reference as an explicit stochastic latent variable.
Ranked #1 on
Recipe Generation
on allrecipes.com
Semantic Parsing with Semi-Supervised Sequential Autoencoders
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
Neural Network-Based Abstract Generation for Opinions and Arguments
We study the problem of generating abstractive summaries for opinionated text.
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning
We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.
Latent Predictor Networks for Code Generation
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs.
Ranked #10 on
Code Generation
on Django
Character-based Neural Machine Translation
We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words.
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs.
Ranked #4 on
Part-Of-Speech Tagging
on Penn Treebank
Privacy-Preserving Multi-Document Summarization
State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties.
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
We propose a technique for learning representations of parser states in transition-based dependency parsers.
Dual Subtitles as Parallel Corpora
In this paper, we leverage the existence of dual subtitles as a source of parallel data.
Linguistic Evaluation of Support Verb Constructions by OpenLogos and Google Translate
This paper presents a systematic human evaluation of translations of English support verb constructions produced by a rule-based machine translation (RBMT) system (OpenLogos) and a statistical machine translation (SMT) system (Google Translate) for five languages: French, German, Italian, Portuguese and Spanish.
Recognition of Named-Event Passages in News Articles
We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes.
