Yoshimasa Tsuruoka

Dynamic Prediction of Minimal Trees in Large-Scale Parallel Game Tree Search

2015 · Journal of Information Processing

Parallelization of the alpha-beta algorithm on distributed computing environments is a promising way of improving the playing strength of computer game programs. Search programs should predict and concentrate the effort on the subtrees that will …

Asymmetric Move Selection Strategies in Monte-Carlo Tree Search: Minimizing the Simple Regret at Max Nodes

2016 · arXiv (Cornell University)

The combination of multi-armed bandit (MAB) algorithms with Monte-Carlo tree search (MCTS) has made a significant impact in various research fields. The UCT algorithm, which combines the UCB bandit algorithm with MCTS, is a good …

Neural Fictitious Self-Play on ELF Mini-RTS

2019 · arXiv (Cornell University)

Despite the notable successes in video games such as Atari 2600, current AI is yet to defeat human champions in the domain of real-time strategy (RTS) games. One of the reasons is that an RTS …

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

2017

Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in …

Neural Machine Translation with Source-Side Latent Graph Parsing

2017

This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser …

Learning to Parse and Translate Improves Neural Machine Translation

2017

There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose …

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

2016 · arXiv (Cornell University)

Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in …

Tree-to-Sequence Attentional Neural Machine Translation

2016

Most of the existing Neural Machine Translation (NMT) models focus on the conversion of sequential data and do not directly use syntactic information. We propose a novel end-to-end syntactic NMT model, extending a sequenceto-sequence model …