80,594 research outputs found
A Shared Task on Bandit Learning for Machine Translation
We introduce and describe the results of a novel shared task on bandit
learning for machine translation. The task was organized jointly by Amazon and
Heidelberg University for the first time at the Second Conference on Machine
Translation (WMT 2017). The goal of the task is to encourage research on
learning machine translation from weak user feedback instead of human
references or post-edits. On each of a sequence of rounds, a machine
translation system is required to propose a translation for an input, and
receives a real-valued estimate of the quality of the proposed translation for
learning. This paper describes the shared task's learning and evaluation setup,
using services hosted on Amazon Web Services (AWS), the data and evaluation
metrics, and the results of various machine translation architectures and
learning protocols.Comment: Conference on Machine Translation (WMT) 201
Reinforcement Learning based NLP
In the field of Natural Language Processing (NLP), reinforcement learning (RL) has drawn attention as a viable method for training models. An agent is trained to interact with a linguistic environment in order to carry out a given task using RL- based NLP, and the agent learns from feedback in the form of rewards or penalties. This method has been effectively used for a variety of linguistic problems, including text summarization, conversation systems, and machine translation. Sequence-to- sequence Two common methods used in RL-based NLP are reinforcement learning and deep reinforcement learning. Sequence-to-sequence While deep reinforcement learning includes training a neural network to discover the optimum strategy for a language challenge, reinforcement learning (RL) trains a model to create a series of words or characters that most closely matches a goal sequence. In several linguistic challenges, RL-based NLP has demonstrated promising results and attained cutting-edge performance. There are still issues to be solved, such as the need for more effective exploration tactics, data scarcity, and sample efficiency. In summary, RL-based NLP represents a potential line of inquiry for NLP research in the future. This method outperforms more established NLP strategies in a variety of language problems and has the added benefit of being able to improve over time with user feedback. To further enhance RL-based NLP's effectiveness and increase its applicability to real-world settings, future research should concentrate on resolving the difficulties associated with this approach.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
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Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Machine translation is a natural candidate problem for reinforcement learning
from human feedback: users provide quick, dirty ratings on candidate
translations to guide a system to improve. Yet, current neural machine
translation training focuses on expensive human-generated reference
translations. We describe a reinforcement learning algorithm that improves
neural machine translation systems from simulated human feedback. Our algorithm
combines the advantage actor-critic algorithm (Mnih et al., 2016) with the
attention-based neural encoder-decoder architecture (Luong et al., 2015). This
algorithm (a) is well-designed for problems with a large action space and
delayed rewards, (b) effectively optimizes traditional corpus-level machine
translation metrics, and (c) is robust to skewed, high-variance, granular
feedback modeled after actual human behaviors.Comment: 11 pages, 5 figures, In Proceedings of Empirical Methods in Natural
Language Processing (EMNLP) 201
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Using touch devices to navigate in virtual 3D environments such as computer
assisted design (CAD) models or geographical information systems (GIS) is
inherently difficult for humans, as the 3D operations have to be performed by
the user on a 2D touch surface. This ill-posed problem is classically solved
with a fixed and handcrafted interaction protocol, which must be learned by the
user. We propose to automatically learn a new interaction protocol allowing to
map a 2D user input to 3D actions in virtual environments using reinforcement
learning (RL). A fundamental problem of RL methods is the vast amount of
interactions often required, which are difficult to come by when humans are
involved. To overcome this limitation, we make use of two collaborative agents.
The first agent models the human by learning to perform the 2D finger
trajectories. The second agent acts as the interaction protocol, interpreting
and translating to 3D operations the 2D finger trajectories from the first
agent. We restrict the learned 2D trajectories to be similar to a training set
of collected human gestures by first performing state representation learning,
prior to reinforcement learning. This state representation learning is
addressed by projecting the gestures into a latent space learned by a
variational auto encoder (VAE).Comment: 17 pages, 8 figures. Accepted at The European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases 2019
(ECMLPKDD 2019
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