130,697 research outputs found
UKP-SQuARE v3: A Platform for Multi-Agent QA Research
The continuous development of Question Answering (QA) datasets has drawn the
research community's attention toward multi-domain models. A popular approach
is to use multi-dataset models, which are models trained on multiple datasets
to learn their regularities and prevent overfitting to a single dataset.
However, with the proliferation of QA models in online repositories such as
GitHub or Hugging Face, an alternative is becoming viable. Recent works have
demonstrated that combining expert agents can yield large performance gains
over multi-dataset models. To ease research in multi-agent models, we extend
UKP-SQuARE, an online platform for QA research, to support three families of
multi-agent systems: i) agent selection, ii) early-fusion of agents, and iii)
late-fusion of agents. We conduct experiments to evaluate their inference speed
and discuss the performance vs. speed trade-off compared to multi-dataset
models. UKP-SQuARE is open-source and publicly available at
http://square.ukp-lab.de
Learning to Interactively Learn and Assist
When deploying autonomous agents in the real world, we need effective ways of
communicating objectives to them. Traditional skill learning has revolved
around reinforcement and imitation learning, each with rigid constraints on the
format of information exchanged between the human and the agent. While scalar
rewards carry little information, demonstrations require significant effort to
provide and may carry more information than is necessary. Furthermore, rewards
and demonstrations are often defined and collected before training begins, when
the human is most uncertain about what information would help the agent. In
contrast, when humans communicate objectives with each other, they make use of
a large vocabulary of informative behaviors, including non-verbal
communication, and often communicate throughout learning, responding to
observed behavior. In this way, humans communicate intent with minimal effort.
In this paper, we propose such interactive learning as an alternative to reward
or demonstration-driven learning. To accomplish this, we introduce a
multi-agent training framework that enables an agent to learn from another
agent who knows the current task. Through a series of experiments, we
demonstrate the emergence of a variety of interactive learning behaviors,
including information-sharing, information-seeking, and question-answering.
Most importantly, we find that our approach produces an agent that is capable
of learning interactively from a human user, without a set of explicit
demonstrations or a reward function, and achieving significantly better
performance cooperatively with a human than a human performing the task alone.Comment: AAAI 2020. Video overview at https://youtu.be/8yBvDBuAPrw, paper
website with videos and interactive game at
http://interactive-learning.github.io
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.Comment: 20 pages, 13 figures, Webpage: https://embodiedqa.org
Learning by Asking Questions
We introduce an interactive learning framework for the development and
testing of intelligent visual systems, called learning-by-asking (LBA). We
explore LBA in context of the Visual Question Answering (VQA) task. LBA differs
from standard VQA training in that most questions are not observed during
training time, and the learner must ask questions it wants answers to. Thus,
LBA more closely mimics natural learning and has the potential to be more
data-efficient than the traditional VQA setting. We present a model that
performs LBA on the CLEVR dataset, and show that it automatically discovers an
easy-to-hard curriculum when learning interactively from an oracle. Our LBA
generated data consistently matches or outperforms the CLEVR train data and is
more sample efficient. We also show that our model asks questions that
generalize to state-of-the-art VQA models and to novel test time distributions
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