21 research outputs found
SMART: A Situation Model for Algebra Story Problems via Attributed Grammar
Solving algebra story problems remains a challenging task in artificial
intelligence, which requires a detailed understanding of real-world situations
and a strong mathematical reasoning capability. Previous neural solvers of math
word problems directly translate problem texts into equations, lacking an
explicit interpretation of the situations, and often fail to handle more
sophisticated situations. To address such limits of neural solvers, we
introduce the concept of a \emph{situation model}, which originates from
psychology studies to represent the mental states of humans in problem-solving,
and propose \emph{SMART}, which adopts attributed grammar as the representation
of situation models for algebra story problems. Specifically, we first train an
information extraction module to extract nodes, attributes, and relations from
problem texts and then generate a parse graph based on a pre-defined attributed
grammar. An iterative learning strategy is also proposed to improve the
performance of SMART further. To rigorously study this task, we carefully
curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show
that the proposed model outperforms all previous neural solvers by a large
margin while preserving much better interpretability. To test these models'
generalization capability, we also design an out-of-distribution (OOD)
evaluation, in which problems are more complex than those in the training set.
Our model exceeds state-of-the-art models by 17\% in the OOD evaluation,
demonstrating its superior generalization ability
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control
Recent progress on physics-based character animation has shown impressive
breakthroughs on human motion synthesis, through imitating motion capture data
via deep reinforcement learning. However, results have mostly been demonstrated
on imitating a single distinct motion pattern, and do not generalize to
interactive tasks that require flexible motion patterns due to varying
human-object spatial configurations. To bridge this gap, we focus on one class
of interactive tasks -- sitting onto a chair. We propose a hierarchical
reinforcement learning framework which relies on a collection of subtask
controllers trained to imitate simple, reusable mocap motions, and a meta
controller trained to execute the subtasks properly to complete the main task.
We experimentally demonstrate the strength of our approach over different
non-hierarchical and hierarchical baselines. We also show that our approach can
be applied to motion prediction given an image input. A supplementary video can
be found at https://youtu.be/3CeN0OGz2cA.Comment: Accepted to AAAI 202