48 research outputs found
Learning a semantic parser from spoken utterances
Gaspers J, Cimiano P. Learning a semantic parser from spoken utterances. In: IEEE International Conference on Acoustics, Speech and Signal Processing. 2014
Compositional Semantic Parsing on Semi-Structured Tables
Two important aspects of semantic parsing for question answering are the
breadth of the knowledge source and the depth of logical compositionality.
While existing work trades off one aspect for another, this paper
simultaneously makes progress on both fronts through a new task: answering
complex questions on semi-structured tables using question-answer pairs as
supervision. The central challenge arises from two compounding factors: the
broader domain results in an open-ended set of relations, and the deeper
compositionality results in a combinatorial explosion in the space of logical
forms. We propose a logical-form driven parsing algorithm guided by strong
typing constraints and show that it obtains significant improvements over
natural baselines. For evaluation, we created a new dataset of 22,033 complex
questions on Wikipedia tables, which is made publicly available
Towards a Visual Turing Challenge
As language and visual understanding by machines progresses rapidly, we are
observing an increasing interest in holistic architectures that tightly
interlink both modalities in a joint learning and inference process. This trend
has allowed the community to progress towards more challenging and open tasks
and refueled the hope at achieving the old AI dream of building machines that
could pass a turing test in open domains. In order to steadily make progress
towards this goal, we realize that quantifying performance becomes increasingly
difficult. Therefore we ask how we can precisely define such challenges and how
we can evaluate different algorithms on this open tasks? In this paper, we
summarize and discuss such challenges as well as try to give answers where
appropriate options are available in the literature. We exemplify some of the
solutions on a recently presented dataset of question-answering task based on
real-world indoor images that establishes a visual turing challenge. Finally,
we argue despite the success of unique ground-truth annotation, we likely have
to step away from carefully curated dataset and rather rely on 'social
consensus' as the main driving force to create suitable benchmarks. Providing
coverage in this inherently ambiguous output space is an emerging challenge
that we face in order to make quantifiable progress in this area.Comment: Published in the NIPS 2014 Workshop on Learning Semantic