8,048 research outputs found
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
Memory Networks
We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in the context of question answering (QA) where the long-term
memory effectively acts as a (dynamic) knowledge base, and the output is a
textual response. We evaluate them on a large-scale QA task, and a smaller, but
more complex, toy task generated from a simulated world. In the latter, we show
the reasoning power of such models by chaining multiple supporting sentences to
answer questions that require understanding the intension of verbs
- …