45,164 research outputs found
Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effectively
to real-world applications like web-level information retrieval and question
answering (QA). We argue that this stems from the nature of MRC datasets: most
of these are static environments wherein the supporting documents and all
necessary information are fully observed. In this paper, we propose a simple
method that reframes existing MRC datasets as interactive, partially observable
environments. Specifically, we "occlude" the majority of a document's text and
add context-sensitive commands that reveal "glimpses" of the hidden text to a
model. We repurpose SQuAD and NewsQA as an initial case study, and then show
how the interactive corpora can be used to train a model that seeks relevant
information through sequential decision making. We believe that this setting
can contribute in scaling models to web-level QA scenarios.Comment: ACL202
The Role of Diverse Strategies in Sustainable Knowledge Production
Online communities are becoming increasingly important as platforms for
large-scale human cooperation. These communities allow users seeking and
sharing professional skills to solve problems collaboratively. To investigate
how users cooperate to complete a large number of knowledge-producing tasks, we
analyze StackExchange, one of the largest question and answer systems in the
world. We construct attention networks to model the growth of 110 communities
in the StackExchange system and quantify individual answering strategies using
the linking dynamics of attention networks. We identify two types of users
taking different strategies. One strategy (type A) aims at performing
maintenance by doing simple tasks, while the other strategy (type B) aims
investing time in doing challenging tasks. We find that the number of type A
needs to be twice as big as type B users for a sustainable growth of
communities.Comment: 10 pages, 3 figure
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Semantic parsing shines at analyzing complex natural language that involves
composition and computation over multiple pieces of evidence. However, datasets
for semantic parsing contain many factoid questions that can be answered from a
single web document. In this paper, we propose to evaluate semantic
parsing-based question answering models by comparing them to a question
answering baseline that queries the web and extracts the answer only from web
snippets, without access to the target knowledge-base. We investigate this
approach on COMPLEXQUESTIONS, a dataset designed to focus on compositional
language, and find that our model obtains reasonable performance (35 F1
compared to 41 F1 of state-of-the-art). We find in our analysis that our model
performs well on complex questions involving conjunctions, but struggles on
questions that involve relation composition and superlatives.Comment: *sem 201
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AQUA: an ontology driven question answering system
This paper describes AQUA our question answering over the Web. AQUA was designed to work over heterogeneous sources. This means that AQUA is equipped to work as closed domain and in addition to open-domain question answering. As a first instance, AQUA tries to answer a question using a Knowledge base. If a query cannot be satisfied over a knowledge base/database. Then, AQUA tries to find an answer on web pages (i.e. it uses as corpus the internet as resource). Our system uses NLP (Natural Language Processing), First order logic and Information Extraction technologies. AQUA has been tested using an ontology which describes academic life. Keywords Ontologies, Information Extraction, Machine Learnin
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