1     Introduction

 

1.1   Abstract

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Refer It Game is a two-player game where users alternate
between generating expressions referring to objects in images of natural
scenes, and clicking on the locations of described objects. The purpose of this
game is to crowdsource natural language referring expressions, very important
for research on natural language generation and dialogue systems such as
Apple’s Siri and Amazon Alexa. Creating a two-player game, allows to gather and
examine referring expressions directly within the game, which later allows to
perform experimental evaluations on collected dataset.

 

1.2   Context

Every day, people around the world communicate with each
other in various ways, but what we discuss, talk and debate about, mostly
concerns the visual world surrounding us. This make understanding the
connection between objects in the physical world and language describing these objects
a very important but challenging issue for artificial intelligence (AI).

Artificial intelligence is technology that is designed to
learn and self-improve. Automation, machine learning or natural language
processing are just few examples of many, various functions AI perform for us
on daily basis. This creates a large range of research fields that can profit
from a better comprehension of how people refer to physical objects in our
world.

Recent progress in automatic computer vision techniques,
have begun to create technologies for perceiving and distinguishing a large
number of object categories very promising (Perronnin et al., 2012; Deng et
al., 2012; Deng et al., 2010; Krizhevsky et al., 2012). As a result, there has
been a surge of recent work trying to estimate higher level semantics, comprising
exciting attempts to generate natural language descriptions of images automatically.

Such approaches, however, are often associated with problems
where descriptions may be highly dependent on the task, open-ended and
difficult to automatically evaluate. This is why we need different but related
approach to problem of referring expression generation (REG). By creating available online, two-player game where
individuals refer to objects in composite images of scenes from surrounding us world,
we enable researchers to retrieve not only referring expressions but also
relevant information. Collected dataset, can then be deeply analysed and later
evaluated.

 

 

 

 

 

 

2    
 Literature & Technology Review

Literature

2.1   Crowdsourcing

Crowdsourcing simply refers to a method of fund sourcing in
which organizations or individuals use contributions from internet users to
achieve a set objective. The word was adopted in 2005 and seems to combine the
word ‘crowd’ and outsourcing.  The belief
is that crowdsourcing has to do with outsourcing work to a crowd people.
There’s a difference between crowdsourcing and outsourcing because, with
crowdsourcing, the work can originate from an undefined public (rather than a
predetermined group). Some of the main benefits of using crowdsourcing include
improved speed, adaptability, costs, quality, diversity, or versatility
(Buettner, 2015).

Crowdsourcing has been highly beneficial in gathering
high-quality gold standard used in making automatic systems in natural language
processing. Promoted by efforts like the ESP game (von Ahn and Dabbish, 2004)
and Peekaboom (von Ahn et al., 2006), Human Computation based games can be a
viable approach to engage users and gather vast quantity of data inexpensively.
Two player games can likewise automate verification of human provided
annotations.

2.1.1    Amazon
Mechanical Turk

Amazon Mechanical Turk (MTurk) simply refers to an online
crowdsourcing marketplace that makes it possible for businesses and individuals
to organize the use of human intelligence to carry out tasks that cannot
currently be performed by computers. 
It’s a website that is owned by Amazon.

Jobs known as Human Intelligence Tasks can be posted by employers
(HITs), such as writing descriptions, picking the very best among multiple images
of a storefront, or identifying performance in music recordings. So-called
workers can later search through a large collection of existing jobs; they can
complete these jobs in exchange for monetary rewards as fixed by the employer.
The requesting programs place jobs using an API, or the more limited MTurk
Requester site which seems to be more limited. 
For a requester to submit an order to be accomplished through the
Mechanical Turk platform, he has to submit a billing address in one of about 30
approved countries.

2.1.2    CrowdFlower

CrowdFlower refers to a San Francisco based crowdsourcing
and data mining company. The company provides a software solution with which
users can gain access to an online workforce to label, clean, and enrich
data.  CrowdFlower uses an online
workforce to clean up messy and incomplete data. Majority of CrowdFlower users
are data scientists who use the solution to build training models as well as machine
learning algorithms.

As soon as data is uploaded into the system, the work is
automatically allocated to contributors and is tested against established
answers which are hidden within the task (this is called “job” in
CrowdFlower). The system trust individuals based on the way they perform on
these hidden tasks. Contributors are allowed to continue working on a
particular job as long as they are still trusted. If they lose that trust, they
lose the job, and their work is disregarded. The judgments of many contributors
are collated and the result is given based on aggregate answers with an
associated confidence score (contributors’ agreement weighed by the trust of
each contributor).

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