  Classic machine learning algorithms learn from labelled examples. For
example, to design a machine translation system, a typical training set will
consist of English sentences and their translation. There is a stronger model,
in which the algorithm can also query for labels of new examples it creates.
E.g, in the translation task, the algorithm can create a new English sentence,
and request its translation from the user during training. This combination of
examples and queries has been widely studied. Yet, despite many theoretical
results, query algorithms are almost never used. One of the main causes for
this is a report (Baum and Lang, 1992) on very disappointing empirical
performance of a query algorithm. These poor results were mainly attributed to
the fact that the algorithm queried for labels of examples that are artificial,
and impossible to interpret by humans.
  In this work we study a new model of local membership queries (Awasthi et
al., 2012), which tries to resolve the problem of artificial queries. In this
model, the algorithm is only allowed to query the labels of examples which are
close to examples from the training set. E.g., in translation, the algorithm
can change individual words in a sentence it has already seen, and then ask for
the translation. In this model, the examples queried by the algorithm will be
close to natural examples and hence, hopefully, will not appear as artificial
or random. We focus on 1-local queries (i.e., queries of distance 1 from an
example in the training sample). We show that 1-local membership queries are
already stronger than the standard learning model. We also present an
experiment on a well known NLP task of sentiment analysis. In this experiment,
the users were asked to provide more information than merely indicating the
label. We present results that illustrate that this extra information is
beneficial in practice.
