Teaching Machines to Classify from Natural Language Interactions

Humans routinely learn new concepts using natural language communications,
even in scenarios with limited or no labeled examples. For example, a
human can learn the concept of a phishing email from natural language explanations
such as ‘phishing emails often request your bank account number’.
On the other hand, purely inductive learning systems typically require a large
collection of labeled data for learning such a concept. We believe that advances
in Computational Linguistics and the growing ubiquity of computing devices
together can enable people to teach computers classification tasks using natural
language interactions.
Learning from language presents some key challenges. A preliminary challenge
lies in the basic problem of learning to interpret language, which refers
to an agent’s ability to map natural language explanations in pedagogical contexts
to formal semantic representations that computers can process and reason
over. A second challenge is that of learning from interpretations, which refers
to the mechanisms through which interpretations of language statements can
be used by computers to solve learning tasks in the environment. We address
aspects of both these problems, and provide an interface for guiding concept
learning methods using language.
For learning from interpretation, we focus on concept learning (binary classification)
tasks. We demonstrate that language can define rich and expressive
features for learning tasks, and show that machine learning can benefit substantially
from this ability. We also investigate assimilation of linguistic cues
in everyday language that implicitly provide constraints for classification models
(e.g., ‘Most emails are not phishing emails’). In particular, we focus on
conditional statements and linguistic quantifiers (such as usually, never, etc.),
and show that such advice can be used to train classifiers even with few or no
labeled examples of a concept.
For learning to interpret, we develop new algorithms for semantic parsing
that incorporate pragmatic cues, including conversational history and sensory
observation, to improve automatic language interpretation. We show that environmental
context can enrich semantic parsing methods by not only providing
discriminative features, but also reducing the need for expensive labeled data
used for training them.
A separate but immensely valuable attribute of human language is that
it is inherently conversational and interactive. We also briefly explore the
possibility of agents that can learn to interact with a human teacher in a mixedinitiative
setting, where the learner can also proactively engage the teacher by
asking questions, rather than only passively listen. We develop a reinforce
learning framework for learning effective question asking strategies in context
of conversational concept learning.