A pinboard by
Shashank Srivastava

PhD student, Carnegie Mellon University


My work focuses on developing algorithms that learn from natural language interactions with humans

When humans learn about a new concept or phenomenon, they rely on rich forms of supervision, including explanations, examples and interactive dialogue. In contrast, modern computers learn through techniques from AI and machine learning, which traditionally depend on large databases of informations (colloquially called big data). If we wish to make computer learning as efficient as human learning, we need to develop methods that can learn from natural language interactions. In my research, I argue that learning from language is a viable paradigm for automated machine learning systems, which presents several advantages that can enable more efficient learning.

First, language can be used to naturally frame new learning tasks, such as by describing relevant features that convey a human understanding of a domain. For example, in predicting the risk of heart attack, a doctor can say: `Check if the patient's BMI is more than 25'. Such a question can parsed by a language interpreter to a structured query, and answered from a patient's health record, thus defining an important attribute to be considered for each new patient.

Second, natural language explanations are also often rich in information that can guide the working of machine learning models, minimizing the need for labeled data. For example, everyday language contains quantification expressions (such as 'all', 'some', 'rarely', 'usually', etc.) that are explicit denoters of generality. Similarly, natural language often conveys explicit declarative knowledge about a domain that may be difficult to learn from data alone (e.g., If a female is above 70 years and has a BMI more than 30, she is definitely at risk of heart disease). Such explanations can be used to guide machine learning models by constraining them to emulate the teacher's advice.

Finally, language allows a natural medium for proactive dialog on the part of a computer, such as by seeking clarifications about specific examples, validating its predictions, asking questions for filling its information gaps, etc. Computers could levarage such interactions with humans to simplify and validate their learning.

The goal of my research is to develop computer algorithms that can leverage such information, and hence provide a conceptual interface for guiding machine learning algorithms using natural language advice. Such interfaces that can be taught by a non-expert could bring the potential of machine learning to the masses.