CORDIS

The explosion of information around us has democratized knowledge and transformed its availability for
people around the world. Still, since information is mediated through automated systems, access is bounded
by their ability to understand language.
Consider an economist asking “What fraction of the top-5 growing countries last year raised their co2 emission?”.
While the required information is available, answering such complex questions automatically is
not possible. Current question answering systems can answer simple questions in broad domains, or complex
questions in narrow domains. However, broad and complex questions are beyond the reach of state-of-the-art.
This is because systems are unable to decompose questions into their parts, and find the relevant information
in multiple sources. Further, as answering such questions is hard for people, collecting large datasets to train
such models is prohibitive.
In this proposal I ask: Can computers answer broad and complex questions that require reasoning over
multiple modalities? I argue that by synthesizing the advantages of symbolic and distributed representations
the answer will be “yes”. My thesis is that symbolic representations are suitable for meaning composition, as
they provide interpretability, coverage, and modularity. Complementarily, distributed representations (learned
by neural nets) excel at capturing the fuzziness of language. I propose a framework where complex questions
are symbolically decomposed into sub-questions, each is answered with a neural network, and the final answer
is computed from all gathered information.
This research tackles foundational questions in language understanding. What is the right representation
for reasoning in language? Can models learn to perform complex actions in the face of paucity of data?
Moreover, my research, if successful, will transform how we interact with machines, and define a role for
them as research assistants in science, education, and our daily life.