  Numerical reasoning is often important to accurately understand the world.
Recently, several format-specific datasets have been proposed, such as
numerical reasoning in the settings of Natural Language Inference (NLI),
Reading Comprehension (RC), and Question Answering (QA). Several
format-specific models and architectures in response to those datasets have
also been proposed. However, there exists a strong need for a benchmark which
can evaluate the abilities of models, in performing question format independent
numerical reasoning, as (i) the numerical reasoning capabilities we want to
teach are not controlled by question formats, (ii) for numerical reasoning
technology to have the best possible application, it must be able to process
language and reason in a way that is not exclusive to a single format, task,
dataset or domain. In pursuit of this goal, we introduce NUMBERGAME, a
multifaceted benchmark to evaluate model performance across numerical reasoning
tasks of eight diverse formats. We add four existing question types in our
compilation. Two of the new types we add are about questions that require
external numerical knowledge, commonsense knowledge and domain knowledge. For
building a more practical numerical reasoning system, NUMBERGAME demands four
capabilities beyond numerical reasoning: (i) detecting question format directly
from data (ii) finding intermediate common format to which every format can be
converted (iii) incorporating commonsense knowledge (iv) handling data
imbalance across formats. We build several baselines, including a new model
based on knowledge hunting using a cheatsheet. However, all baselines perform
poorly in contrast to the human baselines, indicating the hardness of our
benchmark. Our work takes forward the recent progress in generic system
development, demonstrating the scope of these under-explored tasks.
