  Natural Language Inference (NLI) is the task of inferring the logical
relationship, typically entailment or contradiction, between a premise and
hypothesis. Code-mixing is the use of more than one language in the same
conversation or utterance, and is prevalent in multilingual communities all
over the world. In this paper, we present the first dataset for code-mixed NLI,
in which both the premises and hypotheses are in code-mixed Hindi-English. We
use data from Hindi movies (Bollywood) as premises, and crowd-source hypotheses
from Hindi-English bilinguals. We conduct a pilot annotation study and describe
the final annotation protocol based on observations from the pilot. Currently,
the data collected consists of 400 premises in the form of code-mixed
conversation snippets and 2240 code-mixed hypotheses. We conduct an extensive
analysis to infer the linguistic phenomena commonly observed in the dataset
obtained. We evaluate the dataset using a standard mBERT-based pipeline for NLI
and report results.
