Anirudh Srinivasan author Sandipan Dandapat author Monojit Choudhury author 2020-may text English eng European Language Resources Association Marseille, France conference publication 979-10-95546-66-5 In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into “pseudo constituency trees” and find that a parser trained on synthetically generated trees is able to decently parse these as well. srinivasan-etal-2020-code https://www.aclweb.org/anthology/2020.calcs-1.8 2020-may 57 64