Computational Modelling of Plurality and Definiteness in Chinese Noun
  Phrases
Yuqi Liu Guanyi Chen Kees van Deemter
Theoretical linguists have suggested that some languages (e.g., Chinese and
Japanese) are "cooler" than other languages based on the observation that the
intended meaning of phrases in these languages depends more on their contexts.
As a result, many expressions in these languages are shortened, and their
meaning is inferred from the context. In this paper, we focus on the omission
of the plurality and definiteness markers in Chinese noun phrases (NPs) to
investigate the predictability of their intended meaning given the contexts. To
this end, we built a corpus of Chinese NPs, each of which is accompanied by its
corresponding context, and by labels indicating its singularity/plurality and
definiteness/indefiniteness. We carried out corpus assessments and analyses.
The results suggest that Chinese speakers indeed drop plurality and
definiteness markers very frequently. Building on the corpus, we train a bank
of computational models using both classic machine learning models and
state-of-the-art pre-trained language models to predict the plurality and
definiteness of each NP. We report on the performance of these models and
analyse their behaviours.