Pre-trained transformer-based language models have achieved state-of-the-art performance in many areas of NLP. It is still an open question whether the models are capable of integrating syntax and semantics in language processing like humans. This paper investigates if models and humans construct argument hierarchy similarly with the effects from telicity, agency, and individuation, using the Chinese structure “NP1+BA/BEI+NP2+VP”. We present both humans and six transformer-based models with prepared sentences and analyze their preference between BA (view NP1 as an agent) and BEI (NP2 as an agent). It is found that the models and humans respond to (non-)agentive features in telic context and atelic feature very similarly. However, the models show insufficient sensitivity to both pragmatic function in expressing undesirable events and different individuation degrees represented by human common nouns vs. proper names. By contrast, humans rely heavily on these cues to establish the thematic relation between two arguments NP1 and NP2. Furthermore, the models tend to interpret the subject as an agent, which is not the case for humans who align agents independently of subject position in Mandarin Chinese.