  The human ability of deep cognitive skills are crucial for the development of
various real-world applications that process diverse and abundant user
generated input. While recent progress of deep learning and natural language
processing have enabled learning system to reach human performance on some
benchmarks requiring shallow semantics, such human ability still remains
challenging for even modern contextual embedding models, as pointed out by many
recent studies. Existing machine comprehension datasets assume sentence-level
input, lack of casual or motivational inferences, or could be answered with
question-answer bias. Here, we present a challenging novel task, trope
detection on films, in an effort to create a situation and behavior
understanding for machines. Tropes are storytelling devices that are frequently
used as ingredients in recipes for creative works. Comparing to existing movie
tag prediction tasks, tropes are more sophisticated as they can vary widely,
from a moral concept to a series of circumstances, and embedded with
motivations and cause-and-effects. We introduce a new dataset, Tropes in Movie
Synopses (TiMoS), with 5623 movie synopses and 95 different tropes collecting
from a Wikipedia-style database, TVTropes. We present a multi-stream
comprehension network (MulCom) leveraging multi-level attention of words,
sentences, and role relations. Experimental result demonstrates that modern
models including BERT contextual embedding, movie tag prediction systems, and
relational networks, perform at most 37% of human performance (23.97/64.87) in
terms of F1 score. Our MulCom outperforms all modern baselines, by 1.5 to 5.0
F1 score and 1.5 to 3.0 mean of average precision (mAP) score. We also provide
a detailed analysis and human evaluation to pave ways for future research.
