Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks

by   Jiaan Wang, et al.

Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context. The key challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper, we propose a Story Heterogeneous Graph Network (SHGN) to explicitly model both the information of story context at different granularity levels and the multi-grained interactive relations among them. In detail, we consider commonsense knowledge, words and sentences as three types of nodes. To aggregate non-local information, a global node is also introduced. Given this heterogeneous graph network, the node representations are updated through graph propagation, which adequately utilizes commonsense knowledge to facilitate story comprehension. Moreover, we design two auxiliary tasks to implicitly capture the sentiment trend and key events lie in the context. The auxiliary tasks are jointly optimized with the primary story ending generation task in a multi-task learning strategy. Extensive experiments on the ROCStories Corpus show that the developed model achieves new state-of-the-art performances. Human study further demonstrates that our model generates more reasonable story endings.


page 1

page 2

page 3

page 4


Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Story ending generation is a strong indication of story comprehension. T...

Incorporating Structured Commonsense Knowledge in Story Completion

The ability to select an appropriate story ending is the first step towa...

A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation

Story generation, namely generating a reasonable story from a leading co...

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Natural language understanding is a challenging problem that covers a wi...

Narrative Modeling with Memory Chains and Semantic Supervision

Story comprehension requires a deep semantic understanding of the narrat...

DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension

This paper proposes a Distilled-Exposition Enhanced Matching Network (DE...

CIS2: A Simplified Commonsense Inference Evaluation for Story Prose

Transformers have been showing near-human performance on a variety of ta...

Please sign up or login with your details

Forgot password? Click here to reset