Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?
Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cross-task features to enhance the task performance. In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on SCT considered various semantic information, such as sentiment and topic, but lack the logic information between sentences which is an essential element of stories. Thus we propose to extract the logic information during the course of the story to improve the understanding of the whole story. The logic information is modeled with the help of the NLI task. Experimental results prove the strength of the logic information.
Code (0)
등록된 구현이 없습니다.
Tasks
Cloze TestNatural Language InferenceNatural Language UnderstandingRelationSimilar Papers 제목 키워드 기반
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
Story generation, namely generating a reasonable story from a leading context, is an important but challenging task. In spite of the success in modeling fluency and local coherence, existing neural language generation mo…
Multi-Task LearningStory GenerationText GenerationDetermining Secondary Attributes for Credit Evaluation in P2P Lending
There has been an increased need for secondary means of credit evaluation by both traditional banking organizations as well as peer-to-peer lending entities. This is especially important in the present technological era …
Clusteringfeature selectionTowards the Study of Morphological Processing of the Tangkhul Language
There is no or little work on natural language processing of Tangkhul language. The current work is a humble beginning of morphological processing of this language using an unsupervised approach. We use a small corpus co…
ArticlesIncorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks
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 suf…
Multi-Task LearningSpoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling
LLMs have so far failed both to generate consistently compelling stories and to recognize this failure--on the leading creative-writing benchmark (EQ-Bench), LLM judges rank zero-shot AI stories above New Yorker short st…