Story Ending Prediction is a task that needs to select an appropriate ending
for the given story, which requires the machine to understand the story and
sometimes needs commonsense knowledge. To tackle this task, we propose a new
neural network called Diff-Net for better modeling the differences of each
ending in this task. The proposed model could discriminate two endings in three
semantic levels: contextual representation, story-aware representation, and
discriminative representation. Experimental results on the Story Cloze Test
dataset show that the proposed model siginificantly outperforms various systems
by a large margin, and detailed ablation studies are given for better
understanding our model. We also carefully examine the traditional and
BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may
potentially help future studies.

提出了一种用于预测故事结尾的神经网络，名为 Diff-Net，能够在三个语义层面上对两种结尾进行区分，并在 Story Cloze Test 数据集上获得了显著的性能提升，这将有助于未来的相关研究。

基于辨别式句子建模的故事结尾预测

Discriminative Sentence Modeling for Story Ending Prediction

Recent advances, such as GPT and BERT, have shown success in incorporating a
pre-trained transformer language model and fine-tuning operation to improve
downstream NLP systems. However, this framework still has some fundamental
problems in effectively incorporating supervised knowledge from other related
tasks. In this study, we investigate a transferable BERT (TransBERT) training
framework, which can transfer not only general language knowledge from
large-scale unlabeled data but also specific kinds of knowledge from various
semantically related supervised tasks, for a target task. Particularly, we
propose utilizing three kinds of transfer tasks, including natural language
inference, sentiment classification, and next action prediction, to further
train BERT based on a pre-trained model. This enables the model to get a better
initialization for the target task. We take story ending prediction as the
target task to conduct experiments. The final result, an accuracy of 91.8%,
dramatically outperforms previous state-of-the-art baseline methods. Several
comparative experiments give some helpful suggestions on how to select transfer
tasks. Error analysis shows what are the strength and weakness of BERT-based
models for story ending prediction.

本研究旨在研究一种可转移的 BERT 训练框架，该框架不仅可以从大规模未标记数据中转移一般语言知识，还可以从各种语义相关的监督任务中转移特定类型的知识以用于目标任务。在本实验中，提出了利用三种转移任务，包括自然语言推理、情感分类和下一动作预测，以进一步训练预训练模型的思想，并将故事结尾预测作为目标任务进行实验。最终结果，准确率达到了 91.8％，明显优于先前最先进的基线方法。同时，进行了一些比较实验，给出了一些关于如何选择转移任务的有益建议。通过误差分析，还显示了基于 BERT 的模型在故事结尾预测中的优势和劣势。

基于可转移 BERT 的故事结局预测

Story Ending Prediction by Transferable BERT

The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant.

本文提出一种神经模型，通过整合叙事顺序、情感演变和背景常识等三种信息，有效提高了对 ROCStory Cloze 任务的预测准确率。

在故事完成中融合结构化常识知识

Incorporating Structured Commonsense Knowledge in Story Completion

Story comprehension requires a deep semantic understanding of the narrative,
making it a challenging task. Inspired by previous studies on ROC Story Cloze
Test, we propose a novel method, tracking various semantic aspects with
external neural memory chains while encouraging each to focus on a particular
semantic aspect. Evaluated on the task of story ending prediction, our model
demonstrates superior performance to a collection of competitive baselines,
setting a new state of the art.

提出了一种新颖的方法，使用外部神经记忆链跟踪不同的语义方面，每个链条专注于特定的语义方面，以实现对叙述的深层语义理解，该方法在故事结尾预测任务上表现优异，超过了一系列竞争基线，并创造了新的最先进成果。