Venue: NAACL 2022

# A Corpus for Understanding and Generating Moral Stories

Jian Guan, Ziqi Liu, Minlie Huang1

The CoAI group, DCST; Institute for Artificial Intelligence; State Key Lab of Intelligent Technology and Systems; Beijing National Research Center for Information Science and Technology; Tsinghua University, Beijing 100084, China.

{j-guan19, liuzq19}@mails.tsinghua.edu.cn, aihuang@tsinghua.edu.cn

Footnote 1: Corresponding author

###### Abstract

Teaching morals is one of the most important purposes of storytelling. An essential ability for understanding and writing moral stories is bridging story plots and implied morals. Its challenges mainly lie in: (1) grasping knowledge about abstract concepts in morals, (2) capturing inter-event discourse relations in stories, and (3) aligning value preferences of stories and morals concerning good or bad behavior. In this paper, we propose two understanding tasks and two generation tasks to assess these abilities of machines. We present storal, a new dataset of Chinese and English human-written moral stories. We show the difficulty of the proposed tasks by testing various models with automatic and manual evaluation on storal. Furthermore, we present a retrieval-augmented algorithm that effectively exploits related concepts or events in training sets as additional guidance to improve performance on these tasks.

+
Footnote †: _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 5069 - 5087

July 10-15, 2022 ©2022 Association for Computational Linguistics

## 1 Introduction

Stories play an essential role in one's moral development [2]. For example, individuals usually learn morals from life experiences or literature such as fables and tell their morals by representing their lived experience in a narrative form [23]. Accordingly, it is a crucial ability for humans to bridge abstract morals and concrete events in stories. However, this ability has not yet been investigated for machines.

There have been many tasks proposed for evaluating story understanding and generation, including story ending selection [15] and story generation from short prompts [19]. Unlike these tasks, which focus on reasoning plots from context, we emphasize the ability to associate plots with implied morals. As exemplified in Table 1, the challenges mainly lie in (1) grasping knowledge about abstract concepts (e.g., "unity," "strength") and relations among them (e.g., "is") in morals; (2) capturing inter-event discourse relations in stories (e.g., the contrast between endings of the "cows" when they are "united" and "divided"); and (3) aligning value preferences [10] of stories and morals (e.g., the story implies support for "unity", not opposition, which agrees with "is strength" in the moral). To test these abilities of machines, we propose two understanding tasks and two generation tasks. Both understanding tasks require selecting the correct moral from several candidates given a story. And they have respective candidate sets for testing machines in two aspects, including concept understanding (moCpt for short) and preference alignment (moPref for short). The generation tasks require concluding the moral of a story (st2mo for short), and conversely generating a coherent story to convey a moral (mo2st for short).

Furthermore, we collected a new dataset named storal composed of 4k Chinese and 2k English human-written stories paired with morals through human annotation to address the above challenges. We call the Chinese dataset storal-zh and the English dataset storal-en, respectively. And we construct datasets for the proposed tasks based on storal. Our focus of morals is on the social set of standards for good or bad behavior and character, or the quality of being right, honest or accept

\begin{table}
\begin{tabular}{p{341.4pt}} \hline \hline
**Morals: Unity is strength.** \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example in storal

able (Ianinska and Garcia-Zamor, 2006). We conduct extensive experiments on the proposed tasks. Furthermore, we present a retrieval-augmented algorithm to improve model performance by retrieving related concepts or events from training sets as additional guidance. However, the experiment results demonstrate that existing models still fall short of understanding and generating moral stories, which requires a better modeling of discourse and commonsense relations among concrete events and abstract concepts 1.

Footnote 1: All data and evaluation scripts are available at [https://github.com/thu-coai/MoralStory](https://github.com/thu-coai/MoralStory).

## 2 Related Work

Story DatasetsROCStories Mostafazadeh et al. (2016) and WritingPrompts Fan et al. (2018) are two frequently used story datasets in related studies. The former consists of artificial five-sentence stories regarding everyday events, while the latter contains fictional stories of 1k words paired with short prompts. Besides, some recent works collected extra-long stories such as roleplayerguild Louis and Sutton (2018), PG-19 Rae et al. (2020), and storiumAkoury et al. (2020). Guan et al. (2022) proposed a collection of Chinese stories. These stories usually aim to narrate a coherent event sequence but not convince readers of any morals.

Story Understanding and GenerationThere have been many tasks proposed for evaluating story understanding and generation. Firstly, some works tested the machinery commonsense reasoning ability regarding inter-event causal and temporal relations through story ending selection Mostafazadeh et al. (2016), story ending generation Guan et al. (2019) and story completion Wang and Wan (2019). Secondly, a series of studies focused on the coherence of story generation Fan et al. (2018); Yao et al. (2019); Guan et al. (2020). Another line of works concentrated on controllability to impose specified attributes into story generation. These attributes involved outlines Rashkin et al. (2020), emotional trajectories Brahman and Chaturvedi (2020) and story styles Kong et al. (2021). Our tasks investigate not only the above aspects but also the ability to understand abstract concepts and reason value preferences of stories.

A task similar to st2mo is text summarization Finlayson (2012) since both tasks require generating a short text to condense crucial information of a long text. But summarization requires reorganizing a few words of the original text instead of concluding a character-independent moral. For example, a plausible summary of the story in Table 1 is "Four cows were killed by two tigers and a lion" (generated by BARTLarge Lewis et al. (2020) fine-tuned on a summarization dataset XSUM Narayan et al. (2018)), which includes specific characters and events of the original story. Moreover, mo2st is similar to persuasive essay generation Stab and Gurevych (2017), which also requires conveying a viewpoint in generated texts. However, persuasive essays usually convince readers by directly presenting arguments but not narrating a story.

MoralsHaidt and Joseph (2004) provided a theoretical framework named Moral Foundations Theory (MFT) to summarize five basic moral foundations such as "Care/Harm," "Fairness/Cheating," etc. Based on the theory, recent studies have explored to classify the moral foundations of partisan news Fulgoni et al. (2016), tweets Johnson and Goldwasser (2018); Hoover et al. (2020), and crowd-sourced texts Pavan et al. (2020). And Volkova et al. (2017) proposed identifying suspicious news based on the features of moral foundations. However, we focus on morals which are free-form texts far beyond the scope of the five categories in MFT. In addition, recent studies proposed multiple datasets for machine ethics research such as SBIC Sap et al. (2020), Social Chemistry Forbes et al. (2020), Moral Stories Emelin et al. (2020), ETHICS Hendrycks et al. (2021) and Scruples Lourie et al. (2021). But these datasets focus more on how machines behave ethically in some scenario, while storal emphasizes the ability to conclude the moral implied by a story. Moreover, most cases in these datasets consist of short texts of descriptive ethical behavior, typically in the form of one sentence. In contrast, storal provided longer and more context-specific stories for moral understanding.

## 3 Storal Dataset

We collected storal from multiple web pages of moral stories. All stories are allowed to use and redistribute for research and have been reviewed by the website editors as stated on the pages. We show the full list of links to these pages in Section A.1. After de-duplication, we collected 19,197 Chinese and 2,598 English raw texts. Then we adopted human annotation for decoupling the story and moral 

in each raw text. Due to resource limitations, we only constructed 4,209 Chinese and 1,779 English story-moral pairs. We will first show the details of human annotation, then present the topic analysis and statistics of storal, and finally describe the details of dataset construction for the proposed tasks.

### Human Annotation

To narrow down our focus, we define a story as a series of coherent events involving several interrelated characters, and implies support or opposition of some behavior. Such a definition constrains the story to exhibit a moral without any explicit arguments. And we define a moral as a judgment to describe what the story implies concerning good or bad behavior. Note that we do not require morals in storal to be always reflective of normatively virtuous behavior. We emphasize that the morals should align with the story. Then, a key issue is how to extract the story and moral from a raw text. We observe that there are no markers such as "The story tells us" to separate the story and moral in most cases. The moral may be tightly weaved into the plot (e.g., included in a dialogue). Therefore, we adopted human annotation for this extraction task. We hired a commercial team to annotate storalzh. All annotators are native Chinese speakers and well trained for our task. For storal-en, we hired three graduates with good English language proficiency. We did not use AMT since it is inconvenient to train online annotators. Figure 1 shows the annotation pipeline.

We first ask annotators to judge whether the raw text contains a story and moral and whether they meet our constraints shown in Figure 1. We show the examples given to the annotators to inform them of our requirements for stories and morals in Section A.2. If the constraints are not met, we then ask annotators to refine the story and moral. In the refinement stage, annotators have to clean up the data with following heuristics: (1) refusing examples which may violate general ethical principles (e.g., discrimination); (2) deleting noisy words (e.g., links, codes); (3) refining the stories and morals to be coherent and formal. And to ensure the quality of collected data, annotators may refuse to refine the example if it requires much creative writing. Finally, we review the annotation results and provide detailed feedback to the annotators before approving their submissions. We show an annotation example in Table 2.

### Topic Analysis

To provide insight into the taxonomy of morals within storal, we adopt LDA Blei et al. (2003) for topic modeling of morals. Let \(B\) denote the number of topics and \(V\) denote the vocabulary size. Based on the variational parameter for topic word distribution \(\beta\in\mathbb{R}^{B\times V}\), we determine \(B\) as the minimum value that makes the following formula

Figure 1: The pipeline of human annotation for constructing storal (Left) and our constraints (Right).

 

holds true for any \(b\in\{1,2,\cdots,B\}\):

\[s_{b}=\frac{\sum_{v\in\mathcal{V}^{(k)}_{b}}\beta_{bv}}{\sum_{v=1}^ {V}\beta_{bv}}\geq h,\] \[\mathcal{V}^{(k)}_{b}=\text{argmax}_{\mathcal{V}^{*(k)}_{b}}\sum_{ v\in\mathcal{V}^{*(k)}_{b}}\beta_{bv},\]

where \(\beta_{bv}\) is the element at the \(b\)-th row and \(v\)-th column of \(\beta\), \(k\in\{1,2,\cdots,V\}\) is the size of the top-\(k\) vocabulary \(\mathcal{V}^{(k)}_{b}\), and \(h\in[0,1]\) is a predefined threshold. \(s_{b}\) is used to measure the specificity of the \(b\)-th topic. Intuitively, the larger \(s_{b}\), the more specific the topic. We set \(k\) to 20 and \(h\) to 0.5. Finally, we derive 40/24 topics for storal-zh/storal-en, respectively. And the minimum proportion of examples of one topic is 1.6%/3.2% for storal-zh/storal-en, respectively.

Table 3 shows the topic words in \(\mathcal{V}^{(10)}\) of each topic and two morals assigned to each topic with the highest probabilities for the five topics with the largest specificity scores. The topics cover diverse situations ranging from facing others ("honesty," "help"), parents ("love"), ourselves ("self-help," "self-discovery") to facing difficulties ("think") and danger ("safety"). And examples of the same topic present related semantics to some extent, such as "being honest" and "not believing liars" for the first topic in storal-en. We also show the analysis of high-frequency words of stories and morals in Section A.3 and discussion about the commonsense and discourse relations in stories in Section A.4.

### Dataset Statistics of storal

Table 4 shows the statistics of storal. We regard the unlabeled data which contain entangled stories and morals as an in-domain resource for research on unsupervised or semi-supervised learning for the proposed tasks. And the data are also suitable for learning to generate morals stories where the morals are weaved naturally into the story plots.

### Task-Specific Dataset Construction

Based on storal, we build task-specific datasets for our understanding tasks (moCpt and moPref) and generation tasks (st2mo and mo2st). We randomly split the labeled data in storal-zh and storal-en for training/validation/testing by 8:1:1 and 3:1:1, respectively. Table 5 shows the task

\begin{table}
\begin{tabular}{p{79.7pt} p{142.3pt} l} \hline \multicolumn{3}{c}{**Topic Words**} & **Examples** \\ \hline  & (understand), (@g (also), @f (know), @f (method), \% (gain), @f (procerd), @f (brain), @f (abde), @f (pay), @f (progress) & mistakes, we must understand the overall situation. And we are able to have a definition of things only when knowing the overall situation.) \\ \cline{2-3}  & (no), \% (neu), \% (an (t), @f (danger), @f (when), @f (treat), @f (asafety; \(\gamma\) (any) way), @f (well), @f (earth), @f (learn), @f (encounter) & — (keep in mind the word “safety” in your life, and do not take any tones to test safety as a jeble) \\ \cline{2-3}  & (thing), \% (abe), @f (do well), @f (secclient), @f (shoald), @f (achieve), @f (self-conflent), @f (somewhat), @f (mable), @f (may) & — (Do what you should do and be your own master.) \\ \cline{2-3}  & (whe), \% (actually, @f (many), @f (discore), @f (with), @f (mappen), @f (life), @f (already), @f (dump), @f (may) & — (discover), @f (showed the good at @f (move), @f (my) & — (the (howed), 

descriptions and data sizes.

moCptIt requires selecting the correct moral from five candidates given a story. We constructed the dataset by taking the original moral as the correct candidate and four negatively sampled morals as incorrect candidates for each example. To avoid more than one plausible candidate, we ensured that the negative morals are assigned to different topics from the original one by the LDA model (Section 3.2). In this way, moCpt can effectively test the ability to distinguish different concepts.

moPrefIt requires selecting the correct moral from two candidates. Its difference from moCpt is that we created the incorrect candidate by substituting one random token in the original moral to its antonym. For example, the moral "unity is strength" can be transformed to "unity is weakness". We perform the transformation using a rule-based method Ribeiro et al. (2020). Because there exist examples where no words have antonyms, the number of examples for moPref are a little fewer than moCpt. moPref will serve for testing the ability to capture the value preference of stories.

st2moIt requires generating the moral of a given story. We regard the original story as input and the original moral as target output.

mo2stIt requires generating a story to convey a given moral. Unfortunately, automatic evaluation for open-ended story generation is still highly challenging due to the notorious one-to-many issue Zhao et al. (2017): There may be multiple plausible stories with the same moral. For example, the moral in Table 1 can also be conveyed by another story: "bees unite to build their beehives." Such openness makes automatic metrics unreliable for quality evaluation Guan and Huang (2020).

To alleviate this issue, we extract the first sentence and an outline from a target story, and pair them with the moral as input for generating the story. We follow Rashkin et al. (2020) to extract a set of at most eight phrases from a story through RAKE Rose et al. (2010) as the outline. We set the maximum number of words in each phrase to eight. We also filtered those phrases that are substrings of others. For example, the outline for the story in Table 1 is {"lions," "friends fought," "good friends," "grazed," "perfect opportunity"}. Finally, for storal-zh/storal-en, the average number of phrases for each example is 7.5/6.8 and the average number of words in each phrase is 2.87/2.44, respectively.

## 4 Retrieval Augmentation

A critical challenge for tackling the proposed tasks is the sparsity of morals and events makes it difficult to learn relations between them. Prior studies have shown that retrieval improves performance towards infrequent data points across various tasks such as open-domain question answering Chen et al. (2017) and text classification Lin et al. (2021). We present a retrieval-augmented algorithm that exploits the moral-event relations in training sets. We illustrate our model for the moPref task in Fig

\begin{table}
\begin{tabular}{c|c|c c|c|c c|c c|c c c} \hline \multirow{2}{*}{**Datasets**} & \multicolumn{9}{c|}{**Labeled Data**} & \multicolumn{3}{c}{**Unlabeled Data**} \\ \cline{2-13}  & \# Examples & \multicolumn{3}{c|}{Storates} & \multicolumn{3}{c|}{**Morals**} & \multicolumn{3}{c}{**\# Fam Samples**} & \multicolumn{3}{c}{**\# Word**} & \multicolumn{3}{c}{**\# Sent**} & \multicolumn{3}{c}{**Vocab**} \\ \cline{2-13}  & **\# Sort** & **\# Sort** & **Yocab** & **\# Word** & **\# Sent** & **Vocab** & **\# Search** & **Vocab** \\ \hline storal-zh & 4,209 & 321.75 & 17.62 & 63,493 & 25.09 & 1.35 & 10,522 & 14,988 & 487.00 & 26.12 & 147,805 \\ storal-en & 1,779 & 302.33 & 17.71 & 15,873 & 19.77 & 1.45 & 3,384 & 819 & 614.55 & 38.05 & 20,853 \\ \hline \end{tabular}
\end{table}
Table 4: storal statistics. We use Jieba2/NLTK Loper and Bird (2002) for word tokenization of storal-zhi/ storal-en. **# Word** / **\# Sent** is the average number of words/sentences. **Vocab** is the vocabulary size.

\begin{table}
\begin{tabular}{p{42.7pt}|p{42.7pt}|p{42.7pt}|p{42.7pt}|p{42.7pt}} \hline \hline
**Tasks** & **Abilities** & **Inputs \& Outputs** & \multicolumn{1}{p{42.7pt}|}{**Storal-zh**} & \multicolumn{1}{p{42.7pt}}{**Storal-en**} \\ \cline{2-6}  & **MoCpt**t** & **Concept Understanding** & Given a story and five candidate morals, choosing the correct moral. & 3,368 / 420 / 421 & 1,068 / 355 / 356 \\ \hline
**moPref** & Preference Alignment & Given a story and two candidate morals, choosing the correct moral. & 3,276 / 410 / 411 & 988 / 344 / 339 \\ \hline
**st2mO** & Moral Generation & Given a story, generating a moral which is character-independent and generally applicable. & 3,368 / 420 / 421 & 1,068 / 355 / 356 \\ \hline
**mo2ST** & Story Generation & Given a moral and a story beginning and outline, generating a story which has a coherent plot and convinces readers of the moral. & 3,368 / 420 / 421 & 1,068 / 355 / 356 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Description of the proposed tasks about the abilities they investigate, inputs and outputs, and the data sizes.

 

ure 2. Our models for other tasks are similar.

For both moCpt and mopPref, we encode the story and candidates using an input encoder, and then predict a probability distribution over the candidates by normalizing the dot-product scores between the representations of the story and each candidate. We optimize the model by minimizing the cross-entropy loss. We insert special tokens [S] and [C] before the story and each candidate, respectively, and take the corresponding hidden states as their representations. Furthermore, we propose to retrieve related concepts from the training set using the input story. We encode the story using a query encoder, then take the output as the query to retrieve \(m\) most related stories based on a story index, i.e., a set of dense vectors as the representations of stories in the training set. We adopt BERT Devlin et al. (2019) followed by a mean-pooling layer to build the query encoder and story index, which are frozen in the training stage. Finally, we extract the nouns, verbs, adjectives and adverbs from the morals of the top-\(m\) stories and lemmatize them as the retrieved concepts. We feed the concepts together with the original input to the input encoder. For example, the retrieved concepts for the story in Table 1 include "support" and "strength", which may serve as additional guidance for models' prediction.

The retrieval-augmented algorithm can easily adapt to the generation tasks. For st2mo, we take the input story paired with the retrieved concepts into the encoder and then generate the output using the decoder. And for mo2st, we use the input moral as the query to retrieve top-\(m\) stories, and regard their outlines as the retrieved additional information to guide the subsequent story generation.

## 5 Experiments

### Evaluated Models

We evaluated the following baselines for the understanding tasks: _BERT_Devlin et al. (2019), _RoBERTa_Liu et al. (2019) and _T5_Raffel et al. (2020). When evaluating T5, we feed the input to both the encoder and decoder of T5 and optimize the model using the cross-entropy loss. To investigate potential biases of the proposed datasets, we added a baseline called _BERT w/o story_, which is fine-tuned to make prediction without taking the story as input. For the generation tasks, we evaluated _ConvS2S_Gehring et al. (2017), _Fusion_Fan et al. (2018), _GPT2_Radford et al. (2019) and _T5_, which are trained or fine-tuned with the standard language modeling objective. Moreover, we also evaluate a task-specific model _PlotMachines_PM for short) Rashkin et al. (2020), which is proposed for tackling outline-conditioned generation by tracking the dynamic plot states. We use GPT2 as the backbone model of PM.

We also design models to test the adaption of the unlabeled data of storal to the proposed tasks. Specifically, we first post-train RoBERTa and T5 on the unlabeled data with their original pretraining objectives, respectively (i.e., masked language model and text infilling) and then fine-tune them on the labeled data for the downstream tasks Gururangan et al. (2020). We call the baselines _RoBERTa-Post_ and _T5-Post_. We perform our retrieval-augmented algorithm based on the post-trained models, called _RA-RoBERTa_ and _RA-T5_, respectively.

### Experiment Settings

We implement the pretrained models based on the codes and pretrained checkpoints of HuggingFace's Transformers Wolf et al. (2020). We use LongLMbase Guan et al. (2022) as the T5 model for experiments on storal-zh, and set all pre-trained models to the base version due to limited computational resources. As for the hyperparameters, we set the batch size to 16, the maximum sequence length to 1,024, the learning rate to 3e-5, \(m\) to 10 for our retrieval-augmented model. We generate outputs using top-\(k\) sampling Fan et al. (2018) with \(k=40\) and a softmax temperature of 0.7 Goodfellow et al. (2016). We show more details in Section B.1.

### Automatic Evaluation

Evaluation MetricsWe adopt accuracy to evaluate the understanding tasks. For generation tasks, we do not use perplexity since perplexity scores are not comparable among models with different vocabularies. We adopt the following metrics for automatic evaluation: **(1) BLEU (B-\(n\)):** It is used

Figure 2: Model overview for the mOpref task.

 

to measure \(n\)-gram overlaps (\(n=1,2\)) between generated and ground-truth texts Papineni et al. (2002). **(2) BERTScore-F1 (BS):** It is used to measure the semantic similarity between generated and ground-truth texts Zhang et al. (2019). **(3) Repetition (R-\(n\)):** It calculates the ratio of texts that repeat at least one \(n\)-gram in all generated texts Shao et al. (2019). **(4) Distinct (D-\(n\)):** It measure the diversity using the percentage of distinct \(n\)-grams to all \(n\)-grams in generated texts Li et al. (2016). For both R-\(n\) and D-\(n\), we set \(n=2\) for st2mo and \(n=4\) for mo2st considering the much shorter length of morals than stories. Besides, we also report the average number of generated words **(Len)**.

We also adopt the following metrics for automatic evaluation of mo2st: **(1) Coverage (Cov):** It computes Rouge-L recall Lin (2004) between generated stories and phrases in the corresponding outlines. A higher score means the generated stories cover more phrases in the given outlines. **(2) Order (Ord):** It measures the disparity between the positional orders of given phrases in the ground truth and generated story using the percentage of inversions in the generated story Guan et al. (2022). An inversion is a position pair of two phrases that is out of the ground-truth order. Higher order scores mean that the stories arrange the outline more reasonably. In Section B.2, we also construct a learnable automatic metric to measure the faithfulness between morals and stories.

ResultsTable 6 and 7 show the results on the understanding and generation tasks, respectively. To get the human performance on moCpt and mopPref, we sampled 100 examples from the test set and recruited three annotators with good Chinese or English language proficiency to complete these tasks. We made final decisions among the annotators through major voting. The annotation results show an almost perfect agreement with Fleiss's \(\kappa>0.85\)Teiss and Joseph (1971).

We summarize the results on the understanding tasks as follows: **(1)** The moPref datasets suffer from innate biases as indicated by the high accuracy of BERT w/o story. Such biases may result from the noise introduced by the automatic construction technique, i.e., antonym substitution. And models may learn patterns of good behavior (e.g., "unity" is good and "disunity" is bad in general) and make predictions easily without depending on stories. However, moPref is still meaningful as an evaluation task since BERT can achieve much better accuracy when taking stories as input. And we experiment using manually constructed examples for evaluating preference alignment in the appendix. **(2)** T5 performs better than RoBERTa on moCpt but worse on moPref, indicating T5 may not be good at capturing value preferences. **(3)** Post-training on the unlabeled data (i.e., RoBERTaSPost and T5-Post) does not always bring improvement on both tasks, suggesting that it is necessary to develop a better way to exploit these data in future work. **(4)** Retrieving additional concepts improves models' performance effectively, particularly for the moCpt task on storal-en. However, there is still a big gap between our models and human performance.

As for the generation tasks, we draw the following conclusions: **(1)** Almost all pretrained models achieve better lexical and semantic similarity with ground-truth texts than non-pretrained models, as indicated by higher BLEU and BERTScore values. **(2)** Non-pretrained models have less repetition than pretrained ones, and repeat even less than the ground-truth texts when generating morals. It may be because non-pretrained models generate shorter sequences than pretrained models despite the same decoding algorithm, which also accounts for the higher distinct scores of the non-pretrained models on the mo2st task. **(3)** When generating stories, T5-Post can cover more input phrases and arrange them in a correct order than other baselines, as indicated by higher coverage and order scores. **(4)** Retrieval augmentation can improve the generation similarity with the ground-truth texts on both tasks and improve the coverage and order scores on st2mo significantly compared with T5-Post.

\begin{table}
\begin{tabular}{l|c|c c|c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c|}{**mOCPt**} & \multicolumn{2}{c}{**MOPEf**} \\  & & **zH** & **EN** & **ZH** & **EN** \\ \hline
**Random** & N/A & 20.19 & 20.22 & 50.12 & 50.00 \\
**BERT w/o Story** & 110M & 23.52 & 22.47 & 71.81 & 72.57 \\ \hline
**BERT** & 110M & 59.62 & 51.97 & 82.97 & 79.35 \\
**RoBERTa** & 110M & 62.71 & 54.78 & 89.54 & 81.12 \\
**RoBERTa** & _Poot_ & 110M & 64.61 & 51.40 & 87.59 & 81.42 \\
**T5** & 220M & 69.60 & 58.99 & 82.70 & 76.99 \\
**T5-Post** & 220M & 70.07 & 62.64 & 81.75 & 77.29 \\ \hline
**RA-RoBERTa** & 110M & 65.08 & 60.96 & **90.02** & 81.71 \\
**RA-T5** & 220M & **72.68*** & **67.42*** & 82.97 & **82.60** \\ \hline
**Human** & N/A & _95.00_ & _96.00_ & _98.00_ & _99.00_ \\ \hline \end{tabular}
\end{table}
Table 6: Accuracy (%) for moCpt and moPref. # P is the number of parameters. The best performance is highlighted in **bold** and the second best is underlined. The scores marked with \({}^{*}\) and \({}^{**}\) of RA model mean it outperforms the best baseline significantly with p-value<0.1 and p-value<0.05 (sign test), respectively.

 



### Manual Evaluation

On the generation tasks, we conducted a Likert-scale based manual evaluation to measure the gap between existing models and humans. For stoalzh, we hired three graduate students (native Chinese speakers) as annotators. We conducted evaluation on storal-en using Amazon Mechanical Turk (AMT). For both tasks, we randomly sampled 100 examples from the test set, and obtained 300 generated texts from Fusion, T5 and RA-T5. For each text we require three annotators to rate its quality along with the input using a binary score in three following aspects: (1) _linguistic fluency_: correctness in grammaticality; (2) _coherence_: reasonable relations between sentences regarding relatedness, causality and temporal orders; and (3) _moral faithfulness_: exhibition of a faithful moral to the input. Three aspects are independently evaluated. We decided the final score of a text through majority voting. The annotation instruction is shown in Section B.3.

Table 8 shows the manual evaluation results. We show \(p\)-values of the results in Section B.4. For st2mo, T5 achieves a substantial improvement compared with Fusion (\(p<0.01\)), and our model further outperforms T5. The superiority becomes less significant for mo2st. However, the big gap between these models and humans, particularly in terms of faithfulness, proves both tasks challenging for existing models. Furthermore, we evaluate whether machines can capture the value preference of a story using manually constructed examples. And we show error analysis and case study for the proposed tasks in Section C. We believe that explicit modeling of the relations among events and abstract concepts will further promote progress on these tasks, which we regard as future work.

## 6 Conclusion

We present storal, a collection of Chinese and English moral stories. To test the ability to bridge

\begin{table}
\begin{tabular}{l|c|c c c c c c|c c c c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{7}{c}{**Dataset: storal-zh**} \\  & \multicolumn{3}{c}{**B-1†} & \multicolumn{3}{c}{**B-2†} & \multicolumn{3}{c}{**BS†} & \multicolumn{3}{c}{**R-2�} & \multicolumn{3}{c}{**D-2†} & \multicolumn{3}{c}{**Len**} & \multicolumn{3}{c}{**B-1†} & \multicolumn{3}{c}{**B-2†} & \multicolumn{3}{c}{**BS†} & \multicolumn{3}{c}{**R-2�} & \multicolumn{3}{c}{**D-2†} & \multicolumn{3}{c}{**Len**} \\ \hline
**ConvS2S** & 50M & 14.31 & 1.86 & 56.71 & **26.60** & 43.67 & 19.31 & 9.69 & 0.93 & 82.57 & 6.46 & 47.35 & 11.75 \\
**Fusion** & 100M & 14.78 & 2.23 & 56.90 & 27.55 & 41.21 & 29.16 & 9.87 & 8.82 & 68.68 & **6.18** & 45.93 & 13.15 \\ \hline
**GPT2** & 12dM & 14.54 & 2.16 & 60.75 & 35.39 & 48.22 & 20.72 & 10.98 & 1.24 & 79.39 & 20.22 & 60.36 & 16.19 \\
**TS** & 220M & 18.19 & 3.60 & 61.61 & 76.48 & 44.84 & 29.06 & 13.31 & 2.26 & 85.89 & 33.15 & 58.73 & 19.39 \\
**TS-Post** & 220M & 17.98 & **3.91** & 61.52 & 69.12 & **51.97** & 29.14 & 13.83 & 2.11 & 85.85 & 34.83 & 57.12 & 18.49 \\ \hline
**RA-T5** & 220M & **18.32** & 3.64 & **61.93\({}^{**}\)** & 70.78 & 48.41 & 29.44 & **14.59** & **2.61** & **86.16\({}^{**}\)** & 31.46 & **60.61** & 18.54 \\ \hline \end{tabular} 
\begin{tabular}{l|c|c c c c c c c|c c c c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{7}{c}{**Dataset: storal-zh**} \\  & \multicolumn{7}{c}{**B-1†} & \multicolumn{3}{c}{**B-2†} & \multicolumn{3}{c}{**B-1†} & \multicolumn{3}{c}{**B-1†} & \multicolumn{3}{c}{**Covi**†} & \multicolumn{3}{c}{**Oi+ †} & \multicolumn{3}{c}{**Len**} & \multicolumn{3}{c}{**B-1†} & \multicolumn{3}{c}{**B-2†} & \multicolumn{3}{c}{**RS†} & \multicolumn{3}{c}{**R-1†} & \multicolumn{3}{c}{**Coy†} & \multicolumn{3}{c}{**Def†} & \multicolumn{3}{c}{**Len**} \\ \cline{2-15} \cline{7-15}  & **ConySE** & 15.57 & 6.43 & 60.06 & 75.30 & 78.41 & 21.61 & 33.03 & 150 & 16.25 & 6.38 & 79.27 & 61.85 & **80.29** & 6.46 & 41.88 & 122 \\
**Fusion** & 15.53 & 6.45 & 60.06 & **74.11** & **80.51** & 22.86 & 33.33 & 148 & 17.17 & 6.82 & 79.52 & **61.24** & 75.79 & 7.27 & 43.07 & 13.7 \\ \hline
**GFT2** & 14.91 & 6.48 & 63.32 & 91.45 & 58.67 & 48.57 & 51.58 & 282 & 25.83 & 12.91 & 83.25 & 84.27 & 74.63 & 45.18 & 59.95 & 247 \\
**FM** & 15.82 & 7.04 & 63.58 & 90.97 & 57.33 & 50.51 & 52.35 & 280 & 26.34 & 15.92 & 81.63 & 80.90 & 75.26 & 47.07 & 60.31 & 264 \\
**TS** & 17.24 & 9.44 & 65.89 & 91.69 & 61.76 & 58.18 & 56.11 & 160 & 30.36 & 16.75 & 79.89 & 90.17 & 77.53 & 74.21 & 63.45 & 283 \\
**TS-Post** & 18.42 & 9.27 & 65.63 & 94.54 & 58.13 & 60.11 & 56.96 & 176 & 32.26 & 180.4 & 82.80 & 94.10 & 77.27 & 76.09 & 64.13 & 281 \\ \hline \multirow{2}{*}{**RA-T5**} & **23.36** & **12.98** & **67.37** & 95.72 & 59.49 & **69.24** & **60.44** & 241 & **32.46** & **18.31** & **84.07** & 92.42 & 76.74 & **80.21** & **66.10** & 253 \\  & **+ †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** & †** \\ \hline _Truth_ & 1/N & N/A & N/A & _55.34_ & 96.06 & _100.00_ & 100.00 & 324 & N/A & N/A & N/A & _58.77_ & _95.09_ & _100.00_ & _100.00_ & 281 \\ \hline \end{tabular}
\end{table}
Table 7: Automatic evaluation results for st2mo (Top) and mo2st (Bottom). ‡ _I_† means the lower/higher the better. All scores except _Len_ are multiplied by 100. The best result is in **bold** and the second best is underlined. The scores marked with \({}^{*}\) and \({}^{**}\) of RA-T5 mean it outperforms the best baseline significantly with p-value\(<\)0.1 and p-value\(<\)0.05 (sign test), respectively.

\begin{table}
\begin{tabular}{l|c|c|c|c|c} \hline \hline
**Data** & **Task** & **Model** & **Flu (\(s\))** & **Cohen (\(s\))** & **Faith (\(\kappa\))** \\ \hline \multirow{4}{*}{**Stanial**} & \multirow{4}{*}{**Test**} & **fusion** & 0.24 (0.31) & 0.22 (0.37) & 0.08 (0.72) \\  & & & **TS** & 0.75 (0.40) & 0.61 (0.38) & 0.31 (0.32) \\  & & & **RA-T5** & **0.85 (0.65)** & **0.63 (0.26)** & **0.36 (0.27)** \\ \cline{2-6}  & & & **Train** & 1.00 (0.10) & 0.09 (0.96) & 0.86 (0.57) \\ \cline{2

concrete events and abstract morals, we propose new understanding and generation tasks based on storal, including selecting the correct moral from several candidates with different topics or opposite value preferences, concluding the moral of a story and generating a story to convey a moral. Extensive experiments prove these tasks still to be challenging for existing models. We propose a retrieval-augmented algorithm to improve performance by retrieving related concepts or events from training sets. Although it is possible to further increase the dataset size, we expect to make meaningful progress by developing better representations of commonsense and discourse relations among events and abstract concepts in future work.

## 7 Acknowledgement

This work was supported by the National Science Foundation for Distinguished Young Scholars (with No. 62125604) and the NSFC projects (Key project with No. 61936010 and regular project with No. 61876096). This work was also supported by the Guoqiang Institute of Tsinghua University, with Grant No. 2019GQG1 and 2020GQG0005. This work was also sponsored by Tsinghua-Toyota Joint Research Fund. We would also like to thank the anonymous reviewers for their invaluable suggestions and feedback.

## Ethics Statements and Broader Impact

We collected storal from public web resources. All stories are under licenses that allow use and redistribution for research purposes. We asked commercial annotation teams to extract stories and morals from the crawled raw texts. We required the annotators to refuse the examples which violate general ethical principles (e.g., showing discrimination for someone, containing disrespectful content, or encouraging to disturb public order, etc.). Totally, we payed more than $7 (CNY 45) per hour on average for annotating each example in storal, which was far beyond the minimum hourly wage in China (CNY 21). Furthermore, we resorted to AMT for manual evaluation of generated and human-written texts for two proposed generation tasks. We hired three annotators and payed each annotator $0.2 on average for annotating each example.

In this paper, we emphasize the ability to model relations between concrete events and abstract morals, which is also helpful for various scenarios such as reading comprehension (e.g., drawing authors' viewpoints from narratives) and essay writing (e.g., writing essays to convince readers of some arguments by presenting examples or anecdotes). storal provides a good start point for exploring these directions.

## References

* Akoury et al. (2020) Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer. 2020. STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6470-6484, Online. Association for Computational Linguistics.
* Blei et al. (2003) David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. _the Journal of machine Learning research_, 3:993-1022.
* Brahman and Chaturveddi (2020) Faeze Brahman and Snigdha Chaturveddi. 2020. Modeling protagonist emotions for emotion-aware storytelling. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 5277-5294, Online. Association for Computational Linguistics.
* Chen et al. (2017) Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017. Reading Wikipedia to answer open-domain questions. In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_, pages 1870-1879, Vancouver, Canada. Association for Computational Linguistics.
* Cui et al. (2020) Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting pre-trained models for Chinese natural language processing. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings_, pages 657-668, Online. Association for Computational Linguistics.
* Devlin et al. (2019) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_, pages 4171-4186.
* Emelin et al. (2020) Denis Emelin, Ronan Le Bras, Jena D Hwang, Maxwell Forbes, and Yejin Choi. 2020. Moral stories: Situated reasoning about norms, intents, actions, and their consequences. _arXiv preprint arXiv:2012.15738_.
* Fan et al. (2018) Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. In _Proceedings of the 56th Annual Meeting of the Association for 

Computational Linguistics (Volume 1: Long Papers), pp. 889-898. Cited by: SS2.
* M. A. Finlayson (2012)Learning narrative structure from annotated folktales.. Ph.D. Thesis, Massachusetts Institute of Technology. External Links: Link Cited by: SS4.3.
* F. Heiss and L. Joseph (1971)Measuring nominal scale agreement among many raters. Psychological Bulletin76 (5), pp. 378-382. Cited by: SS2.
* M. Forbes, J. D. Hwang, V. Shwartz, M. Sap, and Y. Choi (2020)Social chemistry 101: learning to reason about social and moral norms. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, pp. 653-670. External Links: Document, Link Cited by: SS2.
* D. Fulgoni, J. Carpenter, L. Ungar, and D. Preotiuco-Pietro (2016)An empirical exploration of moral foundations theory in partisan news sources. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pp. 3730-3736. Cited by: SS2.
* J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin (2017)Convolutional sequence to sequence learning. In International Conference on Machine Learning, pp. 1243-1252. Cited by: SS2.
* I. Goodfellow, Y. Bengio, and A. Courville (2016)Deep learning. MIT press. Cited by: SS2.
* J. Guan, Z. Feng, Y. Chen, R. He, X. Mao, C. Fan, and M. Huang (2022)LOT: a Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation. Transactions of the Association for Computational Linguistics10, pp. 434-451. Cited by: SS2.
* J. Guan, F. Huang, Z. Zhao, X. Zhu, and M. Huang (2020)A knowledge-enhanced pretraining model for commonsense story generation. Transactions of the Association for Computational Linguistics8, pp. 93-108. Cited by: SS2.
* J. Guan and M. Huang (2020)UNION: an unreferenced metric for evaluating open-ended story generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 9157-9166. External Links: Link Cited by: SS2.
* J. Guan, Y. Wang, and M. Huang (2019)Story ending generation with incremental encoding and commonsense knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, pp. 6473-6480. Cited by: SS2.
* S. Gururangan, A. Marasovic, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. A. Smith (2020)Don't stop retraining: adapt language models to domains and tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8342-8360. Cited by: SS2.
* J. Haidt and C. Joseph (2004)Intuitive ethics: how innately prepared intuitions generate culturally variable virtues. Daedalus133 (4), pp. 55-66. Cited by: SS2.
* D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt (2021)Aligning {ai} with shared human values. In International Conference on Learning Representations, Cited by: SS2.
* J. Hoover, G. Portillo-Wightman, L. Yeh, S. Havaldar, A. Mostafazadeh Davani, Y. Lin, B. Kennedy, M. Atari, Z. Kamel, M. Mendlen, G. Moreno, C. Park, T. E. Chang, J. Chin, C. Leong, J. Yen Leung, A. Mirinjian, and M. Dehghani (2020)Moral foundations twitter corpus: a collection of 35k tweets annotated for moral sentiment. Social Psychological and Personality Science11 (8), pp. 1057-1071. Cited by: SS2.
* S. Ianinska and J. Garcia-Zamor (2006)Morals, ethics, and integrity: How codes of conduct contribute to ethical adult education practice. Public Organization Review6 (1), pp. 3-20. Cited by: SS2.
* L. Jiang, J. D. Hwang, C. Bhagavatula, R. Le Bras, M. Forbes, J. Borchardt, J. Liang, O. Etzioni, M. Sap, and Y. Choi (2021)Delphi: towards machine ethics and norms. arXiv preprint arXiv:2110.07574. Cited by: SS2.
* K. Johnson and D. Goldwasser (2018)Classification of moral foundations in microblog political discourse. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 720-730. Cited by: SS2.
* X. Kong, J. Huang, Z. Tung, J. Guan, and M. Huang (2021)Stylized story generation with style-guided planning. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, Online, pp. 2430-2436. External Links: Link Cited by: SS2.
* M. Lewis, Y. Liu, N. Goyal, M. Ghavzininejad, A. Mohamed, Omer Levy, V. Stoyanov, and L. Zettlemoyer (2020)BART: denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 7871-7880. External Links: Link Cited by: SS2.
* J. Li, M. Galley, C. Brockett, J. Gao, and W. B. Dolan (2016)A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North 

American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 110-119.
* Lin (2004) Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, Barcelona, Spain. Association for Computational Linguistics.
* Lin et al. (2021) Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, and Fei Wu. 2021. Bertgcn: Transductive text classification by combining gcn and bert. arXiv preprint arXiv:2105.05727.
* Liu et al. (2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
* Loper and Bird (2002) Edward Loper and Steven Bird. 2002. Nltk: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pp 63-70.
* Louis and Sutton (2018) Annie Louis and Charles Sutton. 2018. Deep dungeons and dragons: Learning character-action interactions from role-playing game transcripts. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp 708-713.
* Lourie et al. (2021) Nicholas Lourie, Ronan Le Bras, and Yejin Choi. 2021. Scruples: A corpus of community ethical judgments on 32, 000 real-life anecdotes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, pages 13470-13479.
* Mostafazadeh et al. (2016) Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A corpus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of NAACL-HLT, pages 839-849.
* Narayan et al. (2018) Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018. Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp 1797-1807.
* Papineni et al. (2002) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp 311-318.
* Pavan et al. (2020) Matheus C Pavan, Vitor G Dos Santos, Alex GJ Lan, Joao Martins, Wesley R Santos, Caio Deutsch, Pablo B Costa, Fernando C Hsieh, and Ivandre Paraboni. 2020. Morality classification in natural language text. IEEE Transactions on Affective Computing.
* Radford et al. (2019) Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI blog1(8):9.
* Rae et al. (2020) Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap. 2020. Compressive transformers for long-range sequence modelling. In International Conference on Learning Representations.
* Raffel et al. (2020) Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21:1-67.
* Rashkin et al. (2020) Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outline-conditioned generation with dynamic plot state tracking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4274-4295.
* Ribeiro et al. (2020) Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Behavioral testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 4902-4912, Online. Association for Computational Linguistics.
* Rose et al. (2010) Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010. Automatic keyword extraction from individual documents. Texr mining: applications and theory, 1:1-20.
* Sap et al. (2020) Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. 2020. Social bias frames: Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online. Association for Computational Linguistics.
* Shao et al. (2019) Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, and Xiaoyan Zhu. 2019. Long and diverse text generation with planning-based hierarchical variational model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China. Association for Computational Linguistics.
* Stab and Gurevych (2017) Christian Stab and Iryna Gurevych. 2017. Parsing argumentation structures in persuasive essays. Computational Linguistics, 43(3):619-659.
* Tappan and Brown (1989) Mark Tappan and Lyn Mikel Brown. 1989. Stories told and lessons learned: Toward a narrative approach to moral development and moral education. Harvard Educational Review, 59(2):182-206 

Paul Cvitz. 1990. The use of stories in moral development: New psychological reasons for an old education method. _American psychologist_, 45(6):709.
* Volkova et al. (2017) Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_, pages 647-653.
* Wang and Wan (2019) Tianming Wang and Xiaojun Wan. 2019. T-CVAE: transformer-based conditioned variational autoencoder for story completion. In _Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019_, pages 5233-5239. ijcai.org.
* Wolf et al. (2020) Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020. Transformers: State-of-the-art natural language processing. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations_, pages 38-45.
* Yao et al. (2019) Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019. Plan-and-write: Towards better automatic storytelling. In _Proceedings of the AAAI Conference on Artificial Intelligence_, volume 33, pages 7378-7385.
* Zhang et al. (2019) Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In _International Conference on Learning Representations_.
* Zhao et al. (2017) Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_, pages 654-664.
* Zhao et al. (2019) Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, and Xiaoyong Du. 2019. Uer: An open-source toolkit for pretraining models. _EMNLP-IJCNLP 2019_, page 241.

## Appendix A storal Construction

### Data Source

We show the full list of web pages used for constructing storal in Table 11. We initially collect 52,017 Chinese and 2,630 English raw texts from the web pages. Then we de-duplicate the texts by removing those texts which overlap with others more than twenty words. After de-duplication, we finally collected 19,197 Chinese and 2,598 English texts. And we construct storal based on these texts.

### Data Annotation

Table 9/10 shows the examples given to the annotators to inform them of the requirements for stories/morals, respectively. If the constraints are not met, we ask annotators to refine the story and moral. All workers were paid more than $7 per hour on average.

**Example 1:**_Cone on Bear! What a beautiful day! Go for a walk with your father! Take a deep breath and smell the flowers. But don't pick the flowers. Listen to the birds sing. But don't scare them. How beautiful the world is. Isn't it, dear Bear?_

**Example 2:**_When I was a child, I heard a story that felt very regrettable. I felt sorry for the protagonist of the story._ Long ago, there lived \(\cdots\) Such trees are now found all over Uganda.

**Example 3:** I have a well-off friend. When she first entered college, she had many good wishes and thought she could achieve her goals. \(\cdots\) Now she felt very painful under the strong mental pressure. _I can understand her feelings..._If magnifying your own pain, you will get trapped in the mite of your pain, and even feel that life is too unfair to yout._

**Example 4:**_Raud sat at his door, forening. \(\cdots\) His father told Raul a true story:_A wild wolf escaped into a cave after being wounded by a hunter's arrow. \(\cdots\) After hearing the story, Raul cheered up immediately. \(\cdots\)_

\begin{table}
\begin{tabular}{p{227.6pt}} \hline \hline
**Example 1:**_If you saw a thief in a crowded bus, would you bravely stop him? Please reflect on yourself instead of just complaining that our world is becoming worse. Without the foothd for dirt, the flower of civilization is bound to be fragrant._ \\ \hline
**Example 2:**_The story tells us: we should remember that we should become a polite person and communicate with others carefully._ \\ \hline
**Example 3:** As long as you keep your sanity and make right judgments, all the barriers will not become an obstacle, _just like the beautiful girl in the story_. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Examples of morals provided for the annotators. Each of the examples does not satisfy one of the following constraints in order: (1) describing only the main standpoint and not stating any sub-arguments or proofs, and (2) not stating anything irrelevant to the moral, and (3) not involving any specific characters in the story. We highlight the sentences leading to the above issues in _italic_.

\begin{table}
\begin{tabular}{p{227.6pt}} \hline \hline
**Example 1:**_Some on Bear! What a beautiful day! Go for a walk with your father! Take a deep breath and smell the flowers. But don’t pick the flowers. Listen to the bird sing. But don’t scare them. How beautiful the world is. Isn’t it, dear Bear?_ \\ \hline
**Example 2:**_When I was a child, I heard a story that felt very regrettable. I felt sorry for the protagonist of the story._ Long ago, there lived \(\cdots\) Such trees are now found all over Uganda. \\ \hline
**Example 3:** I have a well-off friend. When she first entered college, she had many good wishes and thought she could achieve her goals. \(\cdots\) Now she felt very painful under the strong mental pressure. _I can understand her feelings. \(\cdots\) If magnifying your own pain, you will get trapped in the mite of your pain, and even feel that life is too unfair to yout._ \\ \hline
**Example 4:**_Raud sat at his door, forening. \(\cdots\) His father told Raul a true story:_A wild wolf escaped into a cave after being wounded by a hunter’s arrow. \(\cdots\) After hearing the story, Raul cheered up immediately. \(\cdots\)_ \\ \hline \hline \end{tabular}
\end{table}
Table 9: Examples of stories provided for the annotators. Each example does not meet one of the following requirements in order: (1) having a clear beginning and ending; (2) not stating anything irrelevant to the main plot; (3) not stating any explicit arguments for the moral; and (4) not telling the story in a nested form. The sentences causing the above issues are in _italic_.

 



### Analysis of High-Frequency Words

To investigate the topic features of storal, we count the top 50 most frequent nouns in storal (excluding stop words) as shown in Figure 3. We roughly categorize these words into four types: **(1) Animals**: animals are popular as protagonists in moral stories since they usually have various but clear characteristics (e.g., "sly foxes"), which embody rich commonsense knowledge; **(2) Relationships**: such nouns are used to describe the inter-character relationships in a story (e.g., "friend"), which are useful for modeling characters' motivation and behavior; **(3) Concrete nouns**: they refer to physical entities that can be observed, such as "water"; and **(4) Abstract nouns**: they refer to abstract concepts, such as "difficulty". We manually check the proportional distribution of the four types for stories and morals, respectively. The results in Figure 3 demonstrate that morals contain significantly less concrete nouns and more abstract nouns than stories. And morals contain little animal words but almost as many relationship words as stories, indicating that morals may be independent of specific characters but relate to general interpersonal relations. The result shows that morals are more abstract than stories.

Furthermore, Table 12 shows the most frequent 4-grams in storal, further indicating that morals are more abstract than stories. Each of the 4-grams in Table 12 comprises less than \(0.01\%\) of all 4

Figure 3: Top 50 most frequent nouns for stories and morals in storal-zh and storal-en. The numbers in the legend show the percentages of the total frequency of the nouns of the same type among the 50 nouns.



grams in the corresponding dataset, showing the diversity of storal.

### Discussion about storal

The high-quality examples in storal are full of commonsense and discourse relations. As exemplified in Table 1 in the main paper, the common sense is mainly regarding the characters' reaction and intention (e.g., "the cows dispersed" and then the "tiger" and "lion" intend to kill them), as well as the nature of physical objects and abstract concepts (e.g. "cows" may be the food of "lions" and "tigers", and "unity" refers to "keeping together for a common goal"). Additionally, the stories usually have a specific discourse structure, i.e., the premise to introduce the story settings (e.g., the characters "four cows" and the location "a meadow"), the right or wrong behavior ("stay together or not") and the endings ("living well or being killed"). We believe it is an essential topic of future work to develop a better approach to model such commonsense and discourse relations.

## Appendix B Experiments

### Implementation

We implement the pretrained models used in our experiment mainly based on the register models of HuggingFace Wolf et al. (2020). Table 13 shows the names of the used register models. Note that we use LongLMbase Guan et al. (2022) as the T5 model for experiments on storal-zh, which has not been registered on HuggingFace.

All results in the main paper and the appendix are based on one NVIDIA Tesla v100 (16G memory). All reported results are based on one single running. The CPU is Intel Xeon Gold 5218. It cost less than 5 hours for fine-tuning each model on storal. We set the hyper-parameters following the default parameters of HuggingFace.

### Automatic Evaluation for Moral Faithfulness

We follow Guan and Huang (2020) to train a learnable metric to evaluate moral faithfulness. Specifically, we fine-tune RoBERT\({}_{\text{BASE}}\) as a classifier to distinguish whether a story matches a moral. We regard ground-truth examples as positive where the story and moral are matched, and construct negative examples by replacing the story or moral with a randomly sampled one. Finally, the classifier achieves an accuracy of 77.32/79.21% on the data constructed based on the test set of storal-zh/storal-en respectively. Then we calculate the faithfulness score as the average classifier score of all generated texts for the inputs.

Table 14 presents the evaluation results. We can see that pretrained models achieve better faithfulness than the non-pretrained models as shown by the much higher faithfulness scores. However, we also observe that the faithfulness score of the ground-truth texts is lower than some models (e.g., T5) when generating morals. Therefore, it is still necessary to manually evaluate faithfulness.

Results on Validation SetsWe show the performance of several baselines and RA-T5 on the validation sets of the understanding tasks and the generation tasks in Table 15 and Table 16, respectively.

 



### Significance of Manual Evaluation Results

Table 17 shows the \(p\)-values (sign test) when comparing the manual evaluation results (Table 8 in the main paper) between each pair of the ground truth, Fusion, T5 and RA-T5.

### Evaluating Value Preference Alignment

Although we have used moPref to evaluate whether machines can capture the value preference of a story, the automatically constructed dataset may bias machines to focus on distinguishing general standards of good behaviour without considering story plots. Therefore, in this section, we construct examples manually to test this ability beyond the token level. Specifically, we randomly sampled 50 examples from the test sets of storal-zh and storal-en respectively. For each example, we manually rewrote the moral to convey a synonymous or antonymous value preference. For example, a synonymous moral with "unity is strength" in Table 1 can be "we are powerful as long as we unite with each other" and an anonymous one can be "everyone can also be powerful enough." Then we expect a model to be able to accept the synonymous moral but reject the antonymous one. We use three typical models, including BERT w/o Story, RA-RoBERTa and RA-T5, to compute the winning

Figure 4: A simplified version of the manual annotation interface for mo2st. The interface for st2mo is similar.



rate of pair-wise comparisons between any two of ground-truth, synonymous and antonymous morals. These models are trained on the training set of the moPref task.

Table 18 shows the evaluation results. We observe that BERT can not distinguish different types of morals without input stories. RA-RoBERTa fails to accept the synonymous morals on storal-en (winning rate of only 36% w.r.t the ground truth, \(p<0.1\)), and can not distinguish synonymous and antonymous morals on both storal-zh and storal-en (winning rate near 50% with \(p>0.1\)). Additionally, it prefers anonymous morals to the ground truth significantly on both datasets (winning rate less than 50% and \(p<0.1\) ). The results indicate that existing models still struggle to capture the value preference of moral stories.

## Appendix C Error Analysis and Case Study

In this section, we conducted a case study and investigated the errors of existing models on the proposed tasks to provide insight into future work. We show several typical error cases in Table 20.

### Understanding Tasks

The example in Table 20 for moCpt shows that the model may not grasp abstract concepts such as "good will" and "good acts" and align them to the story plots. It makes predictions possibly based on only token-level features such as relations between "ask after" and "attention". On the other hand, the example for moPref indicates that the model can not capture the value preference of the story in terms of "whether it is intelligent to regard others are illiterate". The results demonstrate the necessity of introducing concept knowledge and modeling high-level semantic information.

### Generation Tasks

Table 21 shows cases generated by several baselines and our model for the generation tasks. We can see that retrieval can provide effective guidance for both moral and story generation. Baseline models including GPT2 and T5 tend to generate unrelated concepts or non-moral texts.

However, as shown by the manual evaluation results, there is still a big gap between RA-T5 and humans. To provide quantitative error analysis, in the process of manual evaluation on storalen, we required annotators to annotate the error type of a text when it exhibit an unfaithful moral. We summarize three main error types as follows: **(1) Not a moral text (nam)**: not stating or imply

\begin{table}
\begin{tabular}{l|c|c|c|c} \hline \hline
**Models** & **True vs. Syn** & **True vs. Ant** & **Syn vs. Ant** \\ \hline \hline \multicolumn{4}{c}{**Dataset: storal-zh**} \\ \hline
**BERT w/o Story** & 52\% (0.89) & 46\% (0.67) & 58\% (0.32) \\
**RA-RoBERTa** & 40\% (0.21) & 36\% (0.06) & 48\% (0.89) \\ \hline \hline \multicolumn{4}{c}{**Dataset: storal-en**} \\ \hline
**BERT w/o Story** & 54\% (0.67) & 54\% (0.67) & 48\% (0.89) \\
**RA-RoBERTa** & 64\% (0.06) & 34\% (0.03) & 40\% (0.20) \\ \hline \end{tabular}
\end{table}
Table 18: Winning rates of pair-wise comparisons which require selecting a correct moral from two candidates. Each candidate is a ground-truth (**True**), synonymous (**Syn**), or antonymous (**Ant**) moral. The number in the parenthesis is the corresponding \(p\)-value (sign test).

\begin{table}
\begin{tabular}{l|c c|c} \hline \hline
**Models** & **Nam** & **unrel** & **conf** & **Others** \\ \hline \multicolumn{4}{c}{**Task: st2mo**} \\ \hline
**Fusion** & 27\% & 23\% & 7\% & 2\% \\
**T5** & 19\% & 9\% & 12\% & 0\% \\
**RA-T5** & 15\% & 7\% & 6\% & 0\% \\ \hline
**Truth** & 3\% & 4\% & 2\% & 0\% \\ \hline \hline \multicolumn{4}{c}{**Task: mo2st**} \\ \hline
**Fusion** & 25\% & 13\% & 6\% & 1\% \\
**T5** & 19\% & 9\% & 10\% & 1\% \\
**RA-T5** & 16\% & 10\% & 10\% & 0\% \\ \hline
**Truth** & 2\% & 1\% & 1\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 19: Percentage of the texts annotated with a certain error in all annotated 100 texts in terms of moral faithfulness.

 

ing what is right or what is wrong; **(2) Unrelated concepts (unrel)**: containing unrelated concepts with the input; and **(3) Conflicting value preference (conf)**: conveying a value preference conflicting with the input despite related concepts. In addition, we also provide annotators with another option Others. The annotators are allowed to annotate a text with multiple errors. When at least two of three annotators annotate the text with some error, we decide it has the error. We show the distribution of the error types in Table 19, suggesting that existing models still struggle to generate meaningful morals and stories, and align the concepts and value preferences between them.

Furthermore, as exemplified in Table 20, when generating morals, we can see from Case 1 that the

\begin{table}
\begin{tabular}{l l} \hline \hline  & **Generation Task: st2mo** \\ \hline
**Input Story:** In the forest, there was a deer and an owl. The deer loved to scare this fearful owl all the time. One day, the deer scored the out so hard, the owl past out. The deer ran away, and the owl woke up and came to her senses. She had had enough of being scared. She went to the deer and said, “Tm not scared of you.” She said this with such a force that the deer backed down... \\ \hline
**Generated Case 1:** The deer loves to scare ours. (nam) \\ \hline
**Generated Case 2:** We should not be greedy and learn to others the importance of our desires. (unrel) \\ \hline
**Generated Case 3:** It’s good to be scared. (conf) \\ \hline
**True Moral:** Don’t let anyone scare you all the time. \\ \hline \hline  & **Generation Task: mo2st** \\ \hline
**Input Model:** Empty solutions are of no worth. There was a **big fat cat** in there. It was very big. It was a giant cat. It was big, and it was very big and powerful. It was bug and powerful. It was very powerful. The mouse could **move freely** and had a nice time hunting. The cat was very big and powerful. The mouse wanted to live in a very large land .... (nam) \\ \hline
**Generated Case 5:** There was a **greedy shop in a town. Just the mouse and cat lived together. ... The mouse was very active in the shop and in the street. The mouse was very happy. But the mouse was too busy to **move freely**. He was the only mouse in town. He was very busy with work. ... As soon as the mouse had left, he moved slowly. ... So the mouse was so happy and satisfied with everything that it was so happy for him. ... (unrel) \\ \hline
**Generated Case 6:****There was a **grocery shop in a town.** There were two stores. ... The third was an empty solution. Each time the mouse went on a few days. Then, the third was a little while later the next day the mice were gone. They were done. They were happy at last ... (conf) \\ \hline
**Tree Story:** There was a **grocery shop in a town.** Plenty of **mice lived** in that grocer shop. Food was in plenty for them. They are everything and spoiled all the bugs. They have used the bread, biscious and fruits of the shop. In other grocer got really vorried. So, he thought that "should buy a cat and let it stay at the grocery. only then I can save my things." He bought a nice, **Big fat cat** and let him stay there. The cat had a **nice time hunting** the mice and killing them. The mice cold not **move freely** nor they. They were started that anytime the cat would eat them up. The mice **wanted** told so something. They held a meeting and all of them received ”we must get if of the cat, Can someone give a suggestion? All the mice sat and hooded. A smart looking mouse **stop** did me and said,” The cat **moves softly**, that is the problem. If we can tie a bell around her neck, then things will be fine. we can know the movements of the car. ”_Yes_, that is answer," Stated all the mice. An old **mouse slowly stood** up and asked, “Who would tell the bell? After some moments there was no one there to answer this question. \\ \hline \end{tabular}
\end{table}
Table 20: Typical error cases predicted by RA-T5 (for the understanding tasks) or sampled from RA-T5 (for the generation tasks). For the generation tasks, the error types in terms of moral faithfulness include “not a moral text” (nam), “unrelated concepts” (unrel) and “conflicting value preference” (conf). The underlined words are improper concepts/events which leads to corresponding errors. **Bold** words for mo2st are the given first sentence and the outline of multiple phrases.

 

models still often state events involved with specific characters (e.g., _"owls"_) but do not tell what is right and what is wrong. And Case 2 shows that they struggle to conclude related concepts from the story (e.g., _"greedy"_ is not embodied in the story at all). Furthermore, in Case 3, the models conclude a conflicting value preference with the story despite correct concepts (e.g., the story shows that _"it is bad to be scared"_ but not _"good"_). On the other hand, models also are shown to suffer from similar issues when generating stories. In Case 4, the model only describes some scenes (e.g., _"it was

\begin{table}
\begin{tabular}{l} \hline \hline
**Input Story:** Once upon a time there was a spring who lived happily and safely inside a pen. Although he heard many noises coming from outside, he lived believing that outside his world inside the pen, there was nothing good. Even just onto shall taxing his pen made him so scard that he was quite content to spend his life comparing and stretching himself again again against that tiny space. However, one day, like kn out, and when the pen’s owner was busy changing it, there was an accident. The spring was flung through the air and landed in the toilet drain, well out of sight. Terrified, and cursing his bad luck, the spring was flushed through their pace perple, each thinking it might be his end. During the journey, the bltd did more then gives us cut of pure far. Nor did he every stopying. Swerp away that, he travelled untiled on and on, until he ended up in a river. When the river current lost its force, and the spring could see that things had calmed down a bit, he stopped copying and listened all around him. Hearing birdsong and wind in the trees, he felt encouraged to finally open his eyes. What the spring was sure the pure, crystal waves of the river, the rich green rocks of the riverbed, and all kinds of fish may colours, whose skin seemed to dance under the sunlightly. Now we understood that the world was much greater than the tape inside the pen, and that there had always been many thughes outside, waiting to be enjoyed. After spending a while playing within the fish, he went over into the riverbank, and then moved no to a field of flowers. There he heard wearing. He followed the sound, which took him to a lovely flower that had been flattered by a rabbit, and could no longer stand up straight. The spring realised that he could help the flower, so he offered to be his support. The flower accepted, and slipped through the middle of the spring. There they lived happily together. And they would always hugh when remembering how the spring used to think that all there was to life was being a sad and fearful spring. \\ \hline
**GPT2:** Always think of what you can do to make things better. \\ \hline
**T5:** Fear and frustration are one thing, but they can be turned into happiness. \\ \hline
**RA-T5:** Life should not be afraid of the unknown. There should be lots of positive things out there. \\ \hline
* even though it may bring discomfort – thin isolate yourself from people in order to avoid problems. \\ (3) Most fear is without foundation, and plays on what is unknown or different! \\ (4) Friends are for cheering you up in your worst moments. \\ (5) Someone who harm’s no one is still very far from being good. \\ \hline
**True Moral:** Changes are not a bad thing, it is up ourselves to open the eyes and make something positive of them. \\ \hline \hline
**Generation Task: ST2mo** \\ \hline \hline
**Input Moral:** Never give up. \\ \hline
**GFT2: One day dragon and a dino met in the late Jurassic period.** They were friends, and on the way they meet a **huge tree**. Dragon was huge and his **big claws** west full and we saw going to idle. Dino was going to die. Dino was going to die. Dino was going to die. When the **dragon made half** of the tree, he was about to die. Dragon was a **togalue** on using to die, and all the beads and sheets were dead. Dino had idea what to do. He was the **strongest dino** in the whole tree. Dragon was a **total surprise**. Dino was going to die. He was sought to die. He was going to die. The dragon was continued. Enno was not able to die. Dino was so angry, he started to die. Dino was so angry, and the dragon throw this **big claws** at the tree and ran to the forest. This was the best match. Then the dino was nearly killed. \\ \hline
**TS:****One day a dragon and a dino met in the late Jurassic period.** The dragon was standing next to a **huge tree**. It was fall of beads, lions and zebras that were strong enough to carry out their duties. The dragon was so strong that he could move himself without losing his foot. The dino was so strong that he could carry anything, even if that we had much more branches. But the dino was very weak, so he could not carry anything. The dragon came to the tree and said, "I will do everything I can get dry hot ago let meve.” Then the **dingto started running** through the tree. The **dragon made half** way through the tree and put on **his big claws**. Then he took off his **big claws**. Just then a huge **tree started** to go through a storm. Then the monster took out his **fiery breath**. \\ \hline
**RA-T5:****One day a dragon and a dino met in the late Jurassic period.** The dragon was very poorly of the dino because of his **big claws** and **fiery breath**. The dino was going to jump on a **huge tree** free and climb it up. But the dragon didn't know how to jump. The **dragon made half** way to the tree and started to roll down. The dino was completely surprised. The dragon said, “I can’t playe” flagon started to jump. Then the **free started** to roll. The dino started to roll over the loop of the tree and jumped up. The tree started to thrash off. **Studently**, the dino saw that the dragon had **if hery breath**. The dragon said, ”That’s any strength”. The dino was all alone. Then he stood, looking at the dragon. Then the dragon stopped, shouted at him, “Hello dimo? what a **total surprise**.” The **dino started running** and jumping towards the tree, and the dragon got close to him. Then he hit the tree. Now he found a boulder on the tree started to crash and he jumped on the tree. Then the **strengest dino** ever was surprised. \\ \hline
**Top-S Retrieved Story Outins:** \\ (1) [adwin few, halvious scratted/rather, team beat balokun, halukum dogled earlier, football game, goal post, buddown threat rather, balokun started] \\ (2) [agele resting, free09, torious rested, edge answered, deep stoctus sleeping, torticise smiled, hunter suddenly]) \\ (3) [loud hump, man happened, cry intrudec, ugly wild, wolf named pte walked, long neck, man walked, thin air] \\ (4) [at #allobird, left, hotw, startel climbing, cat thanked] \\ (5) [hou won, race stuffated, croc wu]) \\ \hline \end{tabular}
\end{table}
Table 21: Cases generated by different models for the generation tasks. The underlined words are improper concepts/events which leads to incoherence or unfaithfulness. **Bold** words for mo2st are the given first sentence and the outline of multiple phrases. The red moral for st2mo is related to the generated moral of RA-T5 in semantics. Note that we only take concepts in these retrieved morals as input for RA-T5. And red words in the retrieval outlines for mo2st indicate that they also show up in the generated story of RA-T5.

 

very big"_ and _"it was very powerful"_) but does not aims to convince readers of anything. And Case 5 seems to tell a story centered on some concepts such as _"active"_ and _"busy"_, but the concepts do not relate to the input. Case 6 implies _"empty solutions may be useful,"_ which is conflicting with the input. These cases indicate the necessity of modeling the relations between events and abstract concepts for understanding and generating moral stories.