Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment
Authors: Sky CH-Wang
Arkadiy Saakyan
Oliver Li
Zhou Yu
Smaranda Muresan
Venue: EMNLP 2023 Main (2023)
Keywords: social norms
culture
chain-of-thought reasoning

Abstract: Designing systems that can reason across cultures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing computational models of social norms has primarily focused on American society. Here, we propose a novel approach to discover and compare descriptive social norms across Chinese and American cultures. We demonstrate our approach by leveraging discussions on a Chinese Q\&A platform—知乎 (Zhihu)—and the existing SocialChemistry dataset as proxies for contrasting cultural axes, align social situations cross-culturally, and extract social norms from texts using in-context learning. Embedding Chain-of-Thought prompting in a human-AI collaborative framework, we build a high-quality dataset of 3,069 social norms aligned with social situations across Chinese and American cultures alongside corresponding free-text explanations. To test the ability of models to reason about social norms across cultures, we introduce the task of explainable social norm entailment, showing that existing models under 3B parameters have significant room for improvement in both automatic and human evaluation. Further analysis of cross-cultural norm differences based on our dataset shows empirical alignment with the social orientations framework, revealing several situational and descriptive nuances in norms across these cultures.

Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment
Sky CH-Wang Arkadiy Saakyan Oliver Li Zhou Yu
Smaranda Muresan
DDepartment of Computer Science, Columbia University DDaa Science Institute, Columbia University (skywang, a. saakyaa an) @cs cowumbia..aao..oo edu
Abstract
Chinese Situation (#0 Zhihu); A (0 #
US Situation (See-al-
Designing systems that can reason across cul- tures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing com- putational models of social norms has primar- ily focused on American society. Here, we propose a novel approach to discover and com- pare descriptive social norms across Chinese and American cultures. We demonstrate our approach by leveraging discussions on Chi- nese Q&A platform ku-F (Zhihu) and the existing
Algnment Sim. 90%
asking for my money back
(How can ask for my
money back?)
Human Annotations from Social-Cham-101
Extract Norms from Top-Voted Answers by text-davinci-003
Most Relevant Chinese Social Norm:
US Social Norm:
It's OK ask for your money back
polite for money back directly
dataset as prox- ies for contrasting cultural axes, align social situations cross-culturally, and extract social norms from texts using in-context learning. Embedding Chain-of-Thoughh prompting in human-Al collaborative framework, we build a high-quality dataset of 3,069 social norms aligned with social situations across Chinese and American cultures alongside correspond- ing free-text explanations. To test the ability of models to reason about social norms across cultures, we introduce the task of explainable social norm entailment, showing that existing models under 3B parameters have significant room for improvement both automatic and human evaluation. Further analysis of cross- cultural norm differences based on our dataset shows empirical alignment with the social entations framework, revealing several situa- tional and descriptive nuances in norms across these cultures.
Relation: Contradiction
Textual Explanation: The US norm expresses positive attitude towards directly asking for money back, while the Chinese norm expresses negative atritude towards the same action.
An example of descriptive norms conditioned on an aligned cross-cultural situation, together with their inference relation and corresponding textual explana- tion.
Figure 1:
norms can lead to conflict within intercultural inter- actions due to perceptions of deviance (Durkheim, 1951). Creating computational systems that robustly reason and translate across cultures in pragmatic communication requires that they be grounded in these norms and their differences across contexts. As an initial step in addressing this question, we propose a novel approach to discover and compare social norms conditioned on social situations across Chinese and American cultures. Leveraging XI 3 (Zhihu), Chinese Q&A plat- form, alongside the existing SOCIALCHEMISTRY (Forbes al., 2020) dataset on social norms re- spective proxies of Chinese and American cultural axes, our paper offers the following contributions:
1 Introduction
Social norms are normative beliefs that guide behavior in groups and societies (Sherif, 1936). Deviance from these expectations of behavior can cause perceptions of impoliteness (Culpeper, 2011), feelings of offense (Rubington and Wein- berg, 2015), and pragmatic failure (Thomas, 1983). Social norms vary across cultures (Triandis al., 1994; Finnemore, 1996), and different cultural "Equal Contribution.
A human-Al collaboration framework for cross-cultural descriptive norm discovery consisting of (I) automatic situation align- ment using cross-lingual similarity between

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SOCIALCHEMSTR situations and questions from Zhihu, (2) Chinese social norm ex- traction from Zhihu answers using few-shot prompting with GPT-3 (Brown et al., 2020), (3) cross-cultural norm similarity and dif- ference identification as textual entailment with explanations using GPT-3 with Chain of Thought (CoT) Prompting (Wei et al., 2022); and (4) human feedback in verification and editing. An example of outputs is shown in Figure
ments of appropriate and ethical behavior (Emelin et al., 2021; Jiang et al., 2022) and in grounding behavior in areas like dialogue (Ziems et al., 2022) and situated question answering (Gu et al., 2022a) more specifically on underlying knowledge of so- cial norms. In recognizing that social norms are often culturally (Haidt et al.. 1993) and even demo- graphically (Plepi et al., 2022; Wan et al., 2023) specific, prior work in this area has primarily volved around the normative judgments of major- ity English-speaking cultures represented within North America. contrast, here, aligning with the broader goal of creating systems that can effec- tively reason across cultures and languages (Liu et al., 2021), we focus computationally studying norms across Chinese and American cultures, ex- panding the utility of large language models in ways that have the potential transform modern computational social science (Ziems et al., 2023). Contemporary studies of Chinese cultural soci- eties (Yang, 1993) emphasize several broad dif- ferences relative to American culture. Under the framework of social orientations, emphasis in Chi- nese society is placed especially on family, rela- tionship, authority, and personal reputation social orientations. In particular, past work has shown a large significance compared to American culture in familistic collectivism and harmony (Ch' eng- K' K'Un, 1944; Yang, 1988; Campos et al., 2014), re- lational determinism (Chen and Chen, 2004; Chua et al.. 2009), and authority worship (Yang, 1970; Thornton and Fricke, 1987; Hu, 2016), among other factors, in influencing social behavior. Critical re- views of past cross-cultural work have criticized weaknesses in study design and their overly broad generalizations (Voronov and Singer, 2002), in fa- vor of a more fine-grained analysis. Here, under the framework of descriptive norms and sourcing data from social media at scale, we conduct a more nu- anced analysis of how norms vary across cultures under situational controls.
A new dataset for cross-cultural norms un- derstanding with explainable textual entail- ment. Our human-Al collaboration enables us to create novel dataset of 3069 situation- aligned entailment pairs of Chinese and Amer- ican norms together with textual explanations. We introduce the new task of explainable so- cial norm entailment and show that is chal- lenging for models fine-tuned on related tasks; fine-tuning on our task directly still leaves sig- nificant space for improvement.
An analysis of cross-cultural differences in social norms enabled by our dataset. In Section 6, we show that the analysis enabled by our dataset empirically aligns with prior work on differences in Chinese and American cultures. Our empirical results align with the social orientations framework (Yang, 1993) in understanding Chinese-Ammriiaal norm dif- ferences and reveal several situational and de- scriptive nuances in norms across these cul- tures.
2 Related Work
Our work is situated in the broader literature on the study of social norms (Sherif, 1936) and how they vary across cultures (Thomas, 1983). Here, our work is rooted specifically in the study of de- scriptive norms (Rawls, 1951, 2004; Cialdini et al., 1990)-what people actually do, rather than pre- scriptive norms, or what they think people ought to do-and focuses on the differences between Chi- nese and American cultures.
3 Data Sources
Our analysis of social norm variation across Chinese and American contexts draws from the largest Q&A discussion platform in China AITT (Zhihu) and existing data gathered by SOCIAL- CHEMISTRY (Forbes et al., 2020), treating these data sources as different cultural axes.
We build on recent computational work in cre- ating systems capable of situated reasoning in so- cial situations, most closely adapting the rule-of- thumb formalism for descriptive norms introduced in Forbes et al. (2020). This line of work not only spans that of commonsense reasoning (Sap et al., 2019; 9; Rashkin et al., 2018), but also in judg-
Social Chemistry 101 (Forbes et al., 2020) is a natural language corpus of ethical judgments

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AL Rt? (How to respond politely when someone says there is funeral in the family?)
Question:
Entailment Relation
Situation: think I'm jealous of my best friend.
US Norm: You should be proud of people and not jealous them.
15 R ni D i. LE Hi 3d 75 hi 2th tt if 2e 92
Answer:
Chinese Norm: It not appropriate to be jealous of your best friend.
14. (Condolences. You can politely pat on their shoul- ders or tell them to pay attention to their health even though it will be hard for them recently.)
Textual Explanation: Both norms express disapproval of being jealous of someone, especially a best friend.
RoT I: it is appropriate to say # to someone who has lost a family member or friend
Contradiction Relation
Situation: telling my teacher a classmate cheated on a
RoT 2: it is uppropriate to pat someone on the back to show your sympathy to them
test.
US Norm: It's wrong to a tattle-tale.
RoT 3: it is appropriate to tell them take care of themselves though they are sad
Chinese Norm: It is not wrong to report a cheating student to the teacher <: ... >>
Textual Explanation: The US norm expresses disap- proval of telling on classmate, while the Chinese norm expresses neutrality towards the action.
Table Example of a Zhihu question- answer pair (top) with English translations and relevant social norms in rules-of-thumb form (bottom).
Table 2: Examples of similarities (entailments, top) and differences (contradictions, bottom) situated norms between Chinese and American cultures from our dataset, alongside relation explanations.
and social norms on everyday situations. Crowd- sourced social norms on situations scraped from Reddit (i.e., r/AmITheAsshole and other sources are expressed in free-text cule-of-thumil form, where each rule-of-thumb consists of a judg- ment on an action, i.c., "It's rude to run the blender at 5 AM". Annotators of this dataset are 55% women, and 94% of annotators had spent 10 or more years in the United States; as such, we treat the normative judgments represented within this dataset as originating from a predominantly English-speaking, American context'.
quire about the appropriate action to partake in a given situation, i.e., "what do I do if my friend is cheating on their exam?", with other users indicat- ing their views on appropriate courses of action in the answers below. Reasoning that these ques- tions align most closely in form to situations in DOCIALCHEMISTRY we translate and query for all situations present in the SOCIAI.CHEMISTR dataset through Zhihu's 's search function and save the top 100 question posts returned for every trans- lated SOCIALCHEMISTRY situation. Following the rationale that social norms are the most com- mon and broadly accepted judgments of actions in given situations, we take the most up-voted an- swer? to each question as the basis from which we extract social norms, described in the following section. In total, we obtained answers to 508,681 unique questions posed on Zhihu. The study and data collection were approved by an Institutional Review Board prior to data collection; detailed breakdown of this corpus is detailed Appendix Scction A. We reiterate both datasets can only be used as a proxy for social norms in each culture and further discuss the limitations of our approach and these assumptions in Section 7.
T Zhihu has over 101 million monthly active users (Zhihu, 2022) and is one of China's 's largest online knowledge communities. Similar to its in- ternational counterparts like Quora, Zhihu is pri- marily Q&A site where users can post questions, answer existing questions, and up-vote answers to questions by other users, among other platform in- teractions. Users are 59% women (Yang, 2022a) and are primarily educated and middle-class, with research showing that over 80% of its users possess at least bachelor's degree (Zhang, 2020). This population is significant because the middle class is the primary upholder of social stability (Goodman, 2014) and the primary driver of cultural trends and public opinion (Denemark and Chubb, 2016) in China. An example of Zhihu question-answer pair is shown in Table
4 Human-A4 AI Collaboration to Create a Cross-Cultural Social Norm Dataset
Questions on Zhihu are often posed as to in-
'Despite the many demographic similarities between our two cultural axes, a few notable differences remain. Most notably of which is class composition, where only 47% of annotators of Social Chemistry 101 considered themselves to be middle-class compared to over 80% of users on Zhihu, See our Limitations section for discussion on potential impacts.
We enable our computational analysis of social norm variation across Chinese and American cul- tures through a framework of (1) automatic sit-
7Sec Limitations for a discussion on method caveats.

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Situation (SocialChemistny101)
Multilingual Alignment (XLM-R)
Situation $ (Zhihu)
Step 1: Select the Most Relevvnt Norms
S GPT-3
Step Find the Relation Between Chinese and US Nomms Stop 3: Generate Textual Explanation
In-Context Learning
Rule-of-thumb (US Social Norm)
s GPT-3 Chain-of-Thought Prompting
Chinese Social Norms
Expert Verification & Editing
Figure 2: Our human-Al collaboration framework for creating a cross-cultural Chinese Amerrian social norm NLI dataset through (I) situation alignment, aligning cross-lingual situations between Zhihu and Social Chemistry, (2) social norm extraction, from frre-form answers to rules-of-thund with in-context learning, and (3) norm relation inference with textual explanation with CoT prompting, coupled with (4) expert verification and editing.
uation alignment, (2) social norm extraction, (3) cross-cultural similarity/difference identification as textual entailment with explanations, and (4) hu- man feedback. An illustration of our human-AI collaboration framework is presented Figure
Social Norm Extraction. We follow the formal- ism introduced by Forbes et al. (2020) in structur- ing descriptive norms as rules-of-thumb, or judg- ments of an action (i.e.. "It's rude run the blender at 5AM"). Taking 2 random top-aligned situations in our dataset, we manually annotate their corre- sponding Zhihu answer for social norms in rule- of-thumb form (RoT); an example of such an an- notation is shown in Table 1. We then design a 2-shot prompt using these annotations (Table 3) and use GPT-3 (Brown et al., 2020) extract novel oft---fffttbmbb-o from unseen answers. Running this model across all aligned situations and following a manual verification faithfulness, we obtain a to- tal of 6566 unique rules-of offes-o thumb for our Chinese axis, relative to 1 173 unique rules-of-thumb for our American axis sourced from SOCIAI.CHEMISTRY (note, here, that rule-of-thumb may be associated with multiple situations).
Aligning Situations Across Cultures. Descrip- tive judgments of appropriate behavior are often situationally-deeenddee (Leung and Morris, 2015); accurate comparison of norms across cultures must first ensure a similarity of contexts. For instance, Yamagishi et al. (2008) showed that Japanese preferences for conformity disappeared in private compared to when they were being ob- served. To obtain the closest matching situations between US and Chinese contexts, we align situ- ations found in the ROCIALCHEMISTER dataset with similar questions on Zhihu. While the Zhihu search API allows for direct query searches of plat- form content, semantic alignment of returned re- sults with queries remains low due to an observed bias in prioritizing entity matches. To improve this alignment of situations between US and Chinese contexts, we use XLM-R (Conneau et al., 2020) to obtain cross-lingual representations of situations and further perform one-to-one matching through cosine similarity. Setting the similarity threshold 0.895 allowed us to align 3069 situations, of which around 80% were correctly aligned, based on a manual review of a random sample of 100 sit- uations. Here, incorrectly aligned instances remain valuable for our data as negative samples in our later entailment task as they can be assigned a No Relation label.
Identifying Cross-Cultural Norm Similarities & Differences as Textual Entailment with Expla- nations. Under our formalism, a difference cultural norms equates to disagreement in judg- ments for action relative to given situation. Here, structure the identification of social norm differences across cultures entailment task (a.k.a natural language inference (NLJ) similar to e-SNLI (Camburu al., 2018); a difference in cultural norms equates to a contradic- tion between norms of different cultures for a given situation, and vice versa for entailment. Given the recent success of human-Al collaboration frame- works (Wiegreffe et al., 2022; Bartolo et al., 2022; Chakrabarty al., 2022), the complex nature of
explainable textual

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Given an input Text, respond with Social Norms that are assumed by the speakver of the text, English. Social Norms are rules end standards that are understood by members of group, and that guide constrain social behaviors without the force of law. The Social Norms muet be complete, correct different, not contradicting with each other. inferable from text.
Text: # BE K + -
t. A EE
- Ч - 27. 3 T th
IA 10 M iE
Social Norms that we can infer from this text are:
it not polite to confront directly your elders even when elders wrong
2. is appropriate to argue with elders in private when you think they are wrong
is shameful to point their mistakes
crowds though elders are wrong
Table 3: Prompt used for the extraction of social norms in rule-of- form from Zhihu questions and answers. The instruction precedes the in-context example; the original question (in red, equivalent to the situation) is followed directly by the answer, and sample annotated rules-of-thumb are listed in blue.
task, and the need for interoperability in rea- soning, we use Chain-of-Thought prompting (Wei et al., 2022) (see our prompt in Table 4) together with GPT-3 (text -axinidavinc.. -00innii--3)) to generate most of the data automatically before mamual verifi- cation. In order to construct our data in the e-SNLI format and save on computation costs, for every given American norm associated with given situ- ation, we (I) select the most relevant Chinese norm for that situation extracted in the previous step, (2) identify the inference relationship (entailment, contradiction, or no relation) between them, and (3) generate a free-text explanation for the identified relationship. When no Chinese norms are selected as relevant, random norm is sampled from the aligned candidates, and an explanation is generated given the No Relation label. Using this framework, we generate social norm relationships and their jus- tifications for all cross-culturally aligned situations in our dataset.
extracted, relevant Chinese norms for correspond- ing US norms. Only few instances (around 5%) had hallucinated norms; all were further corrected during annotation.
Generated explamations needed no further edit- ing in 58.9% of the cases. In 38.1 % of the cases, the inferred cross-cultural norm relation (and corre- sponding explanations) were deemed incorrect and required the revision of both the label and the ex- planation, while in the remaining instances (3.0%), generated explanations still required major revi- sions even though the relation label was deemed correct. Our final dataset contains 3,069 NLI in- stances of social norm relations across cultures with explanations. Out of these, 432 are contradic- tions (14%), 1059 are entailments (35%), and 1578 have no relation label (51%). In total, aligned norms comprise 1173 unique rules-of- offthhmbb from CHEMISTHE (American axis) and 2273 obtained from Zhihu using our framework (Chinese axis), with social norm description length averag- ing 63.5 characters. For social norm extraction, situation length was limited to 300 characters; ex- amples from our datasct are shown in Table 2.
Human Verification and Editing, To ensure the quality and accuracy of our dataset of situated social norm relationships and their justifications across cultures, three annotators with significant (10+ years) lived experiences in both Chinese and American cultures further acted as annotators by jointly verifying and editing, needed, the outputs of our framework (Figure 2). Specifically, authors ensured that (1) situations are correctly aligned (i.e., entailment or contradiction labels are assigned cor- rectly to aligned norms, while no relation labels are assigned to misaligned norms), (2) norms gen- crated by GPT-3 are not hallucinated, and (3) the explanations are correct, Annotators were able to discuss any questions and resolve any final dis- agreements with each other. In total, we find that 51% % of norms had a no relation label, either due to misaligned situations or due to the absence of
5 Experiments and Evaluation
Task. To test the ability of models to reason about social norms across cultures, we introduce the task of explainable social norm entailment. Our task is closely related to the explainable natural language inference task, e-SNLI (Camburu et al., 2018) 8), where, in addition to the relation label, model has to output a natural language explanation for its prediction well. For every pair of US and Chi- nese norms of a cross-culturally aligned situation, we ask a model to predict the relationship between them (Entailment, Contradiction, or No Relation) and output an explanation for the relation. We test

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Social norms are the informal rules that govem behavior groups and societies. task, you will compare and Chinese social norms. You will be presented with situation, US social norm applicable to it, and liat of Chinese social norms.
Your task has 3 sleps:
1. Select Chinese social norm most relevant to the situaiion.
Determine the relationship between the Chinese norm and the US Entailment, Contradiction, or No Rolation. Entailment means both nomss express similar attitudes towards certain actions/belief given situations.
Contradiction means two norms express opposite attitudes towards certain actions/bolief given situations. Chinese norms applicable to the situation. 3. Justify the relation between the noms that you chose.
Select No Relation if
Situation: not showing up to friend's wedding
US Norm: You should attend your friend's wedding.
Chinese Norms:
your friend's wedding you cannot make 2. If you cannot attend or send gift to your friend's wedding, your friend will understand and not hold' it against youj
[1. is not necessary to attend send
Most Relevant Chinese Norm: If you cannot attend or send gift to your friend's wedding, your friend will
understand and not hold it against you
Relationship between the norms: Contradiction
Justification: The norm expresses negative attituce towards not showing up to a friend's 's wedding, while the Chinese norm expresses more neutral judgement ('your friend will understand").
Table 4: Chain-of-thouglee prompt used identify (1) the most relevant (if any) Chinese norm given American norm from an aligned situation, (2) determine the relationship between these norms, and (3) provide a free-text explanation justifying the inferred relation.
whether models fine-tuned on existing closely re- lated and much larger textual entailment datasets (e.g., e-SNLI) and cnstruction-tuned models like FLAN-T5 can perform the task of explainable so- cial norm entailment addition to a simple fine- tuned baseline. evaluation, we center our focus on explanation plausibility, testing fine-tuning on our data may enhance a model's 's ability to reason about social norm entailment.
elaborations to provide an answer containing a label and explanation. We evaluate this model in 10-shot setting.
FLAN-T5-XL (Chung et al., 2022): an en- hanced version of the T5 (Raffel et al., 2020) 3B model fine-tuned on more than 1000 tasks, including e-SNLI (Camburu al., 2018). We evaluate this model in a 10-shot setting.
Models. We focus smaller models on the order of 3B parameters and on non-OpenAl models, as an OpenAI model was used to generate our data. Here, we test the performance of a model fine- tuned on SOCIALCHEMISTRY rule elaborations (DREAM) and an instruction-tuned model (FLAN- T5) in few-shot settings, and further fine-tune joint self-rationaliizing models on eSNLI and our dataset. Prior work in interpretability (Wiegreffe al., 2021) has shown that rationales from joint self- rationalizing soadees-rredicctngg both the explana- tion alongside the relation label-are capable of producing faithful free-text rationales. Following prior work (Chakrabarty et al., 2022), we fine-tune a joint selffrationalizing T5 model in multiple set- tings, randomly splitting our data into 65% train, 10% validation, and 25% test set splits (Appendix Section C).
T5-cSNLI: a T5 3B model fine-tuned on the e-SNLI (Camburu et al., 2018) dataset. We fine-tune for two epochs with a batch size 4096, with 268 total steps, and an Adam W optimizer with a learning rate of 5e 05. We take the longest explanation per example in e-SNLI, our data contains only one refer- ence explanation. This leaves with 549,367 training and 9,842 validation examples.
(mT5, T5)-SocNorm: T5 3B model fine- tuned our Social Norm Explainable NLI dataset. We fine-tune for 20 epochs with a batch size of 256 (with 140 total steps) and an Adam W optimizer with a learning rate of 5e- 05. Note, here, that our training set of 2,000 examples (see Table 8) is 274 times smaller than e-SNLI. We fine-tune mT5 model in the same setting to explore how multilingual pretraining may impact in multicultural social norm understanding.
DREAM (Gu et al., 2022a): elaboration model that uses T5 generate details about the input. Here, we test the DREAM-FLUTE (social-norm) variant (Gu et al., 2022b), which uses SOCIAL CHEMISTRY rules-of of-thuffbhbb-tooo as
Automatic Metrics. Following Chakrabarty al. (2022), we evaluate the quality of model-generrted

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Model
F1@0
50
@1
%A(J)
Model
Entail.
Contra.
NoRel.
DREAM
17.68
4.31
0.00
100.00
FLAN-TS
1.36
13.56
62.62
FLAN-TS
25.85
8.92
7.11
72.50
TS-eSNLI
21.28
35.63
43.54
T5-eSNLI
33.48
8.27
1.14
96.59
T5-SocNorn
56.6
47.0
59.97
mTT5-SccNorm
29.69
28.61
23.19
21.89
T5-SocNorm 54.52
51.68
43.07
21.00
Table 6: Fl score breakdown by relation label. FLAN- T5 mostly correctly predicts NoRelation class which al- lows it achieve higher Fil @50 and Fl @66 scores these explanations are easier to generate. T5-SocNorm is more robust across relation classes.
Table 5: Automatic evaluation results of series of competitive baseline models measured by F1 scores at three thresholds of the explanation score (0, 50, and 60, indicated as Fl @0, Fl @50, and Fl @ 60, respectively), and models tinettuned on our data. %& represents the percent decrease from F1 @0 to Fl @60.
most classes as No Relation (the majority class). This also explains the better performance of FLAN- '5 when accounting for the explanation score, neutral explanations are easier to generate and typi- cally more templatic in structure. T5-SocNorm and T5-ESNL are seen as more robust in this regard.
explanations using the explanation score metric: an average of BLEURT (Sellam et al., 2020; Pu et al.. 2021) and BERTScore (Zhang et al., 2019). We re- port the macro average Fl score at three thresholds of the explanation score: 0, 50, and 60. Fl @0 is equivalent simply computing the Fl score, while Fl @ 50 counts only the correctly predicted labels that achieve an explanation score greater than 50 as correct.
Explanation Quality. Wiegreffe et al. (2021) in- troduced an automatic metric for textual explana- tion quality, termed as rationale quality, that com- pares the accuracy of the model with and without provided explanations. We fine-tune models to pre- dict the relation label given an input and an expla- nation, in addition to giving only the input. When providing "gold" explanations, accuracy rises from 53.4% to 96.1% (with rationale quality of 42.7), emphasizing the quality of textual explanations pro- vided by our dataset.
As shown in Table 5, current off-the-shelf mod- els under 3B parameters lack heavily in perfor- mance on our dataset, at most achieving an F1 score of 33.48, despite being fine-tuned on closely related tasks like e-SNLI. This verifies the need for domain-specific dataset, general NLI data is observed to be insufficient to perform well on our task. Furthermore, even the model fine-tuned on our data only achieves 54.52 Fl score, showing that there is still ample room for improvement in this domain.
Human Evaluation of Generated Explanation Plausibility. Three students with significant lived experiences (10+ years) in Chinese and Ameri- can cultures assessed the quality of a subset of 50 randomly chosen model-generater explanations for correctly predicted labels, evaluating perfor- mance between the best performing model fine- tuned only on related tasks (T5-eSNLI) and the best-performing model that was directly fine-tuned on our dataset (T5-SocNorm). Each annotator was asked to rate which generated explanation they preferred, allowing for the presence of ties. An- notators showed an annoraan agreement of 62.4 in Fleiss' kappa (Fleiss, 1971), considered to be "substantial" agreement in existing litera- ture. T5-SocNorm explanations are preferred for the vast majority of instances (73%); T5-eSNLI explanations were preferred in only 11% of in- stances, while explanations from both tied for the rest (16%). An example of a bad generation from T5-SSNLI is shown in Table 7; as shown, T5- SocNorm tries to determine the attitude the norms
When considering explanation quality, we see a very steep drop in performance from Fl @0 F1 @1@60 for models fine-tuned related tasks (72.50% for FLAN-T5 and 96.59% for T5-eNNLD, indicating that current datasets do not enable suffi- cient explanation quality for reasoning about social norm entailment. For the models fine-tuned on our data, the performance drop when accounting for explanation quality is not as sharp (> 21 % for T5-SocNorm and mTTS-SccNorm).
Interestingly, FLAN-T5 achieves lower Fl @0 score than model fine-tuned on eSNLI, possibly because of interference from non-eSNLI-related tasks that was fine-tuned on. Further investigat- ing performance differences between T5-eNNI and FLAN-T5 (see Table 6). we observe that FLAN-T5 struggles, in particular, to predict all relation classes equally well, instead predicting

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expresses, while T5-cSNLI instead attempts to gen- erate a rulc-of-thumb (in red) instead. These results are indicative of how our data contains high-quality explanations desipt its small scale, models fine- tuned on are able to produce better explanations compared to model fine-tuned on the entirety of e-SNLI, which is 247 times larger in size.
of Chinese cultural societies under the framework of social orientations (Yang, 1993). In Chinese culture, relational determinism strongly defines the social norms surrounding social interactions in ev- eryday situations (Chen et al., 2013). One's rela- tionship with another determines how one inter- acts with them; much distinction is placed within Chinese culture between one's own kith and kin (ÉEY, zijiren)-wiic mostly includes family members and familiar persons such as friends or classmates- and outsiders (3) A, wairen), at a much stronger degree than that which is present in American cultures (Chen and Chen, 2004; Chua et al.. 2009). Our results here show that in situa- tions where this relationship distinction is appar- ent, or in cases of change in relationship type, the norms surrounding these situations more likely to differ across Chinese and American cultures.
The US norm expresses approval towards asking for money back, while the Chinese norm suggests that it is not polite to ask for it directly
T5.SocNorm
It either OK to ask for your money back or not polite to directly ask
T5-cSNLI
Table 7: Example explanations generated by T5- SocNorm and T5-eSNLI for the situation in Figure 1. T5-eSNLI tries to generate norm, while our model tries to explain the contradiction.
Descriptive Effects. As we have done for sit- uational effects, here, we train a 10-topic LDA model on preprocessed norms in rules-of-thumb form, identify each norm topic's s most prominent theme, and test for the effect of norm-topic factors on predicting differences in these norms between Chinese and American cultures. As before, labeled norm topies alongside their top words are shown Appendix Section D.
6 Cross-Culturee Norm Variation
Recalling that descriptive norms are situationally dependent, here, using our dataset, we test for fac- tors driving these cross-cultural differences in so- cial norms, testing specifically for (1) situational effects (i.e., when do norms differ?) and (2) de- scriptive effects (i.e., what do norms differ about?) across Chinese and American cultures.
Recalling that norms in rule-of-thumb form are judgments of actions, norm topics associate with action topics; a contradiction between cross- culturally aligned norms means that contrasting judgments
Situational Effects. To capture thematic trends across situations, we train a 10-topic LDA model on preprocessed situations and manually label each topic with its most prominent theme; labeled sit- uational topics alongside the top words that are associated with each are shown in Appendix Sec- tion D. Testing for the effect of topic-situational factors differences in social norms, we measure the correlation strength between the probability of a situation belonging to a given topic against how likely norms will contradict for that given situation.
placed those actions across Chi- nese and American cultures. Here, we find that two norm topics positively predicted how likely a contradiction would be present between the norms of our two cultures. They are, specifically, Loss of Trust/hntimacy (p = 0.06, p = 0.05) and Support in Relationship (p = 0.04, p 0.04).
Interpreting these descriptive results in the con- text of relational determinism, our results above situational effects, and through further qualitative analysis. our findings show that differences exist between Chinese and American cultures in judg- ments actions that (1) would cause a loss of trust or intimacy and (2) are taken to support an individ- ual close to you. These findings are consistent with longstanding work in Chinese familism (Ch' eng- K' Un, 1944; Yang. 1988; Campos et al., 2014), showing that exceptional accommodations in cir- cumventing typical social rules are made, relative to American cultures, to pursue interpersonal and
In this regression, we find that three situation topics positively predicted how likely norms would contradict between Chinese and American cul- tures for given situation to statistically signif- icant degree. They are, as follows, Lack of Inri- macy/Separation (p 0.07, p 0.01), Family Discord/Divorce (p 0.06, p 0.01), and Loss of Family Connection/Changes in Life (p- = 0.07, p= 0.02).
Though these effect sizes remain small in scale likely due to the limited size of our dataset, these findings are consistent with contemporary studies

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especially familial harmony for those classed as one's own kith and kin (Dunfee and Warren, 2001; Chen et al., 2013).
social science research scale, it is infeasible obtain explicit consent for large-scale datasets such as ours (Buchanan, 2017). In order to preserve user privacy, we collected only publicly available data and analyzed it in aggregate, not at the individual level. Furthermore, we mirror former Twitter aca- demic data release guidelines in that we release only the set of question and answer ids used in our analyses, which researchers are able together with the Zhihu API obtain original discussion content.
7
Conclusion and Future Work
In this work, we sought to computationally model social norms across Chinese and American cultures, via human-Al AI collaboration framework extract and interpretably compare social norm differences in aligned situations across contexts. Our analyses here reveal several nuances in how social norms vary across these cultural contexts, incorporating principles of descriptive ethics that prior studies have often lacked thus far. We see our present work situated in the broader context of designing sys- tems that are able to reason across languages and cultures in an increasingly interconnected global world. Here, we highlight few directions we find exciting for future work; models, code, and anonymized data are made available for further research.
Limitations
Cross-Cultural Demographic Differences. In studying cultural variations in social norms, we do not argue that culture as a variable alone ex- plains the entirety of the observed variations. Most notably, intra-cultural variation social norms also exists depending on the scale examined; for example, among multiple demographic groups or sub-communities (Plepi et al., 2022; Wan et al., 2023). Further, as the presence of variation in user and participant demographics undoubtedly remains between the data from our opposite cultural axes, is crucial to interpret our results in the context of these differences and to take note that findings our data are biased toward representing the view- points of individuals of these demographic groups
Deviance from social norms can lead to miscom- munication and pragmatic failure (Thomas, 1983). Integrating cross-cultural norm knowledge into so- cially situated communication systems (Hovy and Yang, 2021) could lead to fewer miscommunica- tions in everyday situations, bridge cultures, and increase the accessibility of knowledge and of in- terpersonal technologies to far greater extent than traditional translation technologies.
Value Pluralism. There are often more than one one-and often contradictory-sociin norms that are relevant to any given social situation, which are often in tension with each other in many so- cial dilemmas. By performing norm comparison using only the top-up-voted answer for given so- cial situations, we necessarily limit the scope of our work to the social norms that are, by design assumptions and by proxy, the most accepted social norms for any given social situation. It is important to note that this does not preclude the possibil- ity that similar social norms may remain valid for each culture differing levels of acceptance. Here, while annotators with significant lived experiences in each culture sanity-checked that the entailment relations assigned between norms across cultures corresponded to actual cross-cultural differencees and not simply because of value pluralism, this does not entirely ensure that the cross- norm differences observe not because of value pluralism, it is impossible for annotators be aware of every single norm an ever-evolving so- cial landscape. We believe this to be a rich area
While our work has focused on measuring dif- ferences in social norms across cultures, culture, though important, is only one of many variables like age and gender, among others that affect de- scriptive ethical judgments (Kuntsche al., 2021). Neither are norms fixed in time (Finnemore and Sikkink, 1998; Sethi and Somanathan, 1996); fu- ture work could, with the aid of social platforms like Zhihu and Reddit, quantitatively examine not only the evolution of social norms across cultures and platforms but also compare changes between cultures, further bridging theory and empirical study.
Ethical Considerations
Data Release and User Privacy. The study and data collection was approved by an Institutional Re- view Board prior to data collection. Nonetheless, while online data in platforms such as social media has opened the door to conducting computational

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future work to more quantitatively study instances of value pluralism even for norms of single cul- ture, and to see which norms ultimately "win over" the others for certain situations (Sorensen et al., 2023).
possibilities in human-Al collaboration, as we have done here. Nonetheless, is important recognize that language models ultimately mimic patterns in their training data (Lucy and Bamman, 2021) and regurgitate structural and social biases (Ben- der et al., 2021). While we have incorporated these models under Iluman-Al collaboration framework where we externally verify, validate, and edit their output in certain cases to mitigate this risk, it would be remiss to say that we are capable of doing so en- tirely lieu of effects such as priming influencing our decisions.
Data Coverage. It is unrealistic to expect that ei- ther our data or that of Social Chemistry can cover all possible social situations. As such, our findings only represent a subset of the true underlying cul- tural differences present between the norms of Chi- nese and American cultures. Furthermore, it seems intuitive that questions about non-controversial so- cial situations "everyone" is familiar with will, if not be completely absent from online discourse, otherwise get lower representation and engagement. As we only extract from the most up-voted answer from each Zhihu discussion and treat it as the most broadly adopted norm as a simplifying assumption, an open question remains as to quantifying the "problem" of intra-cultural norm disagreements and to investigate genuine human variations in judg- ments of social norms (Plank, 2022). By releasing our data, we hope that future work can take into account less up-voted answers. In moving towards more representative studies, we encourage further work to examine these variations intra-cultural norms and tease out the details of human behavior.
Acknowledgements
We thank David Jurgens, Tuhin Chakrabarty, and the anonymous reviewers for their helpful com- ments, thoughts, and discussions. This research is being developed with funding from the Defense Ad- vanced Research Projects Agency (DARPA) CCU Program No. HR001 122C0034. The views, opin- ions and/or findings expressed are those of the au- thors and should not be interpreted as representing the official views policies of the Department of Defense the U.S. Government. Sky CH-Wang is supported by National Science Foundation Grad- uate Research Fellowship under Grant No. DGE- 2036197.
Censorship and Moderation. Chinese social media platforms like Weibo, and Wechat are man- dated by law (Xu and Albert, 2014) to implement censorship policies on the content created by their users. Zhihu is no different; the presence of inher- ent content bias introduced by this form of active moderation of user content has an effect on influ- encing the landscape of public discourse (Yang, 2022b) and the data that we rely on to derive Chi- nese social norms. In particular, past work has shown the existence of censorship programs aimed prohibiting "collective action by silencing com- ments that represent, reinforce, or spur social mo- bilization" across primary Chinese social media services (King et al., 2013). Comments that con- tain politically sensitive terms, such as mentions of the names of political leaders, are subject to a higher rate of deletions (Bamman et al., 2012). The extent to which these actions lead to a biased public representation of the norms and beliefs commonly shared in Chinese society remains unclear.
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Appendix
Inference Relation and Explanation Generation. We use text-davinci-000 with the following hyper- parameters:
We provide as supplementary material additional information about our collected dataset from Zhihu, chosen hyperparameters for our models, topic model details from our analysis of cross-cultural norm variation, well as the performance evalua- tions of larger models similar the ones used for data generation (GPT 3.5 and GPT-4) on our task.
temperature=0. max tokens=140, top p=1, frequency penalty=0, presence penalty=0.
DREAM-FLUTE Instruction Modification. We structure the imput of social norm data into the DREAM-FLUTE instruction in the following manner:
Zhihu Statistics
Premise: [Premise social norm] US NORM.
Hypothesis: [Hypothesis social normj CHINESE NORM. Is there a contradiction, entailment, or no relation between the premise and hypothesis?".
Questions in Zhihu are tagged by users with topic categories, which serve as entities that individual users may browse, follow, and subscribe to. Figure 3 shows breakdown of the top 20 user-tagged categories our Zhihu questions dataset 5508661 unique questions) alongside their English transla- tions, as well as a distribution of top-answer length. Following that of Social Chemistry, most topics here are related in content to relationships.
We prefix the prompt with 10 examples from the validation set in the same format.
C
Dataset train test split statisties
Train (14%)
Valid
Test
Contradiction
(13%)
(15%)
Relation
(35%)
110 (37%)
249 (32%)
Fintilmmntt
1022 (51%)
151 (50%)
405 (53%)
Table 8: Statistics of our cross-cultural Chinese- American social norm NLI dataset, stratified by entail- ment relation across train, validation, and test sets.
D Topic Models
Distribuionn Charasters Voled
We train 10-topic LDA models using MALLET to analyze cross-cultural social norm variations on both situational and descriptive effects, manually labeling each topic with its most prominent theme. Labeled topics and their top words are shown in Tables 9 (topics on situations) and 10 (topics on descriptive norms in rule-of-thumb form), with the topics that were statistically significant in the pre- diction of cross-cultural norm contradictions high- lighted in bold.
0.021
0.033
290
502
750
1000
1250
1500
1750
2000
Chamecerr 2hinu Top-Soled Answer
Figure 3: A top 20 user-labeled topics breakdown of Zhihu questions in our dataset (top) and the distribution of top-answer length measured in characters (bottom).
B Hyperparameters
Norm Extraction. We use text-davinci-002 with the following hyperparameters: temperature=0.11 max tokens=255 top p=1, frequency penalty=0, presence penalty=0.

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Topic Theme
Top Tokens
0. Self-care/ Emotional Turmoil
dog boyfriend living sad fears died wife doesn't t speak every- time someone's ' petting hands washing morning teeth brush up- set/irritated truth bed angrying stuff confess lie leads brother's attracted suffers agitated i'm
1. School Bullying/ Toxic Rela- tionship
school she's 's forced bullied pressuring friendship valentines ab- solutely engagement year disgusted people expecting fact bitchy remarry day tears burst criticism threw mother ditching mad crying boyfriend won't play stop can't 't reason don't 't consequences issues ill gravely starts panies lot unprofessional texting i've years that's reconciliation daughter's i've thing pathetic intimacy lacking public vulnerable separated
2. Lack of Intimacy/ Separa- tion
3. Family Discord/ Divorce
hate mother sister hating mom parents dad people wrong brother family father making wife ery starting divorce pissed what's 's resent girl kinda aunt brothers asked parent dislike grandmother marry genuinely
4. Conflicts in Romantic Rela-
girlfriend boyfriend friend wanting mad friends telling birthday girl upset breaking break friend's 's hating family jealous relationship teacher boyfriend's hates angry boyfriends cheated gift dating depression wedding hang christmas likes
tionship
5 Struggles in Marriage
birthday friends wanting fat dream husband night college part dont ashamed working cheat somcone's ' frustrates affection reciprocate inability wife's complaining job open accepting remembered it's lies dropout couple boyfriend's pregnant
6. Loss of Family Connection/ Changes in Life
woman recently meet-up cancelled disappointed passing mother's blaming job dress original wear selling sick care taking form exist disapper fall dad wonders anymore wedding ex ex-bestfriend unfollow instagram despise
7. Emotional Struggles/ Loneli- ness
fear son man trust thinking woman young married time covering welfare mom's 's unhappy constant lives longs toe camel bathtub facebook friending forgetting social co-worker...i 'mm knowing close lazy extremely dogs depressed
8. Fear/ Uncertainty
love m don't ton't boyfriend friend i'm married feel scared don't fon'tinn jeal- ous anymore kids afraid mother life family yelling man younger cancer roommate dad advice can't doesn't 't telling terrified falling fiancée
9. Unhealthy Relationship/ Mis- treatment
mom ballet ago girls bad years girlfriend's ex-girlfriend scaring sounding insensitive ratting classmate hug holding she's doctor bring stressed teen friendship effort puts man's annoyed ex-wife learn lessons lie interested
Table 9: Manually labeled situation topics and their top tokens, as captured from 10-topic LDA model trained on cross-culturally aligned English Social Chemistry 101 situations.

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Topic Theme
Top Tokens
0. Loss of Trust/ Intimacy
abortion depressed hold sense loyal pet hatred talk kind doesn't t hang past you'l re that's disgusted access hurtful brushing fiance's hide thoughts pushy depression pictures change breaking interact hard spite intimate
1. Communication Issues/ Gift-
communication refuse girlfriend decision reach true public christ- mas dress lie grateful relationship lives steal assume enrolled stressful happily jeopardy admit you'll Il accepting interest interac- tion hangout debt loaned escalate friendly presents
ing
Support in Relationship
it's expected people wrong family shouldn't 't friends good partner parents love hate rude bad significant understandable relationship normal feel don't expect things friend you're 'rere children feelings upset important talk friend's
3. Social Media/ Social Bonds
families find you'l 'Il service wedding choose involved songs broken gifts media emotion cohesion foster behave society job online teaching ties sexuality interfere develop hurts schools profession- alism poor health struggling kid's
4. Responsibility/ Respect
put mother relationship long person's 's teacher understanding men- tal deserve lazy situation music partner aunt responsible wash face fix illegal material finances judge anger purposely concerns dog harass exchange college respond
5. Care-taking/ Nurturing in Re- lationships
give relationship health frowned polite attention desires working inconsiderate single encouraged hang rest immoral random ignor- ing routine consistent lot provide assist won't strike arguments afraid information amends sister frequent concerns
6. Personal Autonomy/ Control Over Life
relative fear calm death time members remember inappropriate methods elderly fights intimidate consent frustrate starting step betray relate reasons shy future beds stalk father business mind sense behavior control divorce
Diversity/ Inclusivity
enjoy issues order college couples find aggressive place exclude petty fair gratitude trivial commitment grudges conversations you'll information attention based argue alive selfish lifestyle free live transgender nowadays socially dropout
8. Trust/ Commitment
allowed spend common invite friend made plan supposed crush cheat interested activities hanging values chance contact tooth roommates toe lied betray deal extracurricular reasons member's finish students concerts effort honor
9. Hardships/ Refusal
uncomfortable married accept frustrated interests bitter mom cheater hurtful who's therapy yous nasty taste stand lifetime both- cring fears no-contact arguments phone purpose self-sufficient adults reaction hostile receiving health full shun
Table 10: Manually labeled descriptive norm topics and their top tokens, captured from a 10-topic LDA model trained on relevant cales-of-thumm from aligned cross-cultural situations.

---

E Performance of GPT-3.5 and GPT-4
Here, we evaluate the performance of larger models similar to the one used to generate our GPT- 3.5 and GPT-4. Both models achieve high FI @0 and F1 @60 performance, expected from their size and from the similarity of the data distri- bution their outputs. Notably, however, the FI @60 score of GPT-4 the better performing of the two-remains lower comparison with our best-fine-tuned model (41.18 vs. 43.07); further- more, the relative decrease from @0 to F1 @60 (%A) of GPT-4 remains higher than our model (34% vs 21%) as well, indicative of the possibil- ity that distilled models may even surpass teacher models in explanation quality.
Model
FI@0
F1@50
FI @60
%A())
GPT-3.5
52.21
47.30
30.41
41.75
GPT-4
62.85
55.40
41.18
34.47
Table 11: Fl scores and percent decrease in Fl across explanation score thresholds for GPT-3.5 and GPT-4.


Reviews:
[{"review_id": "IHpOYEYAqE", "reviewer": "Reviewer_3gbm", "summary": "", "questions": "See first reason to reject\n\n**Update post rebuttal** Changing my soundness score from 2->3", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their engagement with our work and insightful comments.\n\n* Reason to reject 1: Absolutely, value pluralism is one limitation for doing alignment in this fashion—On this point, during the data validation stage described in Section 4, we had annotators with significant lived experiences (10+ years) in both cultures sanity check that the entailment relations assigned between norms across cultures corresponded to actual cross-cultural differences and not simply because of value pluralism.  While this does not entirely ensure that the cross-cultural norms we observe are not because of value pluralism (as it’s not possible for annotators to be aware of every single norm), this was a good mitigation step and acted as a good validation check for our method that is based on a human-AI collaboration framework. We thank the reviewer for pointing out this omission and will clarify the details of this in the camera-ready, and include an acknowledgement of value pluralism as a potential limitation in the Limitations section. We believe this to be a rich area of future work to more quantitatively study instances of value pluralism even for norms of a single culture, and to see which norms ultimately “win over” the others for certain situations.\n\n* Reason to reject 2: We thank the reviewer for their suggestions for additional experiments. \n    - Our framework and modeling experiments can be easily adapted for other cultures since they only take the basic NLI set-up as assumptions, and our framework for data generation is also easily extendable to other cultures. \n    - *We fine-tuned a multilingual mT5 model on our dataset to explore how multilingual pre-training aids in social norm understanding.* We found that the fine-tuned mT5-XL model achieves 29.69, 28.61, and 23.19 scores for F1@0, F1@50, and F1@60 respectively. While the absolute scores are lower than the monolingual T5, the relative decrease from @0 to @60 is in line with the T5 model at 22%). We will report this and perhaps other ablations in the paper given the additional page for camera-ready. We also implemented experiments with GPT-3.5 and GPT-4 (see response to reviewer nK7w).\n    - We also would like to note that one of the baselines (DREAM) was fine-tuned on SocialChemistry (lines 372-374, 386-394), but that alone did not seem to yield significant improvements. We may include more experiments with socialChemistry given the additional page for camera-ready.\n\n* Reason to reject 3: We thank the reviewers for pointing this out, and yes—we will adjust the hedging language of our claims to reflect this effect size.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- More work like this one is needed to mitigate the huge US-centrism bias in NLP research\n- The introduced dataset is useful to understand cultural differences in social acceptability in social situations\n- I particularly appreciated the analysis in §6 of why situations and judgments might be different in different cultures, especially since it was well-grounded in socio-cultural studies.", "reasons_to_reject": "- My main concern about this paper is that it seemingly glosses over the fact that there are more than one---often contradictory--- social norms that are relevant to a situation, which could often support opposing judgments for the situation. For example, for the situation \"*staying home instead of going to work because you're sick*\" evokes both norms that supports the situation (\"*it's good to take care of your health*\") and that oppose it (\"*you should go to work*\"). Such a discordance is just evidence that often social norms are in tension with one-another (value pluralism). My question is, how many of the contradictions in the final dataset are actually contradictions due to cross-cultural differences, vs. contradictions that are just due relevant social norms in US-centric situations being sampled and ranked for relevance differently than in Chinese-centric situations, and this really more due to value pluralism that is inherent to many social dilemmas?\n- The choices of modelling experiments is reasonable but does not yield many insights that would be useful for other researchers interested in adapting NLP to other cultures. Authors could, for example:  \n  - Run some experiments or ablations to compare whether multi-lingual pretrained models have better performance at predicting different social acceptability judgments / predicting the NLI-style task compared to monolingual models\n  - Explore whether training specifically on social chemistry would help with the task.\n- Nitpick: though I appreciated the analyses in §6 greatly, the effect sizes of the correlations were very very small (likely due to the size of the dataset, especially given how little contradictions there were). Perhaps authors could hedge their claims more in this part to indicate these small effect sizes?", "questions_to_authors": "See first reason to reject\n\n**Update post rebuttal** Changing my soundness score from 2->3", "paper_topic_and_main_contributions": "This paper presents an interesting exploration into how NLP systems can handle socio-cultural differences in social acceptability of situations. Given the social chemistry dataset (Forbes et al, 2020) of situation-RoT tuples, authors scrape a Chinese advice-seeking forum (Zhihu) to create a parallel corpus of US-centric and Chinese-centric situations. They then generate/obtain RoTs for both situations, and manually label whether the RoTs disagree or agree, to find contradictory/different acceptability judgments, along with an explanation of why. The paper then describes modeling experiments on the pairwise task of predicting agreement/disagreement between social norms for a given situation, showing that models benefit substantially from being finetuned on the task (as opposed to on related tasks like e-SNLI). Authors conclude with a small qualitative investigation of why situations and norms are different.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Tables 1 and 2 are slightly wider than the text columns.\n- Nitpick: I would have appreciated if more details about the various annotations / verifications done were included in the main body of the paper instead of the appendix"}}, {"review_id": "UbdWnVPLrR", "reviewer": "Reviewer_nK7w", "summary": "", "questions": "Same as the \"Reasons to Reject\".", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their comments and appreciation of novelty of our contributions.\n\n* Reason to reject 1: We would like to emphasize 5 modeling strategies investigated in this study: model fine-tuned for elaborations on SocialChemistry, state-of-the-art instruction-tuned model (FLAN T5), model fine-tuned on a large amount of data for a related task (T5 e-SNLI) and the model fine-tuned on the dataset. All of these approaches result in a relatively weak performance which serves as objective evidence of the inherent difficulty of the task (even though personally it may feel easy). Furthermore, the focus of our work is *explainable entailment*, the performance of which we try to measure with F1@50 and F1@60, *which is an even more difficult task.* We agree with the reviewer that more investigations could be explored in future work, and hope that our release of the dataset and the initial investigations of 5 different approaches to this task will be a reasonable first step for future work. \n    * In addition, we conducted an experiment with fine-tuning a multilingual mT5 model (see response to Reviewer 3gbm), as well as few-shot performance of GPT-4 and GPT-3.5 (see below) and plan to include these results in the paper, adding 3 more modeling approaches to our investigation.\n\n* Reason to reject 2: We found that the capacity of text-davinci-003 was enough, in conjunction with meticulous human validation described in Section 4, to generate a high-quality dataset for our task. Due to the time-consuming nature of human validation, certain state-of-the-art models may advance in the meantime. However, we encourage future work to consider state-of-the-art models in our rapidly evolving field. \n    * We also implemented an experiment from Table 5 with GPT-4 in a 10-shot setting (same as for instruction-tuned FLAN-T5 baseline) and found the performance to be 62.85, 55.4, 41.18 for F1@0, F1@50, F1@60 respectively. Note that despite being a much more powerful model (order of magnitude higher number of parameters and reinforcement learning training), it performs worse than the fine-tuned 3B T5-SocNorm both in terms of absolute score for F1@60 (41.18 vs. 43.07) and relative decrease (34% vs. 21%). In a similar experiment with GPT-3.5, the results are 52.21, 47.3, 30.41, respectively, supporting a similar conclusion. We would be happy to report these results if provided additional space in camera-ready. That being said, we note that the scope of our work was focused on smaller, more efficient models under 3B parameters (as defined in abstract, see line 025).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. **An interesting research problem**. To my knowledge, this might be the first paper that addresses the cross-cultural social norm identification task. The authors also conduct experiments to show the challenge for current LMs to cope with this problem, implying the emergency of future studies.\n2. **A high-quality corpus**. This paper proposes a corpus for this task accordingly. By adopting a human-AI collaboration pipeline, the dataset covers more than 3k social situations, the corresponding bi-cultural rule-of-thumb, and explainable relation tags. The thorough analysis also exhibits statistical alignment with existing literature on human societies.\n3. **Wrtting**. This paper is well-formed and is easy-to-follow.", "reasons_to_reject": "1. **Limited contributions**. The authors only test the capacity of some well-known LMs; there are limited insights or investigations on the modeling strategies of this problem. For example, it should be useful to investigate the reason for such bad F1@0 performance. Personally, I think this entailment task is not that difficult; more related analysis should help a lot for future research. To be honest, I didn't learn much after reading this paper.\n2. **Some other LLMs**. The authors used GPT-3 (text-davinci-003) to generate the rule-of-thumb, however, I am curious about why they didn't try with other LLMs with more power capacity, such as ChatGPT. Similarly, I think it is also necessary to test whether the vanilla LLMs (e.g., GPT-4) can efficiently deal with this problem.", "questions_to_authors": "Same as the \"Reasons to Reject\".", "paper_topic_and_main_contributions": "This paper studies the social norm relations (similarities & differences) between the U.S. and Chinese by proposing a novel corpus. Specifically, this paper collects the contexts from the existing English corpus (Social-Chem-101) and Chinese QA platform (Zhihu). In order to investigate the cross-cultural social norm, the authors put these bi-lingual pairs into the same situations and use LLMs to annotate the entailment relations along with explanations. After careful human verification and evaluation, the authors show the high quality and challenge of this dataset, where some widely-adopted LMs fail at solving this task.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "mWkViURGm5", "reviewer": "Reviewer_7iE3", "summary": "", "questions": "1. Do you have any plan to release this dataset  in further?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their comments and appreciation of our contributions to the study of cross-cultural social norms. We also thank the reviewer for pointing out a very relevant work and will cite it in our paper.\n\n* Reason to reject 1: We would like to emphasize 5 modeling strategies investigated in this study: model fine-tuned for elaborations on SocialChemistry (DREAM), state-of-the-art instruction-tuned model (FLAN T5), model fine-tuned on a large amount of data for a related task (T5 e-SNLI) and the model fine-tuned on the dataset. In addition, we conducted an experiment with fine-tuning a multilingual mT5 model (see response to Reviewer 3gbm), as well as few-shot performance of GPT-4 and GPT-3.5 (see response to Reviewer nK7w) and plan to include these results in the paper, adding 3 more modeling approaches to our investigation. We are not sure what is meant by a simple tailored model, perhaps that is satisfied with the model fine-tuned on the dataset (T5-SocNorm)?\n\n* Reason to reject 2:  Please refer to Appendix C for dataset split. The average length of the social norms is 63.5 characters. For social norm extraction, situation length was limited to 300 characters. We will provide more detailed statistics in the appendix.\n\n* Question 1: Yes, the dataset will be publicly released.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper is in well-written, easy and clear to follow.\n2. This paper explored a task on cross-cultural social norm discovery between English and Chinese, two main languages around the world. The work would be meaningful and significantly contribute to the developmente of cross-cultural language development.\n3. This paper proposed a Human-AI collaboration framework, which is easy to be extended for further work.\n4. This paper provided supportive and comprehensive analysis and experimental to inspire how to ultize this datset in further.", "reasons_to_reject": "1. Besides baseline models such as T5, a simple tailored model should be introduced to help formulate the task of cross-cultural norm discovery and expainable textual entailment.\n2. Details about this dataset should be introduced. e.g., how to split the 3069 social norms in train/dev/test? what's average length of each social norm and dialogue context?", "questions_to_authors": "1. Do you have any plan to release this dataset  in further?", "paper_topic_and_main_contributions": "This paper proposed a human-AI collaboration framework for cross-culturaldescriptive norm discovery, as well as provided a new dataset for cross-cultural norms understanding with explainable textual entailment.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {"Missing_References": "A recent paper that is relevant to yours. You are encouraged to cite this paper in your further version (not a reason to reject). \n- SocialDial: A Benchmark for Socially-Aware Dialogue Systems (SIGIR'23)"}}]

Author rebuttal:

Meta-review: This paper contributes a new task and dataset: comparing social norms across Chinese and American cultures. They build upon the Social Chemistry dataset to present a novel corpus of 3k social situations in the Chinese language and analyze cross-cultural norm differences. 

Several reviewers commented on the importance of this paper as the “first paper that addresses the cross-cultural social norm identification task” and “mitigat[ing] the huge US-centrism bias in NLP research”.

The author response was very thorough, causing at least one reviewer to increase their soundness score. Although I see this paper as primarily a task and dataset paper, the authors have also conducted many empirical experiments that show there is still room for models to improve on this task and dataset. For reviewers 7iE3 and nK7w reasons to reject as “some other LLMs” and “a simple tailored model”, these arguments seem to fall under the “The authors could also do [extra experiment X]” heuristic/shortcut discouraged in the reviewer guidelines: https://2023.aclweb.org/blog/review-acl23/#2-check-for-lazy-thinking
Decision: Accept-Main