NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Authors: Yi Fung
Tuhin Chakrabarty
Hao Guo
Owen Rambow
Smaranda Muresan
Heng Ji
Venue: EMNLP 2023 Main (2023)
Keywords: social norms
discovery
grounding
TL;DR: Norm Discovery from Conversations On-the-Fly with language model self-verification mechanism

Abstract: Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address these limitations by proposing a new framework, NormSage,  to automatically extract 
 culture-specific norms from multi-lingual conversations. NormSage uses GPT-3 prompting to 1) extract candidate norms directly from conversations and 2) provide explainable self-verification to ensure correctness and relevance. Comprehensive empirical results show the promise of our approach to extract high-quality culture-aware norms from multi-lingual conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NormSage achieves an AUC of 94.6\% in this grounding setup, with generated explanations matching human-written quality.

NORMSAGE:
Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
Yi R. Fung, TTuhin Charkaborty, "Ho Guo, O Owen Rambow, Smaranda Muresan, *Heng Ji
University of Illinois Urbana- Chhaaaan Columbia University, Stony Brook University fyifung2, hengnn jeaiiiieelliiiiin edu
Abstract
Yet, research on computational modeling of so- cial norms is still in its early stages. SOCIAL- CHEM- 101 (Forbes et al., 2020) and the MORAL INTEGRITY CORPUS (Ziems et al., 2022) intro- duce large crowdsourced datasets of social norms and moral judgements conceptualized as free-text Rules-of-Thumb (RoT) grounded in given situa- tions. While this has led to significant advances in computational modeling of social norms, there are two important limitations. First, while mod- els trained on these grounded RoTs can adapt to new situations, they often struggle to uncover new norms and to model richer context. Second, the norms covered in these studies are primarily Reddit- based and US- US-centric (Bianchi, 2022).
Knowledge of norms is needed understand and reason about acceptable behavior in hu- man communication and interactions across sociocultural scenarios. Most computational research on norms has focused on single cul- ture, and manually built datasets, from non- conversational settings. We address these limitations by proposing a new framework, NORMSAGE', to automatically extract culture- specific norms from multi-lingual conversa- tions. NORMSAGE uses GPT-3 prompting extract candidate norms directly from conversations and 2) provide explainable self- verification to ensure correctness and rele- vance. Comprehensive empirical results show the promise of our approach to extract high- quality culture-aware norms from multi- lingull conversations (English and Chinese), across several quality metrics. Further, our relevance verification can be extended assess the ad- herence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation. NORMSAGE achieves AUC of 94.6% in this grounding setup, with generated explanations matching numan-written quality.
In this paper, we propose to address current short- comings with the following contributions:
A new task: conversation-based multilingual and multi-cultural norm discovery (Sec 2). Norms manifest themselves in day-to-day conver- sations, either by being implicitly adhered to violated. For example, we observe in Fig input a conversation between Taiwanese-America parent and son about "not wasting food". We define a new task aimed at extracting norms from conversation context, beyond single statements and question-answer pairs from recollected sum- marics. To support this task, we also introduce a collection of conversations in English and Chi- nese spanning multiple genres (e.g.. TV shows, videos, text messages), topics, and cultures.
1 Introduction
Norms are rules that embody the shared standards of behaviors amongst cultural groups and societies (Schwartz al., 2012). These include social con- ventions (e.g., it's good shake hand with your opponent even you lost); behavior guidances (e.g., it's wrong to hurt a pet); and general con- cepts (e.g., it's nice to be smart) (Forbes et al., 2020; Ziems et al., 2022). Knowledge of norms in general, and sociocultural norms in particular, is essential if are to equip AI systems with capa- bility understand and reason about acceptable behavior in communication and interaction across cultures.
NORMSAGE, a zero-shot language model prompting and self-verification framework for norm discovery from conversations (Sec 3). First, we extract candidate norms using GPT-3 (Brown et al., 2020) prompting that takes the conversational and social context into account (e.g., culture, social relations, discussion topic, etc.), as shown the Fig dvr(-) operation for initial norm discovery. Second, to ensure norm correctness and relevance, we propose the idea
OOr code and data are available at the Github repo here.

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a) Initial Discovery: dvr()
Seeeliiveiiiiitiion Correctness cor()) Grounding grd(.)
Di. Gives the following conversstion:
Coneider this sentencer
D: Given the fullowing conversation:
hurgyy.
coolonghan pact
good be honeet with
Collec but
NormsKB Population
your family
trerel
And the somm
Jevalon:
d: this comret/accaptalle norm?
it good to oe herust with your
The
Diay. about food, Edibic Jesico.
pibai
explain why.
Answer "yes" or "no" and then
<entails, contradicts, inrllvant with the norm:
@i Explain whether the conversation
S Norm
Topic Category
Culture
Description
Q: some soclo caltural norms relabed altuation:
Correctness Verdict (C,):
Grounding Verdict [C,]] controdict Inrelesont entall
Source and Otheet
Correctness & Explanation
-it is goodt to be honest with your family.
norm
Correctness Explonation (6,): Wes, honestry is the founditiin of trust, and strong family relationchips are built on trust.
Grounding Explanation (fyl norm was honest with Jussica whan
Groundlng Explanation
accepned show sporecietion
food your family prepares foryou. food.
nomo
unaacoppable
is expected to show respect to diders Talwonese Americon
longhen she parked oten did any.
D: diallogue
S: structure Q: question
nome
[Niter out Incoerect)
(fiter outimmlevant
N: norm
The key idea NORMSAGE E. is prompting verification for cuiversation-groundees norm discovery. Norm candidates are first discovered from dialogues, through the dvr(-) operation. They are then checked for correctness and relevance, through cor(-) and grd(-), before populating into the knowledge base of norms, NormsKB.
Figure I:
of explanation-aware self-verification through prompting based operations cor(-) and grd(-). As shown in Fig 1(b), our formulation here involves question assessing the correctness of a candi- date norm, and another probing how the norm grounds to its source dialogue. The entailment reasoning in grd(.) () can be further used to assess the adherence or violation of any norm in con- versation, with textual explanation.
of the pre-defined target languages (e.g., English, Chinese), we aim to derive set of relevant norms N [m in English, for a unified repre- sentation. If background information (X₂) about the conversation context or speaker profile is avail- able, such as from the Wikipedia summary of a TV show, it can optionally be incorporated to enrich norm discovery. Because handling hallucination and achieving transparency are important for norm discovery, we introduce checking norm correct- ness and grounding as supplementary verification tasks. We aim filter out incorrect norms, by deriv- ing correctness verdict C, {1 yes, -1 no), along with a confidence probability and natural lan- guage explanation. Additionally, we aim to filter out non-insightful norms, by deriving a grounding inference G, E {1 entail, 0 irrelevant, -1 contradict). along with confidence probability and explanation.
Comprehensive evaluation setup (Sec 4). We assess the quality of culture-aware norm extrac- tions along several dimensions. Our method dis- covers significantly more relevant and insightful norms for conversations on-the-fly (selected as "best" 270% cases by human judgement) com- pared to baselines developed over Reddit-based rules-of-thumb annotations (Forbes et al., 2020; Ziems et al., 2022). Additionally, the norms discovered from Chinese conversation exhibit limited performance difference compared to the norms discovered from English conversation in terms of relevance and correctness (A < 5%). The culture speciffc norms are also promising in quality, with humans being able to identify the culture from these norms at 80% accuracy.
Our proposed task is innovative in several key aspects. It the first task define automatically discovering norms from dialogue data, which best reflects human communication on- on-the-fly. In addi- tion, it is the first task on discovering multicultural norms from multilingual sources, which is then used to construct a NormsKB that can better repre- sent diverse socioethnic demographic groups. Fi- nally, grounding the discovered norm with dialogue examples, confidence score, and natural language explanations enables our norm discovery approach to be explainable and self-supervised. This veri- fication process can be used to check whether a conversation adheres or violates any given norm.
NormKB, a knowledge base of over 20k unique norms with correctness and grounding explana- tions, extracted from multi- minti-linguul multi-cultrra conversations.
2
Task Formulation and Source of Data
2.1 Task Formulation
We define the conversation-based. multi-lingual multi-cultural norm discovery problem as fol-
2.2 Source of Data In collecting source data for norm discovery from
lows. Given a conversation scenario (Xaa) in one

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Source of Data
Lang
Topic
#Tok
# TTr
Multil Ling Cross- Culture Single Culture
Big Bang Theory (BBT)
EN
the life nerdy, brilliant scientists Califomia
29,682
3,468
Friends (F)
EN
Manhattan adult life, friendship. dating
26,197
2,849
How I Met Your Mother (HIMYM)
EN
romance centered around NYC medical environments centered Seattle law and justice centered around NY police
29,423
3,785
Grey's Anatomy (GA)
EN
23,341
3,117
Castle (C)
EN
38,880
4,142
Fresh off the Boat (FOB)
EN
Taiwanese family in America Indian girl coming-of-age the US African American family in the suburbs Pakistani family in Britain, patriarchism American salesman, outsourced to India
26,056
4,129
Never Have Ever (NHIE)
EN
39,847
5,637
Blackish (B)
EN
33,993
5,103
Citizen Khan (CK)
EN
22,985
3,198
Outsourced (O)
1,464
American Factory (AF)
EN,CY CN
Chinese company open factory in Ohio
10,840
1,138
Real-Worke Negotiations (RWN)
EN.CN
US-China talks, street vendor purchase talks
19,487
1,758
LDC2022B11 CCU TAI Dev.
CN
text msg. phone calls, online videos
1.2M
102k
Total
1.5M
140k
Table 1: We list out the sources of our raw data here, along with description of their language (EN English, CN Chinese), topic domain, and statistics (# tok number of spoken tokens, tr number of dialogue turns).
conversations, we seek data that involve dialogue exchanges mimicing reflecting real-world com- munication. Secondly, the data should ideally span diverse topics and societal or cultural groups. In practice, it is generally difficult to obtain large- scale, in-the-wild data for norm discovery due to privacy concerns, as well as sparsity of interest- ing human interaction occurrences. TV shows and movies offer an interesting alternative, as they rep- resent naturally occurring conversations often in- volving multicultural exchanges. We expand on the predominantly single-cultured TVQA dataset (Lei al., 2018), and collect a set of TV and movies that cover different cultures, all which are pri- marily in English. We also include several multi- lingual (Chinese and English) conversations from real-world chats, negotiations, and documentaries, to explore norm discovery adaptability in diverse data settings. detailed in Table 1. In partic- ular, the LDC data is large, compiled release of several thousand SMS/chat, phone conversa- tions, and You TouTube/Bilibili video scenarios (LDC, 2022). For each source of data, we collect the corresponding textual summary from Wikipedia, if available. Lastly, we preprocess dialogue tran- scripts into chunks (X.ial,..x)) every &o = 5 lines as the individual data points in our task consideration.
norm discovery as series of natural language prompts, each with directed question for the pre- trained GPT-3 Davinci language model to reason with its internal knowledge and generate an answer response. To discoyer an initial set of candidate norms from conversation data, we introduce the dvr(.) operator, which concatenates D, a template header describing the nature of the context data followed by the dialogue input (Xdiat, with Q directed question describing the norm discovery task, input for the PLM to generate response. We follow the general prompt development guideline of wording D and Q in clear and concise language.
Structure Enhancement A shortcoming ob- served in standard prompting is that the norms dis- covered may lack well-formedness and taxonomy for categorizing information specific to different cultures and topics. To encourage greater level of detail and structure in dvr(-) outputs, we investigate adding to the prompt input:
S a building block in the text template consist- ing of either frames defining the expected struc- ture of norms (see Fig 2), or cultural indicators encouraging topic-specific taxonomy (see Fig la). These cultural indicators are extracted automati- cally through prompting on the background sum- mary if the data is available. Further details of this process are included in A.
3 NORMSAGE Framework
3.1 Initial Norm Discovery
3.2 Self- Self-Verification with Correctness Checking & Explainable Grounding
Pretrained language models store implicit knowl- edge about the world learned from large-scale text collected around the internet (Petroni et al., 2019; et al., 2023b). We frame conversation based
For each discovered norm, we add a cor()) oper- ator to check the correctness of norms through prompting. This prompting operator follows the

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Base No Frame, No Indicators
(Devlin et al., 2019) exceed threshold of cosine similarity with any previously discovered norm. The threshold is empirically set to o = 0.95.
Given the following conversation:
Honesty
X
(Kaa)
Gratitude
Q: List some socio cedt-cultural norms
related the situation:
Consideatioo
With Frame
Evaluation and Results
D: Given the following conversation: (Kow)
It's wrong lie, for the reason of being untrustworthy.
5: Norms typically follow one of the following templates:
We organize this section as follows. In Sec 4.1, we detail experiments on norm discovery from conver- sations, including multi-lingual and cross-culture scenarios. Then, in Sec 4.2 and Sec 4.3, we an- alyze the effectiveness of self-verification mech- anisms for norm discovery correctness, and rele- vance grounding between the norm and conversa- tion, respectively. Finally, in Sec 4.4, we discuss the cost and resource contributions.
It's important acknowledge the efforts others, avan the outcome not what expected.
It's (gnosshwlpiid https//wlpp//mmpttmmmm/eo /ttp///ttt///aaarttt (opecctd) to do action Mi under aituation [2)
It's (wrong/evol/nat norwa/(naawful)
(M) reason [7].
some soclo-cultural norms related to the situation:
It's not good
away food,
for the reason of being wasteful.
Figure 2:
A comparison of dvr()) d NORMSAGE in base form and with framing structure.
natural language template of: "Consider this sen- tence: (ni). Is this a correct/ucceptalle social norm? Answer 'yes' or 'no', and then explain why.". ". The output from cor(.) consists of both correctness verdict, C, and a subsequent expla- nation. Ce. in single natural language response generation (Fig lb). We further derive a confi- dence score (Cp) for the correctness verdict, by normalizing the probability of token generation for C = ='yes' with the probability of token generation for C, ='no'. Norm candidates with correctness probability below a tunable threshold of 0 = 0.7 are filtered out.
4.1 Intrinsic Norm Discovery Evaluation
Baselines While there has been prior work norm discovery based on conversations, we include the following baselines trained on different data format, as valuable points of comparison. NMT gen is a GPT2-XL trained on SOCIALCHEMI 101 (Forbes et al., 2020), while SOCIALCHEM, re- trieves the most relevant SOCIAI,CHFM ()1 rule-of- thumbs for a dialogue based on their embeddings encoded from pre-trained BERT (Devlin al., 2019). We also consider pMT gen- which is gen- erator trained on the MORAL INTEGRITY CORPUS (MIC) (Ziems et al., 2022), and MIC which retrieves the most relevant MIC rules-of-thumb for a dialogue based on their embeddings encoded from pre-trained BERT. In addition, we include TO pp- T5 model 16x smaller than GPT-3 that is trained tasks formulated natural language prompts (Sanh et al., 2022). In our proposed GPT- prompting with self-verification approach, we consider the variant that utilizes structural enhance- ment in initial norm discovery as NORMSAGE* We also include GPT-3 prompting, without self- verification and without structure enhancement, as an additional bascline. Morcover, we include an open and accessible alternative, NORMSAGE AGMMSiG which consists of GPT2-XL finetuned on the norm discovery outputs from NORMSAGE.
As norms are subjective in nature and language models have the risk of hallucination in their output predictions, we further safeguard norm discovery with grd(.) operator for determining whether the hypothesized norm discovery can be groundable to its situation premise. We reformulate the explain- able NLI setting (Camburu et al.. 2018; Huang et al., 2022, 2023) for our new conversation-bant based norm grounding task, and devise the following nat- ural language template: "Explain whether the con- versation <entails, contradicts, or irrelevant with> the given norm". The output from grd(-) consists of the grounding verdict, Gy. along with the explanation, Ge- We get the grounding rele- vance score by normalizing the token probability being either "entail" or "contradict" in Gy over the sum of probability of generating "entail", "contra- dict", "irrelevant". Then, we filter out the norm candidates with grounding score below tunable threshold, 7 0.6.
Metrics We proposed to evaluate norm discov- ery from conversations along the dimensions of relevance, well-formedness, correctness, insight- fulness, and relatableness, using Likert scale of ("awful") to 5 ("excellent"). Definitions and as- sessment guidelines of these metric categories are included in A.2. We also assess what percentage of norms discovered from the various methods are
Finally, when populating NormsKB with new norm discoveries, we disregard norms that dupli- cate existing ones in the norms library. We flag norms as duplication if their BERT(n) embeddings

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Relevance
Well-Formedness
Correctness
Insightfulness
Relatableness
Best
SOCIALCHEM,v
3,8
3.9
3.8
6.2%
NMT gen
3.1
3.9
3.5
3,4
3.7
0.0%
MICIv
3.3
3.1
3.6
3,4
3.2
7.1%
PMT pon
2.2
3.2
3.1
3.0
2.5
0.0%
T0ge
2.7
2.0
2.0
2.1
2.1
0.0%
NORMSAGE
3.9
2.8
4.I
3.5
3.6
51.3%
NORMSAGE"
4.5
4.6
4.2
70.4%
GPT--
3.0
2.8
2.8
2.8
3.6
25.5%
NORMSAGE.mi 3.8
4.2
3.7
3.4
4.0
11.9%
Table 2: Likert scale (1-5) ratings across five quality metrics and the percentage of norm discovery outputs considered as "best" for a given dialogue scenario, averaged over 100 data samples.
rated as best overall, for each dialogue scenario. More than one norm can be considered as "best".
for conversation reasoning and norm discovery.
4.1.1 Multi-Lingual Norm Discovery
Setting We crowdsource on Amazon Mechani- cal Turk for human assessment. Each HIT ("sub- mit" task) consists of a dialogue scenario, an as- sessment metric, and sets of norms representing the norms discovered from each de-identified gen- eration or retrieval approach. Following crowd- sourcing guidelines (Sheehan, 2018), we provide definitions and examples for each assessment met- ric. Workers undergo a qualification test ensuring a 295% HIT rate, comprehension of task instruc- tions, and native fluency in the conversation lan- guage. For assessing norms in cross-cultural (pair- wise) scenarios, ensure that the workers have basic familiarity with both cultures. Based these worker selection criteria, we assign each example distinct workers, and reject poor quality hits. Workers take 1-2 minutes per norm comparison task, and are rewarded a $15 hourly rate.
As case study of the multi-lingual task setting, investigate norm discovery performance Chi- nese conversations, since Chinese widely spo- ken and studied language separate from the west- ern language systems. As visualized in Fig 3, the norms discovered from Chinese conversations are high-quality in detail and correctness.
198799 IE 3169
Mi 98 25
to try and parsuade someone to da something, rather than force them.
S/BUS (t.
converstiions negotations, if onn wants to come agreement, both sides should compromise beneficil solution.
20
that strengthon mutual cooperation Commonic-ate
must
properly monage differences, and strice to promote cooperation. We should not fight
In the contest of conversations and negotattion,, anne agreement, both parties avoid making demands that the other libely
there hav been times confront-
results
no good impact get? What benefits don't see any benefits.
Results Table 2 shows our norm discovery in- trinsic evaluation results on English conversation scenarios. As we can see, our proposed norm dis- covery approach, NORMSAGE, outperforms base- lines across all dimensions when enhanced with either frame structured indicators. A major lim- itation of baseline approaches is poor portability to conversation domains. The performance of So- CIALCHEMyy and MIC shows that simply re- trieving pre-annotated norms results in the norms being less relevant and insightful for new conver- sations. Compared to the retrieval baselines, the generation baselines, NMT gen and PMT gen- per- form even worse. This suggests that the domain gap in situation context between curated Reddit post headers (previous approaches) and in-situ conversa- greater bottleneck for norm discovery here. NORMSAGE overcomes challenges in domain portability through opera- tionalizing zero-shot language model prompting
Figure Example illustration of NORMSAGE norm discovery results on Chinese conversations, demonstrat- ing our method's multi-lingual capability beyond En- glish data.
We also perform investigation the qual- ity of norms discovered from Chinese conversa- tions compared to norms discovered from English conversations. We measure the stand-alone cor- rectness and insightfulness of norms discovered from multi-lingual setting, on a 1-5 Likert scale. The results in Table 4 indicate that NormSage exhibits relative consistency in norm discovery across both English and Chinese conversations. with A <9.5% observed in terms of relevance and correctness. Interestingly, norms discovered from English data receive slightly higher rating for in- sightfulness (59.5%) but lower ratings for relat- ableness ($2.1%) potentially due to the dialogue
tions (current task) poses

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Sulture-Specific Norm Discoveries
Source
In India, it is considered polite to always offer food and drink to guests, even if they decline." each other until wedding day."
In Pakistani culture, is uncommon for the bride and groom
CK
0
Taiwanese culture, it more common have heavier lunch, such as rice vegetables.
FOB
In British culture, it is normal for the bride and groom to meet cach other before wedding."
CK
In American culture, is common to have a light lunch, such as salad or sandwich."
0
In America,
common just let guests deeline if they don't want anything."
FOB
Table 3:
Visualization of some examples in culture- speuure-specifie norm discovery. We denote the pairs of contrasting norms across cultures with special symbols (o, o,
nature. The English sources involve movies and shows, which tend to be more creative (insightful) and less formal (biased dialogues may lower norm correctness). Finally, prompting-basee based method for norm discovery from conversations plied other languages that the large language model backbone, GPT-3, has been pretrained on.
Table 5, indicate that the culture-specific norms dis- covered from NORMSAGE" are promising, with human annotators achieving 80% identification accuracy. The inter-annotator agreement for the illture-identfficattinn task, measured by Fleiss Kappa, is 60.8%, which signals moderately good agreement. Some of the error cases in culture com- parison of norm discoveries may be due to subjec- tivity of the assessment task (for example, norms about whether the Caucasian American or African American cultural group is more likely to discuss controversial topic).
be ap-
CN Conv. Norm
A% w. Conv. Norm
Relevance
4.3
-.44
Well-Formedness
4.8
+6.6
Correctness
4.6
0.0
Insightfulness
3.8
-9.5
Relatableness 4.8
+2.1
4.2 Extrinsic Evaluation on Norm Correctness Self- Verificciin
Table 4: Likert-scale (1-5) rating of norms discovered from Chinese (CN) conversation, and comparison with norms discovered from English (EN) data.
Baselines We compare cor())-NORMSAGE, the mechanism which checks for correctness in proposed norm discovery approach, with:
4.1.2 Culture-Specific Norm Discovery To evaluate the correctness of culture- speeure- norms, we design a pairwise culture comparison setting. Specifically, we run a pretrained BART- LARGE-MNLI model (Lewis et al., 2020; Williams et al., 2018) on pairs of norm from different cul- tures, and randomly select 10 pairs of norm that are determined "contradiction" with each other, for each of the cross-culture scenarios in our dataset (e.g, Fresh off the Boat, Outsourced, etc.). Then, we mask the culture identities in the pairs of norm, and ask human annotators familiar with both cul- tures to identity which culture each norm belongs to from binary options. The results, shown in
CUEM- We finetune a BART-LARGE model (Lewis et al., 2020) to per- form regression and predict anticipated agree- ment scores in the SOCIAL-CHEM- 101 dataset (Forbes et al., 2020), which reports 1-5 scale how much people agree with the rules-of-thumb. TOppp: For prompt template, we follow the same principles as for vor(-)NNOMSAGE to first intro- duce the norm and then ask a directed question about its correctness.
For reproducibility in this classification task, we temperature to 0 during prompting generation.
Norm correctness verification is two- class classification problem. We measure perfor- mance in terms of classification accuracy (Acc) and area under the ROC-curve (AUC). We also assess the textual explanations of correctness predictions through human rating on a 1-5 Likert scale.
Metrics
Culture Comparison
Source
Id
Rating
American vs Taiwanese
FOR
74
American Indian
0
82
4.0
Western vs Muslim
African American vs
Caucasian American
73
4.0
Data Setting We randomly select set of norms discovered from NoRRSSSE and ask crowd workers (paid $15/hr) to provide correctness labels and explanations. We keep the data in which the correctness label has been agreed upon by at least
Average
80
4.1
Table 5: We evaluate culture-specific norm discovery via culture identification accuracy (%) between pairs of norms and also Likert scale rating (1-5) on the norms.

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three annotators. In total, we derive a set of 100 norms that are class-balanced with half "correct" and half "incorrect" labels.
prompt template here, we follow the same princi- ples as for )rd(3--NOM introduce the di- alogue context and norm, and question about its ad- herence/violation relevance. We also evaluate train- ing a BART-LARGE model on the grounding outputs of NORMSAGE, as (yd(()-NoRMSAC Emini. Simi- lar to Sec 4.2, we remove all model randomness.
Results Table 6 shows the results of norm cor- rectness verification. The zero-shot prompting ap- proaches, -oORMSSE and pppp. achieve the best performance for this task. Interestingly, we observe that p0pp than AG. We believe this may be due to the fact that the norms have been generated by the same underlying GPT-3 language model backbone now used to verify the data. As a result, the language model may tend associate the se- mantic content or linguistic artifacts in the norm as correct. However, cor())-NoRMSAN remains a competitive method for verifying norm correct- ness and has the added advantage of generating high-quality explanations for its predictions, en- abling transparent reasoning. In contrast, T0pp prompting cannot generate reasonable explanation norm correctness beyond circular reasoning. Fi- nally, even though MART-SOCIALCHEM-A. is finetuned on SOCIAL-CHEN 101 "agreement" an- notations, it does not perform as well in our norm correctness task. This is likely due to the lack of culture-specific rules-of-thumb annotated in the SOCIAL-CHEM-100 dataset to train a robust cor- rectness verification model.
pp has a higher Acc and AUC score
Metrics Similar to the norm correctness task, we evaluate norm grounding in terms of classifica- tion accuracy (Acc) and area under the ROC-curve (AUC). For both metrics, we consider a two-class scenario to determine whether norm is relevant (either entailed or contradicted) or irrelevant to di- alogue. We also consider finer-grained three-class scenario on whether the norm is entailed, contra- dicted, or irrelevant. As one speaker may violate a norm while another points out adherence issues, we evaluate three-class norm grounding conditioned on speaker localization (i.e., for the dialogue of a given speaker), if speaker identity is available. Fi- nally, use human assessment to evaluate ground- ing prediction explanations on 1-5 Likert scale.
Data Setting We randomly sampled dialogue chunks and norm candidates to create set of 100 data annotations with baass-balaneed <contradict, irrelevant, entail> labels and corresponding planations. The annotations include speaker iden- tities for dialogues with known speakers. During three-class inference, we consider two data input variants for each method, with and without speaker localization, to investigate whether deeper differ- entiating of who violates and who adheres to a norm helps. Speaker localization is incorporated into NLI classification models by using only the dialogue lines from the speaker of interest as input. incorporated into the prompting- -hased grd(-)- NORMSAGE and T0pp approaches via a question in the prompt template, "Explain whether what's ss spoken by [speaker ID] <entails, contradicts, is irrelevant with the norm". We report three-class grounding results utilizing the data setting, with or without speaker localization, that maximizes each model's performance and clarity the details next.
Acc
AUC
Expl
SAAT-SSOIILCHEM--E
75
78.0
N/A
T0pp
82
92.0
1.0
car(. NNORMSAGE
81
85.3
4.2
Table Classification results (%) and Likert rating (1-
5) on the generated explanations for norm correctness.
4.3 Extrinsic Evaluation on Norm Grounding Norm grounding is utilized in the serffieriiication process of our norm discovery, as grd(.)- NORMSAGE. This subtask extends to identifying norm adherence and violation in conversations, and stands important downstream application.
with existing NLI-based approaches, including BART- MNLI, a BART-LARGE model (Lewis et al., 2020) pretrained on Multi-genre NLI (Williams et al., 2018); BART-DIALNLI, a BART-LARGE pre- trained on Dialogue NLI (Welleck et al., 2018); and T5-eSNLII a T5 (Raffel al., 2020) pretrained on explainable NLI (Camburu et al., 2018). We fur- ther consider TO pp. introduced in Sec 4.1. For its
Baselines
We compare grd(
Results Speaker localization does not bene- fit NLI classification models for two-class norm grounding, possibly due to domain shift in seman- tics after isolating speaker's dialogue, but it plays crucial role in prompting-bas based approaches for three-class norm grounding. particular, grd(-)- NORMSAGE significantly outperforms all other

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Dialogue Situation
Discovered Norms
Grounding Explanation
Label
Dave: No. Defimitlly booked.
Mr. Khan listening
Mr. Khan: What?! Do know who I amm? Hello! Mr Khan, community
It's important listen others and give them chance to speak.
leader! Next President of Sparkhill Pakistani Business Association!
Dave and he is not giving Dave a chance speak
-1
Dave: I'm sorry
Mr. Khan: Right, that's it want to speak to the proper manager.
Dave: I am the property manager.
Beckett: Sure I can, until jury tells me otherwise.
Creason: You are wasting my time. Detective, look, T told you exactly what I was doing last night.
is generally considered impolite make lewd comments.
What's spoken by Creason is irrele- vant with the norm.
0
Beckett: Right. You were at the club. They said that you made quite the entrance [...]
Jessica: L..J Well, those kids, they just don't know, that's all. It just it just take time to gel used to something different.
Eddie: hate here! want to go back to D.C.
sidered polite try make best of a situation, if you do not like it
It also con-
The mother is trying to make the best of the situation though she does not like it
Jessica: Eddie, that's not possible. We are here now. We have to make the best of it Like am doing with this neighbor woman. You think like pretending Samantha isn't carrying haggie dog poops
in her hand? No! don't t like this Bu. am trying!
Table 7: Norm grounding example results, randomly sampled for each class from f-1: Contradict, O: Irrelevant, 1: Email]. We underline the utterance-level provenance of the input, where entailment or contradiction is found.
baselines in norm grounding, as shown in Table 8. For three-class norm grounding, it achieves ac- curacies of 68% without and 80% with speaker localization. The findings suggest that speaker localization can allow for fairer evaluation ac- counting for differences in norm grounding across speakers, and help models reason over the perspec- tive and context of speaker. We further highlight that grd(() (rr(())-NOMMSGGE generates easy-to-follow and insightful norm grounding explanations that are preferred over numan-written ones in around 40% of cases. Examples of the grounding and explanation of norms discovered from conversa- tions are visualized in Table 7. It is worth noting that even though the dialogue inputs may contain improper grammar, involving noisy or informally styled textual dialogue, this is exactly the nature of real-world conversations which makes our novel norm discovery on-the-fly task setting introduced unique and challenging. Despite the noisy nature of dialogue inputs, Table shows that our large language model prompting mechanism can still ef- fectively reason over the <entailment, irrelevance, contradiction> nature of grounding between a given dialogue input and given norm with proper expla- nations.
(2-Clnss)
(3-Clnss)
ACC
AUC
ACC
AUC
Expl
BART MNLI TS-eSNLI
36
31.5
32
27.3
N/A
50
22.9
20.0
1.92
BART-DIALNC DIALNLI
67
70.1
44,9
N/A
T0
67
94.2
29
34.1
1.3
grdl J.NOON
69
NORM-SAE
79
91.6
80
92.7
4.1
Table 8: Classification results (%) on norm grounding. along with rating (1-5) on the generated explanations.
onds per dialogue, and performs norm grounding at a rate of 3.8 seconds per dialogue. This process is done through OpenAI API, which may fluctuate slightly due to internet speed, but has the benefit of requiring no GPU usage from the researcher's end. The NORMSAGE norm discovery process is over 10x faster the human annotation efforts. The cost of OpenAI GPT-3 API access currently $0.06 per 1 tokens, which still less expensive than human annotation efforts. While quality may be factor of difference, we showed that NORMSAGEmin a variant of the proposed approach trained on silver- standard NORMS AGE outputs, perform competi- tively for norm discovery and verification tasks.
Related Work
The domain of norms is closely related to behav- ioral psychology and moral judgement. Early stud- ies investigated the pragmatic cooperative princi- ples (Grice, 1975), politeness implicatures (Kallia, 2004), and relationship between norms and law (Posner, 2009) governing human behavior. As
4.4 Resource Contribution
We discovered over 20,500 unique norms, of which 1,250 are culture-speciffi On average, NORMSAGE discovers norms at a rate of 8.6 sec-

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judgements of behavior are communicated through linguistics, (Graham et al., 2009) introduced lex- icon of evocative words based on moral founda- tion theory, which later attempts utilize for pre- dicting the moral value from text messages (Lin et al., 2018; Mooijman et al., 2018). Recent ap- proaches explore modeling moral and ethical judge- ment of real-life anecdotes from Reddit (Emelin et al., 2021; Sap et al., 2019a; Lourie et al., 2021; Botzer al., 2022), with DELPHI (Jiang et al., 2021a) unifying the moral judgement prediction on these related benchmarks. Related is another line of work modeling legal judgement on judicial corpora (Chalkidis et al., 2022).
technical perspective, our norm discovery approach based on language model prompting and knowl- edge elicitation can be seen as a form of prompt engineering (Le Scao and Rush, 2021), where we prefix a question with an elaborated scene, with the underlying core intuition based on leverag- ing the zero-shot question answering capability of language models (Gangi Reddy et al., 2022). The norm grounding with explanation task is intu- itively similar to the explainable natural language inference problem setting (Welleck et al., 201 2018; Wiegreffe et al., 2021). Our proposed framework, NORMSAGE, achieves norm discovery and ground- ing without intensive prompt-tuning (Jiang et al.. 202 lb) or finetuming (Forbes et al., 2020; Ziems et al., 2022).
Norm discovery is a unique, emerging task, which aims to catalogue the underlying principles behind behavioral judgements, and can be seen as similar to distilling reactions, explanations, and im- plications from situations (Vu al., 2014; Ding and Riloff, 2016; Rashkin al., 2018: Sap al., 2019b). Separate from explicit information ex- traction (Wen al., 2021), such as the extraction of events/eniitfessrllatioss which may tend to be overly granular and situation-specific, norm discov- ery involves inferring the underlying stand-alone abstracted social or cultural rules. Towards this end, Forbes et al. (2020); Ziems et al. (2022) are the main existing norm discovery approaches. Each presents a large-scale catalogue of manually cu- rated rule-of-thumbs from Reddit post headers, and trains a language model to generate rule-of-thumble based on this data. In contrast, our work focuses on norm discovery from conversations on-the-fly and without needing manual curation.
6 Conclusions
We introduced new NLP paradigm of guid- ing cross-culture communication with on-the-fly sociocultural norm discovery, violation de- tection, and explanations. This conversation- based norm discovery and grounding problem goes beyond the US-centric data predominent in prior work. To address the new challenges, we present framework called NORMSAGE that lever- ages knowledge elicitation from large language model (LM) prompting and incorporates novel self- verification mechanisms. Our approach surpasses baselines by improving the discovery of dialogue norms across diverse social and cultural groups, including multi-lingual conversations and culture- specific norms, while providing natural language explanations for interpretable norm discovery and violation detection. Our work has broad impacts empowers the discovery of norms which may differ from each other across cultures, thus en- abling individuals to navigate communication bar- riers more effectively and find common grounds that avoid norm violations.
Modeling the social and moral dynamics in hu- man interaction and communication have diverse applications. such the detection of cyberbullying (Van Hee et al., 2015) and hate speech (Mathew al., 2021), detoxification (Han et al., 2023), debiasing (Yu al., 2023; Omrani et al., 2023; Yang et al., 2023), model alignment (Ouyang et al.. 2022; Dong et al., 2023), bipartisan news framing (Fulgoni et al., 2016), social media understanding (Sun et al., 2023), emotions analysis (Zadeh et al., 2018; Yu et al., 2020), and situational report gen- cration (Reddy al., 2023), amongst other inter- disciplinary human-centered NLP applications (Li et al., 2023a). In particular, discovering norms es- sential for explicitly detecting norm adherence and violations instances (our work), as well as implic- itly guiding dialogues (Ziems et al., 2022). From a
Limitations
Our norm discovery process makes use of the GPT- from OpenAI² as a strong pre-trained language model to elicit groundable knowledge about the rules and judgements of acceptable behavior from human dialogue interactions. We recognize that norms may shift with context over time. Our dis- covery of norms applies to the time period that aligns with the conversation scenario in which a
compappp

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norm is discovered from. While the newer GPT- 4 has now been released, we chose stick with GPT-3 our experiments due to the tight rate limit of GPT-4, currently capped at 25 request calls ev- ery 3 hours. We further point out that the GPT-3 model acquired its implicit knowledge from ultra large-scale data, and has added in mechanisms to address bias (Solaiman and Dennison, 2021). Nev- ertheless, all computational models still come with a risk of potential bias. For example, the behavior of some closed source models, such as ChatGPT, is not always guaranteed to be consistent over time (Chen et al., 2023). Moreover, explanations gen- erated by language models may not always entail the models' predictions nor be factually grounded in the input (Ye and Durrett, 2022). We encourage researchers and practitioners to exercise caution and check-guards in their endeavors.
Risks and Mitigations Our task involves source data that may contain explicit conversations about race, gender, religion, etc. We recognize the emotional burden that this presents to annotators (Roberts, 2016). In mitigation, we include the fol- lowing content warning in the header of each task: This HIT may contain text that disturbs some work- ers. If at any point you do not feel comfortable, please feel free to skip the HIT take a break. The study has been thoroughly reviewed and ap- proved by a national level internal review board. The resources and findings presented in this work are intended for research purposes only. To en- sure proper, rather than malicious, application of dual-use technology, we require users of our norm discovery data to complete Data Usage Agree- ment that link in project repository. We also intend to make our software available as open source for public auditing, and explore measures to protect vulnerable groups.
Ethical Considerations
A Acknowledgement
We recognize that the automatic generation of norms and judgements could be seen as normative and authoritative (Talat et al., 2021; ; Ziems et al., 2022). Here, we emphasize that the discovered norms are not intended to form a global and univer- sally binding ethical system, but rather to provide a set of discrete intuitions and principles to help differentially explain the underlying assumptions that exist latently. The present work supports an ex- plainable system to automatically discover norms from conversations on-the on-the-fly, fly,, and verify whether the discovered norms can be sufficiently grounded to the data source, as well as the (entail vs. contra- dict) relation characteristic. Currently, we do not specifically distinguish between different levels in the cultural hierarchy (such as the relationship be- tween Cantonese and Chinese culture), and resort to cultural references in the source data. It is also important to note that some automatically discov- ered norms might be viewed as stereotypical. Our transparent and flexible system should be seen as human-Ar AI collaboration tool that domain experts interface with, facilitating the moderation efforts.
This research is based upon work supported by U.S. DARPA CCU Program No. HR00112220034, The opinions, views and conclusions contained herein are those of the authors and should not be inter- preted as necessarily representing the official poli- cies, either expressed or implied, of DARPA or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copy- right annotation therein.
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A
Appendix
A.1
Speaker Profiling and Cultural Indicator Extraction for Norm Discovery with Frames
As discussed in Sec 3, profiling speakers with cultural indicators help guide better structure and

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culture-specific insights in norm discovery from conversations on-the-fly. The prompting procedure is intuitive with the presentation of the dialogue content and a directed question describing the task, similar to the prompting logic we introduced for conversation-basee norm discovery, norm correct- ness verification, and relevance grounding. The specific prompt template used for cultural indicator extraction is:
Insightfulness: does the norm convey enlight- ening understanding about what's 's considered ac- ceptable and standard the society that pertain to the conversation scenario.
Relatableness: how well does the norm balance vagueness against specificity, so that it can gen- eralize across multiple situations without being too specific.
We also provide good and bad examples of norms discovered, according to each of the metric dimen- sions. Consider a dialogue instance of "I think we should divide the project tasks based on everyone's 's expertise and skills. It will ensure better efficiency and quality". We list good and bad examples of social norms with respect to the dialogue scenario as follows.
Context: (X&)
For each person, extract the country- or state- level culture they are affiliated (in a if the information is avail- able, and skip the person if the informa- tion is not available:
Relevant social norm: "It's important to play peo- ple's 's strengths utilize their individual knowledge to deliver the best results." "
For instance, the imput for a dialogue snippet from "Outsourced" comes with a metadata description of summary from its Wikipedia page, containing text content such as:
Irrelevant social norm: "It is good to earn money and retire carly."
Well-formed social norm: "It good to utilize play to people's 's strength and deliver the best results".
"Todd Anderson (Josh Hamilton). a salesman for a Seattle novelty products company, learns he has to travel to India when his department is outsourced. Todd is not happy but when his boss Dave in- forms him that quitting would mean los- ing his stock options, he goes to train his Indian replacement Puro (Asif Basra).",
Poor-formed social norm: "Deliver the hest results."
Correct social norm: "Assigning tasks based on indi-
vidual expertise and skills leads better project outcomes".
Incorrect social norm: "Assigning tasks based the alphahetical order of employees" last names leads to hetter project outcomes".
Insightful social norm: "Assigning tusks based on indi- vidual expertise not only maximizes efficiency and quality but also fosters sense of ownership, motivation, and col- laboration among team members."
which we feed as value into {Xa} in the prompt template. The output of this cultural indicator ex- traction is "Todd Anderson: American" and "Puro: Indian" in separate lines, which follows consis- tent textual pattern that is easy to parse and is then plugged into the AGE input for norm discovery with framing.
Non-insightful social norm: is nice to deliver good
results."
Relatable social norm: "Assigning tasks based on each team member's proficiency in relevant software tools (such as Excel for data analysis and presentation task) can lead to better project outcomes."
A.2 Norm Discovery Intrinsic Evaluation
Non-relatable social norm: "Assigning tasks based each team memher's proficiency in the specific Excel software tool leads better project outcomes".
A.2.1
Assessment Metric Guidelines
We provide definitions of each intrinsic metric to the crowdsourced annotation workers.
We ask annotators to rate social norms discov- ered from conversations on 1-5 Likert scale, in which the spectrum of scoring guidelines illus- trated as:
Relevance: is the norm inspired from the situa- tion (lower bound on norm applicability).
Fell-doormd:sss:: how well is the norm struc- tured is the norm self-contained, and does it include both judgment of acceptability or oc- currence, and an action societal/cultural phe- nomena that is assessed.
Dnuiided
SwiMd
Saishl
Saided
Correctness: to the best of their knowledge, do people agree that the described norm holds true?


Reviews:
[{"review_id": "uIGrwtlivo", "reviewer": "Reviewer_6CHs", "summary": "", "questions": "1. What is your positioning for your articles? A tech paper, resource paper or position paper?", "limitations": "", "rating": 3, "confidence": 5, "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 Reviewer 6CHs for the review. We appreciate the reviewer's recognition of our paper in tackling a useful/promising task domain, and also its well-writteness with detailed examples and resources for future work to explore further upon. We address the questions and feedback raised in the review as follows, and hope they clarify potential misconceptions or concerns of our work.\n\n> **I.** On the applicability of previous multilingual information extraction (IE) work and tools for norm discovery\n\nNorms are often implicitly mentioned, so they need to be inferred from the entire dialogue discourse. Most IE work, on the other hand, focuses on explicitly extracting specific pieces of information (e.g., entities, events, relations, etc.) that are often too granular and situation-specific to represent a stand-alone abstracted social or cultural norm. In reference to the Figure 1 input dialogue as example, IE may parse out entities such as “I” (the speaker) and “xiaolongbao”, and events such as “threw”. Yet, the norm discovery task aims to identify the underlying social or cultural rules, such as “It is good to be honest with your family”, which are often not readily apparent from the results of explicit information extraction. Due to this reason, we did not include information extraction work into our current set of baselines for norm discovery. But if there is a particular IE tool that you strongly recommend, we can look into incorporating it for our camera-ready version.  \t\t \t \t \t\t\n\n> **II.** Clarification on how NormSAGE helps solve multi-cultural challenges\n\nWe motivate in the Introduction and Task Formulation sections that knowledge of norms in general, and sociocultural norms in particular, is essential if we are to equip AI systems with capability to understand and reason about acceptable behavior in communication and interaction across cultures. We concretize this objective in the Experiment and Results section through visualization of culture-specific norms discovered, and further highlight the norms that contrast each other across specific cultures (please refer to Table 6). For example, in Pakistani culture it is not uncommon for the bride and groom to not meet each other until the wedding day, whereas in British culture, it is normal for the bride and groom to meet each other before the wedding. Heated events revolving around this cultural clash actually occur in “Citizen Khan” (a TV show about a British Pakistani family), and we extract this contrasting pair of norms from the source conversation data. An important impact of our work is that discovering such norms can help humans better navigate communication challenges and find common grounds that avoid norm violations. We demonstrate this key concept through effective empirical performances of grounding (i.e., identifying the <entail,contradict,irrelevant> relation between a given norm and a dialogue input) in Sec 4.3. We will make these points clearer with the extra page allotment for the camera-ready version.\n\n> **III.** Framework Novelty and Contribution\n\nThe technical contribution of our NormSage framework lies in the fundamental design philosophy of leveraging correctness and relevance verification for enabling norm discovery on-the-fly and addressing the important LM hallucination problem. We empirically show that our two-step verification mechanism proposed in NormSage improves the intrinsic property of discovered norms by over 14% in relevance and correctness (on an absolute scale), through simple yet effectively designed prompting mechanisms. Our study findings reveal important insights that large language models are capable of performing knowledge elicitation and self-verification to effectively reason over conversation scenarios across cultures and languages, discover underlying norms insightful for facilitating harmonious human interactions, and also detect norm violations with grounded explanations.\n\n> **IV.** Choice of Baselines and Model Size\n\nWe would like to address the concern about competitive baselines. Our current choice of baseline models were selected based on either domain relevance (e.g., LM variants trained or based on SocialChem and Moral Integrity Corpus) or their established/published zero-shot performance across NLP tasks (e.g., the T5-based T0pp), while bearing in mind to keep methods that are runnable and accessible on a typical CPU machine or GPU that is V100 or less in size. It is thus true that the baselines in Table 2, such as the pMT model adapted from GPT-2, are smaller than GPT-3 in parameter size. However, we clarify that the fundamental design of our NormSage framework in combining initial norm discovery with correctness and grounding self-verification mechanisms can be extended beyond its current GPT-3 backbone to other powerful large language model backbones, such as LLaMA-2 70B, as well as compact language models finetuned on prediction data from NormSage (i.e., the NormSage_mini method in our paper).\n\n> **V.** Applying NormKB for downstream tasks\n\nWe agree that we can improve the submission through additional illustration of how to use NormKB for downstream tasks. However, we believe that our current experimental setup and findings in the primary task of norm discovery and the subsidiary tasks of norm correctness and grounding verification, which construct the NormKB, deserve to be considered already as important milestones for knowledge acquisition in socially and culturally important norms that guide human-human interactions. We also point out that the grounding of norms to a dialogue input to identify entailment/contradiction/irrelevance relationship (Sec 4.3) extends to identifying the adherence or violation occurrence of any norm in the NormsKB to a particular conversation. We plan to elaborate this further with clearer examples and explanations in the next version of our paper. For future research directions, we believe it may be meaningful to explore leveraging NormsKB as a data resource for pretraining large language models to be better equipped with commonsense knowledge that aligns with human preferences.\n\n========\n\n> Question regarding the positioning of our paper\n\nOverall, our submission is designed as a research paper with primary contributions in: i)  presenting an important new task of conversation-based multilingual and multicultural norm discovery, which is useful for guiding harmonious human communication interactions; and ii)  proposing a technical approach for tackling challenges involved in the task (i.e., the prompt-based framework, NormSage, with novel self-verification mechanisms and designs supporting explainability).  Our technical contributions also include the setup of comprehensive evaluation objectives (e.g., norm correctness, pairwise culture comparison identification, etc.) to benchmark language model performance for the new task. We point out that these involve insightful discussions, such as the difference between 2-class and 3-class grounding classification in terms of design considerations and model performance observations (lines 446-455, 486-492). Beyond technical contributions, we also present NormsKB as a useful resource for future research work, thought this aspect is positioned as more of a ramification impact from our work worthwhile to point out.", "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. A promising track. Automatic norm discovery in the multi-lingual multi-cultural context is useful for those cross-cultural conversations.\n\n2. A large-scale Norm knowledge base has been proposed for further exploration.\n\n3. Overall writing is in good format and easy to follow. Detailed baseline code and data samples are provided for robustness demonstration.", "reasons_to_reject": "1. My main concern is about the contributions: (1) Although this paper claimed their first trial on multilingual and multi-cultural norm discovery in multi-turn conversations, some previous multi-lingual information extraction work and tools can be easily applied here. Besides, I cannot find any specific design in NormSage to resolve the multi-cultural challenges (Please correct me if I'm wrong). It seems that this framework can be applied to other tasks? (2) NormSage is a reasonable pipeline framework to prompt GPT-3 to automatically discover and verify norms. Nevertheless, the contribution of a framework with several simple prompts is not sufficient for a technical paper.\n\n2. NormSage is a GPT-3 based framework; however, most baselines in Table 2 are much smaller than GPT-3, such as GPT-2. That's not enough to demonstrate the advantages of NormSage. More competitive baseline models should be incorporated. The same problem occurs for Table 7 regarding the comparison between NormSage (GPT-3 based) and BART or T5-based models.\n\n3. Lack of illustration about how to use the NormKB for downstream tasks.", "questions_to_authors": "1. What is your positioning for your articles? A tech paper, resource paper or position paper?", "paper_topic_and_main_contributions": "This paper proposed a new framework, NormSage, to automatically extract culture-specific norms from multi-lingual conversations. Specifically, NormSage uses GPT-3 prompting to extract norms directly from conversations and provides explainable self-verification to ensure correctness and relevance.", "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": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}, {"review_id": "J4qQexF7Kl", "reviewer": "Reviewer_HuvK", "summary": "", "questions": "", "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 Reviewer HuvK for the review. We are glad that the reviewer found our paper well-motivated in relation to the diverse related work in the field, and well-documented in experimental details (ranging from method implementation and data setting for reproducibility, to the human evaluation process). In response to questions and feedback raised in the review, we address them as follows and hope our response clarify possible misconceptions or concerns you may have.\n\n> **I.** Our Research Questions/Objectives and whether Results Address Them\n\nOur work focuses on addressing the core research questions of i) how can norms insightful for guiding human-human interaction be effectively extracted from diverse conversational sources across different cultures and in different languages; and ii) can we validate the norms discovered within conversational contexts in order to improve robustness against hallucination in language model generation, while also considering cultural and social nuances.\n\nWe address these research questions through NormSage, our proposed framework for norm discovery with self-verification. Specifically, with regards to the first research question on extrinsic norm discovery, we show in Sec 4.1 and Table 2 that our method discovers significantly more relevant and insightful norms for conversations on-the-fly (selected as \"best\" ≥70% cases) compared to baselines such as NMT and pMT developed over Reddit-based rules-of-thumb annotations. Additionally, the norms discovered from Chinese conversation exhibit limited performance differences compared to the norms discovered from English conversation in terms of relevance and correctness (∆ ≤ 5%), as presented in lines 332-334 of Sec 4.1 results. We further show in Sec 4.1.1 that the culture-specific norms are also promising in quality, with humans being able to identify the culture from these norms at 80% accuracy. \n\nWith regards to the second research question on norm reliability verification, we demonstrate through correctness prediction experiments in Sec 4.2 and grounding prediction experiments in Sec 4.3 the superiority of NormSage over pre-existing baselines. In particular, NormSage achieves an AUC of 94.6% in three-class classification for grounding, with generated explanations matching human-written quality.\n\n> **II.** Threshold Selection for the filtering process of NormSage in Correctness Verification and Grounding Classification\n\nWe randomly selected a small sample of ten data points that are held out separately from the test set used in Section 4.2-4.3 evaluation, and searched at increments of 0.1 for the threshold value that is optimal for correctness and grounding filtering. Our small-scale threshold comparison results will be further detailed into our paper appendix, and the exact data sample used will be made public for reproducibility.\n\n> **III.** Claim about no Prior Work in Line 256\n\nWe are not aware of any prior work on norm discovery from multilingual conversations across cultures. We positioned our work with respect to other work on computational social norms in related work. We would welcome any references that rebut our claim. \n\n> **IV.** Presentation Structure of our Paper\n\nRegarding our paper’s structure, we clarify that the sole table/figure under the Methodology section (i.e., Fig 2) is to present a high-level motivational overview of our two versions of prompt templates for initial norm discovery, with and without framing structure, which we believed were a crucial component to understanding how the proposed dvr() mechanism in our NormSage framework operates. In contrast, the tables of visualizations under Experiments and Results (i.e., Tables 4,6) are designed to present more comprehensive coverage and details on the results of the primary norm discovery task and secondary verification tasks, which were not immediately crucial to the understanding of the core method proposed.\n\nIn particular, Table 4 under Experiment and Results is intended to illustrate various norms covering different topics that are discovered from different cultures, while further highlighting norms that contrast each other across certain cultures. Table 6 under Experiment and Results is intended to illustrate, through detailed examples, results grounding norms to dialogue situations, for each of the grounding classes <contradict, irrelevant, entail>, with utterance-level provenance underlined. Because these details are insightful for results analysis but not immediately crucial for understanding the grounding mechanism, which can be sufficiently described in detail by line 232-240 in Methods, we believe it is most reasonable to include this information under Experiment and Results as opposed to the Methodology section.\n\nFinally, lines 256-310 is a description of our choice of baseline methods for experimental comparison, which we believe is fitting to be included under the Experiment and Results, as the majority of other works do in EMNLP/ACL-related venues.  \n\nWe hope this explanation helps address your previous concern and clear potential misunderstanding for this aspect here.\n\n> **V.** Clarification on the line 382 statement of our norm correctness verification process\n\nIn Line 382, we wrote that “norm correctness verification is a two-class classification problem”. This statement falls in line with how we defined our norm correctness verification task in Sec 2.1, as a classification problem involving two classes with a norm being either correct or incorrect. Our intention in line 382 is to refresh the reader’s memory of the task when we lay out the evaluation metrics applicable to the task in the Sec 4.2 experiments on norm correctness verification.  \t\t\t\t\t\n\n> **VI.** Clarification about Potential Grammar/Correctness Issues in Table 6 and the effect on Results\n\nWe acknowledge that the dialogue inputs shown in the experimental results visualization of Table 6 may contain improper grammar. However, we point out that this is exactly the nature of real-world conversations, involving noisy or informally styled textual dialogue, which makes our novel norm discovery on-the-fly task setting introduced unique and challenging. We will explicitly clarify this in our paper. Moreover, despite the noisy nature of dialogue inputs, Table 6 shows that our large language model prompting mechanism can still effectively reason over the <entailment, irrelevance, contradiction> nature of grounding between a given dialogue input and a given norm with proper explanations.", "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": "The authors plan to release their data and code to the public, thereby aiding reproducibility.\n\nThe references seem adequate, relevant and recent enough.\n\nHuman annotators are involved in the evaluation of the work and some details of the process of hiring them are discussed.", "reasons_to_reject": "Although the paper mentions existing gaps in the field it covers and its contributions, it does not explicitly state its research question or objectives, making it hard to determine if the paper’s results answer its question.\n\nThe reason for the choices of the threshold of 0.7 and 0.6 for the correctness probability and grounding score, respectively, are not given.\n\nLine 256 makes a claim about no prior work without any reference or mentioning what study the authors carried out that informed this claim.\n\nThe structure of the paper seems a bit scattered – where there are several Result paragraphs under different sections, resulting in tables of results being mixed with tables of methods. Also, line 256 to 310, which one would expect under a Methodology section, is under Evaluation and Results.\n\nLine 382 makes a statement about norm correctness without any reference or justification.\n\nSome of the examples provided (e.g. Table 6) have grammar/correctness issues and it is unclear if this may have affected the results as presented.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper introduces NORMSAGE to automatically extract culture-specific norms from multi-lingual conversations, though in reality it is bilingual conversations the authors addressed. They also provide relevance verification that can be extended to assess the adherence and violation of any norm with respect to a conversation on-the-fly, along with textual explanation.", "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": "The work focused on two languages (bilingual) instead of multilingual, as claimed in the paper.\n\nGrammar check and proof-reading required. For example line 36 “cability”, line 357, Fleiss not “Fless”.\n\nSome of the in-text references are not adequately formatted, e.g. line 53."}}, {"review_id": "yxkCBNHmBI", "reviewer": "Reviewer_sQGj", "summary": "", "questions": "Line 250: How do you get the BERT embedding of a norm? (Embeddings are usually at the token-level rather than the phrase- or sentence-level). What is a BERT(n) embedding? ", "limitations": "", "rating": 4, "confidence": 3, "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 Reviewer sQGj for the positive review and thoughtful suggestion. We are pleased that the reviewer found our paper exciting in its approach to tackling an important research domain, supported by strong experimental results, compelling discussions/findings, and clear visualizations. We address the questions and feedback in the review as follows, and hope they clarify potential concerns.\n\n> **I.** Closed Source Model vs Open Source Model in the Framework Backbone\n\nWe understand the reviewer’s concern that the current usage of the closed source GPT-3 model as backbone in our NormSage framework may involve a certain degree of limitation in reasoning transparency and predictability. We will enrich our paper with discussion about this, and include the relevant references you suggested. Moreover, with the recent advent of open-source language model alternatives that have also been finetuned to align with human feedback, such as Llama-2, we can add experimental results utilizing these open-sourced models as backbone in our NormSage framework for our camera-ready version, and we hope other research groups can further experiment with various models as NormSage’s backbone. \n \n> **II.** The current size of TV show selection as source of data input for cultural norm discovery\n\nThe reviewer’s note that our norm discovery framework, NormSage, should be extended in future work to include a  broader selection of TV shows as source of input data to enable more sufficient reflection of an entire culture’s norm \naligns indeed with our vision for the future directions built upon this work. For the present paper submission, \twe do acknowledge in the Limitations section (lines 621-626) that the discovered norms are not intended to form a global and universally binding ethical system, but rather to provide a set of discrete intuitions and principles to help differentially explain the underlying assumptions that exist latently in human-human interaction.  \n\n\n========\n\nFinally, in regards to how the BERT embedding for a norm is computed for similarity comparison, we derive the embedding pooled across the tokens in a text input through a *means()* operation. The current implementation in the transformer library for BERT models computes this pooled embedding, across the sentence-level, as one of its options. Our code is included, and the full software will be released to the rest of the NLP community as well upon paper publication.\n\n> from transformers import AutoTokenizer, AutoModel\n>\n\n> tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n> \n> model = AutoModel.from_pretrained(\"bert-base-uncased\").eval()\n>\n> \n> ex_s = \"It's important to communicate clearly and effectively.\"\n> \n> ex_s_toks = tokenizer(ex_s,return_tensors=\"pt\")\n> \n> embed = model(**ex_s_toks).pooler_output\n\n> print(embed.size())   # embedding shape is (1, 768) signaling that mean pooling occurred across the sentence-level", "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": "This paper is well-written and broadens research on social norms to additional perspectives beyond a predominantly white, U.S.-centric one. The experiments and findings are compelling and the results and examples are presented in a clear manner. ", "reasons_to_reject": "The authors use closed source models (e.g. GPT-3) that may be difficult to reproduce in future studies, as OpenAI models are often updated in unpredictable and non-transparent ways (e.g. “How is ChatGPT's behavior changing over time?” https://arxiv.org/abs/2307.09009). \n\nA (small) limitation of this paper is that it assumes that a few select TV shows are sufficient to reflect entire cultures’ norms. I say “small” limitation because the author’s approach for extracting norms could be applied to additional data by future work that builds upon this current contribution. \n", "questions_to_authors": "Line 250: How do you get the BERT embedding of a norm? (Embeddings are usually at the token-level rather than the phrase- or sentence-level). What is a BERT(n) embedding? ", "paper_topic_and_main_contributions": "The authors present a framework for automatically extracting social norms from English & Chinese conversation datasets. Their framework involves prompting GPT-3 to extract candidate norms, and then self-verify those norms. NORMSAGE outperforms other approaches at norm discovery, as determined by human evaluators in multiple experiments.  ", "excitement": 4, "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": {"Missing_References": "For your limitations section: The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning by Ye & Durrett NeurIPS 2022. "}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "Line 53: the citation is missing the year (2022). \n\nLine 138-139: “better representing the diverse socioethnic or demographic groups” -> “which can better represent diverse socioethnic or demographic groups” \n\nLine 178: “learnt” -> “learned” \n\nChange “Muslim vs. Western” in Table 3 to “Pakistani vs. British” as you say “British” in Table 4. The authors do say they define culture based on “cultural references in the source data,” so this change would also assume a Pakistani vs. British comparison captures the dimension that is most emphasized by the writers of that particular show. "}}]

Author rebuttal:

Meta-review: This is a very interesting paper that proposes an approach to automatically extract cultural norms from multilingual conversations. A strong reason to accept this is that this paper presents an effective and innovative solution to an under-explored, important problem which will broaden the NLP field by looking beyond the cultural norms of English-spoken western societies. 

While there were concerns about the novelty of the work, pointing out that previous work in IE can be applied here, authors addressed that concern in a convincing way that social norms cannot be described in a word or phrase which is what IE does. The concerns about the language models used and the narrow scope of the data are valid, but I think the authors' replies are sufficient. I would like to see additional experiments done with open-source large language models, as well as discussions about extracting social norms from other sources.

Overall, this is fascinating work which will lead to further research in this important topic.
Decision: Accept-Main