Identifying Distributional Perspectives from Colingual Groups (2021)

Abstract: Discrepancies exist among different cultures or languages. A lack of mutual understanding among different colingual groups about the perspectives on specific values or events may lead to uninformed decisions or biased opinions. Thus, automatically understanding the group perspectives can provide essential back-ground for many natural language processing tasks. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. Ona held out set of diverse topics, including marriage, corruption, democracy, etc., our model achieves high correlation with human judgements regarding intra-group values and inter-group differences

## introduction sociologists have defined culture as a set of shared understandings, herein called perspectives, adopted by the members of that culture @xcite . languages and cultures have radical correlations @xcite @xcite , because individuals communicate with each other by language, which carries the aspects of their cultures, experiences, beliefs, and values, thus will shape their perspectives. lacking of understanding for these perspective differences could lead to biased predictions. selection bias @xcite can often lead to misinformation as it sometimes ignores facts that do not reflect the entire population intended to be analyzed. for example, to verify a controversial statement like "the 1 a group of people that share the same language ( https://www.merriam-webster.com/dictionary/colingual ). ## task definition in this paper, we focus on predicting a group's distributional perspective towards a claim and identifying claims that reflect contrasting perspectives from different groups on a particular topic. we further focus on english, chinese and japanese as the targeted colingual groups. here, we define several key concepts and the task. we also explain why our task is different from stance detection. ## data preparation in this section, we describe the procedure of composing our training data from multi-lingual wikipedia articles. we then introduce an out-ofdomain test dataset retrieved from reddit that contain opinions regarding wide range of topics and the procedure of collecting human annotations on the test set. ## methodology in this section, we present the procedure of training our cluster model. we explain how to learn group perspective models for english, chinese, and japanese colingual groups. we then raise the issue of pattern bias in negative samples and provide our corresponding solution. lastly, we introduce the inference process. ## qualitative analysis while section 6 shows quantitative results and correlation values, we want to understand the advan- for each colingual group pair in {e-c, c-j, j-e} and a given topic, we report the visualization of 50 claim pairs in figure 2 and 3. here, each dot (or triangle) represents one of the 50 claims which are randomly selected from imho, with the x-y axis representing the {e-c, c-j, j-e} model predictions. the blue dots that fall along the diagonals are where the two models agree. on the contrary, dots that fall on the upper left or the lower right part are where the models do not agree with each other. for example, sentence 1 in figure 2 is closer to the chinese culture (upper left corner), while english speakers tend to agree more with sentence 2 (lower right corner). we select representative examples in each region and list them in the captions. first, from figure 2 we observe that the model pairs have zero or negative correlation on three topics: marriage, corruption and cuisine, suggesting that the corresponding language speakers take contrasting stances towards these topics. second, figure 3 shows that 1) the english and chinese speakers hold similar views on baseball, and 2) the chinese and japanese speakers share similar views on christmas. for example, christmas, which is not a traditional holiday in east asia, is adopted directly from the western world. the chinese and japanese speakers both follow the western customs and hence view christmas likewise. ## related work online disagreement most works about online disagreement focus on a single culture or language @xcite @xcite , thus are restricted to a single group. while these works try to computationally model disagreement or stance in debates, they do not target at finding cultural or cross-group differences. we, on the other hand, aim at understanding the disagreement in perspectives through different colingual groups according to their respective languages. @xcite present a framework to visualize the cross-cultural differences in multilingual blogs. @xcite show that using emotion terms as culture features is effective in analyzing cross-cultural difference in social media data. however, it is only restricted to a single topic (love and relationship). in contrast, we use wikipedia to study cross-group differences in perspectives on a much larger scale and do not restrict ourselves to one single topic. @xcite investigate the cross-cultural differences in word usages between australian and american english through socio-linguistic features supervisedly. @xcite use social network structures and user interactions, to study how to quantify the controversy of topics within a marriage is not about meeting someone you connect to, but both people being matured, and in the same headspace. 2. if he cannot share his concerns with her, he is poor marriage material. 3. if you don't reveal others' corruption you are culpable as well. 4. there is plenty of corruption pulled out in the open these days, and that has been happening at a faster pace than ever before. 5. mexican, mediterranean, indian and thai cuisines have the most delicious vegetarian dishes. 6. grilled fish is much better cooked at home and shared with friends. ## conclusion we present cluster, a computational method to identify distributional differences in cross-group perspectives, and evaluate it with human judgements. through detailed experiments, we show that cluster is straightforward and effective. furthermore, we show cluster generalizes well for out-of-domain scenarios by training the group perspective models on wikipedia and test on claims collected from reddit. this means that the proposed method learns the task, not the data. besides, the general model of perspective difference identification can be useful in many nlp tasks such as fact checking, sentiment analysis, as well as crosscultural studies in computational social science or multilingual debate forums. as a first attempt towards automatic identification of cross-cultural differences, our work still has much room for improvement. future directions include more complicated ways of composing negative samples, more well-crafted models, and extending our pipeline to fine-grained subgroups speaking the same language, especially for english as a global language spoken by many nations. to train the classifiers, we start the sentence representations with the pre-trained bert @xcite model, and then fine-tune them. for all models, we set sequence length as 128, batch size as 64 and learning rate as 2e-5. we train each clus-ter model for 5 epochs and save the best model only.