The Importance of Modeling Social Factors of Language: Theory and Practice (2021)

Abstract: Natural language processing (NLP) applications are now more powerful and ubiquitous than ever before. With rapidly developing (neural) models and ever-more available data, current NLP models have access to more information than any human speaker during their life. Still, it would be hard to argue that NLP models have reached human-level capacity. In this position paper, we argue that the reason for the current limitations is a focus on information content while ignoring language’s social factors. We show that current NLP systems systematically break down when faced with interpreting the social factors of language. This limits applications to a subset of information-related tasks and prevents NLP from reaching human-level performance. At the same time, systems that incorporate even a minimum of social factors already show remarkable improvements. We formalize a taxonomy of seven social factors based on linguistic theory and exemplify current failures and emerging successes for each of them. We suggest that the NLP community address social factors to get closer to the goal of human-like language understanding.

## taxonomy of social factors systemic functional linguistics (sfl) @xcite , studies precisely this re-lationship between language and its functions in social settings. it gives us a sense of the different language areas that, instead of formal factors like syntax and semantics, rely on social factors for interpretation. by detailing those factors, we can understand what is missing in current nlp approaches, and how to incorporate them into our systems to go beyond information content. however, sfl alone can not explain why "what is said is not what is meant." for that, we borrow from grice (1975), who laid out four maxims that govern effective communication in social situations. these four maxims are those of quality ("make your contribution true, do not lie or make unsupported claims"), quantity ("make your contribution as informative as is required (but not more informative)"), relevance ("make your contribution relevant"), and manner ("be brief and orderly and avoid obscurity of expression and ambiguity"). together, these maxims are known as the cooperative principle, and govern successful conversations, as long as all conversational partners adhere to them. however, we can also deliberately break selected maxims, for example, for comical effect, sarcasm, politeness, when we playact, or outright lie (i.e., saying things that are not true, not relevant, or obtuse). if this violation is apparent, the conversational partner can use the resulting inconsistency to construct an alternative meaning. e.g., inferring that "take your time, i love waiting for you" violates the maxim of quality and is probably not true lets us assume sarcasm. gricean maxims and their selective violations can explain why "what is said is not what is meant." this inference process is called conversational implicature, and can help explain why nlp applications struggle with tasks such as sarcasm detection or entailment. some previous works have consequently used them to evaluate the quality of nlp systems @xcite . building upon these two frameworks, we lay out a set of seven social factors that nlp systems need to be aware of to overcome current limitations (see figure 2 ). we cover speaker characteristics (section 2.1), receiver characteristics (section 2.2), social relations (section 2.3), context (section 2.4), social norms (section 2.5), culture and ideology (section 2.6), and communicative goals (section 2.7). we first outline each factor and its relation to sfl and the cooperation principle and then discuss the associated limitations for current nlp systems, as well as existing approaches that address these factors. note that the seven social factors in this taxonomy are not mutually exclusive. most language use can be categorized according to multiple factors, such as the use of goal and norm. ## conclusion in this work, we have argued that there are seven social factors of language that impact nlp applications: speaker, receiver characteristics, social relations, context, social norms, culture and ideology, and communicative goals. at present, nlp models often ignore these factors. we have shown that this ignorance limits the kinds of applications we can tackle. it can also can introduce mistakes, ranging from the hilarious to the severe. however, several extant approaches incorporate these social factors, all of them showing substantial improvements in a wide range of applications. by systematically addressing the social aspects of language as a field, we will improve the performances of existing nlp systems, open up new applications, and increase fairness and usability for all users. dirk hovy. 2015. demographic factors improve classification performance. in proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), pages 752-762, beijing, china. association for computational linguistics. dirk hovy. 2016. the enemy in your own camp: how well can we detect statistically-generated fake reviews -an adversarial study. in proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers), pages 351-356, berlin, germany. association for computational linguistics. dirk hovy. 2018. the social and the neural network: how to make natural language processing about people again. in proceedings of the second workshop on computational modeling of people's opinions, personality, and emotions in social media, pages 42-49, new orleans, louisiana, usa. association for computational linguistics. dirk hovy, federico bianchi, and tommaso fornaciari. 2020. "you sound just like your father" commercial machine translation systems include stylistic biases. in proceedings of the 58th annual meeting of the association for computational linguistics, pages 1686-1690, online. association for computational linguistics.