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 humanlike language understanding. [T]he common misconception [is] that language use has primarily to do with words and what they mean. It doesn't. It has primarily to do with people and what they mean. Clark and Schober (1992) Until the 1970s, economics assumed that individuals, markets, and firms always acted rationally, based on all the available information. This assumption allowed researchers to use linear models and worked well for several applications. However, it came at the cost of ignoring essential aspects of human decision making, which oversim-plified an inherently complex matter in a way that limited possible insights and applications. The seminal work by Tversky and Kahneman (1973) showed that people would make irrational decisions, time and again, even with full information, and that simple models could not account for this behavior. By introducing the human factor into the equation, they opened up a new research field: behavioral economics. Like economics in the mid-twentieth century, Natural Language Processing (NLP) still makes a limiting assumption: language is only about information, i.e., message content alone. This assumption makes it possible to model language statistically and works for several applications. However, it completely ignores the fact that people use language to achieve (social) goals; like economists before 1973, NLP researchers are oversimplifying an inherently complex matter in a way that limits possible insights and applications. And like introducing behavior transformed economics, introducing social factors into NLP will similarly transform the field: it will open up new avenues of research, enable new insights and applications, and provide more performant, equitable tools. The focus on information content is rooted in early research on quantifying text and making it usable for information retrieval. While it over-simplifies its subject matter, this focus has enabled many NLP applications, with increasing commercial success over the last few decades. The statistical revolution and introduction of machine learning in the late 1980s and deep learning in the last five years (Manning, 2015) has dramatically improved robustness and performance, and produced industrial-strength everyday applications like machine translation (Wu et al., 2016), search (Shen et al., 2014), and personal assistants (Serban et al., 2016; Radford et al., 2019). Recently, BERT (De-vlin et al., 2019) and GPT-3 (Brown et al., 2020) seemingly picked up enough language behavior to produce natural-looking sentences that show pragmatic constraints and interact in dialogues. However, recent work has pointed out (Bender and Koller, 2020; Bisk et al., 2020) that language is more than just words strung together: it has a social function and relates to non-linguistic context. Nonetheless, current NLP systems still largely ignore the social aspect of language. Instead, they only pay attention to what is said, not to who says it, in what context , and for which goals . We go further to argue that the simplifying focus on information content has effectively limited NLP to a narrow range of information-based applications. Consequently, NLP systems struggle with applications related to pragmatics and interaction, or when what is said is not what is meant, e.g., sarcasm, irony, deception, and any other situation that requires a social interpretation (Aber-crombie and Hovy, 2016). This approach is especially crucial for any system related to pragmatics, such as dialogue systems, machine translation (Mirkin and Meunier, 2015), text-to-speech, and mental healthcare tools (Benton et al., 2017). Examples include conversational agents' inconsistent personality in conducting dialogues with humans (Cercas Curry et al., 2020), the failure of machine translation systems in generating culturally appropriate and polite outputs (Jones and Irvine, 2013; Matusov, 2019; Vanmassenhove et al., 2019), or the general struggles of current systems with social intelligence (Cercas Curry and Rieser, 2018). Ultimately, the goal of NLP is to process language at a human level. However, NLP's current approachignoring social factorsprevents us from reaching human-level competence and performance because language is more than just information content. Unless we start paying attention to the social factors of language, we are arti-ficially limiting NLP's potential as a field and the applications we can develop, including the performance of the applications that exist today. We want to be clear that the idea of language as a social construct is itself nothing new: linguistics and philosophy have long modeled it this way (Wittgenstein, 2010; Eckert, 2012, inter alia). However, as we are reaching a point where this idea can become implemented in systems, it is a message that bears repeating in the NLP community (see also Hovy (2018) and Flek (2020) for similar points, as well as Nguyen et al. (2016) for an overview of the closely related issue of computational sociolinguistics). There have in-0 20 40 60 2 0 1 0 2 0 1 2 2 0 1 4 2 0 1 6 2 0 1 8 2 0 2 0 Social Media Sentiment Analysis Discourse and Pragmatics Sum Figure 1: Trend of interest in social factors in NLP papers, using ACL as an example deed been ongoing and emerging efforts to overcome these limitations. Over the last ten years, research interest in social factors and social context has increased, as shown in Figure 1. Here, we counted the number of accepted papers for the track of computational social science and social media, sentiment analysis, discourse and pragmatics, and their sum at the ACL conference per year, and visualized the overall trend 1 . However, to further highlight and formalize these social factors in language and their use in NLP, we propose a set of seven social factors, explain why they are needed, and show encouraging evidence of approaches that have used them. We hope that this work can inspire more research into the social factors of language in NLP, and push the boundary of what we can achieve as a research field. Contributions We formalize the notion of social factors via two linguistic theories: systemic functional linguistics (Halliday and Matthiessen, 2013, SFL) and the Cooperative Principle (Grice, 1975). We build on these frameworks to provide a taxonomy of seven increasingly complex social factors that help tease out the limitations of NLP models. These seven factors are: 1) speaker and 2) receiver , 3) social relations , 4) context , 5) social norms , 6) culture and ideology , and 7) communicative goals . For each factor, we explain why it presents an obstacle to current information-based approaches and show work that has started to address them. Systemic functional linguistics (SFL) (Halliday and Matthiessen, 2013), studies precisely this re-1 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 bor-row 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 ambi-guity). 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 (Jwalapuram, 2017; Qwaider et al., 2017). 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 Figure 2: Taxonomy of social factors (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. An individual or agent uses language for different social goals, such as constructing their identity. Characteristics of speakers include age, gender, ethnicity, social class, dialect, etc. A speaker determines the speech act, text, tone, language style, and consciously encoded personal signatures of an utterance. Certain speaker attributes are expected to be consistent or unchanged across different scenarios, such as basic demographics and personality traits. Other can vary according to situation, such as tone and style. In both cases, the speaker has a certain amount of agency over the expression of some of these attributes, but will be unaware of others. In sociolinguistics, this hierarchy is called saliency , ranging from obvious to all speakers (e.g., "howdy" for Texans) to apparent only to speakers of the variety (e.g., when to unround a vowel or not), or only to researchers (e.g., syntactic inversion) (Silverstein, 2003). Successful speaker models should thus use the cooperative principle as a set of constraints and know when to break them for effect. the message of a 20-year-old German female read-ing like it was from a 75-year-old American male after translation (Hovy et al., 2020). This effect is a big issue for any text generation, where the lack of speaker personality can create incongruous responses in conversational agents. Despite conversational agents' recent successes (Rit-ter et al., 2011; Banchs and Li, 2012; Serban et al., 2016), their lack of a consistent personality is still one of the common issues in using data-driven approaches. The main reason is that these models are often trained over conversations by different people, averaging and thereby virtually ignoring individual speakers' personalities (Li et al., 2016; Wei et al., 2017; Zhang et al., 2018; Wu et al., 2021). There have not been many attempts to make NLP systems more robust to language variation across speakers (Yang and Eisenstein, 2017), though attempts at creating personalized language technologies exist in information retrieval (Shen et al., 2005), recommender systems (Basilico and Hofmann, 2004), machine translation (Mirkin and Meunier, 2015), and language modeling (Federico, 1996). Meanwhile, various approaches have shown the positive impact of incorporating speaker characteristics into NLP applications, either as explicit features (Volkova et al., 2013), through conditional embeddings (Hovy, 2015; Lynn et al., 2017), or via neural models for multi-task learning (Ben-ton et al., 2017; Li et al., 2018). By accounting for a speaker's specific demographic attributes, models achieve better performance in a variety of tasks, such as sentiment analysis, user attributes, part-of-speech tagging, and response generation (Wu et al., 2021). Rashkin et al. (2016) showed the value of modelling speaker perspective to discover opinions or biases in the way things are expressed. Hovy (2016) showed that demographically-conditioned generated text also is more convincing. 2.2 Receiver Audiences that receive text from a speaker are made up of receivers, depending on the situation and medium. The number of receivers can vary substantially, ranging from zero (monologue) to one (dialogue), multiple (conversation), or massive (broadcast). Receivers may be known or unknown. For instance, in any given dialogue or conversation, the speaker knows the identity of the specific and fixed target or group to whom he/she is talking. However, when it comes to broadcasting or highly public spaces, receivers are often imagined by the speaker (Litt, 2012) and are potentially numerous and invisible. This imagined audience is a speaker's mental conceptualization of the people with whom he or she is communicating. This conceptualization of receiver characteristics influences the conversation: a speaker who calls on Newton's Celestial Mechanics to respond to a child's question Where does the sun go at night? has grossly misconceptualized the receiver characteristics in the situation. Successful receiver models should thus use the cooperative principle as a set of constraints on what to expect from a counterpart. However, they should also assume that the receiver will perform conversational implicature when they notice a maxim violation. Right now, conversational agents tend to take any input as adhering to all maxims, so they are bad at recognizing sarcasm, irony, or overly polite forms (all of which violate the maxim of quality by saying things that are not true: you really do want another piece of cake). Applications Spellchecking and stylistic models currently fail to consider receiver characteristics. For instance, when writing to the president of a company vs. messaging your best friend , the politeness levels and register differ substantially, but current large, pretrained models cannot deal with this difference effectively (for an exception, see Fu et al. (2020)). What is more, they can generate messages that are actively hurtful to receivers (Nozza et al., 2021). In other cases like hateful-content detection (Warner and Hirschberg, 2012), a message might be toxic to outsiders but perceived as appropriate among close friends (Sap et al., 2019a). This self-reference or joking use of slurs by a group of intimates might introduce significant noise to the automatic recognition of hate speech, causing existing classifiers to fail in many instances. Detecting such hateful or toxic speech online might require classifiers to take into account both content and receivers, as well as a broader context. Receiver differences markedly add to the complexity and difficulty in machine translation from, say, English to Korean. Korean speech has strict rules about politeness in language depending on who you are talking to; misusing these measures would be viewed as quite rude by native speakers of Korean (Kim and Lee, 2017). The distance or relation between speaker and receiver matters. Examples of social relations include family, friendship, rival, ally, competitor, professional hierarchies, seniority, follower, and followee. One of the core communicative functions of language is to establish, modulate, and reproduce these social dynamics and social relations (Hymes, 1972). The interplay between speakers, receivers, and their relations introduces variations and flexibility into the resulting text. It also provides a shared background knowledge and context (this function of social relations has also in-fluenced work on meaning frames by Fillmore (1982)). The incorporation of social relations is closely related to the consideration of speakers and receivers, but with different roles. In various social relations, we can flaunt the maxim of manner by being obscure, since much of the missing information will be filled in by shared knowledge. Applications We could improve the detection of self-referential or joking use of hateful content with close friends if we could understand such social relations in the first place, similar to the context of response generation for different audiences. For the sentiment classification task, Yang and Eisenstein (2017) argue that models fail to leverage the tendency of socially proximate individuals (e.g., friends) to use language similarly. Ignoring this phenomenon of linguistic homophily usually means they suffer from limited accuracy. In practice, such social relations often can be reasonably inferred from text (Kr-ishnan and Eisenstein, 2015; Iyyer et al., 2016; Rashid and Blanco, 2017; Rashid et al., 2020). They go a long way to explaining other socially motivated constructs, such as power imbalances or politeness, which in turn can also be inferred from dialogue (Prabhakaran et al., 2012; Danescu-Niculescu-Mizil et al., 2013a). Radfar et al. (2020) showed that including friendship relations in their hate-speech detection improved performance by up to 5%. Similarly, Del Tredici et al. (2019) showed that modeling the social graph of a user improves performance in sentiment analysis, as well as stance and hate speech detection. Incorporating user networks into geolocation substantially improves performance (Rahimi et al., 2018; Fornaciari and Hovy, 2019) and Dinan et al. (2020) show that the different roles of speaking-as, speaking-to, and speaking-about affect gender bias in NLP models. Certain word choices or pronunciations might signal social class, status, or membership in a dialect group. Labov (1972) famously showed how realization of the /r/ sound in phrases like fourth floor was correlated with social hierarchy. In sociolinguistics (Trudgill, 2000), these distinguishing terms are called shibboleths , based on a story from the Old Testament in which pronouncing the word shibboleth a certain way decided whether a person was allowed to pass a checkpoint or was killed. Dialectal areas still play an important role, even in online communication (Hovy and Purschke, 2018), and identifying and integrating them can be vital for fairer NLP tools (Jrgensen et al., 2016; Blodgett et al., 2016; Dorn, 2019). Language-based communication usually takes place in a limited number of social contexts. These contexts reflect the detailed settings speakers and receivers are in, including (but not limited to) the language (e.g., English), domain (e.g., Twitter), occasion (e.g., presentation or discussion), and topic (e.g., work or life). As the containers or holders of communication (Yang, 2019, p. 20), (interpersonal) contexts set the specific boundaries for exchanging language. Prior research on dialogue (Schank and Abelson, 1975) accounted for (social) context as scripts, but framed it in terms of content rather than social factors. Social context is related to the Gricean maxims of quantity and relevance, as it governs what is appropriate and required. Randomly (i.e., without context) saying I have never smuggled live animals in my underwear would probably raise some justified suspicion. In contrast, it is a perfectly acceptable response to the question, Did you hide that parrot in your underpants? (whether the question is appropriate is another matter). Applications NLP models, by their nature, are usually unaware of the (extralinguistic) context. For instance, text or response generation may need to adaptively adjust to the social context of communication, rather than relying on background conversations from different communicators in different contexts. Models have mostly learned to relate words to other words. For instance, current machine translation models are trained on huge corpora of text. However, nuances in language often make it difficult to provide an accurate and direct translation from one social context to another. Studies show that current popular industrial MT systems and recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all tested target languages (Stanovsky et al., 2019; Vanmassenhove et al., 2019; Hovy et al., 2020). There is hilarious content caused by translation fails (see #translation-fail on Twitter), especially when it comes to the social context or cultural-specific nuances of language. Current text generation models also usually fail to account for social context, generating text that lacks nuance. This factor is one of the most difficult ones to overcome, because 1) social context is almost always extralinguistic, and 2) the focus of NLP models has always been on learning applications based on text alone (amplified by the seeming ability of neural approaches to do so, see Collobert et al. (2011)). Some recent papers have commented on the artificial limitation of relying solely on text (Bender and Koller, 2020; Bisk et al., 2020), demonstrating how even large pretrained language models are essentially just mimicking people's language use, instead of actual use. Several works have shown, though, how incorporating non-textual information can improve performance, specifically in conjunction with images (Lazaridou et al., 2015; Caglayan et al., 2019). These approaches help various tasks, from concept learning to machine translation, and improve inherently multimodal applications such as scene descriptions and image labeling. However, even including more linguistic context (i.e., text beyond the current sentence) can drastically improve performance of text classification (Yang et al., 2016) and the detection of irony (Wallace et al., 2014) and sarcasm (Abercrombie and Hovy, 2016). 2 2.5 Social Norm Social norms refer to acceptable group conduct, shared understandings, or informal rules, representing speakers' and receivers' basic knowledge of what others do and what others think they should and should not do (Fehr and Fischbacher, 2004), such as dining etiquette, community norms on Reddit (Chandrasekharan et al., 2018), or hierarchical greetings. Norms are therefore closely related to the factors of relation (Section 2.3) and context (Section 2.4). For instance, greet-2 Note that the latter two show that human speakers depend on context as well, though. ing messages are usually full of positive words and phrases and rarely contain expressions carrying strong negative connotations. Product representatives are expected to communicate with customers in a professional manner rather than teasing or using slang and informal words. The scope of norms also include social commonsense about what is expected and normal in a given situation (Sap et al., 2019b), similar to scripts in Schank and Abelson (1975). Social norms are related to the Gricean maxims of manner and quality: in some situations, it is very much expected to say too much and make unsupported claims, for example, when giving a laudatory speech or a eulogy;  Good evening. Martin didn't stand out while he was alive. Now he is dead. Thank you.  is not much of a speech. Applications Social norms are subtle constructs that are not easy to define, so we still do not have many computational techniques to reliably quantify them, let alone assessing whether certain model behaviors should be rewarded or sanctioned (Anastassacos et al., 2020). Consequently, most NLP models still fail to recognize social norms (for an exception, see Forbes et al. (2020)). Failing to measure social norms, and to detect the alignment between expected or unexpected behaviors and models' actual behaviors, can introduce severe damage and negatively impact society, especially as more conversational agents or chatbots have been developed and deployed for real-world applications, such as customer services, travel or flight reservation, or therapy. In 2016, Microsoft released its now infamous chatbot on Twitter: Tay 3 . Microsoft initially expected Tay's language patterns to resemble a 19-year old American girl, but the chatbot quickly transformed into a fountain of racist, sexist, and abusive slurs, by interacting with people espousing these views. A similar issue played out recently with a Korean chatbot. 4 Sap et al. (2019a) showed that lack of awareness of social norms around taboo words led to annotation bias being integrated into the models. However, norms are subject to change, as Danescu-Niculescu-Mizil et al. (2013b) have shown, and 3 https://www.theguardian.com/world/20 16/mar/29/microsoft-tay-tweets-antisemitic-racism 4 https://www.theguardian.com/world/20 21/jan/14/time-to-properly-socialise-hate-speech-ai-chatbot-pulled-from-facebook can affect standing and integration of members. Language and culture are intertwined. Language reflects the society, ideology, cultural identity, and customs of communicators, as well as their values. It is therefore intertwined with social norms (Sec-tion 2.5). For example, in Japanese (Gao, 2005), the expression of hierarchy necessitates more fine-grained politeness and formality levels than in Western cultures. The terms of address also vary in terms of social and age differences, i.e., inferior members address superior ones with a relationship term instead of using personal names (see also Section 2.3). In many Asian cultures, family terms like  uncle  or  big sister  are used as hon-orifics. While it is common amongst native speakers of North American English to use  please  in requests even to close friends, such an act would be considered awkward, if not rude, in Arabic-speaking cultures (Kdr and Mills, 2011; Madaan et al., 2020). Cultural norms can impose a hierarchy on Gricean maxims. For example, whether it is better to give made-up directions (which violates the maxim of relevance) instead of not saying anything (adhering to the maxim of quality) if you do not know the right answer. Context and social and cultural norms can combine in unexpected ways, such as in the case of Korean Airline co-pilots not correcting pilot mistakes (a social and cultural taboo in ordinary con-texts), which resulted in a series of accidents. Differing perceptions of the context, respect for seniority and age, and a hierarchical communication style can lead to one-way communication, in these cases resulting in the deaths of hundreds. 5 The solution here was to change the context by making the working language English, which in turn removed associated social and cultural norms around hierarchical communication (Gladwell, 2008). Applications Culture and ideology are probably the most complicated language constructs. Despite their substantial influence on communication interpretation and language understanding, most NLP models, like text generation or translation, have not included politeness or other similar subtle cultural signatures. A growing body of research has paid attention to the biases and cul-5 https://www.cnbc.com/id/100869966 tural stereotypes encoded and amplified by current NLP models, e.g., inappropriate occupation predictions by large pretrained language models like  the black woman who worked as a babysitter  (Sheng et al., 2019). These findings call for work to look at the ideology, beliefs, and culture behind language content to mitigate biases and social stereotypes beyond data-level manifestations. The fact that embeddings reflect these stereotypes, cultural beliefs, and ideologies make them also an ideal diagnostic tool for social science scholars (Garg et al., 2018; Kozlowski et al., 2018). However, it also creates fundamental biases that cannot easily be mitigated (Gonen and Goldberg, 2019), which poses severe problems for their use in predictive models. Adding cultural awareness can also help counteract the overexposure (Hovy and Spruit, 2016) to the English language (Joshi et al., 2020) 6 and Anglo-Western culture. Finally, communicative goals cover what people want to achieve with their language use, e.g., information, decision making, social chitchat, negotiation, etc. SFL represents this factor as multiple metafunctions of language. Two metafunctions are of particular relevance here: the interpersonal metafunction, whereby language enables us to enact social relationships, to cooperate, form bonds, negotiate, ask for things, and instruct; and the ideational metafunction, whereby language enables us to talk about inner and outer experiences, people and things, or circumstances in which events occur. Goals introduce an essential layer on top of content, and a good understanding of them can reveal the intent and implication behind the text structure. All of the Gricean maxims are used (or deliberately flaunted) in the ser-vice of achieving these goals. For example, when trying to convince someone to join us in a project, we might adhere to the maxims of relevance and concisely lay out the reasons we need them to join. However, to make it more likely that they agree, we might choose to exaggerate the expected payoff and to leave out some of the difficulties involved, which violates the maxims of quality and quantity, respectively. Applications Communicative goals shape how speakers arrange their words and styles. For in-6 https://thegradient.pub/the-benderru le-on-naming-the-languages-we-study-and-why-it-matters/ stance, text that aims to convince others often uses various persuasion strategies (Yang et al., 2019a; Chen and Yang, 2021), argumentation techniques (Stab and Gurevych, 2014), rhetorical structures (Rapp, 2011), and the exchange of social support (Wang and Jurgens, 2018; Yang et al., 2019b). Messages trying to entertain audiences need to be structured in ways that can trigger humor (Yang et al., 2015). People might use informal language or text with a high level of intimacy to indicate close relations (Pei and Jurgens, 2020) or reduce social distance between speakers and receivers (Bernstein, 1960; Keshavarz, 2001). Therefore, it is essential for NLP systems like text generation models to be aware of communicative goals in order to arrange word choice, and styles to form a grammatically responsible and coherent text. Ongoing research has shown that style can be controlled independently of con-tent(Prabhumoye et al., 2018; John et al., 2019). Some of the early work on NLP (Hovy, 1987) explicitly considered communicative goals in sentence generation, albeit modeled explicitly. More recently, Sap et al. (2020) modeled speaker intent to resolve conversational implicature. Social Factors in Different NLP Tasks When and how, though, should we consider these various social factors for an NLP application? NLP practitioners should feel free to use our social factor taxonomy as a guide to examine what social factors should be used, and whether integrating each confers additional benefits (e.g., better design, performance, user experience, or cultural fit) for their use cases. Different NLP tasks will likely benefit differently from our social factor taxonomy. There is some evidence that the earlier factors (such as speaker and receiver characteristics) can be applied to most tasks, as they are fundamental aspects of language. Social relations and context are likely to apply more to dialogue and text generation tasks than to, say, sentiment analysis. Lastly, high-level factors such as social norms and culture and ideology likely require more research to inform individual applications, but are likely to shape our community approaches. We would be well-advised to incorporate the findings of fields that have studied these issues for longer, such as philosophy, sociology, or sociolinguistics. As NLP tasks and algorithms are being now applied to different aspects of everyday interaction and around the world, how we will equip NLP models with a grounding in social factors becomes extremely important, especially these two dimensions. Detailed modeling of these social factors is essential if NLP systems are to have any impact. It can also help avoid hegemonic approaches from assuming all conversations follow Western norms, culture, and ideology. Real-world interaction involves more than the exchange of information or decision making via language; it involves a wide range of aspects related to social factors and interpersonal relations, reflected in rich modalities such as voice or facial expression. Though this work's focus is on the language side, we argue that the introduced taxonomy can be beneficial in broader scenarios for next-level multi-modal models. Data, Ethics, and Privacy Our work here is related to some of the recent work on bias in NLP (Hovy and Spruit, 2016; Shah et al., 2020). On the one hand, the cooperative principle can be seen as a possible positive bias: a pre-existing expectation of how we interact, the violation of which signals an alternative approach. So far, models do not integrate this positive bias. On the other hand, work on speaker and receiver characteristics is affected by the models' predictive biases: exaggerating or overestimating one particular group's attributes can skew the results, for example, in the case of machine-translated texts sounding older and more male (Hovy et al., 2020). Recently, Blodgett et al. (2020) have discussed the role of bias conceptions, which serves as a meta-discussion of the conceptualization of social norms. Integrating social factors into NLP poses a double challenge: on the one hand, it requires additional data to model those social factors. We need representative annotation samples for, e.g., the demographics and network information of speaker, receiver, and social relations, which requires us to collect and document our annotations (Bender and Friedman, 2018). Social media already contains some information from personal or socially grounded conversations, but other domains might suffer from data sparsity for these factors, and require advances in unsupervised learning or few-shot learning techniques. On the other hand, collecting all this information raises questions about privacy, data protec-tion, and ethics. Some data we need to collect to work with social factors might be personal or protected data, which comes with risks for de-anonymization and privacy leaks. Collecting sensitive data (i.e., membership in a protected category) requires the participants' approval and rigorous procedures to ensure that this information cannot be connected to them individually. These considerations also pose a challenge to data sharing; even if properly anonymized, data can contain clues as to participants' identity (Eckert and Dewes, 2017). We need to strengthen ethical considerations for this emerging direction to guide practice in the field and ensure our models are used in beneficial ways. Evaluation and Metrics A central question in these efforts is How do we evaluate whether NLP models have learned the social factors of language, beyond performance improvements? Current models optimize performance metrics, but these metrics might fail to capture the nuances of NLP systems' understanding when considering social content. Thus, better metrics are needed to measure and visualize such additional benefits introduced by modeling language's social factors. These metrics will become essential to diagnose failure. Failed or improper incorporation of social factors could lead to awkward social consequences. E.g., a system misjudging its social relation to the speaker and being a bit too chummy, or a conversational agent disre-specting social norms of turn taking and formality. To some extent, such problems might be unavoidable: interacting through language is always a trial-and-error process, even for humans. However, such errors become extremely important in high-stakes scenarios, such as inappropriate responses from conversational agents in mental health counseling applications. We need metrics to capture this failure and mechanisms to explain the decision-making process behind socially aware NLP models. Multi-modal Social Interaction Real-world interaction involves more than the exchange of information or decision making via language; it involves a wide range of aspects related to social factors and interpersonal relations, reflected in rich modalities (Simmons et al., 2011) such as images, voice or facial expression. Though this work's focus is on the language side, we argue that the introduced taxonomy can be beneficial in broader scenarios for the next level multi-modal models. 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. This project has partially received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (No. 949944, INTEGRATOR). DY is supported in part by grants from Google and Salesforce. DH is the scientific director of the Data and Marketing Insights Unit at the Bocconi Institute for Data Science and Analysis. We would like to thank Maxwell Forbes, Christoph Purschke, and Maarten Sap for comments on the drafts, as well as the anonymous reviewers who suggested valuable additions.
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