SlowGuess/NAACL_2021_OCR huggingface dataset:
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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.
"[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 oversimplified 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 oversimplifies 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 (Devlin et al., 2019) and GPT-3 (Brown et al., 2020) seemingly picked up enough language behavior to produce natural-looking sentences that show prag
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 588-602 June 6-11, 2021. ©2021 Association for Computational Linguistics
matic 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 (Abercrombie 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 approach—ignoring social factors—prevents 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 artificially 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
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.
2 Taxonomy of Social Factors
Systemic functional linguistics (SFL) (Halliday and Matthiessen, 2013), studies precisely this re
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relationship 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 let 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.
2.1 Speaker
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.
Applications Failing to consider speaker characteristics might result in inaccurate models, e.g.,
the message of a 20-year-old German female reading 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 (Ritter 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 (Benton 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).
2.3 Social Relation
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 influenced 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 (Krishnan 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 (Jorgensen et al., 2016; Blodgett et al., 2016; Dorn, 2019).
2.4 Context
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 di
rect 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).
2.5 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-