This is an excerpt from my book “The 9 Most Common Language Technology Mistakes (and how to avoid them!)”. If you’d like to grab a copy (plus a handy checklist) you can get it on my Ko-fi. If you’d prefer a video version of the same information, you can check out the livestream I did here.

The single most common mistake I see language technologists make, especially newer ones, is failing to consider the domain of their data.

I’m talking about “domain” here in the linguistic sense, not the networking sense. Basically it means that you are attempting to train a piece of language technology using data that is different from what it will see in production. (This is a special case of out-of-distribution data or, if it occurs due to change over time, distribution shift.) The reason this trips up so many new language technology developers is because of a lack of understanding of what counts as “different”: there may be more factors to consider than you initially realize. Language varies systematically and fairly predictably depending on the situation where it is used. What this means from a machine learning standpoint is that the type of language data used is very strong signal and that systems are very likely to pick up on it during training.

Topic

The most obvious type of domain mismatch is topic: what is the text about? If I want to build a system to handle medical text and I only train it with legal text, or customer support chat logs, obviously my system will not be able to handle the specific tokens and expressions that exist in the target medical data but not in the training data.

“Domain” includes a lot more than topic however, and some of the most relevant domain differences are modality, formality, and intended audience.

Modality

First up is modality: how was this language originally produced? Was it spoken aloud, written by hand, typed or signed? Each of these is going to have a systematic effect on the language data that you see, even if the topic is more or less the same and they have all been transcribed into the same format. For example, it’s pretty rare to see emojis in handwritten text. You also won’t see spatial referencing in written text or spoken language the way that you do in signed languages.

Even when comparing different inputs–typed as opposed to spoken or dictated–you’ll see pretty big differences. Written text tends to have more rare words while spoken language tends to have more pronouns and a more skewed frequency distribution (i.e. more of the tokens used are very common). This 1998 paper looking at Swedish is a good citation if you’re interested in a deeper dive.

Formality

Next up is formality. Often you’ll hear people discussing things like “noisy user generated text”. This means that this text has been produced in an informal setting, like social media, rather than a formal setting like a published paper. (Or even this document!) Informal text has its own patterns that you need to consider. For example, misspellings may sometimes be the result of actual mistakes such as typos but often they encode specific information. This study on French is a great example of this.

You are also more likely to see examples of language use that has been stigmatized in an educational setting. For example, code-switching, or using more than one language or language variety in a single text span. You are also more likely to see different language varieties. In the United States African American English, which is a set of related language varieties predominantly used by the Black community, has been historically extremely stigmatized. As a result you are much more likely to find examples of grammatical structures or words from African American English in informal texts than formal ones (although that is changing and does depend on the specific audience for which the formal text was written). As a result a lot of tools trained on more formal text, like language identifiers, will fail when encountering this particular language variety.

You are also much more likely to see slang in informal text and these slang term generally change very quickly. Consider terms like “based” or “lit” that, depending on when you’re reading this, may already sound extremely dated. On the other hand, very formal text is likely to be more stable over time but also to have its own type of rare tokens, especially jargon.

Intended audience

Speaking of jargon, another common source of variation is due to the intended audience. Consider this in your own life: do you write an email to a close friend the same way that you do to your boss? Even though the format of the text is very similar and it may be on the same topic, who you are producing the language output for has a large effect on what you say and how. And this goes even further than just the specific person you were talking to. Is this language being produced to be broadcast (shared with a large number of people who may not be able to each respond individually to the same degree) or has it been produced as part of a dyadic discussion (with a single other individual)? Broadcast text is likely to assume less background knowledge on the part of the listener whereas dyadic text will assume that you have access to the entire rest of the conversation.

If you have a good idea about what type of language use your system will encounter once it’s in production, you can tailor your training data to include a lot of examples of that type of language use. If you don’t, you are likely to have surprising errors that are extremely difficult to debug since they arise from the data and not the modeling itself.

I recently posted on Twitter the strong recommendation that individuals do not attempt to use large language models (like BERT or GPT-3) to replace the services of mental health professionals. I’m writing this blog post to specifically address individuals who think that this is a good idea.

CW: discussions of mental illness, medical abuse, self-harm and suicide. If you are in the US and currently experiencing crisis, 988 is now the nationwide number for mental health, substance use and suicidal crises, if you are outside the US this is a list of international suicide hotlines.

First: I want to start by acknowledging that if you are in the position where you are considering doing this, you are probably coming from a well-meaning place. You may know someone who has experienced mental illness or experienced it yourself and you want to help. That is a wonderful impulse and I applaud it. However, attempting to use large language models to replace the services of mental health professionals is *not* better than nothing. In fact, it is worse than nothing and has an extremely high probability of causing harm to the very people that you are trying to help. And this isn’t just my stance. Replacing clinicians with automated systems goes DIRECTLY against the recommendations of clinicians working in clinical applications of ML in mental health:

“ML and NLP should not lead to disempowerment of psychiatrists or replace the clinician-patient pair [emphasis mine].” – Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. J Med Internet Res. 2021 May 4;23(5):e15708. doi: 10.2196/15708. PMID: 33944788; PMCID: PMC8132982. 

Let me discuss some of the possible harms in more detail, however, since I have found that often when someone is particularly enthused of using large language models in a highly sensitive application they have not considered the scale of the potential harms or the larger societal impact of that application.

First, large language models are fundamentally unfit for purpose. They are trained on a general selection of language data much of which has been scraped from the internet, and I think most people would agree that “random text from the internet” is a very poor source of mental health advice. If you were to attempt to fine-tune a model for clinical practice, you would need to use clinical notes. These are extremely sensitive personal identifying information. Previous sharing of similar information, specifically text from the Crisis Text Line for training machine learning models, has been resoundingly decried as unethical by both the machine learning and clinical communities. Further, we know that PII included as few as one time in the training data of a large language model can be re-identified via model probing. As a result, any of the extremely sensitive data used to tune the model has the potential of being leaked to end users of the model.

You would also open patients up to other kinds of adversarial attacks. In particular the use of universal triggers by a man-in-the-middle attacker could be used to intentionally serve text to patients that encouraged self harm or suicide. And given the unpredictable nature of the text output from large language models in the first place, it would be impossible to ensure that the model didn’t just do that on its own, regardless of how much prompt engineering was done.

Further, mental health support is not generic. Advice that might be helpful to someone in one situation may be actively harmful to another. For example, someone worried about intrusive thoughts may find it very comforting to be told “you are not alone in your experience”. However, telling someone suffering from paranoia who says they are being followed “you are not alone in your experience” may help to reinforce that belief and further harm their mental health. Encouraging someone suffering from depression to “add some moderate intensity exercise to your daily routine” may be helpful for them. Encouraging someone who is suffering from compulsive exercising to “add some moderate intensity exercise to your daily routine” would be actively harmful. Since large language models are not medical practitioners, I hesitate to call this “malpractice”, however it is clear that there is an enormous potential for harm even when serving seemingly innocuous, general advice.

And while you may be tempted to address this by including diagnosing as part of the system, that in itself offers extremely high potential for harm. Mental health misdiagnosis can be extremely harmful to patients, even when it happens during consultation with a medical services provider. Adding on the veneer of supposed impartiality from an automated system may increase that harm.

And of course, once diagnosis has been made (even if incorrectly) it then becomes personal identifiable information linked with the patient. However, since an automated system is not actually a clinician, even the limited data privacy protections provided for people in the US by something like HIPAA just doesn’t apply. (In fact it’s a big issue even with online services that use human service providers; Better Help in particular has sold Facebook data from therapy sessions.) As a result, this data can be legally sold by third parties like data brokers.  Even if you don’t intend to sell that data, there’s no way for you to ensure that it never will be, including after your death or if whatever legal entity you use to create the service is bought or goes into bankruptcy.

And this potential secondary use of the data, again even if the diagnosis is incorrect, has an enormous potential to harm individuals. In the US it could be used to raise their insurance rates, have them involuntarily committed or placed under an extremely controlling conservatorship (which may strip them of their right to vote or even allow them to be forcibly sterilized, which is still legal in 31 states). Mental illness is extremely stigmatized and creating a process for linking a diagnosis with an individual has extremely high potential for harm.

Attempting to replicate the services of a mental health clinician through the use of LLMs has the potential to harm the individuals who attempt to use that service. And I think there’s a broader lesson here as well: mental illness and mental health issues are fundamentally not an ML engineering problem. While we may use these tools to support clinicians or service providers or public health workers, we can’t replace them. 

Instead think about what it is specifically you want to do and spend your time doing something that will have an immediate, direct impact. If your goal is to support someone you love, support them directly. If you want to help your broader community, join existing organizations doing the work, like local crisis centers. If you need help, seek it out. If you are in crisis, seek immediate support. If you are merely looking for an interesting engineering problem, however, look elsewhere.

Two things spurred me to write this post. First, I’d given the same advice three times which, according to David Robinson‘s rule, meant it was time. And, second, this news story on a startup that claims that they can detect student emotions over Zoom. With those things in mind, here is my very simple guidance on sentiment analysis:

You should almost never do sentiment analysis.

Thanks for reading, hope that cleared things up. 🙂 In all seriousness, though, the places where it makes sense for a data scientist or NLP practitioner working in industry to use sentiment analysis are vanishingly rare. First, because it doesn’t work very well and second, because even when it does work it’s usually measuring the wrong thing.

What do I mean that sentiment analysis doesn’t work very well?

Let’s consider the most common approach. You have a list of words that are “positive” and a list of words that “negative” (or, more rarely, a list of words associated with different emotions) and you count how many words from each list appear in your target text. If there are more positive words you assign a positive sentiment, more negative words a negative one and if they are equal or (much more likely) none of the words on either list show up you assign a neutral sentiment. Of course, there are a variety of other, more sophisticated approaches, but this is the most common one.

As you can see, there’s a lot you’ll miss using this approach. The construction of the lists may not have been done with the target text producers in mind, for example. A list from five years ago may possibly have “lit” as a word with positive sentiment, but what about “based”? If you’re attempting to characterize young internet users you’ll need to be very careful with your sentiment lists.

And a word-based approach cannot account for context. “Our flight was late but Cindy at O’Hare managed to change our connection”, for example, may have gotten a negative sentiment assigned to it due to “late” (assuming you’re working within the transportation domain) but in context this is actually a pretty positive review.

Plus, of course, sarcasm will be completely missed by such an approach. If someone says “lovely weather outside” in the middle of a tornado warning then you, as a human, know that they probably aren’t very happy about the weather. Which ties in to my next point: the question of what you’re measuring.

The specific thing you’re attempting to measure using sentiment analysis is the sentiment expressed in the text, but often you’ll see folks (generally tacitly) make a leap to assuming that what you’re measuring is who someone actually felt when they were writing the text. That’s pretty clearly not the case: I can write “I’m absolutely livid” and “I feel ecstatic joy” without experiencing those emotions and I’d expect most sentiment analysis tools would give those statements very strong negative and positive sentiments, respectively.

This is important because, generally, people tend to care more about what people are feeling than what they’re expressing in text. (A good counterexample would be a digital humanities project showing the sentiment of different passages in a novel.) And figuring out someone’s emotions from text is much, much more difficult and in most cases completely utterly impossible. And speaking about what people are feeling….

Sentiment isn’t generally actually that useful

So given that sentiment analysis of text isn’t likely to tell you what people are feeling with any fidelity, where would you want to use it? A great question, and one that I think folks should ask more often. Usually when I see it being used, it’s in a case where there’s another, actually more useful, thing you want to know. Let’s look at some examples.

• In a chatbot to know if the conversation is going well
  • Since most words are neutral and most turns are pretty short, you’re pretty unlikely to get helpful information unless things have already gone very, very wrong (as Zeerak Waseem points out,you can look for swearing). A far better thing to measure would be patterns that you shouldn’t see in efficient conversations, like lots of repetition or slightly rephrasing.
• To determine if reviews are good or bad
  • This one in particular I find baffling: most reviews are associated with a star rating, which is a clear measure directly from the person. A better use of time would probably be to do topic modelling or some other sort of unsupervised text analysis to see if there are persistent issues that should be addressed.
• To predict customer churn based on call center logs
  • If you have the raw input text and the labelled outcomes (churned or not) then I’d just build a raw classifier. You’re also likely to get more mileage out of other metadata features, like how often they’ve contacted support, number of support tickets filed or something similar. Someone can be very polite to a customer service rep and still churn because the product just doesn’t meet their needs.

In all of these cases “what is the sentiment being expressed in the text” just isn’t the useful question. Sure, it’s quick enough to do a sentiment analysis that you might add it as a new feature to just see if it adds anything… but I’d say your time would be better spent elsewhere.

So why do people still use sentiment analysis?

Great question. Probably one reason is that its often used as an example in teaching. It has a pretty simple intuition, there are lots of existing tools for it and can it help students develop intuitions about corpus methods. That means that a *lot * of people have done sentiment analysis already at some point, and it’s much simpler to use a method you are already familiar with. (I get it, I do it too.)

And another, more pernicious reason, is that it’s harder to define a new problem (for which a tool or measure might not exist) than it is to redefine it as an existing one where an existing, simple-to-use tool is available. Even if that redefinition means that the work is no longer actually all that useful.

So, with all that said, the next time you’re thinking that you need to do sentiment analysis I’d encourage you to spend some time really considering if you’re sure before you decide to dive in.

One of the things I really enjoy about my current job is chatting with other data science folks. Almost inevitably in the course of these conversations, the old “Python vs. R” debate comes up.

For those of you who aren’t familiar, Python and R are both programming languages often used by data scientists and other folks who work with data. Python is a general-purpose programming language (originally designed as a teaching language) that has some popular packages used for data analysis. R is a computer language specifically designed for doing statistics and visualization. They’re both useful languages, but R is much more specialized.

I use both Python & R, but I tend to prefer R for data analysis and vitalization. I also love kitchen gadgets. (I own and routinely use a melon baller, albeit only very rarely for actually balling melon.) My hypothesis is that my preference for R and love of kitchen gadgets share the same underlying cause: I really like specialized tools.

I was curious to see if there was a similar relationship for other people, so I reached out to my Twitter followers with a simple two-question poll:

---

Do you prefer Python or R?

• Python
• R

How do you feel about specialized kitchen gadgets (e.g. veggie peelers, egg slicers, specialized knives).

• Hate ’em
• Love ’em

---

185 people filled out the poll (if you were one of them, thanks!). Unfortunately for my hypothesis, a quick analysis of the results revealed no evidence that there was any relationship between whether someone prefers Python or R and if they like kitchen gadgets. You can check out the big ol’ null result for yourself:

Regardless of how poorly this experiment illustrates my point, however, it still stands: R is a specialized tool, while Python is general purpose one.

I like to think of R as a bread knife and Python as a pocket knife. It’s much easier to slice bread with a bread knife, but sometimes it’s more convenient to use a pocket knife if you already have it to hand.

If you spend a lot of time cleaning and analyzing data that’s already in a tabular format or doing statistical analysis, you might consider checking out R. It’s certainly saved me a lot of time. (Oh, and I juuuust so happen to have a couple of short R tutorials for folks with little to no programming background.)

I recently wrote the acknowledgements section my dissertation and it really put into perspective how much help I’ve received during my degree. I’ve decided to pass some of that on by helping out others! Specifically, I’ve decided to help make travelling to conferences a little more affordable for linguistics students who are from underrepresented minorities (African American, American Indian/Alaska Native, or Latin@), LGBT or have a disability.

To enter:

Entry is open to any student (graduate or undergraduate) studying any aspect of language (broadly defined) who is from an underrepresented minority (African American, American Indian/Alaska Native, or Latin@), LGBT or has a disability.  E-mail me and attach:

• An abstract or paper that has been accepted at an upcoming (i.e. starting after June 23, 2017) conference
• The acceptance letter/email from the conference
• A short biography/description of your work

One entry per person, please!

Prizes:

I’ll pick up to two entries. Each winner will receive 100 American dollars to help them with costs associated with the conference, and I’ll write a blog post highlighting each winner’s research.

Contest closes July 31, I’ll contact winners by July 5

Good luck!

 

If you follow me on Twitter (@rctatman) you probably already know that I defended my dissertation last week. That’s right: I’m now officially Dr. Tatman! [party horn emoji]

I’ve spent a lot of time focusing on all the minutia of writing a dissertation lately, from formatting references to correcting a lot of typos (my committee members are all heroes). As a result, I’m more than ready to zoom out and think about big-picture stuff for a little while. And, in academia at least, pictures don’t get much bigger than whole disciplines. Which brings me to the title of this blog post: computational sociolinguistics. I’ve talked about my different research projects quite a bit on this blog (and I’ve got a couple more projects coming up that I’m excited to share with y’all!) but they can seem a little bit scattered. What do patterns of emoji use have to do with how well speech recognition systems deal with different dialects with how people’s political affiliation is reflected in their punctuation use? The answer is that they all fall within the same discipline: computational sociolingustics.

Computational sociolinguistics is a fairly new field that lies at the intersection of two other, more established fields: computational linguistics and sociolinguistics. You’re actually probably already familiar with at least some of the work being done in computational linguistics and its sister field of Natural Language Processing (commonly called NLP). The technologies that allow us to interact with computers or phones using human language, rather than binary 1’s and 0’s, are the result of decades of research in these fields. Everything from spell check, to search engines that know that “puppy” and “dog” are related topics, to automatic translation are the result of researchers working in computational linguistics and NLP.

Sociolinguistics is another well-established field, which focuses on the effects of social context on language how we use language and understand. “Social context”, in this case, can be everything from someone’s identity–like their gender or where they’re from–to the specific linguistic situation they’re in, like how much they like the person they’re talking to or whether or not they think they can be overheard. While a lot of work in sociolinguistics is more qualitative, describing observations without a lot of exact measures, of it is also quantitative.

So what happens when you squish these to fields together? For me, the result is work that focuses on research questions that would be more likely to be asked by sociolinguistics, but using methods from computational linguistics and NLP. It also means asking sociolinguistic questions about how we use language in computational context, drawing on the established research fields of Computer Mediated Communication (CMC), Computational Social Science (CSS) and corpus linguistics, but with a stronger focus on sociolingusitics.

One difficult thing about working in a very new field, however, is that it doesn’t have the established social infrastructure that older fields do. If you do variationist sociolinguistics, for example, there’s an established conference (New Ways of Analyzing Variation, or NWAV) and journals (Language Variation and Change, American Speech, the Journal of Sociolinguistics). Older fields also have an established set of social norms. For instance, conferences are considered more prestigious research venues in computational linguistics, while for sociolinguistics journal publications are usually preferred. But computational sociolinguistics doesn’t really have any of that yet. There also isn’t an established research canon, or any textbooks, or a set of studies that you can assume most people in the field have had exposure to (with the possible exception of Dong et al.’s really fabulous survey article). This is exciting, but also a little bit scary, and really frustrating if you want to learn more about it. Science is about the communities that do it as much as it is about the thing that you’re investigating, and as it stands there’s not really an established formal computational sociolinguistics community that you can join.

Fortunately, I’ve got your back. Below, I’ve collected a list of a few of the scholars whose work I’d consider to be computational sociolinguistics along with small snippets of how they describe their work on their personal websites. This isn’t a complete list, by any means, but it’s a good start and should help you begin to learn a little bit more about this young discipline.

• Jacob Eisenstein at Georgia Tech
  • “My research combines machine learning and linguistics to build natural language processing systems that are robust to contextual variation and offer new insights about social phenomena.”
• Jack Grieve at the University of Birmingham
  • “My research focuses on the quantitative analysis of language variation and change. I am especially interested in grammatical and lexical variation in the English language across time and space and the development of new methods for collecting and analysing large corpora of natural language.”
• Dirk Hovy at the University of Copenhagen
  • “The goal of my research is to integrate sociolinguistic knowledge into NLP models. Concretely, I use large-scale statistics to detect and model the interaction between people’s demographic profile and their language use (see here or here). I am also interested in semantics (modeling what words mean in context), and non-standard language. I am associate professor at the computer science department (DIKU)at the University of Copenhagen.”
• Michelle A. McSweeney at Columbia
  • “My research can be summed up by the question: How do we convey tone in text messaging? In face-to-face conversations, we rely on vocal cues, facial expressions, and other non-linguistic features to convey meaning. These features are absent in text messaging, yet digital communication technologies (text messaging, email, etc.) have entered nearly every domain of modern life. I identify the features that facilitate successful communication on these platforms and understand how the availability of digital technologies (i.e., mobile phones) has helped to shape urban spaces.”
• Dong Nguyen at the University of Edinburgh & Alan Turing Institute
  • “I’m interested in Natural Language Processing and Information Retrieval, and in particular computational text analysis for research questions from the social sciences and humanities. I especially enjoy working with social media data.”
• Sravana Reddy at Wellesley
  • “My recent interests center around computational sociolinguistics and the intersection between natural language processing and privacy. In the past, I have worked on unsupervised learning, pronunciation modeling, and applications of NLP to creative language.”
• Tyler Schnoebelen at Decoded AI Consulting
  • “I’m interested in how people make meaning with language. My PhD is from Stanford’s Department of Linguistics (my dissertation was on language and emotion). I’ve also founded a natural language processing start-up (four years), did UX research at Microsoft (ten years) and ran the features desk of a national newspaper in Pakistan (one year).”
• (Ph.D. Candidate) Philippa Shoemark at the University of Edinburgh
  • “My research interests span computational linguistics, natural language processing, cognitive modelling, and complex networks. I am currently using computational methods to identify social and individual factors that condition linguistic variation and change on social media, under the supervision of Sharon Goldwater and James Kirby.”
• (Ph.D. Candidate) Sean Simpson at Georgetown University
  • “My interests include computa­tional socio­linguistics, socio­phonetics, language variation, and conservation & revitalization of endangered languages. My dissertation focuses on the incorporation of sociophonetic detail into automated speaker profiling systems.”

There is currently a bill in the US House to make English the official language of the United States. These bills have been around for a while now. H.R. 997, also known as the “The English Language Unity Act”, was first proposed in 2003. The companion bill, last seen as S. 678 in the 114th congress, was first introduced to the Senate as S. 991 in 2009, and if history is any guide will be introduced again this session.

So if these bills have been around for a bit, why am I just talking about them now? Two reasons. First, I had a really good conversation about this with someone on Twitter the other day and I thought it would be useful to discuss  this in more depth. Second, I’ve been seeing some claims that President Trump made English the official language of the U.S. (he didn’t), so I thought it would be timely to discuss why I think that’s such a bad idea.

As both a linguist and a citizen, I do not think that English should be the official language of the United States.

In fact, I don’t think the US should have any official language. Why? Two main reasons:

• Historically, language legislation at a national level has… not gone well for other countries.
• Picking one official language ignores the historical and current linguistic diversity of the United States.

Let’s start with how passing legislation making one or more languages official has gone for other countries. I’m going to stick with just two, Canada and Belgium, but please feel free to discuss others in the comments.

Canada

Unlike the US, Canada does have an official language. In fact, thanks to a  1969 law, they have two: English and French. If you’ve ever been to Canada, you know that road signs are all in both English and French.

This law was passed in the wake of turmoil in Quebec sparked by a Montreal school board’s decision to teach all first grade classes in French, much to the displeasure of the English-speaking residents of St. Leonard. Quebec later passed Bill 101 in 1977, making French the only official language of the province. One commenter on this article by the Canadian Broadcasting Corporation called this “the most divisive law in Canadian history”.

Language legislation and its enforcement in Canada has been particularity problematic for businesses. On one occasion, an Italian restaurant faced an investigation for using the word “pasta” on thier menu, instead of the French “pâtes”. Multiple retailers have faced prosecution at the hands of the Office Québécois de la langue Française for failing to have retail websites available in both English and French. A Montreal boutique narrowly avoided a large fine for making Facebook posts only in English. There’s even an official list of English words that Quebec Francophones aren’t supposed to use. While I certainly support bilingualism, personally I would be less than happy to see the same level of government involvement in language use in the US.

In addition, having only French and English as the official languages of Canada leave out a  very important group: aboriginal language users. There are over 60 different indigenous languages used in Canada used by over 213 thousand speakers. And even those don’t make up the majority of languages spoken in Canada: there are over 200 different languages used in Canada and 20% of the population speaks neither English nor French at home.

Belgium

Another country with a very storied past in terms of language legislation is Belgium. The linguistic situation in Belgium is very complex (there’s a more in-depth discussion here), but the general gist is that there are three languages used in different parts of the country. Dutch is used in the north, French is the south, and German in parts of the east. There is also a deep cultural divide between these regions, which language legislation has long served as a proxy for. There have been no fewer than eight separate national laws passed restricting when and where each language can be used. In 1970, four distinct language regions were codified in the Belgium constitution. You can use whatever language you want in private but there are restrictions on what language you can use for government business, in court, in education and employment.  While you might think that would put a rest to legislation on language, tensions have continued to be high. In 2013, for instance, the European Court of Justice overturned a Flemish law that contracts written in Flanders had to be in Dutch to be binding after a contractor working on an English contract was fired. Again, this represents a greater level of official involvement with language use than I’m personally comfortable with.

I want to be clear: I don’t think multi-lingualism is a problem. As a linguist, I value every language and I also recognize that bilingualism offers significant cognitive benefits. My problem is with legislating which languages should be used in a multi-lingual situation; it tends to lead to avoidable strife.

The US

Ok, you might be thinking, but in the US we really are just an English-speaking country! We wouldn’t have that same problem here. Weeeeelllllll….

The truth is, the US is very multilingual. We have a Language Diversity Index of .353, according to the UN. That means that, if you randomly picked two people from the United States population, the chance that they’d speak two different languages is over 35%. That’s far higher than a lot of other English-speaking countries. The UK clocks in at .139,  while New Zealand and Australia are at .102 and .126, respectively. (For the record, Canada is at .549 and Belgium .734.)

The number of different languages spoken in the US is also remarkable. In New York City alone there may be speakers of as many as 800 different languages, which would make it one of the most linguistically-diverse places in the world; like the Amazon rain-forest of languages. In King County, where I live now, there are over 170 different languages spoken, with the most common being Spanish, Chinese, Vietnamese and Amharic. And that linguistic diversity is reflected in the education system: there are almost 5 million students in the US Education system who are learning English, nearly 1 out of 10 students.

Multilingualism in the United States is nothing new, either: it’s been a part of the American experience since long before there was an America. Of course, there continue to be many speakers of indigenous languages in the United States, including Hawaiian (keep in mind that Hawaii did not actually want to become a state). But even within just European languages, English has never had sole dominion. Spanish has been spoken in Florida since the 1500’s. At the time of the signing of the Deceleration of Independence, an estimated 10% of the citizens of the newly-founded US spoke German (although the idea that it almost became the official language of the US is a myth). New York city? Used to be called New Amsterdam, and Dutch was spoken there into the 1900’s. Even the troops fighting the revolutionary war were doing so in at least five languages.

Making English the official language of the United States would ignore the rich linguistic history and the current linguistic diversity of this country. And, if other countries’ language legislation is any guide, would cause a lot of unnecessary fuss.