How can deep learning be combined with theoretical linguistics?

Natural language processing is mostly done using deep learning and neural networks nowadays. In a typical NLP paper, you might see some Transformer models, some RNNs built using linear algebra and statistics, but very little linguistic theory. Is linguistics irrelevant to NLP now, or can the two fields still contribute to each other?

In a series of articles in the Language journal, Joe Pater discussed the history of neural networks and generative linguistics, and invited experts to give their perspectives of how the two may be combined going forward. I found their discussion very interesting, although a bit long (almost 100 pages). In this blog post, I will give a brief summary of it.

Generative Linguistics and Neural Networks at 60: Foundation, Friction, and Fusion

Research in generative syntax and neural networks began at the same time in 1957, and were both broadly considered under AI, but the two schools mostly stayed separate, at least for a few decades. In neural network research, Rosenblatt proposed the perceptron learning algorithm and realized that you needed hidden layers to learn XOR, but didn’t know of a procedure to train multi-layer NNs (backpropagation wasn’t invented yet). In generative grammar, Chomsky studied natural language like formal languages, and proposed controversial transformational rules. Interestingly, both schools faced challenges from learnability of their systems.

Above: Frank Rosenblatt and Noam Chomsky, two pioneers of neural networks and generative grammar, respectively.

The first time these two schools were combined was in 1986, when a RNN was used to learn a probabilistic model of past tense. This shows that neural networks and generative grammar are not incompatible, and the dichotomy is a false one. Another method of combining them comes from Harmonic Optimality Theory in theoretical phonology, which extends OT to continuous constraints and the procedure for learning them is similar to gradient descent.

Neural models have proved to be capable of learning a remarkable amount of syntax, despite having a lot less structural priors than Chomsky’s model of Universal Grammar. At the same time, they fail with certain complex examples, so maybe it’s time to add back some linguistic structure.

Linzen’s Response

Linguistics and DL can be combined in two ways. First, linguistics is useful for constructing minimal pairs for evaluating neural models, when such examples are hard to find in natural corpora. Second, neural models can be quickly trained on data, so they’re useful for testing learnability. By comparing human language acquisition data with various neural architectures, we can gain insights about how human language acquisition works. (But I’m not sure how such a deduction would logically work.)

Potts’s Response

Formal semantics has not had much contact with DL, as formal semantics is based around higher-order logic, while deep learning is based on matrices of numbers. Socher did some work of representing tree-based semantic composition as operations on vectors.

Above: Formal semantics uses higher-order logic to build representations of meaning. Is this compatible with deep learning?

In several ways, semanticists make different assumptions from deep learning. Semantics likes to distinguish meaning from use, and consider compositional meaning separately from pragmatics and context, whereas DL cares most of all about generalization, and has no reason to discard context or separate semantics and pragmatics. Compositional semantics does not try to analyze meaning of lexical items, leaving them as atoms; DL has word vectors, but linguists criticize that individual dimensions of word vectors are not easily interpretable.

Rawski and Heinz’s Response

Above: Natural languages exhibit features that span various levels of the Chomsky hierarchy.

The “no free lunch” theorem in machine learning says that you can’t get better performance for free, any gains in some problems must be compensated by decreases in performance on other problems. A model performs well if it has an inductive bias well-suited for the type of problems it applies to. This is true for neural networks as well, and we need to study the inductive biases in neural networks: which classes of languages in the Chomsky hierarchy are NNs capable of learning? We must not confuse ignorance of bias with absence of bias.

Berent and Marcus’s Response

There are significant differences between how generative syntax and neural networks view language, that must be resolved before the fields can make progress with integration. The biggest difference is the “algebraic hypothesis” — the assumption that there exists abstract algebraic categories like Noun, that’s distinct from their instances. This allows you to make powerful generalizations using rules that operate on abstract categories. On the other hand, neural models try to process language without structural representations, and this results in failures in generalizations.

Dunbar’s Response

The central problem in connecting neural networks to generative grammar is the implementational mapping problem: how do you decide if a neural network N is implementing a linguistic theory T? The physical system might not look anything like the abstract theory, eg: implementing addition can look like squiggles on a piece of paper. Some limited classes of NNs may be mapped to harmonic grammar, but most NNs cannot, and the success criterion is unclear right now. Future work should study this problem.

Pearl’s Response

Neural networks learn language but don’t really try to model human neural processes. This could be an advantage, as neural models might find generalizations and building blocks that a human would never have thought of, and new tools in interpretability can help us discover these building blocks contained within the model.

Launching Lucky’s Bookshelf

I recently launched a new book review website / blog:

Here, I will post reviews of all the books I read (about one book a week). It has over 100 book reviews already, all the reviews I wrote since 2016. My books cover a wide range of topics, including science, linguistics, economics, philosophy, and more. Come check it out!

The biggest headache with Chinese NLP: indeterminate word segmentation

I’ve had a few opportunities to work with NLP in Chinese. English and Chinese are very different languages, yet generally the same techniques apply to both. But there is one source of frustration that comes up from time to time, and it’s perhaps not what you’d expect.

The difficulty is that Chinese doesn’t put words between spaces. Soallyourwordsarejumbledtogetherlikethis.

“Okay, that’s fine,” you say. “We’ll just have to run a tokenizer to separate apart the words before we do anything else. And here’s a neural network that can do this with 93% accuracy (Qi et al., 2020). That should be good enough, right?”

Well, kind of. Accuracy here isn’t very well-defined because Chinese people don’t know how to segment words either. When you ask two native Chinese speakers to segment a sentence into words, they only agree about 90% of the time (Wang et al., 2017). Chinese has a lot of compound words and multi-word expressions, so there’s no widely accepted definition of what counts as a word. Some examples: 吃饭,外国人,开车,受不了. It is also possible (but rare) for a sentence to have multiple segmentations that mean different things.

Arguably, word boundaries are ill-defined in all languages, not just Chinese. Hapselmath (2011) defined 10 linguistic criteria to determine if something is a word (vs an affix or expression), but it’s hard to come up with anything consistent. Most writing systems puts spaces in between words, so there’s no confusion. Other than Chinese, only a handful of other languages (Japanese, Vietnamese, Thai, Khmer, Lao, and Burmese) have this problem.

Word segmentation ambiguity causes problems in NLP systems when different components expect different ways of segmenting a sentence. Another way the problem can appear is if the segmentation for some human-annotated data doesn’t match what a model expects.

Here is a more concrete example from one of my projects. I’m trying to get a language model to predict a tag for every word (imagine POS tagging using BERT). The language model uses SentencePiece encoding, so when a word is out-of-vocab, it gets converted into multiple subword tokens.

“expedite ratification of the proposed law”
=> [“expedi”, “-te”, “ratifica”, “-tion”, “of”, “the”, “propose”, “-d”, “law”]

In English, a standard approach is to use the first subword token of every word, and ignore the other tokens, like this:

This doesn’t work in Chinese — because of the word segmentation ambiguity, the tokenizer might produce tokens that span across multiple of our words:

So that’s why Chinese is sometimes headache-inducing when you’re doing multilingual NLP. You can work around the problem in a few ways:

  1. Ensure that all parts of the system uses a consistent word segmentation scheme. This is easy if you control all the components, but hard when working with other people’s models and data though.
  2. Work on the level of characters and don’t do word segmentation at all. This is what I ended up doing, and it’s not too bad, because individual characters do carry semantic meaning. But some words are unrelated to their character meanings, like transliterations of foreign words.
  3. Do some kind of segment alignment using Levenshtein distance — see the appendix of this paper by Tenney et al. (2019). I’ve never tried this method.

One final thought: the non-ASCII Chinese characters surprisingly never caused any difficulties for me. I would’ve expected to run into encoding problems occasionally, as I had in the past, but never had any character encoding problems with Python 3.


  1. Haspelmath, Martin. “The indeterminacy of word segmentation and the nature of morphology and syntax.” Folia linguistica 45.1 (2011): 31-80.
  2. Qi, Peng, et al. “Stanza: A python natural language processing toolkit for many human languages.” Association for Computational Linguistics (ACL) System Demonstrations. 2020.
  3. Tenney, Ian, et al. “What do you learn from context? Probing for sentence structure in contextualized word representations.” International Conference on Learning Representations. 2019.
  4. Wang, Shichang, et al. “Word intuition agreement among Chinese speakers: a Mechanical Turk-based study.” Lingua Sinica 3.1 (2017): 13.

Representation Learning for Discovering Phonemic Tone Contours

My paper titled “Representation Learning for Discovering Phonemic Tone Contours” was recently presented at the SIGMORPHON workshop, held concurrently with ACL 2020. This is joint work with Jing Yi Xie and Frank Rudzicz.

Problem: Can an algorithm learn the shapes of phonemic tones in a tonal language, given a list of spoken words?

Answer: We train a convolutional autoencoder to learn a representation for each contour, then use the mean shift algorithm to find clusters in the latent space.


By feeding the centers of each cluster into the decoder, we produce a prototypical contour that represents each cluster. Here are the results for Mandarin and Chinese.


We evaluate on mutual information with the ground truth tones, and the method is partially successful, but contextual effects and allophonic variation present considerable difficulties.

For the full details, read my paper here!

I didn’t break the bed, the bed broke: Exploring semantic roles with VerbNet / FrameNet

Some time ago, my bed fell apart, and I entered into a dispute with my landlord. “You broke the bed,” he insisted, “so you will have to pay for a new one.”

Being a poor grad student, I wasn’t about to let him have his way. “No, I didn’t break the bed,” I replied. “The bed broke.”


Above: My sad and broken bed. Did it break, or did I break it?

What am I implying here? It’s interesting how this argument relies on a crucial semantic difference between the two sentences:

  1. I broke the bed
  2. The bed broke

The difference is that (1) means I caused the bed to break (eg: by jumping on it), whereas (2) means the bed broke by itself (eg: through normal wear and tear).

This is intuitive to a native speaker, but maybe not so obvious why. One might guess from this example that any intransitive verb when used transitively (“X VERBed Y”) always means “X caused Y to VERB“. But this is not the case: consider the following pair of sentences:

  1. I attacked the bear
  2. The bear attacked

Even though the syntax is identical to the previous example, the semantic structure is quite different. Unlike in the bed example, sentence (1) cannot possibly mean “I caused the bear to attack”. In (1), the bear is the one being attacked, while in (2), the bear is the one attacking something.

broke-attackAbove: Semantic roles for verbs “break” and “attack”.

Sentences which are very similar syntactically can have different structures semantically. To address this, linguists assign semantic roles to the arguments of verbs. There are many semantic roles (and nobody agrees on a precise list of them), but two of the most fundamental ones are Agent and Patient.

  • Agent: entity that intentionally performs an action.
  • Patient: entity that changes state as a result of an action.
  • Many more.

The way that a verb’s syntactic arguments (eg: Subject and Object) line up with its semantic arguments (eg: Agent and Patient) is called the verb’s argument structure. Note that an agent is not simply the subject of a verb: for example, in “the bed broke“, the bed is syntactically a subject but is semantically a patient, not an agent.

Computational linguists have created several corpora to make this information accessible to computers. Two of these corpora are VerbNet and FrameNet. Let’s see how a computer would be able to understand “I didn’t break the bed; the bed broke” using these corpora.


Above: Excerpt from VerbNet entry for the verb “break”.

VerbNet is a database of verbs, containing syntactic patterns where the verb can be used. Each entry contains a mapping from syntactic positions to semantic roles, and restrictions on the arguments. The first entry for “break” has the transitive form: “Tony broke the window“.

Looking at the semantics, you can conclude that: (1) the agent “Tony” must have caused the breaking event, (2) something must have made contact with the window during this event, (3) the window must have its material integrity degraded as a result, and (4) the window must be a physical object. In the intransitive usage, the semantics is simpler: there is no agent that caused the event, and no instrument that made contact during the event.

The word “break” can take arguments in other ways, not just transitive and intransitive. VerbNet lists 10 different patterns for this word, such as “Tony broke the piggy bank open with a hammer“. This sentence contains a result (open), and also an instrument (a hammer). The entry for “break” also groups together a list of words like “fracture”, “rip”, “shatter”, etc, that have similar semantic patterns as “break”.


Above: Excerpt from FrameNet entry for the verb “break”.

FrameNet is a similar database, but based on frame semantics. The idea is that in order to define a concept, you have to define it in terms of other concepts, and it’s hard to avoid a cycle in the definition graph. Instead, it’s sometimes easier to define a whole semantic frame at once, which describes a conceptual situation with many different participants. The frame then defines each participant by what role they play in the situation.

The word “break” is contained in the frame called “render nonfunctional“. In this frame, an agent affects an artifact so that it’s no longer capable of performing its function. The core (semantically obligatory) arguments are the agent and the artifact. There are a bunch of optional non-core arguments, like the manner that the event happened, the reason that the agent broke the artifact, the time and place it happened, and so on. FrameNet tries to make explicit all of the common-sense world knowledge that you need to understand the meaning of an event.

Compared to VerbNet, FrameNet is less concerned with the syntax of verbs: for instance, it does not mention that “break” can be used intransitively. Also, it has more fine-grained categories of semantic roles, and contains a description in English (rather than VerbNet’s predicate logic) of how each semantic argument participates in the frame.

An open question is: how can computers use VerbNet and FrameNet to understand language? Nowadays, deep learning has come to dominate NLP research, so that VerbNet and FrameNet are often seen as relics of a past era, when people still used rule-based systems to do NLP. It turned out to be hard to use VerbNet and FrameNet to make computers do useful tasks.

But recently, the NLP community is realizing that deep learning has limitations when it comes to common-sense reasoning, that you can’t solve just by adding more layers on to BERT and feeding it more data. So maybe deep learning systems can benefit from these lexical semantic resources.

Edge probing BERT and other language models

Recently, there has been a lot of research in the field of “BERTology”: investigating what aspects of language BERT does and doesn’t understand, using probing techniques. In this post, I will describe the “edge probing” technique developed by Tenney et al., in two papers titled “What do you learn from context? Probing for sentence structure in contextualized word representations” and “BERT Rediscovers the Classical NLP Pipeline“. On my first read through these papers, the method didn’t seem very complicated, and the authors only spend a paragraph explaining how it works. But upon closer look, the method is actually nontrivial, and took me some time to understand. The details are there, but hidden in an appendix in the first of the two papers.

The setup for edge probing is you have some classification task that takes a sentence, and a span of consecutive tokens within the sentence, and produces an N-way classification. This can be generalized to two spans in the sentence, but we’ll focus on the single-span case. The tasks cover a range of syntactic and semantic functions, varying from part-of-speech tagging, dependency parsing, coreference resolution, etc.


Above: Examples of tasks where edge probing may be used. Table taken from Tenney (2019a).

Let’s go through the steps of how edge probing works. Suppose we want to probe for which parts of BERT-BASE contain the most information about named entity recognition (NER). In this NER setup, we’re given the named entity spans and only need to classify which type of entity it is (e.g: person, organization, location, etc). The first step is to feed the sentence through BERT, giving 12 layers, each layer being a 768-dimensional vector.


Above: Edge probing example for NER.

The probing model has several stages:

  1. Mix: learn a task-specific linear combination of the layers, giving a single 768-dimensional vector for each span token. The weights that are learned indicate how much useful information for the task is contained in each layer.
  2. Projection: learn a linear mapping from 768 down to 256 dimensions.
  3. Self-attention pooling: learn a function to generate a scalar weight for each span vector. Then, we normalize the weights to sum up to 1, and take a weighted average. The purpose of this is to collapse the variable-length sequence of span vectors into a single fixed-length 256-dimensional vector.
  4. Feedforward NN: learn a multi-layer perceptron classifier with 2 hidden layers.

For the two-span case, they use two separate sets of weights for the mix, projection, and self-attention steps. Then, the feedforward neural network takes a concatenated 512-dimensional vector instead of a 256-dimensional vector.

The edge probing model needs to be trained on a dataset of classification instances. The probe has weights that are initialized randomly and trained using gradient descent, but the BERT weights are kept constant while the probe is being trained.

This setup was more sophisticated than it looked at first glance, and it turns out the probe itself is quite powerful. In one of the experiments, the authors found that even with randomized input, the probe was able to get over 70% of the full model’s performance!

Edge probing is not the only way of probing deep language models. Other probing experiments (Liu et al., 2019) used a simple linear classifier: this is better for measuring what information can easily be recovered from representations.


  1. Tenney, Ian, et al. “What do you learn from context? probing for sentence structure in contextualized word representations.” International Conference on Learning Representations. 2019a.
  2. Tenney, Ian, Dipanjan Das, and Ellie Pavlick. “BERT Rediscovers the Classical NLP Pipeline.” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019b.
  3. Liu, Nelson F., et al. “Linguistic Knowledge and Transferability of Contextual Representations.” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

What if the government hadn’t released any Coronavirus economic stimulus?

It is March 23, 2020. After a month of testing fiascos, COVID-19 is ravaging the United States, with 40,000 cases in the US and 360,000 worldwide. There is a growing sense of panic as cities begin to lock down. Market circuit breakers have triggered four times in quick succession, with the stock market losing 30% of its value in mere weeks. There’s no sign that the worst is over.

covid-mar-23Above: Global Coronavirus stats on March 23, 2020, when the S&P 500 reached its lowest point during the pandemic (Source).

With businesses across the country closed and millions out of work, it’s clear that a massive financial stimulus is needed to prevent a total economic collapse. However, Congress is divided and is unable to pass the bill. Even when urgent action is needed, they squabble and debate over minor details, refusing to come to a compromise. The president denies that there’s any need for action. Both the democrats and the republicans are willing to do anything to prevent the other side from scoring a victory. The government is in a gridlock.

Let the businesses fail, they say. Don’t bail them out, they took the risk when the times were good, now you reap what you sow. Let them go bankrupt, punish the executives taking millions of dollars of bonuses. Let the free market do its job, after all, they can always start new businesses once this is all over.

April comes without any help from the government. Massive layoffs across all sectors of the economy as companies see their revenues drop to a fraction of normal levels, and layoff employees to try to preserve their cash. Retail and travel sectors are the most heavily affected, but soon, all companies are affected since people are hesitant to spend money. Unemployment numbers skyrocket to levels even greater than during the Great Depression.

Without a job, millions of people miss their rent payments, instead saving their money for food and essential items. Restaurants and other small businesses shut down. When people and businesses cannot pay rent, their landlords cannot pay the mortgages that they owe to the bank. A few small banks go bankrupt, and Wall Street waits anxiously for a government bailout. But unlike 2008, the state is in a deadlock, and there is no bailout coming. In 2020, no bank is too big to fail.

Each bank that goes down takes another bank down with it, until there is soon a cascading domino effect of bank failures. Everyone rushes to withdraw cash from their checking accounts before the bank collapses, which of course makes matters worse. Those too late to withdraw their cash lose their savings. This is devastation for businesses: even for those that escaped the pandemic, but there is no escaping systemic bank failure. Companies have no money in the bank to pay suppliers or make payroll, and thus, thousands of companies go bankrupt overnight.

Across the nation, people are angry at the government’s inaction, and take to the streets in protest. Having depleted their savings, some rob and steal from grocery stores to avoid starvation. The government finally steps in and deploys the military to keep order in the cities. They arrange for emergency supplies, enough to keep everybody fed, but just barely.

The lockdown lasts a few more months, and the virus is finally under control. Everyone is free to go back to work, but the problem is there are no jobs to go back to. In the process of all the biggest corporations going bankrupt, society has lost its complex network of dependencies and organizational knowledge. It only takes a day to lay off 100,000 employees, but to build up this structure from scratch will take decades.

A new president is elected, but it is too late, the damage has been done and cannot be reversed. The economy slowly recovers, but with less efficiency than before, and with workers employed in less productive roles, and the loss of productivity means that everyone enjoys a lower standard of living. Five years later, the virus is long gone, but the economy is nowhere close to its original state. By then, China emerges as the new dominant world power. The year 2020 goes down in history as a year of failure, where through inaction, a temporary health crisis led to societal collapse.

In our present timeline, fortunately, none of the above actually happened. The democrats and republicans put aside their differences and on March 25, swiftly passed a $2 billion dollar economic stimulus. The stock market immediately rebounded.

There was a period in March when it was seemed the government was in gridlock, and it wasn’t clear whether the US was politically capable of passing such a large stimulus bill. Is an economic collapse likely? Not really — no reasonable government would have allowed all of the banks to fail, so we would likely have a recession and not a total collapse. Banks did fail during the Great Depression, but macroeconomic theory was in its infancy at that time, and there’s no way such mistakes would’ve been repeated today. Still, this is the closest we’ve come to an economic collapse in a long time, and it’s fun to speculate about the consequences of what it would be like.

Why do polysynthetic languages all have very few speakers?

Polysynthetic languages are able to express complex ideas in one word, that in most languages would require a whole sentence. For example, in Inuktitut:


“I’ll have to go to the airport”

There’s no widely accepted definition of a polysynthetic language. Generally, polysynthetic languages have noun incorporation (where noun arguments are expressed affixes of a verb) and serial verb construction (where a single word contains multiple verbs). They are considered some of the most grammatically complex languages in the world.

Polysynthetic languages are most commonly found among the indigenous languages of North America. Only a few such languages have more than 100k speakers: Nahuatl (1.5m speakers), Navajo (170k speakers), and Cree (110k speakers). Most polysynthetic languages are spoken by a very small number of people and many are in danger of becoming extinct.

Why aren’t there more polysynthetic languages — major national languages with millions of speakers? Is it mere coincidence that the most complex languages have few speakers? According to Wray (2007), it’s not just coincidence, rather, languages spoken within a small, close-knit community with little outside contact tend to develop grammatical complexity. Languages with lots of external contact and adult learners tend to be more simplified and regular.

It’s well known that children are better language learners than adults. L1 and L2 language acquisition processes work very differently, so that children and adults have different needs when learning a language. Adult learners prefer regularity and expressions that can be decomposed into smaller parts. Anyone who has studied a foreign language has seen tables of verb conjugations like these:



For adult learners, the ideal language is predictable and has few exceptions. The number 12 is pronounced “ten-two“, not “twelve“. A doctor who treats your teeth is a “tooth-doctor“, rather than a “dentist“. Exceptions give the adult learner difficulties since they have to be individually memorized. An example of a very predictable language is the constructed language Esperanto, designed to have as few exceptions as possible and be easy to learn for native speakers of any European language.

Children learn languages differently. At the age of 12 months (the holophrastic stage), children start producing single words that can represent complex ideas. Even though they are multiple words in the adult language, the child initially treats them as a single unit:

whasat (what’s that)

gimme (give me)

Once they reach 18-24 months of age, children pick up morphology and start using multiple words at a time. Children learn whole phrases first, then only later learn to analyze them into parts on an as-needed basis, thus they have no difficulty with opaque idioms and irregular forms. They don’t really benefit from regularity either: when children learn Esperanto as a native language, they introduce irregularities, even when the language is perfectly regular.

We see evidence of this process in English. Native speakers frequently make mistakes like using “could of” instead of “could’ve“, or using “your” instead of “you’re“. This is evidence that native English speakers think of them as a single unit, and don’t naturally analyze them into their sub-components: “could+have” and “you+are“.

According to the theory, in languages spoken in isolated communities, where few adults try to learn the language, it ends up with complex and irregular words. When lots of grown-ups try to learn the language, they struggle with the grammatical complexity and simplify it. Over time, these simplifications eventually become a standard part of the language.

Among the world’s languages, various studies have found correlations between grammatical complexity and smaller population size, supporting this theory. However, the theory is not without its problems. As with any observational study, correlation doesn’t imply causation. The European conquest of the Americas decimated the native population, and consequently, speakers of indigenous languages have declined drastically in the last few centuries. Framing it this way, the answer to “why aren’t there more polysynthetic languages with millions of speakers” is simply: “they all died of smallpox or got culturally assimilated”.

If instead, Native Americans had sailed across the ocean and colonized Europe, would more of us be speaking polysynthetic languages now? Until we can go back in time and rewrite history, we’ll never know the answer for sure.

Further reading

  • Atkinson, Mark David. “Sociocultural determination of linguistic complexity.” (2016). PhD Thesis. Chapter 1 provides a good overview of how languages are affected by social structure.
  • Kelly, Barbara, et al. “The acquisition of polysynthetic languages.” Language and Linguistics Compass 8.2 (2014): 51-64.
  • Trudgill, Peter. Sociolinguistic typology: Social determinants of linguistic complexity. Oxford University Press, 2011.
  • Wray, Alison, and George W. Grace. “The consequences of talking to strangers: Evolutionary corollaries of socio-cultural influences on linguistic form.” Lingua 117.3 (2007): 543-578. This paper proposes the theory and explains it in detail.

Predictions for 2030

Now that it’s Jan 1, 2020, I’m going to make some predictions about what we will see in the next decade. By the year 2030:

  • Deep learning will be a standard tool and integrated into workflows of many professions, eg: code completion for programmers, note taking during meetings. Speech recognition will surpass human accuracy. Machine translation will still be inferior to human professionals.

  • Open-domain conversational dialogue (aka the Turing Test) will be on par with an average human, using a combination of deep learning and some new technique not available today. It will be regarded as more of a “trick” than strong AI; the bar for true AGI will be shifted higher.

  • Driverless cars will be in commercial use in a few limited scenarios. Most cars will have some autonomous features, but full autonomy still not widely deployed.

  • S&P 500 index (a measure of the US economy, currently at 3230) will double to between 6000-7000. Bitcoin will still exist but its price will fall under 1000 USD (currently ~7000 USD).

  • Real estate prices in Toronto will either have a sharp fall or flatten out; overall increase in 2020-2030 period will not exceed inflation.

  • All western nations will have implemented some kind of carbon tax as political pressure increases from young people; no serious politician will suggest removing carbon tax.

  • About half of my Waterloo cohort will be married, but majority will not have any kids, at the age of 35.

  • China will overtake USA as world’s biggest economy, but growth will slow down, and PPP per capita will still be well below USA.

Directionality of word class conversion

Many nouns (like google, brick, bike) can be used as verbs:

  • Let me google that for you.
  • The software update bricked my phone.
  • Bob biked to work yesterday.

Conversely, many verbs (like talk, call) can be used as nouns:

  • She gave a talk at the conference.
  • I’m on a call with my boss.

Here, we just assumed that {google, brick, bike} are primarily nouns and {talk, call} are primarily verbs — but is this justified? After all, all five of these words can be used as either a noun or a verb. Then, what’s the difference between the first group {google, brick, bike} and the second group {talk, call}?

These are examples of word class flexibility: words that can be used across multiple part-of-speech classes. In this blog post, I’ll describe some objective criteria to determine if a random word like “sleep” is primarily a noun or a verb.

Five criteria for deciding directionality

Linguists have studied the problem of deciding what is the base / dominant part-of-speech category (equivalently, deciding the directionality of conversion). Five methods are commonly listed in the literature: frequency of occurrence, attestation date, semantic range, semantic dependency, and semantic pattern (Balteiro, 2007; Bram, 2011).

  1. Frequency of occurrence: a word is noun-dominant if it occurs more often as a noun than a verb. This is the easiest to compute since all you need is a POS-tagged corpus. The issue is the direction now depends on which corpus you use, and there can be big differences between genres.
  2. Attestation date: a word is noun-dominant if it was used first as a noun and only later as a verb. This works for newer words, Google (the company) existed for a while before anyone started “googling” things. But we run into problems with older words, and the direction then depends on the precise dating of Middle English manuscripts. If the word is from Proto-Germanic / Proto-Indo-European then finding the attestation date becomes impossible. This method is also philosophically questionable because you shouldn’t need to know the history of a language to describe its current form.
  3. Semantic range: if a dictionary lists more noun meanings than verb meanings for a word, then it’s noun-dominant. This is not so reliable because different dictionaries disagree on how many senses to include, and how different must two senses be in order to have separate entries. Also, some meanings are rare or domain specific (eg: “call option” in finance) and it doesn’t seem right to count them equally.
  4. Semantic dependency: if the definition of the verb meaning refers to the noun meaning, then the word is noun-dominant. For example, “to bottle” means “to put something into a bottle”. This criterion is not always clear to decide, sometimes you can define it either way, or have neither definition refer to the other.
  5. Semantic pattern: a word is noun-dominant if it refers to an entity / object, and verb-dominant if refers to an action. A bike is something that you can touch and feel; a walk is not. Haspelmath (2012) encourages distinguishing {entity, action, property} rather than {noun, verb, adjective}. However, it’s hard to determine without subjective judgement (especially for abstract nouns like “test” or “work”), whether the entity or action sense is more primary.

Comparisons using corpus methods

How do we make sense of all these competing criteria? To answer this question, Balteiro (2007) compare 231 pairs of flexible noun/verb pairs and rated them all according to the five criteria I listed above, as well as a few more that I didn’t include. Later, Bram (2011) surveyed a larger set of 2048 pairs.

The details are quite messy, because applying the criteria are not so straightforward. For example, polysemy: the word “call” has more than 40 definitions in the OED, and some of them are obsolete, so which one do you use for attestation date? How do you deal with homonyms like “bank” that have two unrelated meanings? With hundreds of pages of painstaking analysis, the researchers came to a judgement for each word. Then, they measured the agreement between each pair of criteria:

bram-thesis-tableTable of pairwise agreement (adapted from Table 5.2 of Bram’s thesis)

There is only a moderate level of agreement between the different criteria, on average about 65% — better than random, but not too convincing either. Only frequency and attestation date agree more than 80% of the time. Only a small minority of words have all of the criteria agree.

Theoretical ramifications

This puts us in a dilemma: how do we make sense of these results? What’s the direction of conversion if these criteria don’t agree? Are some of the criteria better than others, perhaps take a majority vote? Is it even possible to determine a direction at all?

Linguists have disagreed for decades over what to do with this situation. Van Lier and Rijkhoff (2013) gives a survey of the various views. Some linguists maintain that flexible words must be either noun-dominant or verb-dominant, and is converted to the other category. Other linguists note the disagreements between criteria and propose instead that words are underspecified. Just like a stem cell that can morph into a skin or lung cell as needed, a word like “sleep” is neither a noun or verb, but a pre-categorical form that can morph into either a noun or verb depending on context.

Can we really determine the dominant category of a conversion pair? It seems doubtful that this issue will ever be resolved. Presently, none of the theories make any scientific predictions that can be tested and falsified. Until then, the theories co-exist as different ways to view and analyze the same data.

The idea of a “dominant” category doesn’t exist in nature, it is merely an artificial construct to help explain the data. In mathematics, it’s nonsensical to ask if imaginary numbers really “exist”. Nobody has seen an imaginary number, but mathematicians use them because they’re good for describing a lot of things. Likewise, it doesn’t make sense to ask if flexible words really have a dominant category. We can only ask whether a theory that assumes the existence of a dominant category is simpler than a theory that does not.


  1. Balteiro, Isabel. The directionality of conversion in English: A dia-synchronic study. Vol. 59. Peter Lang, 2007.
  2. Bram, Barli. “Major total conversion in English: The question of directionality.” (2011). PhD Thesis.
  3. Haspelmath, Martin. “How to compare major word-classes across the world’s languages.” Theories of everything: In honor of Edward Keenan 17 (2012): 109-130.
  4. Van Lier, Eva, and Jan Rijkhoff. “Flexible word classes in linguistic typology and grammatical theory.” Flexible word classes: a typological study of underspecified parts-of-speech (2013): 1-30.