Climbing towards NLU : On Meaning, Form, and Understanding in the Age of Data (2020)

Abstract: The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as “understanding” language or capturing “meaning”. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of “Taking Stock of Where We’ve Been and Where We’re Going”, we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.

## introduction the current state of affairs in nlp is that the large neural language models (lms), such as bert @xcite or gpt-2 @xcite , are making great progress on a wide range of tasks, including those that are ostensibly meaningsensitive. this has led to claims, in both academic and popular publications, that such models "understand" or "comprehend" natural language or learn its "meaning". from our perspective, these are overclaims caused by a misunderstanding of the relationship between linguistic form and meaning. we argue that the language modeling task, because it only uses form as training data, cannot in principle lead to learning of meaning. we take the term language model to refer to any system trained only on the task of string prediction, whether it operates over characters, words or sentences, and sequentially or not. we take (linguistic) meaning to be the relation between a linguistic form and communicative intent. our aim is to advocate for an alignment of claims and methodology: human-analogous natural language understanding (nlu) is a grand challenge of artificial intelligence, which involves mastery of the structure and use of language and the ability to ground it in the world. while large neural lms may well end up being important components of an eventual full-scale solution to human-analogous nlu, they are not nearly-there solutions to this grand challenge. we argue in this paper that genuine progress in our field -climbing the right hill, not just the hill on whose slope we currently sitdepends on maintaining clarity around big picture notions such as meaning and understanding in task design and reporting of experimental results. after briefly reviewing the ways in which large lms are spoken about and summarizing the recent flowering of "bertology" papers ( §2), we offer a working definition for "meaning" ( §3) and a series of thought experiments illustrating the impossibility of learning meaning when it is not in the training signal ( §4,5). we then consider the human language acquisition literature for insight into what information humans use to bootstrap language learning ( §6) and the distributional semantics literature to discuss what is required to ground distributional models ( §7). §8 presents reflections on how we look at progress and direct research effort in our field, and in §9, we address possible counterarguments to our main thesis. ## large lms: hype and analysis publications talking about the application of large lms to meaning-sensitive tasks tend to describe the models with terminology that, if interpreted at face value, is misleading. here is a selection from academically-oriented pieces (emphasis added): one important consequence of imprudent use of terminology in our academic discourse is that it feeds ai hype in the popular press. as nlp gains public exposure and is more widely used in applied contexts, it is increasingly important that the actual capabilities of our systems be accurately represented. in some cases, nlp experts speaking with the media are being appropriately careful, as in these two quotes in the new york times: foot_0 (4) these systems are still a really long way from truly understanding running prose. in sum, it is not clear from our academic literature whether all authors are clear on the distinction between form and meaning, but it is clear that the way we speak about what neural lms are doing is misleading to the public. part of the reason for this tendency to use imprecise language may well be that we do not yet fully understand what exactly it is about language that the large lms come to implicitly represent. their success, however, has sparked a subfield ('bertology') that aims to answer this question. the methodology of probing tasks (e.g. @xcite has been used to show that large lms learn at least some information about phenomena such as english subject-verb agreement @xcite , constituent types, dependency labels, ner, and (core) semantic role types (again, all in english) @xcite . 4 @xcite find information analogous to unlabeled dependency structures in the word vectors provided by elmo and bert (trained on english). and of course it is well established that vector-space representations of words pick up word classes, both syntactic (pos, e.g. @xcite and semantic (lexical similarity, e.g. @xcite . others have looked more closely at the success of the large lms on apparently meaning sensitive tasks and found that in fact, far from doing the "reasoning" ostensibly required to complete the tasks, they were instead simply more effective at leveraging artifacts in the data than previous approaches. @xcite find that bert's unreasonably good performance on the english argument reasoning comprehension task @xcite falls back to chance if the dataset is modified by adding adversarial examples that just negate one piece of the original, thus mirroring the distribution of lexical cues for each label. similarly, @xcite find that bert's performance on the english multi-genre natural language inference dataset @xcite is predicated on its ability to leverage syntactic heuristics involving overlap (of full constituents, subsequences, or simply bags of words). in a dataset carefully designed to frustrate such heuristics, bert's performance falls to significantly below chance. in this brief overview of bertology papers we have highlighted both the extent to which there is evidence that large lms can learn aspects of linguistic formal structure (e.g. agreement, dependency structure), and how their apparent ability to "reason" is sometimes a mirage built on leveraging artifacts in the training data (i.e. form, not meaning). our contribution is an argument on theoretical grounds that a system exposed only to form in its training cannot in principle learn meaning. we start by defining two key terms: we take form to be any observable realization of language: marks on a page, pixels or bytes in a digital representation of text, or movements of the articulators. 5 we take meaning to be the relation between the form and something external to language, in a sense that we will make precise below. ## the octopus test in order to illustrate the challenges in attempting to learn meaning from form alone, we propose a concrete scenario. say that a and b, both fluent speakers of english, are independently stranded on two uninhabited islands. they soon discover that previous visitors to these islands have left behind telegraphs and that they can communicate with each other via an underwater cable. a and b start happily typing messages to each other. meanwhile, o, a hyper-intelligent deep-sea octopus who is unable to visit or observe the two islands, discovers a way to tap into the underwater cable and listen in on a and b's conversations. o knows nothing about english initially, but is very good at detecting statistical patterns. over time, o learns to predict with great accuracy how b will respond to each of a's utterances. o also observes that certain words tend to occur in similar contexts, and perhaps learns to generalize across lexical patterns by hypothesizing that they can be used somewhat interchangeably. nonetheless, o has never observed these objects, and thus would not be able to pick out the referent of a word when presented with a set of (physical) alternatives. at some point, o starts feeling lonely. he cuts the underwater cable and inserts himself into the conversation, by pretending to be b and replying to a's messages. can o successfully pose as b without making a suspicious? this constitutes a weak form of the turing test (weak because a has no reason to suspect she is talking to a nonhuman); the interesting question is whether o fails it because he has not learned the meaning relation, having seen only the form of a and b's utterances. the extent to which o can fool a depends on the task -that is, on what a is trying to talk about. a and b have spent a lot of time exchanging trivial notes about their daily lives to make the long island evenings more enjoyable. it seems possible that o would be able to produce new sentences of the kind b used to produce; essentially acting as a chatbot. this is because the utterances in such conversations have a primarily social function, and do not need to be grounded in the particulars of the interlocutors' actual physical situation nor anything else specific about the real world. it is sufficient to produce text that is internally coherent. now say that a has invented a new device, say a coconut catapult. she excitedly sends detailed instructions on building a coconut catapult to b, and asks about b's experiences and suggestions for improvements. even if o had a way of constructing the catapult underwater, he does not know what words such as rope and coconut refer to, and thus can't physically reproduce the experiment. he can only resort to earlier observations about how b responded to similarly worded utterances. perhaps o can recognize utterances about mangos and nails as "similarly worded" because those words appeared in similar contexts as coconut and rope. so o decides to simply say "cool idea, great job!", because b said that a lot when a talked about ropes and nails. it is absolutely conceivable that a accepts this reply as meaningful -but only because a does all the work in attributing meaning to o's response. it is not because o understood the meaning of a's instructions or even his own reply. finally, a faces an emergency. she is suddenly pursued by an angry bear. she grabs a couple of sticks and frantically asks b to come up with a way to construct a weapon to defend herself. of course, o has no idea what a "means". solving a task like this requires the ability to map accurately between words and real-world entities (as well as reasoning and creative thinking). it is at this point that o would fail the turing test, if a hadn't been eaten by the bear before noticing the deception. 7 having only form available as training data, o did not learn meaning. the language exchanged by a and b is a projection of their communicative intents through the meaning relation into linguistic forms. without access to a means of hypothesizing and testing the underlying communicative intents, reconstructing them from the forms alone is hopeless, and o's language use will eventually diverge from the language use of an agent who can ground their language in coherent communicative intents. the thought experiment also illustrates our point from §3 about listeners' active role in communication. when o sent signals to a pretending to be b, he exploited statistical regularities in the form, i.e. the distribution of linguistic forms he observed. whatever o learned is a reflection of a and b's communicative intents and the meaning relation. but reproducing this distribution is not sufficient for meaningful communication. o only fooled a into believing he was b because a was such an active listener: because agents who produce english sentences usually have communicative intents, she assumes that o does too, and thus she builds the conventional meaning english associates with o's utterances. because she assumes that o is b, she uses that conventional meaning together with her other guesses about b's state of mind and goals to attribute communicative intent. it is not that o's utterances make sense, but rather, that a can make sense of them. ## more constrained thought experiments the story of the octopus considers the problem of learning not only the full communicative system, including the relations m and c, but also the reasoning required to come up with answers that are both coherent and also helpful in the real world. here, we provide two more constrained thought experiments, to focus more narrowly on the problem of learning the meaning relation, for both natural languages and programming languages. because programming languages are designed to be unambiguous and relatively insensitive to execution context, the distinction between standing and speaker meaning is less important than for natural languages. a java program e, when compiled and executed on the java virtual machine, can be interpreted as a function i which maps program inputs to program outputs. we take the meaning relation j ⊆ e × i of java to contain all such pairs (e, i). java imagine that we were to train an lm on all of the well-formed java code published on github. the input is only the code. it is not paired with bytecode, nor a compiler, nor sample inputs and outputs for any specific program. we can use any type of lm we like and train it for as long as we like. we then ask the model to execute a sample program, and expect correct program output. english as as second example, imagine training an lm (again, of any type) on english text, again with no associated independent indications of speaker intent. the system is also given access to a very large collection of unlabeled photos, but without any connection between the text and the photos. for the text data, the training task is purely one of predicting form. for the image data, the training task could be anything, so long as it only involves the images. at test time, we present the model with inputs consisting of an utterance and a photograph, like how many dogs in the picture are jumping? or kim saw this picture and said "what a cute dog!" what is cute? and the photos in figure 1 , where the appropriate answers are a number or a region of the photo, respectively. reflections in both cases, the tests are ridiculous. it seems patently unfair to ask the model to perform them, given what it was trained on. but that is precisely the point we are trying to make: a system that has learned the meaning (semantics) of a programming language knows how to execute code in that language. and a system that has learned the meaning of a human language can do things like answer questions posed in the language about things in the world (or in this case, in pictures). in other words, what's interesting here is not that the tasks are impossible, but rather what makes them impossible: what's missing from the training data. the form of java programs, to a system that has not observed the inputs and outputs of these programs, does not include information on how to execute them. similarly, the form of english sentences, to a system that has not had a chance to acquire the meaning relation c of english, and in the absence of any signal of communicative intent, does not include any information about what language-external entities the speaker might be referring to. accordingly, a system trained only on the form of java or english has no way learn their respective meaning relations. ## human language acquisition one common reason for believing lms might be learning meaning is the claim that human children can acquire language just by listening to it. this is not supported by scholarly work on language acquisition: rather, we find that human language learning is not only grounded in the physical world around us, but also in interaction with other people in that world. kids won't pick up a language from passive exposure such as tv or radio: @xcite note in passing that dutch-speaking kids who watch german tv shows by choice nonetheless don't learn german. @xcite shows experimentally that english-learning infants can learn mandarin phonemic distinctions from brief interac-tions with a mandarin-speaking experimenter but not from exposure to mandarin tv or radio. baldwin (1995) and others argue that what is critical for language learning is not just interaction but actually joint attention, i.e. situations where the child and a caregiver are both attending to the same thing and both aware of this fact. this theoretical perspective is substantiated with experimental results showing that toddlers (observed at 15 and 21 months) whose caregivers "follow into" their attention and provide labels for the object of joint attention more have larger vocabularies @xcite ; that toddlers (18-20 months old) don't pick up labels uttered by someone behind a screen, but do pick up labels uttered by someone performing joint attention with them @xcite ; and that at around 10-11 months of age babies pay attention to whether a person's eyes are open or not in terms of whether to follow their gaze, and the degree to which infants in fact follow gaze at 10-11 months while vocalizing themselves predicts vocabulary comprehension 7-8 months later @xcite . 8 in summary, the process of acquiring a linguistic system, like human communication generally, relies on joint attention and intersubjectivity: the ability to be aware of what another human is attending to and guess what they are intending to communicate. human children do not learn meaning from form alone and we should not expect machines to do so either. ## distributional semantics distributional semanticists have long been aware that grounding distributional representations in the real world is challenging. the lexical similarity relations learned by distributional models trained on text don't in themselves connect any of those words to the world @xcite @xcite , and the distributions of words may not match the distribution of things in the world (consider four-legged dogs). one approach to providing grounding is to train distributional models on corpora augmented with perceptual data, such as photos @xcite or other modalities @xcite . another is to look to interaction data, e.g. a dialogue corpus with success annotations, including low-level success signals such as emotional stress (mcduff and kapoor, 2019) or eye gaze @xcite , which contains a signal about the felicitous uses of forms. the idea that as the learner gets access to more and more information in addition to the text itself, it can learn more and more facets of meaning is worked out in detail by @xcite . we agree that this is an exciting avenue of research. from this literature we can see that the slogan "meaning is use" (often attributed to @xcite , refers not to "use" as "distribution in a text corpus" but rather that language is used in the real world to convey communicative intents to real people. speakers distill their past experience of language use into what we call "meaning" here, and produce new attempts at using language based on this; this attempt is successful if the listener correctly deduces the speaker's communicative intent. thus, standing meanings evolve over time as speakers can different experiences (e.g. @xcite , and a reflection of such change can be observed in their changing textual distribution (e.g. @xcite . ## on climbing the right hills what about systems which are trained on a task that is not language modeling -say, semantic parsing, or reading comprehension tests -and that use word embeddings from bert or some other large lm as one component? numerous papers over the past couple of years have shown that using such pretrained embeddings can boost the accuracy of the downstream system drastically, even for tasks that are clearly related to meaning. our arguments do not apply to such scenarios: reading comprehension datasets include information which goes beyond just form, in that they specify semantic relations between pieces of text, and thus a sufficiently sophisticated neural model might learn some aspects of meaning when trained on such datasets. it also is conceivable that whatever information a pretrained lm captures might help the downstream task in learning meaning, without being meaning itself. recent research suggests that it is wise to interpret such findings with caution. as noted in §2, both @xcite and @xcite found that bert picked up idiosyncratic patterns in the data for their tasks, and not "meaning". beyond such diagnostic research on why large pretrained lms boost such tasks so much, we think there is a more fundamental question to be asked here: are we climbing the right hill? ## some possible counterarguments in discussing the main thesis of this paper with various colleagues over the past 18 months, we have observed recurring counterarguments. in this section, we address those counterarguments, plus a few more that might arise. ## conclusion in this paper, we have argued that in contrast to some current hype, meaning cannot be learned from form alone. this means that even large language models such as bert do not learn "meaning"; they learn some reflection of meaning into the linguistic form which is very useful in applications. we have offered some thoughts on how to maintain a healthy, but not exaggerated, optimism with respect to research that builds upon these lms. in particular, this paper can be seen as a call for precise language use when talking about the success of current models and for humility in dealing with natural language. with this we hope to encourage a top-down perspective on our field which we think will help us select the right hill to climb towards human-analogous nlu. dani yogatama, cyprien de masson d'autume, jerome connor, tomas kocisky, mike chrzanowski, lingpeng kong, angeliki lazaridou, wang ling, lei yu, chris dyer, and phil blunsom. 2019. learning and evaluating general linguistic intelligence. arxiv preprint.