The publication of the Penn Treebank Wall Street Journal (WSJ) corpus in the late 80s has undoubtedly pushed NLP from symbolic computation to statistical approaches, which dominate our field up to this day. The WSJ has become the NLP benchmark dataset for many tasks (e.g., part-of-speech tagging, parsing, semantic role labeling, discourse parsing), and has developed into the de-facto “standard” in our field.
However, while it has advanced the field in so many ways, it has also introduced almost imperceptible biases: why is newswire considered more standard or more canonical than other text types? Journalists are trained writers who make fewer errors and adhere to a codified norm.111We do not explicitly concern us here with issues of language prescription, but rather on the assumption-heavy perceptions of some instances of language as ‘more normal’. But let us pause for a minute. If NLP had emerged only in the last decade, would newswire data still be our canon? Or would, say, Wikipedia be considered canonical? User-generated data is less standardized, but is highly available. If we take this thought further and start over today, maybe we would be in an ‘inverted’ world: social media is standard and newswire with its ‘headlinese’ is the ‘bad language’ [eisenstein:2013:bad]. It is easy to collect large quantities of social media data. Whatever we consider canonical, all data comes with its biases, even more democratic media like Wikipedia carry their own peculiarities.222For instance, the demographics of Wikipedia shows that mostly young single men aged 18-30 contribute, see https://strategy.wikimedia.org/wiki/Wikimedia_users#Demographics
It seems that what is considered canonical hitherto is mostly a historical coincidence and motivated largely by availability of resources. Newswire has and actually still does dominate our field. For example, in Figure 1, I plot domains versus languages for the treebank data in version 1.3 of the on-going Universal Dependencies333http://universaldependencies.org/ project [universal1.2]. Almost all languages include newswire, except ancient languages (for obvious reasons), English (since most data comes from the Web Treebank) and Khazak, Chinese (Wikipedia). While including other domains and languages is highly desirable, it is impossible to find unbiased data.444This is related to the problem of overexposure in ethics, e.g., [hovy:spruit:2016]. Let’s be aware of this fact and try to collect enough biased data.
Processing non-canonical (or non-canonical) data is difficult. A series of papers document large drops in accuracy when moving across domains [mcclosky:2010, foster:ea:2011, inter alia]. There is a large body of work focusing on correcting for domain differences. Typically, in domain adaptation (DA) the task is to adapt a model trained on some source domain to perform better on some new target domain. However, it is less clear what really folds into a domain. In Section 5, I will review the notion of domain and propose what I call variety space.
Is the annotation of non-canonical also more difficult, just like its processing appears to be? Processing and annotating are two aspects, and the difficulty in one, say processing, does not necessarily propagate the same way to annotation [plank-martinezalonso-sogaard:2015:LAW]. However, very little work exists on disentangling the two. The same is true for examining what really constitutes a domain. What remains is clear: the challenge is all about variations of data. Language continuously changes, for various reasons (different social groups, communicative purposes, changes over time), and so we will continuously face interesting challenges, both for processing and annotation.
In the remainder I will look at the NLP community’s approach to face these challenges. I will outline one potential way to go about it, arguing for the use of fortuitous data, and end by returning to the question of domain.
2 What to do about non-standard data
There are generally three main approaches to go about non-standard data.
2.1 Annotate more data
Annotating more data is a first and intuitive solution. However, it is naïve, for several reasons.
Domain (whatever that means) and language (whatever that comprises) are two factors of text variation. Now take the cross-product between the two. We will never be able to create annotated data that spans all possible combinations. This is the problem of training data sparsity, illustrated in Figure 1. The figure only shows a tiny subset of the world’s languages, and a tiny fraction of potential domains out there. The problem is that most of the data that is available out there is unlabeled. Annotation requires time. At the same time, ways of communication change, so what we annotate today might be very distant to what we need to process tomorrow. We cannot just “annotate our way out” [eisenstein:2013:bad]. Moreover, it might not be trivial to find the right annotators; annotation schemes might need adaptation as well [zinsmeister:ea:2014] and tradeoffs for doing so need to be defined [schneider:2015].
What we need is quick ways to semi-automatically gather annotated data, and use more unsupervised and weakly supervised approaches.
2.2 Bring training and test data closer to each other
The second approach is based on the idea of making data resemble each other more. The first strategy here is normalization, that is, preprocess the input to make it closer to what our technology expects, e.g. han:baldwin:2013. A less known but similar approach is to artificially corrupt the training data to make it more similar to the expected target domain [vanderplas:ea:2009]. However, normalization implies “norm”, and as eisenstein:2013:bad remarks: whose norm are we targeting? (e.g., labor vs labour). Furthermore, he notes that it is surprisingly difficult to find a precise notion of the normalization task.
Corrupting the training data is a less explored endeavor. This second strategy though hinges on the assumption that one knows what to expect.
What we need are models that do provide nonsensical predictions on unexpected inputs, i.e., models that include invariant representations. For example, our models should be capable of learning similar representations for the same inherent concept, e.g., kiss vs :* or love vs 3. Recent shifts towards using sub-token level information can be seen as one step in this direction.
2.3 Domain adaptation
There is a large body of work on adapting models trained on some source domain to work better on some new target domain. Approaches range from feature augmentation, shared representation learning, instance weighting, to approaches that exploit representation induced from general background corpora. For an overview, see [plank:2011, weiss2016survey]. However, what all of these approaches have in common is an unrealistic assumption: they know the target domain. That is, researchers typically have a small amount of target data available that they can use to adapt their models.
An extreme case of adaptation is cross-lingual learning, whose goal is similar: adapt models trained on some source languages to languages in which few or no resources exist. Also here a large body of work assumes knowledge of the target language and requires some in-domain, typically parallel data. However, most work has focused on a restricted set of languages, only recently approaches emerged that aim to transfer from multiple sources to many target languages [agic:ea:2016].
What we need are methods that can adapt quickly to unknown domains and languages, without much assumptions on what to expect, and use multiple sources, rather than just one. In addition, our models need to detect when to trigger domain adaptation approaches.
In the next parts I will outline some possibilities to address these challenges. However, there are other important areas that I will not touch upon here (e.g., evaluation).
3 Fortuitous data
What we need are models that are more robust, work better on unexpected input and can be trained from semi-automatically or weakly annotated data, from a variety of sources. In order to build such models, I argue that the key is to look for signal in non-obvious places, i.e., fortuitous data.555Thanks to Anders Johannsen for suggesting fortuitous when I was in search for a name for serendipitous casual data.
Fortuitous data is data out there that just waits to be harvested. It might be in plain sight, but is neglected (available but not used), or it is in raw form and first needs to be refined (almost ready but needs refinement). Availability and ease-of-use (or readiness) are therefore two important dimensions that define fortuitous data. Fortuitous data is the unintended yield of a process, a promising by-product or side benefit.
|Side benefit of:||availability||readiness|
In the following I will outline potential sources of fortuitous data. An overview is given in Table 1.
Side benefit of user-generated content
This is data of high availability and high readiness, but it is often not used or “preprocessed away”. This source of fortuitous data includes user-generated content like webpages, social media posts, community-efforts like Wikipedia or Wiktionary. Concrete examples include hyperlinks that can be used to build more robust named entity and part-of-speech taggers [plank:ea:14:coling], or HTML markup for parsing [spitkovsky:ea:2010]. Similarly, Wiktionary can be used to mine large pools of data for unambiguous instances [hovy2015mining], or can guide constrained inference like in type-constrained POS tagging [tackstrom:ea:2013, plank:ea:2014]. Broadly speaking, exploiting the web to process the web.
Side benefit of annotation
Another yield that is often disregarded is annotator disagreement. Such data has high readiness, but low availability. It is still rare for annotation efforts to release intermediate or preliminary stages of the annotation project, but such data contains precious signal.
In fact, instead of adjudicating annotator decisions, we should embrace it. Annotator disagreement contains actual signal informative for a variety of tasks, including tagging, parsing, supersense tagging and relation extraction, e.g., [plank:ea:2014, aroyo2015truth].
Side benefit of behavior
When people produce or read texts, they produce loads of by-product in form of behavior data. Examples here include click-through data, but also more distant sources such as cognitive processing data like eye tracking or keystroke dynamics. In a pilot study, I found keystroke logs carry signal that can be used to inform NLP. Such data represents a potentially immense resource (imagine logging devices build into online editors or mobile phones, or eye tracking build into mobile devices). However, only very little work explored this source yet, e.g., [barrett:ea:2015, klerke:ea:2016]. It is also the “most distant” fortuitous source, having high availability and low readiness, as data often first needs to be refined.
Using fortuitous data can thus be seen as a way to quickly obtain semi-automatically labeled data, from a variety of sources. If we pair fortuitous data with appropriate learning algorithms (transfer/multi-task learning), this will enable language technology that can adapt quickly to new language varieties. However, one question remains.
4 But what’s in a domain?
As already noted earlier [plank:2011], there is no common ground on what constitutes a domain. blitzer2006domain attribute domain differences mostly to differences in vocabulary, biber explores differences between corpora from a sociolinguistics perspective. mcclosky:2010 considers it in a broader view: “By domain, we mean the style, genre, and medium of a document.” Terms such as genre, register, text type, domain, style are often used differently in different communities [lee2002genres], or interchangeably.
While there exists no definition of domain, work on domain adaptation is plentiful but mostly focused on assuming a dichotomy: source versus target, without much interest in how they differ. In fact, there is surprisingly little work on how texts vary and the consequence for NLP. It is established that out-of-vocabulary (OOV) tokens impact NLP performance. However, what are other factors?
Interest in this question re-emerged recently. For example, focusing on annotation difficulty, zeldes-simonson:2016 remark “that domain adaptation may be folding in sentence type effects”, motivated by earlier findings by silveira2014gold who remark that “[t]he most striking difference between the two types of data [Web and newswire] has to do with imperatives, which occur two orders of magnitude more often in the EWT [English Web Treebank].” A very recent paper examines word order properties and their impact on parsing taking a control experiment approach [gulordava2016multi]. On another angle, it has been shown that tagging accuracy correlates with demographic factors such as age [hovy:soegaard:2015:age].
I want to propose that ‘domain’ is an overloaded term. Besides the mathematical definition, in NLP it is typically used to refer to some coherent data with respect to topic or genre. However, there are many other (including yet unknown factors) out there, such as demographic factors, communicational purpose, but also sentence type, style, medium, technology/medium, language, etc. At the same time, these categories are not sharply defined either. Rather than imposing hard categories, let us consider a Wittgensteinian view.
5 The variety space
I here propose to see a domain as variety in a high-dimensional variety space. Points in the space are the data instances, and regions form domains. A dataset is a sample from the variety space, conditioned on latent factors :
The variety space is a unknown high-dimensional space, whose dimensions (latent factors ) include (fuzzy) aspects such as language (or dialect), topic or genre, and social factors (age, gender, personality, etc.), amongst others. A domain is a variety that forms a region in this complicated network of similarities, with some members more prototypical than others. However, we have neither access to the number of latent factors nor to their types. This vision is inspired by the notion of prototype theory in Cognitive Science and Wittgenstein’s graded notion of categories. Figure 2 shows a hypothetical example of this variety space.
Our datasets are subspaces of this high-dimensional space. Depending on our task, instances are sentences, documents etc. In the following I will use POS tagging as a running example to analyze what’s in a domain, by referring to the datasets with the typically used categories.
Some empirical evidence - Taggers and Data
Let us examine two POS taggers representative for different tagging approaches and evaluate them on several varieties. We use TnT,666http://www.coli.uni-saarland.de/~thorsten/tnt/ an HMM-based tagger, and Bilty, a bidirectional LSTM tagger [plank:ea:2016]. Both taggers are trained on the WSJ training portion converted to Universal POS tags [petrov:ea:2012]. As test sets we consider parts of the Web Treebank (emails and answers), two Twitter datasets (Foster and Gimpel/oct27, Twitter sample 1 and 2 respectively), review data from two different age groups [hovy:soegaard:2015:age], above 45 and below 35 years, and data from the CoNLL-X dataset from other Indogermanic languages.777http://ilk.uvt.nl/conll/free_data.html except Dutch because of joined MWU units. These datasets were chosen to represent different varieties.
Table 2 shows POS tagging accuracies. First, as is well known, we see that all taggers suffer when applied to other domains. However, models trained on WSJ fare worse on data from the younger age group, thus age is a covariate. This confirms the age bias reported in [hovy:soegaard:2015:age] for the same data but using different taggers. If we stretch the notion of variety to other languages, we see that performance unsurprisingly drops dramatically. Remember, we just apply an in-domain single language tagger to other languages, although only trained on WSJ here.888Subtoken representations are used train a single tagger for multiple languages [gillick:ea:2015]. Bilty performs much better on other languages than TnT
. Although the neural network-based tagger that uses both word and character embeddings fares better overall, both taggers suffer similarly, their accuracy variation is highly correlated (=0.95, over all test sets; =0.96 if we exclude the other languages, and =0.94 if we also include oct27).
While the two age samples have similar OOV rates, the two Twitter samples differ substantially. Twitter sample 1 (Foster) has an OOV rate close to others (28), while sample 2 has the highest OOV (52), every other token is an OOV word. Thus, although both come from the same medium (Twitter), they are very different samples. In general, OOV words are a major cause of performance drop. If we correlate all accuracies with OOV rate, we see a significant correlation ( = -0.70,
=0.02274). However, caution is needed here, the high correlation could be influenced by outliers. In fact, if we exclude the other Twitter sample (oct27, which seems to form an outlier) and other languages, there is no significant correlation (=0.23, -val 0.6584), see Figure 3, explained next.
Rather than just inspecting numbers of single test sets, we will now plot data characteristics versus accuracy. In order to do so we take 10 bootstrap samples ( sentences) from the original test data, tag it with the best variant of Bilty, which uses word and character features, and evaluate it against gold POS. Figure 3 shows accuracy rates versus OOV rate (above) and accuracy vs KL-divergence between gold and predicted tag bigram distributions (lower plot). Each data point in the plot is a bootstrap sample.
The plots show that Twitter sample 2 (dark green, Foster) is similar in OOV rate to emails and answers; In fact, it is very close to the original dataset (WSJ), it differs the least from WSJ in terms of POS KL-divergence (lower plot). In contrast, Twitter sample 2 (green, oct27) has not only high OOV rate, but it also differs highly in KL div from WSJ. The dataset contains many unusual POS sequences that are hard to predict. The same is true for age, the KL plot confirms that the tags of the younger group are harder to predict.
We see that performance varies greatly on different samples of Twitter data, as also reported earlier [hovy2014when]. This suggest that Twitter is not a ‘single domain’. It spans an entire range of varieties (social groups, agents, topics, even languages, etc.). Relating back to variety space, it seems that our two samples span different subspaces. Although the two samples used here do not resemble each other, they still share the commonality of being drawn from the same category (in this case, medium), mirroring Wittgenstein’s theory of family resemblance, cf. [givon1986prototypes]. In fact, if we think about data from Twitter, we will have a prototypical member in mind, but members might vary highly. Whenever we build models for, say, Twitter, we need to be aware of these properties. The more the data varies, the more test samples we will need to achieve higher confidence in our models.
Current NLP models still suffer dramatically when applied to non-canonical data, where canonicity is a relative notion; in our field, newswire was and still often is the de-facto standard, the canonical data we typically train our models on.
While newswire has advanced the field in so many ways, it has also introduced almost imperceptible biases. What we need is to be aware of such biases, collect enough biased data, and model variety. I argue that if we embrace the variety of this heterogeneous data by combining it with proper algorithms, in addition to including text covariates/latent factors, we will not only produce more robust models, but will also enable adaptive language technology capable of addressing natural language variation.
I would like to thank the organizers for the invitation to the keynote at KONVENS 2016. I am also grateful to Héctor Martínez Alonso, Dirk Hovy, Anders Johannsen, Zeljko Agić and Gertjan van Noord for valuable discussions and feedback on earlier drafts of this paper.