Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development.While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world's ≈6,500 languages.We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP.Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection).In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidencebased policy making aimed at promoting more global and equitable language technologies.1

While there exist scores of natural languages, each with its unique features and idiosyncrasies, they all share a unifying theme: enabling human communication.We may thus reasonably predict that human cognition shapes how these languages evolve and are used.Assuming that the capacity to process information is roughly constant across human populations, we expect a surprisal-duration trade-off to arise both across and within languages.We analyse this trade-off using a corpus of 600 languages and, after controlling for several potential confounds, we find strong supporting evidence in both settings.Specifically, we find that, on average, phones are produced faster in languages where they are less surprising, and vice versa.Further, we confirm that more surprising phones are longer, on average, in 319 languages out of the 600.We thus conclude that there is strong evidence of a surprisal-duration trade-off in operation, both across and within the world's languages.

Tiago Pimentel, Irene Nikkarinen, Kyle Mahowald, Ryan Cotterell, Damián Blasi. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Tiago Pimentel, Brian Roark, Søren Wichmann, Ryan Cotterell, Damián Blasi. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Abstract We use large-scale corpora in six different gendered languages, along with tools from NLP and information theory, to test whether there is a relationship between the grammatical genders of inanimate nouns and the adjectives used to describe those nouns. For all six languages, we find that there is a statistically significant relationship. We also find that there are statistically significant relationships between the grammatical genders of inanimate nouns and the verbs that take those nouns as direct objects, as indirect objects, and as subjects. We defer deeper investigation of these relationships for future work.

Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and therefore making language more efficient.If ambiguous words cannot be disambiguated from context, however, this gain in efficiency might make language less clearresulting in frequent miscommunication.For a language to be clear and efficiently encoded, we posit that the lexical ambiguity of a word type should correlate with how much information context provides about it, on average.To investigate whether this is the case, we operationalise the lexical ambiguity of a word as the entropy of meanings it can take, and provide two ways to estimate this-one which requires human annotation (using WordNet), and one which does not (using BERT), making it readily applicable to a large number of languages.We validate these measures by showing that, on six high-resource languages, there are significant Pearson correlations between our BERT-based estimate of ambiguity and the number of synonyms a word has in Word-Net (e.g.ρ = 0.40 in English).We then test our main hypothesis-that a word's lexical ambiguity should negatively correlate with its contextual uncertainty-and find significant correlations on all 18 typologically diverse languages we analyse.This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.

Embedding a clause inside another ("the girl [who likes cars [that run fast]] has arrived") is a fundamental resource that has been argued to be a key driver of linguistic expressiveness.As such, it plays a central role in fundamental debates on what makes human language unique, and how they might have evolved.Empirical evidence on the prevalence and the limits of embeddings has however been based on either laboratory setups or corpus data of relatively limited size.We introduce here a collection of large, dependencyparsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution.Our results indicate that there is no evidence for hard constraints on embedding depth: the tail of depth distributions is heavy.Moreover, although deeply embedded clauses tend to be shorter, suggesting processing load issues, complex sentences with many embeddings do not display a bias towards less deep embeddings.Taken together, the results suggest that deep embeddings are not disfavored in written language.More generally, our study illustrates how resources and methods from latest-generation big-data NLP can provide new perspectives on fundamental questions in theoretical linguistics.

When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation.Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language.We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages.The results are evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.

A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade?For instance, does the character bigram gl have any systematic relationship to the meaning of words like glisten, gleam and glow?In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks.We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages.We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation.Encouragingly, we also recover wellattested English examples of systematic affixes.We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small-despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.

Adina Williams, Damian Blasi, Lawrence Wolf-Sonkin, Hanna Wallach, Ryan Cotterell. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.