Damian Blasi


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Speakers Fill Lexical Semantic Gaps with Context
Tiago Pimentel | Rowan Hall Maudslay | Damian Blasi | Ryan Cotterell
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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 clear—resulting 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 WordNet (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.


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Quantifying the Semantic Core of Gender Systems
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)

Many of the world’s languages employ grammatical gender on the lexeme. For instance, in Spanish, house “casa” is feminine, whereas the word for paper “papel” is masculine. To a speaker of a genderless language, this categorization seems to exist with neither rhyme nor reason. But, is the association of nouns to gender classes truly arbitrary? In this work, we present the first large-scale investigation of the arbitrariness of gender assignment that uses canonical correlation analysis as a method for correlating the gender of inanimate nouns with their lexical semantic meaning. We find that the gender systems of 18 languages exhibit a significant correlation with an externally grounded definition of lexical semantics.

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Meaning to Form: Measuring Systematicity as Information
Tiago Pimentel | Arya D. McCarthy | Damian Blasi | Brian Roark | Ryan Cotterell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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 well-attested 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.

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Is Word Segmentation Child’s Play in All Languages?
Georgia R. Loukatou | Steven Moran | Damian Blasi | Sabine Stoll | Alejandrina Cristia
Proceedings of the 57th Annual Meeting of the Association for Computational 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 consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.

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On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study
Damian Blasi | Ryan Cotterell | Lawrence Wolf-Sonkin | Sabine Stoll | Balthasar Bickel | Marco Baroni
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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, dependency-parsed 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 disfavoured 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.


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Modeling infant segmentation of two morphologically diverse languages
Georgia-Rengina Loukatou | Sabine Stoll | Damian Blasi | Alejandrina Cristia
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

A rich literature explores unsupervised segmentation algorithms infants could use to parse their input, mainly focusing on English, an analytic language where word, morpheme, and syllable boundaries often coincide. Synthetic languages, where words are multi-morphemic, may present unique difficulties for segmentation. Our study tests corpora of two languages selected to differ in the extent of complexity of their morphological structure, Chintang and Japanese. We use three conceptually diverse word segmentation algorithms and we evaluate them on both word- and morpheme-level representations. As predicted, results for the simpler Japanese are better than those for the more complex Chintang. However, the difference is small compared to the effect of the algorithm (with the lexical algorithm outperforming sub-lexical ones) and the level (scores were lower when evaluating on words versus morphemes). There are also important interactions between language, model, and evaluation level, which ought to be considered in future work.