Faculty member Ivano Caponigro just a published a paper entitled “Unveiling multiple wh– free relative clauses and their functional wh-words” on Semantics & Pragmatics together with Dr. Anamaria Fălăuş.
Faculty member Eva Wittenberg has been awarded a UC San Diego Division of Social Sciences research grant to investigate counterfactuals like, “If I had bought toilet paper in February, I would have one fewer worry right now.” Congratulations, Eva!
Faculty member Ivano Caponigro is giving a talk on “Logic and Grammar: Richard Montague’s Turn towards Natural Language” at the Working Group in the History and Philosophy of Logic, Mathematics, and Science at UC Berkeley on March 18, 2020. Ivano will present some of the findings from the intellectual and personal biography of Richard Montague (1930-1971) that he is currently working on.
Faculty member Eva Wittenberg and Dr. Angela He (Chinese University of Hong Kong) have a new paper on the acquisition of event nominals and light verb constructions in Language & Linguistics Compass:
He AX, Wittenberg E. “The acquisition of event nominals and light verb constructions.” Lang Linguist Compass. 2019;1–18. https://onlinelibrary.wiley.com/doi/full/10.1111/lnc3.12363
Abstract. In language acquisition, children assume that syntax and semantics reliably map onto each other, and they use these mappings to guide their inferences about novel word meanings: For instance, at the lexical level, nouns should name objects and verbs name events, and at the clausal level, syntactic arguments should match semantic roles. This review focuses on two cases where canonical mappings are broken—first, nouns that name event concepts (e.g., “a nap”) and second, light verb constructions that do not neatly map syntactic arguments onto semantic roles (e.g., “give a kiss”). We discuss the challenges involved in their acquisition, review evidence that suggests a close connection between them, and highlight outstanding questions.
Faculty member Will Styler was invited to give a talk at UCLA’s Department of Linguistics on November 15th.
Title: Using Transparent Machine Learning to study Human Speech
Machine learning, the use of nuanced computer models to analyze and predict data, has a long history in speech recognition and natural language processing, but have largely been limited to more applied, engineering tasks. This talk will describe two more research-focused applications of transparent machine learning algorithms in the study of speech perception and production.
For speech perception, we’ll examine the difficult problem of identifying acoustic cues to a complex phonetic contrast, in this case, vowel nasality. Here, by training machine learning algorithms on acoustic measurements, we can more directly measure the informativeness of the various acoustic features to the contrast. This by-feature informativeness data was then used to create hypotheses about human cue usage, and then, to model the observed human patterns of perception, showing that these models were able to predict not only the utilized cue, but the subtle patterns of perception arising from less informative changes.
For speech production, we’ll focus on data from Electromagnetic Articulography (EMA), which provides position data for the articulators with high temporal and spatial resolution, and discuss our ongoing efforts to identify and characterize pause postures (specific vocal tract configurations at prosodic boundaries, c.f. Katsika et al. 2014) in the speech of 7 speakers of American English. Here, the lip aperture trajectories of 800+ individual pauses were gold-standard annotated by a member of the research team, and then subjected to principal component analysis. These analyses were then used to train a support vector machine (SVM) classifier, which achieved a 96% classification accuracy in cross-validation tests, with a Cohen’s Kappa showing machine-to-annotator agreement of 0.79, suggesting the potential for improvements in speed, consistency, and objective characterization of gestures.
These methods of modeling feature importance and classifying curves using transparent and interpretable machine learning both demonstrate concrete methods which are potentially useful and applicable to a variety of questions in phonetics, and potentially, in linguistics in general.
The first talk (on November 5th) will take place at the conference Crosslinguistic Perspectives on Processing and Learning (X-PPL) in Zurich, and Eva will present joint work with Ashwini Vaidya (IIT Delhi) on processing light verbs in Hindi in the their talk Practice makes perfect: Frequency of language-wide predicational strategy eases processing cost in Hindi light verb constructions.
The second talk (on November 9th) will take place at the 14. Bayerisch-Österreichischen Dialektologentagung in Salzburg, where Eva and her collaborator Andreas Trotzke will talk about their work on a variety of Bavarian: Mogst a weng a Schnitzala? Eine psycholinguistische Untersuchung zur referenziellen Verkleinerungsfunktion in ostfränkischen Nominalphrasen. (‘Would you like a little schnitzl? A psycholinguistic study about the referential function in East Franconian noun phrases’).