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1. Syntactic Structure from Deep Learning

   https://doi.org/10.1146/annurev-linguistics-032020-051035  

   Linzen, Tal; Baroni, Marco (January 2021, Annual Review of Linguistics)

   null (Ed.)

   Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey representative studies of the syntactic abilities of deep networks and discuss the broader implications that this work has for theoretical linguistics. 

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2. Predicting syntactic priming from sentence embedding vectors

   Kelly, M. A. (January 2020, In Proceedings of the 42nd Annual Conference of the Cognitive Science Society)
3. Prosody Prediction from Syntactic, Lexical, and Word Embedding Features

   https://doi.org/10.21437/SSW.2019-48  

   Sloan, Rose; Akhtar, Syed Sarfaraz; Li, Bryan; Shrivastava, Ritvik; Gravano, Agustin; Hirschberg, Julia (September 2019, 10th ISCA Speech Synthesis Workshop)

   Accurate prosody prediction from text leads to more natural-sounding TTS. In this work, we employ a new set of fea- tures to predict ToBI pitch accent and phrase boundaries from text. We investigate a wide variety of text-based features, in- cluding many new syntactic features, several types of word em- beddings, co-reference features, LIWC features, and specificity information. We focus our work on the Boston Radio News Corpus, a ToBI-labeled corpus of relatively clean news broad- casts, but also test our classifiers on Audix, a smaller corpus of read news, and on the Columbia Games Corpus, a corpus of conversational speech, in order to test the applicability of our model in cross-corpus settings. Our results show strong per- formance on both tasks, as well as some promising results for cross-corpus applications of our models. 

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4. Tiers, Paths, and Syntactic Locality: The View from Learning

   https://doi.org/10.7275/scil.2135  

   Hanson, Kenneth (January 2024, Proceedings of the Society for Computation in Linguistics)

   Many long-distance linguistic dependencies across domains can be modeled as tier-based strictly local (TSL) patterns (Graf 2022a). Such patterns are in principle efficiently learnable, but known algorithms require unrealistic conditions. Heuser et al. (2024) present an algorithm for learning syntactic islands which involves memorizing local bigrams along attested paths; no tiers are involved. I propose a method for inferring tier membership which augments the latter algorithm to produce a TSL grammar, and show that this model also derives a version of the Height-Locality Connection (Keine 2019). 

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5. Predicting syntactic structure

   https://doi.org/10.1016/j.brainres.2021.147632  

   Ferreira, Fernanda; Qiu, Zhuang (November 2021, Brain Research)