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1. Beyond c-structure and f-structure: On the argument-adjunct distinction in O’dam

   Everdell, Michael; Melchin, Paul B.; Asudeh, Ash; Siddiqi, Daniel (April 2022, The proceedings of the LFG Conference)

   Butt, Miriam; Findlay, Jamie Y.; Toivonen, Ida (Ed.)

   In this paper we examine the argumenthood properties of Controlled Complement Clauses and Non-Complement Subordinate Clauses in O’dam. We show that in O’dam only controlled COMPs are arguments, while other putative complement clauses are adjunct relative clauses that elaborate on a pronominal OBJ incorporated in the matrix verb. We use the LRFG framework to capture both the argumenthood properties of the two types of clauses in O’dam as well as the patterns of object marking on the matrix verb by taking advantage of mismatches between c-structure (phrase structure and f-descriptions) and v-structure (the vocabulary items realizing this structure). 

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2. The typology of actual clauses in Eastern Bantu

   https://doi.org/10.4000/lla.9572  

   Sikuku, Justine Mukhwana; Mulalu, Joseph Wanyonyi; Safir, Ken (June 2023, Linguistique et langues africaines)

   This article reports on the existence of actual clause morphology and interpretation in selected Bantu languages. Essentially, we treat the actual clause as an embedded assertion whereby the utterer is committed not only to the truth of the proposition described by the actual clause: It must be the case that the event in the proposition cannot be unrealized (or describe a future state) at the time of the utterance. The Bantu languages in our sample mark the actual clause by a verbal prefix in a typical tense position on the lower verb. This prefix occurs as a single vowel or as a consonant/vowel combination. When the actual clause is a syntactic complement, it co-occurs with verbs that may be incompatible with indicative clauses. The clause is also semantically distinct from other clause types such as the infinitive and the subjunctive. Our analysis of actual clauses as assertions explains why they are not complements of factive verbs. We argue that the source of the speaker’s commitment to truth arises in part from the way actual clauses are licensed by the clauses they are dependent on. That is, we propose that actual clauses are licensed by a “contingent antecedent clause” which is taken to be a precondition for the actual clause assertion. Our approach generalizes to explain other non-complement uses of actual/narrative clause types, typically described as “narrative” tense in Bantu, which bears the exact same morphology 

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3. The Verbal Phrase in Paraguayan Guarani: A Case Study on the Role of Prosody in Linearization

   https://doi.org/10.3390/languages7030221  

   Zubizarreta, Maria Luisa (September 2022, Languages)

   This paper examines in detail the morpho-syntax of the verbal phrase in Paraguayan Guarani, in root and complement clauses, and argues that while the ordering relation between the verb and its associated functional morphemes is congruent with the syntax (cf. the Mirror Principle), the ordering of post-verbal arguments is best understood in terms of phonological linearization. More specifically, it is argued that there are language-particular prosodic requirements that force the post-verbal arguments to be phonologically linearized outside the accentual domain defined by the verb and its associated functional morphemes. 

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4. UMR annotation of Chinese Verb compounds and related constructions

   Sun, Haibo; Zhu, Yifan; Zhao, Jin; Xue, Nianwen (March 2023, Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023))

   This paper discusses the challenges of annotating the predicate-argument structure of Chinese verb compounds in Uniform Meaning Representation (UMR), a recent meaning representation framework that extends Abstract Meaning Representation (AMR) to cross-linguistic settings. The key issue is to decide whether to annotate the argument structure of a verb compound as a whole, or to annotate the argument structure of their component verbs as well as the relations between them. We examine different types of Chinese verb compounds, and propose how to annotate them based on the principle of compositionality, level of grammaticalization, and productivity of component verbs. We propose a solution to the practical problem of having to define the semantic roles for Chinese verb compounds that are quite open-ended by separating compositional verb compounds from verb compounds that are non-compositional or have grammaticalized verb components. For compositional verb compounds, instead of annotating the argument structure of the verb compound as a whole, we annotate the argument structure of the component verbs as well as the semantic relations between them as creating an exhaustive list of such verb compounds is infeasible. Verb compounds with grammaticalized verb components also tend to be productive and we represent grammaticalized verb compounds as either attributes of the primary verb or as relations. 

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5. CAN NEURAL NETWORKS ACQUIRE A STRUCTURAL BIAS FROM RAW LINGUISTIC DATA?

   Warstadt, Alex; Bowman, Samuel R. (January 2020, Proceedings of the Annual Meeting of the Cognitive Science Society)

   null (Ed.)

   We evaluate whether BERT, a widely used neural network for sentence processing, acquires an inductive bias towards forming structural generalizations through pretraining on raw data. We conduct four experiments testing its preference for structural vs. linear generalizations in different structure-dependent phenomena. We find that BERT makes a structural generalization in 3 out of 4 empirical domains---subject-auxiliary inversion, reflexive binding, and verb tense detection in embedded clauses---but makes a linear generalization when tested on NPI licensing. We argue that these results are the strongest evidence so far from artificial learners supporting the proposition that a structural bias can be acquired from raw data. If this conclusion is correct, it is tentative evidence that some linguistic universals can be acquired by learners without innate biases. However, the precise implications for human language acquisition are unclear, as humans learn language from significantly less data than BERT. 

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