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In the 20 years that have passed since Heleen Bos’ original work on serial verb constructions in Sign Language of the Netherlands (NGT), continuing work on verb serialization has consolidated the topic. A greater breadth and depth of (spoken) language data has made it possible to characterize the phenomenon and to develop a taxonomy of different types of serial verb constructions. Unfortunately, these developments have not been accompanied by a parallel growth in the topic in sign linguistics. Since Supalla’s (1986, 1990) seminal work on classifier forms and verbs of motion, sign linguists have been aware of the presence of serial verb constructions in sign languages, but very little has happened since then. The work on classifier constructions in sign languages has acknowledged the existence of serial verb constructions of this type (e.g. Slobin & Hoiting 1994; Hong 2003; Tang 2003), provided formal models of such constructions (Benedicto, Cvejanova & Quer 2008) or even suggested alternative analyses (see Tang & Yang 2006 for the proposal that double verb constructions in Hong Kong Sign Language are verb-verb compounds). However, all the cited works focus on verb constructions that involve classifiers. In contrast, Bos identified serial verb constructions made up of lexical verbs. In this commentary, I intend to point out why that difference is important, and to show what Bos’ data can add to what we know about signed languages and about serial verb constructions. In order to do this, I adopt recent typological work on serial verb constructions: Aikhenvald (2006) provides a thorough overview that allows us to situate the NGT structures within the range of serial verb constructions that appear in many different languages of the world (section 2); Haspelmath (2016) proposes a narrower approach aimed at allowing crosslinguistic comparison by means of a stricter definition of serial verb constructions and a set of accompanying generalizations that follow from this definition

Sign language serial verb constructions fit into the bigger picture