Traceback and Chunk-Based Learning: Comparing Usage-Based Computational Approaches to Child Code-Mixing

Abstract

Recent years have seen increased interest in code-mixing from a usage-based perspective. In usage-based approaches to monolingual language acquisition, a number of methods have been developed that allow for detecting patterns from usage data. In this paper, we evaluate two of those methods with regard to their performance when applied to code-mixing data: the traceback method, as well as the chunk-based learner model. Both methods make it possible to automatically detect patterns in speech data. In doing so, however, they place different theoretical emphases: while traceback focuses on frame-and-slot patterns, chunk-based learner focuses on chunking processes. Both methods are applied to the code-mixing of a German–English bilingual child between the ages of 2;3 and 3;11. Advantages and disadvantages of both methods will be discussed, and the results will be interpreted against the background of usage-based approaches.

Building languages: Estonian–English two-year-old bilingual’s reliance on patterns in code-mixed utterances

“…Dabrowska (2014) also showed that a higher frequency threshold leads to more failed derivations, though the difference is rather small. The same finding has been confirmed by Dabrowska & Lieven (2005) and Koch, Hartmann & Quick (2022).…”

“…Answering the above questions will also enable discussion of whether the traceback method can be applied to a child's spontaneous speech data from two genealogically unrelated languages. The last question has been posed in several studies (for example Gaskins et al 2022, Koch, Hartmann & Quick 2022, and while the current study sheds some light on that question, it was felt that answering the question properly would warrant a study of its own. This paper is structured as follows.…”

“…It is assumed that the child has that fixed string available as an entrenched unit. Most traceback studies use a frequency threshold of two occurrences (for example Dabrowska & Lieven 2005, but see Koch, Hartmann & Quick 2022 for a discussion and results of manipulating the threshold). According to Dabrowska (2014), a threshold of two is enough because the corpora only capture a very small proportion of the child's speech.…”

“…The same constructional patterns that form the basis of monolingual speech can, according to Koch, Hartmann & Quick (2022), be assumed also to be the basis for code-mixed utterances. For example, I want X can be the basis for I want see (from here on, Estonian is marked in bold) 'I want it' (Fiona, 2;7) if the child has uttered the frame I want as well as the slot filler see 'it' before.…”

“…For better comparability with other similar studies, it is also important to report the child's mean length of utterance (MLU), as it has been discussed that higher MLU leads to more traceback fails (Koch, Hartmann & Quick 2022). The MLU in words for Estonian for the entire recording period is 2.31 and for English 3.04.…”