Out-of-distribution generalisation in spoken language understanding

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

5 Lataukset (Pure)

Abstrakti

Test data is said to be out-of-distribution (OOD) when it unex-
pectedly differs from the training data, a common challenge in
real-world use cases of machine learning. Although OOD gen-
eralisation has gained interest in recent years, few works have
focused on OOD generalisation in spoken language understand-
ing (SLU) tasks. To facilitate research on this topic, we intro-
duce a modified version of the popular SLU dataset SLURP,
featuring data splits for testing OOD generalisation in the SLU
task. We call our modified dataset SLURP For OOD gener-
alisation, or SLURPFOOD. Utilising our OOD data splits, we
find end-to-end SLU models to have limited capacity for gen-
eralisation. Furthermore, by employing model interpretability
techniques, we shed light on the factors contributing to the gen-
eralisation difficulties of the models. To improve the generali-
sation, we experiment with two techniques, which improve the
results on some, but not all the splits, emphasising the need for
new techniques.
AlkuperäiskieliEnglanti
OtsikkoInterspeech 2024
KustantajaInternational Speech Communication Association (ISCA)
Sivumäärä5
DOI - pysyväislinkit
TilaJulkaistu - 5 syysk. 2024
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInterspeech - Kos Island, Kreikka
Kesto: 1 syysk. 20245 syysk. 2024

Julkaisusarja

NimiInterspeech
KustantajaInternational Speech Communication Association
ISSN (elektroninen)2958-1796

Conference

ConferenceInterspeech
Maa/AlueKreikka
KaupunkiKos Island
Ajanjakso01/09/202405/09/2024

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