Sumit Neelam, Udit Sharma, et al.
EMNLP 2022
We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events. This is done by including reasoning layers that implement finite-domain quantification over objects and time. The structure allows them to generalize directly to input instances with varying numbers of objects in temporal sequences of varying lengths. We evaluate TOQ-Nets on input domains that require recognizing event-types in terms of complex temporal relational patterns. We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.
Sumit Neelam, Udit Sharma, et al.
EMNLP 2022
Don Joven Ravoy Agravante, Michiaki Tatsubori
JSAI 2022
Manling Li, Tengfei Ma, et al.
EMNLP 2021
Hanieh Hanieh Khorashadizadeh, Nandana Mihindukulasooriya, et al.
ESWC 2023