Sanskrit sandhi splitting using Seq2(Seq)22
Rahul Aralikatte, Neelamadhav Gantayat, et al.
EMNLP 2018
Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features; then employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.
Rahul Aralikatte, Neelamadhav Gantayat, et al.
EMNLP 2018
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Kevin Gu, Eva Tuecke, et al.
ICML 2024
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021