Papers with sequence encoder

2 papers
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)

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Challenge: Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information.
Approach: They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form .
Outcome: The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880.
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders (2020.emnlp-main)

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Challenge: Named entity recognition and relation extraction are two important fundamental problems.
Approach: They propose to design two separate encoders to capture two different types of information in the representation learning process.
Outcome: The proposed methods show significant improvements on standard datasets.

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