Abstract
When the semantics of a sentence are not representable in a semantic parser's output schema, parsing will inevitably fail. Detection of these instances is commonly treated as an out-of-domain classification problem. However, there is also a more subtle scenario in which the test data is drawn from the same domain. In addition to formalizing this problem of domain-adjacency, we present a comparison of various baselines that could be used to solve it. We also propose a new simple sentence representation that emphasizes words which are unexpected. This approach improves the performance of a downstream semantic parser run on in-domain and domain-adjacent instances.
Cite
CITATION STYLE
Ferguson, J., Christensen, J., Li, E., & Gonzàlez, E. (2018). Identifying domain adjacent instances for semantic parsers. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 4964–4969). Association for Computational Linguistics. https://doi.org/10.18653/v1/d18-1539
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