Practical Semantic Parsing for Spoken Language Understanding
By Marco Damonte, Rahul Goel, Tagyoung Chung. 2019
28 citations recorded by the source corpus.
Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response. We build a transfer learning framework for executable semantic parsing. We show that the framework is effective for Question Answering (Q&A) as well as for Spoken Language Understanding (SLU). We further investigate the case where a parser on a new domain can be learned by exploiting data on other domains, either via multi-task learning between the target domain and an auxiliary domain or via pre-training on the auxiliary domain and fine-tuning on the target domain. With either flavor of transfer learning, we are able to improve performance on most domains; we experiment with public data sets such as Overnight and NLmaps as well as with commercial SLU data. The experiments carried out on data sets that are different in nature show how executable semantic parsing can unify different areas of NLP such as Q&A and SLU.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, background): “Therefore, semantic parsers are widely adopted in dialogue systems to map natural language (NL) utterances to executable programs or logical forms ∗corresponding author (LFs) (Damonte et al., 2019; Rongali et al., 2020).”
- FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations (2022, unknown): “…al., 2019, 2021a; Opitz et al., 2020, 2021; Fu et al., 2021) and has been shown to benefit down-stream tasks such as spoken language understanding (Damonte et al., 2019), machine translation (Song et al., 2019), commonsense reasoning (Lim et al., 2020), and question answering (Kapanipathi et al.,…”