Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
| Autor: | Dipendra Misra, Xiaodong He, Ming-Wei Chang, Wen-tau Yih |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Parsing Computer Science - Computation and Language Computer science business.industry 02 engineering and technology computer.software_genre 03 medical and health sciences 0302 clinical medicine 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering Key (cryptography) Question answering 020201 artificial intelligence & image processing Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | EMNLP |
| Popis: | Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy. Accepted at EMNLP 2018 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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