Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs
| Autor: | Karthik Sankaranarayanan, Amrita Saha, Abhishek Laddha, Soumen Chakrabarti, Ghulam Ahmed Ansari |
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| Rok vydání: | 2019 |
| Předmět: |
Linguistics and Language
Computer science business.industry Communication lcsh:P98-98.5 02 engineering and technology computer.software_genre Computer Science Applications Human-Computer Interaction Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence lcsh:Computational linguistics. Natural language processing business computer Natural language processing |
| Zdroj: | Transactions of the Association for Computational Linguistics, Vol 7, Pp 185-200 (2019) |
| ISSN: | 2307-387X |
| DOI: | 10.1162/tacl_a_00262 |
| Popis: | Recent years have seen increasingly complex question-answering on knowledge bases (KBQA) involving logical, quantitative, and comparative reasoning over KB subgraphs. Neural Program Induction (NPI) is a pragmatic approach toward modularizing the reasoning process by translating a complex natural language query into a multi-step executable program. While NPI has been commonly trained with the ‘‘gold’’ program or its sketch, for realistic KBQA applications such gold programs are expensive to obtain. There, practically only natural language queries and the corresponding answers can be provided for training. The resulting combinatorial explosion in program space, along with extremely sparse rewards, makes NPI for KBQA ambitious and challenging. We present Complex Imperative Program Induction from Terminal Rewards (CIPITR), an advanced neural programmer that mitigates reward sparsity with auxiliary rewards, and restricts the program space to semantically correct programs using high-level constraints, KB schema, and inferred answer type. CIPITR solves complex KBQA considerably more accurately than key-value memory networks and neural symbolic machines (NSM). For moderately complex queries requiring 2- to 5-step programs, CIPITR scores at least 3× higher F1 than the competing systems. On one of the hardest class of programs (comparative reasoning) with 5–10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times. 1 |
| Databáze: | OpenAIRE |
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