SLING: A framework for frame semantic parsing
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Abstract (arXiv)
We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2017.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 3; 2024: 3.
12 papers.
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- 2024A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala3 Dec 2024
- 2024Knowledge Authoring with Factual English, Rules, and Actions9 Nov 2024
- 2024Automatic Question-Answer Generation for Long-Tail Knowledge3 Mar 2024
- 2023Single Sequence Prediction over Reasoning Graphs for Multi-hop QA1 Jul 2023
- 2023Benchmarking Machine Translation with Cultural Awareness23 May 2023
- 2023Knowledge Authoring for Rules and Actions12 May 2023
- 2022A Unified Encoder-Decoder Framework with Entity Memory7 Oct 2022
- 2022Knowledge Authoring with Factual English5 Aug 2022
- 2021DEEP: DEnoising Entity Pre-training for Neural Machine Translation14 Nov 2021
- 2021Cross-Lingual Fine-Grained Entity Typing15 Oct 2021
- 2021Time-Aware Language Models as Temporal Knowledge Bases29 Jun 2021
- 2021Biomedical Interpretable Entity Representations17 Jun 2021