# A Transition-based Parser for Unscoped Episodic Logical Forms

arXiv: https://arxiv.org/abs/2103.08759 · HTML: https://mlsanity.com/paper/2103.08759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [AMR Parsing as Sequence-to-Graph Transduction](https://mlsanity.com/paper/1905.08704/) (2019)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Broad-Coverage Semantic Parsing as Transduction](https://mlsanity.com/paper/1909.02607/) (2019)
- [A Transition-Based Directed Acyclic Graph Parser for UCCA](https://mlsanity.com/paper/1704.00552/) (2017)
- [Robust Incremental Neural Semantic Graph Parsing](https://mlsanity.com/paper/1704.07092/) (2017)
- [SemBleu: A Robust Metric for AMR Parsing Evaluation](https://mlsanity.com/paper/1905.10726/) (2019)
- [A Spoken Dialogue System for Spatial Question Answering in a Physical Blocks World](https://mlsanity.com/paper/1911.02524/) (2019)
- [A Type-coherent, Expressive Representation as an Initial Step to Language Understanding](https://mlsanity.com/paper/1903.09333/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient](https://mlsanity.com/paper/2207.07276/) (2022)

## Abstract (arXiv)

"Episodic Logic:Unscoped Logical Form" (EL-ULF) is a semantic representation capturing predicate-argument structure as well as more challenging aspects of language within the Episodic Logic formalism. We present the first learned approach for parsing sentences into ULFs, using a growing set of annotated examples. The results provide a strong baseline for future improvement. Our method learns a sequence-to-sequence model for predicting the transition action sequence within a modified cache transition system. We evaluate the efficacy of type grammar-based constraints, a word-to-symbol lexicon, and transition system state features in this task. Our system is available at https://github.com/genelkim/ulf-transition-parser We also present the first official annotated ULF dataset at https://www.cs.rochester.edu/u/gkim21/ulf/resources/.