# Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability

arXiv: https://arxiv.org/abs/2309.06460 · HTML: https://mlsanity.com/paper/2309.06460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [An Incremental Parser for Abstract Meaning Representation](https://mlsanity.com/paper/1608.06111/) (2016)
- [Improving AMR Parsing with Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2010.01771/) (2020)
- [Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/1908.05859/) (2019)
- [Cross-lingual Abstract Meaning Representation Parsing](https://mlsanity.com/paper/1704.04539/) (2017)
- [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://mlsanity.com/paper/1811.00207/) (2018)
- [Pushing the Limits of AMR Parsing with Self-Learning](https://mlsanity.com/paper/2010.10673/) (2020)
- [Separating Argument Structure from Logical Structure in AMR](https://mlsanity.com/paper/1908.01355/) (2019)
- [StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations](https://mlsanity.com/paper/2109.09853/) (2021)
- [Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope](https://mlsanity.com/paper/2109.09858/) (2021)

## Abstract (arXiv)

This paper presents a novel semantic representation, WISeR, that overcomes challenges for Abstract Meaning Representation (AMR). Despite its strengths, AMR is not easily applied to languages or domains without predefined semantic frames, and its use of numbered arguments results in semantic role labels, which are not directly interpretable and are semantically overloaded for parsers. We examine the numbered arguments of predicates in AMR and convert them to thematic roles that do not require reference to semantic frames. We create a new corpus of 1K English dialogue sentences annotated in both WISeR and AMR. WISeR shows stronger inter-annotator agreement for beginner and experienced annotators, with beginners becoming proficient in WISeR annotation more quickly. Finally, we train a state-of-the-art parser on the AMR 3.0 corpus and a WISeR corpus converted from AMR 3.0. The parser is evaluated on these corpora and our dialogue corpus. The WISeR model exhibits higher accuracy than its AMR counterpart across the board, demonstrating that WISeR is easier for parsers to learn.