Papers with DRSs
DRS Parsing as Sequence Labeling (2022.starsem-1)
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| Challenge: | a new semantic parser for English, German, Italian, and Dutch discourse representation structures is developed . we present a system that maps tokens to finite set of meaning fragments and is more transparent . a comprehensive error analysis highlights areas for future work on semantic parses . |
| Approach: | They propose a fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures that maps each token to one of a finite set of meaning fragments. |
| Outcome: | The proposed system is more transparent and useful for human-in-the-loop annotations. |
Text Generation from Discourse Representation Structures (2021.naacl-main)
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| Challenge: | Existing models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs) . |
| Approach: | They propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). |
| Outcome: | The proposed model achieves competitive performance on the GMB benchmark against several strong baselines. |
Discourse Representation Structure Parsing (P18-1)
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| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese (2021.acl-short)
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| Challenge: | Neural semantic parsers have obtained acceptable results in parsing DRSs . previous studies have focused on parse of DRS in English, but have focused only on a few languages . |
| Approach: | They propose to use character sequences as input to map meaning representations to string format. |
| Outcome: | The proposed models learn the meaning of a series of semantic phenomena by taking sentences as input and outputting the corresponding DRSs, without the aid of any extra linguistic information. |
Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have been used for tasks in computational semantics but meaning representations are not included in PLMs. |
| Approach: | They propose to include meaning representations besides natural language texts in the same model . they propose to use DRSs to improve performance of non-English tasks . |
| Outcome: | The proposed approach achieves the best performance on multilingual parsing and DRS-to-text generation tasks. |
Dscorer: A Fast Evaluation Metric for Discourse Representation Structure Parsing (2020.acl-main)
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| Challenge: | Discourse representation structures (DRSs) are scoped semantic representations for texts of arbitrary length. |
| Approach: | They propose a new metric which converts box-style DRSs to graphs and measures the overlap of n-grams. |
| Outcome: | Experiments show that Dscorer computes accuracy scores that are correlated with Counter at a fraction of the time. |