Papers with DRS
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. |
Exploring Data Augmentation in Neural DRS-to-Text Generation (2024.eacl-long)
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| Challenge: | Neural networks are notoriously data-hungry, resulting in ungrammatical texts . data augmentation requires a specific design for a structurally rich input format . |
| Approach: | They propose to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |
| Outcome: | The proposed approach selectively augments a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |
Adversarial Learning for Discourse Rhetorical Structure Parsing (2021.acl-long)
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| Challenge: | Existing top-down discourse rhetorical structure parsers make local decisions and ignore global parsing. |
| Approach: | They propose a method to transform gold standard and predicted constituency trees into tree diagrams with two color channels. |
| Outcome: | The proposed method improves performance on RST-DT and CDTB corpora and can leverage global context. |
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing (2021.emnlp-main)
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| Challenge: | Existing training data is limited for languages other than English, so is the performance of the developed parsers. |
| Approach: | They propose to apply a pre-trained multilingual model to Italian, German and Dutch parsers where only a small number of manually annotated parses are available. |
| Outcome: | The proposed model improves on six parsers in English and Italian, German and Dutch, with the addition of universal dependency relations and universal POS tags as model-agnostic features. |
A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure (2020.acl-main)
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| Challenge: | Text-level discourse parsing of discourse rhetorical structure (DRS) is a fundamental research topic in natural language processing. |
| Approach: | They propose a top-down neural architecture for text-level discourse parsing . they cast the parser as a recursive split point ranking task . |
| Outcome: | The proposed top-down approach is more suitable for text-level discourse parsing. |
DRS: Deep Question Reformulation With Structured Output (2025.findings-acl)
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| Challenge: | Existing models like GPT-3 and Instruct-GPT lack the ability to reformulate unanswerable questions. |
| Approach: | They propose a zero-shot method that combines the strengths of LLMs with a DFS-based algorithm to iteratively explore potential entity combinations and constrain outputs using predefined entities. |
| Outcome: | The proposed method outperforms all baselines, including the GPT-3.5 model, on the unanswerable question reformulation task. |
Model-Agnostic Cross-Lingual Training for Discourse Representation Structure Parsing (2024.lrec-main)
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| Challenge: | Discourse Representation Structure (DRS) parsers are constrained when trained exclusively on monolingual data. |
| Approach: | They propose a cross-lingual training strategy that leverages cross-linguistic training data to train models in multiple languages. |
| Outcome: | The proposed method improves clause and graph parsing in English, German, Italian and Dutch. |