Papers with DRT
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. |
DRT: Deep Reasoning Translation via Long Chain-of-Thought (2025.findings-acl)
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| Challenge: | Recent studies have shown the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. |
| Approach: | They propose to use Qwen2.5 and LLama-3.1 as backbones to train long thought models to bring the success of long chain-of-thought (CoT) to neural machine translation. |
| Outcome: | The proposed model outperforms vanilla LLMs and LLM models which are fine-tuning on paired sentences without long thought and outperformed vanilla LRMs. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
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| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Scope-enhanced Compositional Semantic Parsing for DRT (2024.emnlp-main)
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| Challenge: | Existing compositional semantic parsers for DRT struggle to produce well-formed representations due to the complexity of the sentence. |
| Approach: | They propose a compositional, neurosymbolic semantic parser for DRT that uses a novel mechanism for predicting quantifier scope. |
| Outcome: | The proposed model produces well-formed outputs and performs well on complex sentences. |