Papers with top-down approach
Top-down Discourse Parsing via Sequence Labelling (2021.eacl-main)
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| Challenge: | Discourse analysis is a systematic way to understand how texts are segmented hierarchically into discourse units. |
| Approach: | They propose a top-down approach to discourse parsing that is conceptually simpler than its predecessors. |
| Outcome: | The proposed model eliminates the decoder and reduces the search space for splitting points. |
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts (2025.naacl-short)
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| Challenge: | Training conversational question-answering systems requires in-domain data, which is often scarce in practice. |
| Approach: | They propose a bottom-up approach where QA pairs are generated first and combined into a coherent dialogue. |
| Outcome: | The proposed approach produces more realistic and higher-quality dialogues compared to top-down methods. |
DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization (D19-1)
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| Challenge: | Existing models for natural language video localization are top-down and bottom-up . however, both approaches suffer several limitations, leading to performance degradation . |
| Approach: | They propose a top-down approach for localizing a natural language description in a video sequence . they propose 'DEnse Bottom-Up Grounding' which uses the temporal boundaries of each video frame . |
| Outcome: | The proposed framework matches the speed of top-down models while surpassing the state-of-the-art models. |
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. |
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn Interaction (2024.emnlp-main)
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| Challenge: | Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage. |
| Approach: | They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy. |
| Outcome: | The proposed approach scales up the diversity of test cases using a top-down approach based on an extensible, fine-grained risk taxonomy and leverages reinforcement learning techniques to facilitate multi-turn adversarial probing in a human-like manner. |
Modal Dependency Parsing as Structured Prediction over Source-Cue Scope (2026.acl-long)
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| Challenge: | Existing work on identifying sources only focuses on defining source-introducing cues . a structured model focuses learning at the source-cue level and constrains event-level decisions to a small, scope-defined candidate set. |
| Approach: | They propose a framework that leverages large language models to explicitly identify source-cue pairs and their respective scope to define modal contexts. |
| Outcome: | The proposed framework surpasses state-of-the-art results by 3 and 4% for English and Chinese datasets. |