| Challenge: | Existing models that use context and type-matching heuristics do not provide realistic evaluation of reasoning capabilities. |
| Approach: | They propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find supporting facts and the answer jointly. |
| Outcome: | The proposed network shows competitive performance on the HotpotQA distractor setting benchmark compared to the state-of-the-art models. |
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Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
Semantic Role Labeling with Associated Memory Network (N19-1)
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| Challenge: | Existing work on semantic role labeling has been focused on using deep learning methods to solve the task. |
| Approach: | They propose a syntax-agnostic SRL model enhanced by the proposed associated memory network which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL. |
| Outcome: | The proposed model achieves state-of-the-art on CoNLL-2009 benchmark datasets showing that it is not dependent on external resources. |
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling (2020.emnlp-main)
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| Challenge: | Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. |
| Approach: | They propose to use graph convolutional networks to encode constituents and inform an SRL system by combining word representations of the first and last words in a constituent tree. |
| Outcome: | The proposed model is compared with other models and shows that it is more efficient than dependency trees. |
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)
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| Challenge: | Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process. |
| Approach: | They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it. |
| Outcome: | The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge. |
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)
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| Challenge: | Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale. |
| Approach: | They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
| Outcome: | The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
Is Graph Structure Necessary for Multi-hop Question Answering? (2020.emnlp-main)
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| Challenge: | Existing studies focus on multi-hop question answering across multiple documents or paragraphs. |
| Approach: | They propose a graph neural network to deal with graph structure in textual multi-hop reasoning . they propose 'self-attention' and propose removing entire graph structure may not hurt the final results . |
| Outcome: | The proposed model shows that graph-attention or the entire graph structure can be replaced by self-attention . hotpotQA is a widely used benchmark for multi-hop question answering . |
High-order Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
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Transformer-based Swedish Semantic Role Labeling through Transfer Learning (2024.lrec-main)
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| Challenge: | Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. |
| Approach: | They propose to build a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. |
| Outcome: | The proposed model outperforms two different cross-lingual transfer models and shows that the multilingual learning outperformed the other models. |
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing (2022.coling-1)
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| Challenge: | Using end-to-end span-based SRL, we propose a word-based graph parsing task for word-level representation of spans . compared with word-driven SRL, span-Based SRL is more complex due to difficulties in determining argument boundaries. |
| Approach: | They propose to cast end-to-end span-based SRL as a word-based graph parsing task . they propose a constrained Viterbi procedure to ensure the legality of the output graph . |
| Outcome: | The proposed model can parse 669/252 sentences per second without and with pre-trained models. |
Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)
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| Challenge: | Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening. |
| Approach: | They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning. |
| Outcome: | The proposed method improves on existing methods at CommonsenseQA and OpenBookQA. |