| Challenge: | Existing methods for reading comprehension tasks ignore semantic relations between sentences or use sliding window scanning over the words of the passage without sentence breaks. |
| Approach: | They propose a method to integrate information from multiple sentences to answer complex questions. |
| Outcome: | Experiments on RACE and MCTest show that the proposed approach improves state-of-the-art methods on simple factoid questions. |
Similar Papers
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020.emnlp-main)
Copied to clipboard
| Challenge: | Current machine reading comprehension benchmarks have no questions that test temporal phenomena . a new study studies reading comprehension for temporal relations . |
| Approach: | They propose a reading comprehension benchmark built on news snippets and 21k human-generated questions querying temporal relationships. |
| Outcome: | The new reading comprehension benchmark TORQUE achieves an exact-match score of 51% on the test set . the benchmark is built on 3.2k news snippets with 21k human-generated questions . |
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for extracting temporal information from text are not suitable for time-sensitive questions. |
| Approach: | They propose to use existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. |
| Outcome: | The proposed method outperforms graph convolution-based approaches on SituatedQA and TimeQA. |
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding (2022.naacl-main)
Copied to clipboard
| Challenge: | Temporal reading comprehension (TRC) is a natural way to study temporal relations since natural language questions are flexible to capture divergent temporal relationships. |
| Approach: | They propose a reading comprehension approach that uses precise question understanding . they embed a temporal ordering question into two vectors and evaluate the temporal relation based on that . |
| Outcome: | The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. |
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations. |
| Approach: | They propose a machine reading comprehension dataset that leverages natural language queries to reason about the five most common event semantic relations. |
| Outcome: | The proposed dataset shows that current SOTA systems achieve 22.1%, 63.3% and 83.5% for token-based exact-match, **F1** and event-based **HIT@1** scores. |
Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)
Copied to clipboard
| Challenge: | Temporal relationship extraction is crucial for understanding complex events and reasoning over them. |
| Approach: | They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain. |
Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure . |
| Approach: | They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events . |
| Outcome: | The proposed model improves on the ACE05, RAMS, and WikiEvents datasets. |
Once Upon a Time in Graph: Relative-Time Pretraining for Complex Temporal Reasoning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing work focuses on strengthening the knowledge-time association between text and time-stamps, but this is insufficient for downstream tasks. |
| Approach: | They propose a model that explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences. |
| Outcome: | The proposed model outperforms baseline T5 on multiple temporal question answering datasets . it is especially good at modeling long-range complex temporal dependencies, the authors say . |
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs (2020.coling-main)
Copied to clipboard
| Challenge: | Using conceptual graphs, we can embed a sentence into a knowledge embedding in a graph to solve slot-filling challenges in question answering and capture neighbouring connections of reference concept nodes. |
| Approach: | They propose a miscellaneous-context-based method to embed a sentence into a directed graph and a task-agnostic semantics module which integrates the syntactic-semantic information. |
| Outcome: | The proposed method can accommodate slot-filling challenges in typical question answering and capture the neighbouring connections of reference concept nodes. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
Copied to clipboard
| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
Annotating Temporal Dependency Graphs via Crowdsourcing (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing temporal annotation schemes have been limited due to the complexity of temporal relations between events. |
| Approach: | They propose to build a corpus of Wikinews articles annotated with temporal dependency graphs . they also propose a crowdsourcing strategy to annotate TDGs based on the corpus . |
| Outcome: | The proposed method achieves a good trade-off between completeness and practicality in temporal annotation. |