Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport (2022.starsem-1)
Copied to clipboard
| Challenge: | Event Detection (ED) is a critical task in Information Extraction. |
| Approach: | They propose a word-label alignment task for event detecting . they propose to incorporate word-labeled alignment biases into the equation . |
| Outcome: | The proposed model facilitates incorporation of word-label alignment biases on a benchmark dataset to demonstrate its effectiveness. |
Similar Papers
Event Causality Identification via Generation of Important Context Words (2022.starsem-1)
Copied to clipboard
| Challenge: | Prior work focused on identifying causal relation between two event mentions . current models do not output important contexts for causal prediction of two mentions. |
| Approach: | They propose to use dependency path generation as a complementary task for ECI. |
| Outcome: | The proposed model can generate both causal relation and dependency path words from input sentences. |
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)
Copied to clipboard
Paolo Pedinotti, Giulia Rambelli, Emmanuele Chersoni, Enrico Santus, Alessandro Lenci, Philippe Blache
| Challenge: | Prior work has explored the ability of computational models to predict word semantic fit with a given predicate. |
| Approach: | They compare Transformers Language Models to SDM to assess their performance . they found that TLMs do not capture important aspects of event knowledge . people can discriminate between typical and atypical events, they say . |
| Outcome: | The proposed models can achieve comparable performance to SDM, but they lack important aspects of event knowledge. |
Capturing the Content of a Document through Complex Event Identification (2022.starsem-1)
Copied to clipboard
| Challenge: | Recent work grouped granular events into more general events, called complex events . however, this approach assumes that a given complex event is always described in consecutive sentences . |
| Approach: | They propose a context-augmented representation learning approach that uses contextual information to model pairwise relation between granular events. |
| Outcome: | The proposed approach outperforms baselines on the complex event identification task. |
Pairwise Representation Learning for Event Coreference (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing work induces mention representations independently by extracting features from the sentence that contains the mention, without using the context of the other mention. |
| Approach: | They propose a Pairwise Representation Learning scheme for the event mention pairs that jointly encodes a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. |
| Outcome: | The proposed scheme outperforms state-of-the-art representations on cross-document and within-document benchmarks. |
Script Parsing with Hierarchical Sequence Modelling (2021.starsem-1)
Copied to clipboard
| Challenge: | Script knowledge is a category of commonsense knowledge that describes how people conduct everyday activities sequentially. |
| Approach: | They propose a hierarchical sequence model and transfer learning to do script parsing with a sequence model that accurately tags script participants. |
| Outcome: | The proposed model improves state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants. |
EDM3: Event Detection as Multi-task Text Generation (2024.starsem-1)
Copied to clipboard
| Challenge: | Existing methods for Event Detection (ED) cannot easily leverage pre-trained semantic knowledge. |
| Approach: | They propose to decompose and reformulate ED and fine-tune over its atomic subtasks to enhance knowledge transfer while mitigating prediction error propagation inherent in pipelined approaches. |
| Outcome: | The proposed method achieves state-of-the-art performance on RAMS, MAVEN, and MLEE, while achieving 90% accuracy over rare event types. |
Measuring Alignment Bias in Neural Seq2seq Semantic Parsers (2022.starsem-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence semantic parsers with attention mechanisms have changed the research landscape . emergence of seq2seq models have led to questions about alignments . |
| Approach: | They investigate whether seq2seq models can handle both simple and complex alignments. |
| Outcome: | The proposed model performs better on monotonic and complex alignments compared to monotonic models . |
Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals (2022.starsem-1)
Copied to clipboard
| Challenge: | Existing methods to derive sentence embeddings have not been well understood what properties are captured in the resulting sentences depending on the supervision signals. |
| Approach: | They propose to combine two types of sentence embedding methods with similar architectures and tasks to investigate their properties. |
| Outcome: | The proposed methods perform better on unsupervised and downstream tasks than the proposed methods on untrained STS tasks and probing tasks. |
Event Semantic Knowledge in Procedural Text Understanding (2023.starsem-1)
Copied to clipboard
| Challenge: | Annotators’ reliance on commonsense knowledge to annotate implicit state information is a challenge for entity state tracking. |
| Approach: | They propose a method for entity state tracking that incorporates commonsense entity-centric knowledge from ConceptNet into a BERT-based neural-symbolic architecture. |
| Outcome: | The proposed model outperforms existing models on the ProPara dataset and is domain-agnostic. |
Multilingual and Code-Switched Sentence Ordering (2024.starsem-1)
Copied to clipboard
| Challenge: | Prior research has focused on English language structures and multilingual contexts . however, there are several shortcomings with specialized sentence ordering models and advanced Large Language Models like GPT-4. |
| Approach: | They propose a multilingual sentence order task that extends SO to diverse narratives across 12 languages and code-switched texts. |
| Outcome: | The proposed task extends SO to diverse narratives across 12 languages, including challenging code-switched texts. |