| 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. |
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What do Large Language Models Learn about Scripts? (2022.starsem-1)
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| Challenge: | Script Knowledge is important for language understanding but expensive to produce manually and difficult to induce from text due to reporting bias. |
| Approach: | They propose a pipeline-based script induction framework which can generate good quality ESDs for unseen scenarios. |
| Outcome: | The proposed framework produces good quality ESDs for unseen scenarios, but manual evaluation shows there is room for improvement. |
Event Semantic Knowledge in Procedural Text Understanding (2023.starsem-1)
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| 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. |
Capturing the Content of a Document through Complex Event Identification (2022.starsem-1)
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| 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)
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| 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. |
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets (2021.starsem-1)
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| Challenge: | Existing semantic parsing datasets lack a single standard for meaning representations . lack of a standard led to the creation of plethora of datasets requiring expert annotators . |
| Approach: | They propose to use multi-task learning to unify different datasets and train a single model for them. |
| Outcome: | The proposed architectures yield better parsing accuracies and composition generalization than single-task models. |
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)
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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. |
BiQuAD: Towards QA based on deeper text understanding (2021.starsem-1)
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| Challenge: | Recent question answering and machine reading benchmarks require systems to pinpoint the span of the answer to a given text. |
| Approach: | They propose a dataset that requires deeper comprehension to answer questions extractively and deductively. |
| Outcome: | The proposed dataset outperforms existing benchmarks on extractive and deductive questions. |
DRS Parsing as Sequence Labeling (2022.starsem-1)
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| Challenge: | a new semantic parser for English, German, Italian, and Dutch discourse representation structures is developed . we present a system that maps tokens to finite set of meaning fragments and is more transparent . a comprehensive error analysis highlights areas for future work on semantic parses . |
| Approach: | They propose a fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures that maps each token to one of a finite set of meaning fragments. |
| Outcome: | The proposed system is more transparent and useful for human-in-the-loop annotations. |
Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis (2021.starsem-1)
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| Challenge: | Compchains are a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
| Approach: | They introduce compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
| Outcome: | The proposed model performs poorly on sentences with predicate-argument structure with more than one level of embedding. |
JSEEGraph: Joint Structured Event Extraction as Graph Parsing (2023.starsem-1)
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| Challenge: | Existing approaches model event extraction using simplified datasets or sequence-labeling-based encodings. |
| Approach: | They propose a graph-based event extraction framework that explicitly encodes entities and events in a single semantic graph. |
| Outcome: | The proposed framework can handle nested event structures and solve different IE tasks jointly. |