| 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. |
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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. |
Script Parsing with Hierarchical Sequence Modelling (2021.starsem-1)
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| 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. |
When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs) (2023.starsem-1)
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| Challenge: | In this paper, we examine the ability of large language models (LLMs) to accommodate different pragmatic sentence types, such as questions, commands, and sentence fragments for natural language inference (NLI). |
| Approach: | They propose to fine-tune large language models to accommodate different sentence types for natural language inference (NLI) they also explore ChatGPT's concept of entailment by using a symbolic semantic parser. |
| Outcome: | The proposed models can accommodate different sentence types without losing too much accuracy on MNLI-matched 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. |
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. |
Seeking Clozure: Robust Hypernym extraction from BERT with Anchored Prompts (2023.starsem-1)
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| Challenge: | Existing methods for extracting hypernym knowledge from large language models are unclear whether they fail due to a lack of knowledge or shortcomings. |
| Approach: | They propose to use pattern-based hypernym extraction as a diagnostic tool to examine hypernomy knowledge encoded in BERT. |
| Outcome: | The proposed method compares the results of two different methods on six English data sets and on challenge sets of rare and abstract concepts. |
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. |
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. |
Event Causality Identification via Generation of Important Context Words (2022.starsem-1)
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| 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. |
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. |