| Challenge: | Recent work shows that explicit modeling entity states benefits LMs in procedural tasks. |
| Approach: | They propose a dataset where entities and attributes are fully canonicalized and additional entity salience annotations are added. |
| Outcome: | The proposed dataset outperforms existing models on question answering and classical planning tasks. |
Similar Papers
A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
Copied to clipboard
Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension (N18-1)
Copied to clipboard
| Challenge: | Using synthetic data, existing models struggle with questions that require inference. |
| Approach: | They propose a dataset and two new neural models that exploit alternative mechanisms for state prediction. |
| Outcome: | The proposed dataset improves accuracy by 19% over previous models. |
Leveraging Contextual Information for Effective Entity Salience Detection (2024.findings-naacl)
Copied to clipboard
Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro
| Challenge: | Prior work on salient entity detection focused on machine learning models that require heavy feature engineering. |
| Approach: | They propose to fine-tune medium-sized language models with a cross-encoder style architecture to achieve significant performance gains over feature engineering approaches. |
| Outcome: | The proposed model fine-tunes medium-sized pre-trained language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. |
WN-Salience: A Corpus of News Articles with Entity Salience Annotations (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing work on entity salience does not distinguish between salient and non-salient entities. |
| Approach: | They propose a dataset to measure entity salience using WikiNews dataset . WN-Salience is built on top of Wikinews, a Wikimedia project . |
| Outcome: | The proposed dataset can be used to benchmark tasks such as entity salience detection and salient entity linking. |
Chain and Causal Attention for Efficient Entity Tracking (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to handle entity tracking require at least log2 (n+1) layers to handle n state changes. |
| Approach: | They propose an efficient enhancement to the standard attention mechanism to handle long-term dependencies with a single layer. |
| Outcome: | The proposed model can handle entity tracking with n state changes with a single layer. |
OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets are not consistently formatted and use a variety of chunk encodings (IOB, BIO, etc.), often without documentation. |
| Approach: | They present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. |
| Outcome: | The proposed datasets correct annotation format issues and provide a structure that enables research in multilingual and multi-ontology NER. |
Conundrums in Entity Coreference Resolution: Making Sense of the State of the Art (2020.emnlp-main)
Copied to clipboard
| Challenge: | despite significant progress on entity coreference resolution, there is a general lack of understanding of what has been improved. |
| Approach: | They present an empirical analysis of entity coreference resolvers to provide an understanding of what has been improved. |
| Outcome: | The proposed model improves the performance of entity coreference resolvers. |
Proceedings of the First Workshop on Aggregating and Analysing Crowdsourced Annotations for NLP (D19-59)
Copied to clipboard
| Challenge: | The first workshop on crowdsourcing for NLP is open to all . |
| Approach: | The first workshop on crowdsourcing annotations for NLP is held at the acl.com . the workshop will focus on methods for aggregating and analysing crowdsourced data for Nl-specific tasks. |
| Outcome: | The first workshop on crowdsourcing for NLP received 16 submissions and accepted 7 . the workshop will focus on ambiguous, subjective or ambiguity analysis of crowdsourced data . |
EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes. |
| Approach: | They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes. |
| Outcome: | The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available. |
An annotated dataset of literary entities (N19-1)
Copied to clipboard
| Challenge: | Existing datasets built on news focus on non-named entities, but not literary texts. |
| Approach: | They propose to annotate 210,532 tokens from 100 different English-language literary texts for ACE entity categories (person, location, geo-political entity, facility, organization, and vehicle). |
| Outcome: | The proposed dataset includes 210,532 tokens drawn from 100 different English-language literary texts. |