Probing Representations for Document-level Event Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level information extraction tasks require a more comprehensive understanding that often extends to the entire input document. |
| Approach: | They propose to use probing to analyze document-level information extraction representations by embedding probes into a standard dataset. |
| Outcome: | The proposed models improve argument detections but struggle with document length and cross-sentence discourse. |
Similar Papers
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
| Approach: | They examine the probing paradigm through a set of controlled synthetic tasks and show that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself. |
| Outcome: | The proposed model can encode linguistic properties above chance-level even when distributed in the data as random noise, reversing the interpretation of absolute claims on probing tasks. |
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Neural relation extraction models capture linguistic and semantic properties of the input, a recent study shows. |
| Approach: | They introduce 14 probing tasks targeting linguistic properties relevant to RE . they add contextualized word representations to enhance probing performance . |
| Outcome: | The proposed models achieve state-of-the-art on two datasets, TACRED and SemEval 2010 Task 8 . they show that the models capture linguistic and semantic properties relevant to the downstream task . |
Modeling Document-Level Context for Event Detection via Important Context Selection (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for Event Detection (ED) do not encode long-range document-level context . e.g., BERT cannot encode long text-level contextual information . |
| Approach: | They propose a method to model document-level context for Event Detection using transformer-based language models. |
| Outcome: | The proposed model can predict event prediction of target sentence in document-level context . the proposed model is effective on multiple benchmark datasets . |
Classifier Probes May Just Learn from Linear Context Features (2020.coling-main)
Copied to clipboard
| Challenge: | Current probing methods can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations. |
| Approach: | They propose to use token embeddings to test whether probing tasks contain linguistic structure . they argue that current probing methods do not provide enough information to support this hypothesis . |
| Outcome: | The proposed method can be scrutinized and proves that representations encode linguistic structure even without additional linguistic structures. |
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
Copied to clipboard
| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
| Outcome: | The proposed tasks capture key linguistic features of sentences, but they are difficult to infer from them. |
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
Discourse Probing of Pretrained Language Models (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on probing of pretrained language models has focused on sentence-level syntactic tasks. |
| Approach: | They introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document- level relations. |
| Outcome: | The proposed model performs best in encoder, but only in the encoder layer. |
Intrinsic Probing through Dimension Selection (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing research on probing for linguistic structure in word embeddings has focused on intrinsic probing, but what these representations encode about linguistic structures remains unclear. |
| Approach: | They propose a framework that allows us to determine whether linguistic information in word embeddings is dispersed or focal. |
| Outcome: | The proposed framework allows us to determine whether linguistic information in word embeddings is dispersed or focal. |
Thinking about how to extract: Energizing LLMs’ emergence capabilities for document-level event argument extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing models for document-level event argument extraction (D-EAE) lack key feature forgetting and cross-event argument confusion. |
| Approach: | They propose a document-level event argument extraction method based on guided summarization and reasoning that leverages the emergence capabilities of large language models to highlight key event information. |
| Outcome: | The proposed method outperforms baseline models by 1.3% F1 and 1.6% F1 on WIKIEVENTS and RAMS. |
Rule-Guided Extraction: A Hierarchical Rule Optimization Framework for Document-Level Event Argument Extraction (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level event argument extraction (EAE) is a critical task in natural language processing. |
| Approach: | They propose an LLM-driven HiErarchical Rule Optimization framework that iteratively generates and selects optimal hierarchical rules. |
| Outcome: | The proposed framework outperforms few-shot supervised methods and outperformed state-of-the-art prompting baselines. |