Papers with TACRED
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction (2022.naacl-demo)
Copied to clipboard
Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
| Challenge: | Vacareanu et al., 2021) proposes a system that helps users build transparent information extraction models . rule-based methods address the opacity of neural architectures by producing models that are transparent . |
| Approach: | They propose a system that assists a user in constructing transparent information extraction models . the system generates high-precision rules even in a 1-shot setting, they show . |
| Outcome: | The proposed system generates high-precision rules even in a 1-shot setting . it outperforms manually written patterns on a widely-used relation extraction dataset . |
An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)
Copied to clipboard
| Challenge: | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. |
| Approach: | They propose to improve sentence-level relation extraction by adding entity representations with typed markers to the model. |
| Outcome: | The proposed model outperforms existing methods on entity representation and noisy labels on TACRED dataset. |
Improving Relation Extraction with Knowledge-attention (D19-1)
Copied to clipboard
| Challenge: | Existing attention mechanisms are data-driven, but most are data driven. |
| Approach: | They propose a knowledge-attention encoder which integrates prior knowledge from external lexical resources into deep neural networks for relation extraction task. |
| Outcome: | The proposed system outperforms existing CNN, RNN, and self-attention based models on a large-scale relation extraction dataset. |
Relation Classification with Entity Type Restriction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods regard all relations as candidate relations for the two entities, which leads to inappropriate relations being candidate relations. |
| Approach: | They propose a paradigm which exploits entity types to restrict candidate relations by mutual restrictions. |
| Outcome: | The proposed paradigm improves GCN and SpanBERT on a standard dataset by 6.9 and 4.4 F1 points. |
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Relation extraction systems require large amounts of labeled examples which are costly to annotate. |
| Approach: | They propose to use hand-made relation extraction tasks to refine a pretrained textual entailment engine which is run as-is or further fine-tuned on labeled examples. |
| Outcome: | The proposed system achieves 63% F1 zero-shot, 69% with 16 examples per relation and 4 points short of the state-of-the-art system on the same conditions. |
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | a number of information extraction tasks require task-specific training. |
| Approach: | They propose a text-to-triple translation framework for information extraction tasks . they propose enabling task-agnostic translation by leveraging latent knowledge of a pre-trained language model . |
| Outcome: | The proposed framework outperforms the existing methods on open information extraction tasks. |
Dependency Position Encoding for Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to extract relation extraction from sentence are limited in focusing on leveraging dependency information. |
| Approach: | They propose dependency position encoding (DPE) that incorporates dependency connections and dependency types into the self-attention mechanism to distinguish the importance of different word dependencies. |
| Outcome: | The proposed method significantly outperforms the previous methods on SemEval 2010 Task 8, KBP37, and TACRED. |
Bootstrapping Relation Extractors using Syntactic Search by Examples (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for supervised relation extraction still require a large quantity of training data. |
| Approach: | They propose a process for bootstrapping training datasets which can be performed quickly by non-NLP-experts. |
| Outcome: | The proposed method outperforms models trained on manual and distant data augmentation techniques and the search-based approach with the NLG method. |
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 . |
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for Relation Extraction (RE) still show a high error rate . label errors account for 8% absolute F1 test error, and more than 50% of examples need to be relabeled. |
| Approach: | They validate the most challenging 5K examples using trained annotators and analyze misclassifications on the challenging instances. |
| Outcome: | The proposed methods perform well on the most challenging datasets and improve on the relabeled test set. |
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset (2023.acl-long)
Copied to clipboard
| Challenge: | Relation extraction (RE) is a fundamental task in information extraction, but its extension to multilingual settings is hindered by the lack of supervised resources comparable in size to large English datasets. |
| Approach: | They propose a dataset to analyze relation extraction (RE) in multilingual settings . they find machine translation is a viable strategy to transfer RE instances . |
| Outcome: | The proposed dataset covers 12 typologically diverse languages from 9 language families and is compared with existing datasets. |
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on distant supervision denoising introduces false-positive (FP) and falsenegative (FN) training instances to the generated datasets. |
| Approach: | They propose a hierarchical false-negative denoising framework for distant supervision relation extraction that denoises false-positive and false- negative training instances. |
| Outcome: | The proposed framework can revise FN instances correctly and maintain high F1 scores even when 50% of instances have been turned into negatives. |
Matching the Blanks: Distributional Similarity for Relation Learning (P19-1)
Copied to clipboard
| Challenge: | Efforts to build general purpose relation extractors that can model arbitrary relations are limited in their ability to generalize. |
| Approach: | They propose to build task-agnostic relation representations solely from entity-linked text to extend Harris’ distributional hypothesis to relations. |
| Outcome: | The proposed representations outperform previous methods on SemEval 2010 Task 8, KBP37, and TACRED even without using any of the task’s training data. |
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for relation extraction (RE) use shallow heuristics that do not generalize to challenge-set data. |
| Approach: | They propose to annotate a dataset to test whether relation extraction models are generalized to the challenge-set data. |
| Outcome: | The proposed model performs better on the challenge-set compared with the SOTA models on the same dataset. |
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on rule-based and neural sequence-to-sequence (seq2sequ) TS is a technique that reduces text complexity for human consumption. |
| Approach: | They evaluate two possible uses of neural TS: simplifying input texts at prediction time and augmenting training data to provide machines with additional information during training. |
| Outcome: | The proposed approach improves performance on two datasets. |
Learning from Noisy Labels for Entity-Centric Information Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent information extraction approaches can easily overfit noisy labels and suffer from performance degradation. |
| Approach: | They propose a co-regularization framework for entity-centric information extraction that optimizes neural models with task-specific losses and regularizes them to generate similar predictions based on agreement loss. |
| Outcome: | The proposed framework is optimized with task-specific losses and generates similar predictions based on agreement loss. |
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for entity representations do not capture information in a knowledge base, and cannot represent entities that do not exist in the KB. |
| Approach: | They propose a pretrained contextualized representation of words and entities based on the bidirectional transformer. |
| Outcome: | The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. |
A Spectral Viewpoint on Continual Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to solve the Continual Relation Extraction problem have been proposed . |
| Approach: | They propose a class-wise regularization method that preserves eigenvectors for each class shape . they propose spectral regularization to preserve eenvector shape after learning new tasks . |
| Outcome: | The proposed method improves performance on two benchmark datasets. |
On the Use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing zero-shot methods for information extraction use large amounts of gold standard data. |
| Approach: | They propose a framework to utilize silver data to enhance zero-shot classification methods . they propose to use off-the-shelf models of other NLP tasks to perform inference on test data . |
| Outcome: | The proposed framework outperforms baseline methods on TACRED and Wiki80 datasets by 5% and 6% on the zero-shot relation classification task and by 3% 7 % on Smile (Korean and Polish) |
Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies investigate Relation Extraction task from two different aspects. |
| Approach: | They propose to use Large Language Model (LLM) to do data augmentation and propose a bidirectional prompt template for prompt learning. |
| Outcome: | The proposed model outperforms the state-of-the-art on four datasets and outperformed existing methods on TACREV, RETACRED and Semeval. |