Papers with absolute F-score
Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining (D19-61)
Copied to clipboard
| Challenge: | Entities can be used as effective signals to generate less ambiguous semantic representations and align multiple languages. |
| Approach: | They propose a method to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. |
| Outcome: | The proposed method can generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia . it provides reliable alignment on word/entity level and sentence level, and thus can be used for unsupervised cross-linguistic entity linking. |
Joint Multimedia Event Extraction from Video and Article (2021.findings-emnlp)
Copied to clipboard
Brian Chen, Xudong Lin, Christopher Thomas, Manling Li, Shoya Yoshida, Lovish Chum, Heng Ji, Shih-Fu Chang
| Challenge: | Existing methods to extract multimedia events from video and text are limited to video and images. |
| Approach: | They propose a task to jointly extract events from video and text documents . they propose 'self-supervised' cross-modal event coreference model and cross-mod transformer architecture . |
| Outcome: | The proposed method achieves 6.0% and 5.8% absolute F-score gain on video-article pairs . the proposed method can resolve coreference and extract multimodal event frames more accurately than existing methods. |
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding (D18-1)
Copied to clipboard
| Challenge: | a new approach to multilingual word embedding is needed to achieve this goal . a multilingual common semantic space is a language-agnostic semantic continuous space . |
| Approach: | They propose a multilingual common semantic space where words from multiple languages are mapped into a shared space so that resources and knowledge can be shared across languages. |
| Outcome: | The proposed approach achieves 14.6% absolute F-score gain over state-of-the-art methods on cross-lingual direct transfer. |
Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)
Copied to clipboard
| Challenge: | Temporal relationship extraction is crucial for understanding complex events and reasoning over them. |
| Approach: | They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain. |
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages (D19-61)
Copied to clipboard
| Challenge: | a novel name retrieval method is proposed for languages with no annotations or training data. |
| Approach: | They propose a method which relies on zero annotation or resources from the target language . they pre-train an orthographic encoder using Wikipedia inter-lingual links from dozens of languages . |
| Outcome: | The proposed method shows 11.6% improvement over state-of-the-art methods. |
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling (P18-1)
Copied to clipboard
| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
| Approach: | They propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. |
| Outcome: | The proposed architecture achieves 4.3%-50.5% absolute gains compared to mono-lingual model . the proposed model is particularly effective in low-resource settings . |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging (N19-1)
Copied to clipboard
| Challenge: | Low-resource language name tagging is an important but challenging task. |
| Approach: | They propose a neural architecture that leverages multi-level adversarial transfer to improve name tagging for low-resource languages. |
| Outcome: | The proposed approach outperforms previous approaches on CoNLL data sets. |
RE2: Region-Aware Relation Extraction from Visually Rich Documents (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on relation extraction from visually rich documents focus on layout structure and Optical Character Recognition (OCR) results. |
| Approach: | They propose a relation extraction tool that leverages layout structure among entity blocks to improve relation prediction. |
| Outcome: | The proposed model outperforms existing models on a wide range of domains and languages. |
Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation (2021.acl-long)
Copied to clipboard
| Challenge: | Compared with general natural language texts, sentences from scientific papers usually possess wider contexts between knowledge elements. |
| Approach: | They propose a novel biomedical Information Extraction model to extract scientific entities and events from English research papers using Abstract Meaning Representation (AMR) they construct a sentence-level knowledge graph from an external knowledge base and encode it to improve the model's understanding of complex scientific concepts. |
| Outcome: | The proposed model can extract scientific entities and events from scientific literature and improve its understanding of complex scientific concepts. |