Papers with distant supervision
Entity-Consistent End-to-end Task-Oriented Dialogue System with KB Retriever (D19-1)
Copied to clipboard
| Challenge: | Existing work on sequence-to-sequence dialogues treats the KB query as an attention over the entire KB without the guarantee that the generated entities are consistent with each other. |
| Approach: | They propose a framework which queries the knowledge base in two steps to improve consistency . they first return the most relevant KB row given a dialogue history . |
| Outcome: | The proposed framework outperforms baseline models and produces entity-consistent responses. |
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
Copied to clipboard
| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes (2021.naacl-industry)
Copied to clipboard
| Challenge: | Existing methods for training numeric attributes are based on manual labeling and distant supervision leads to incomplete training annotations. |
| Approach: | They propose a multi-task learning architecture to deal with missing attribute values in training data, removing dependency on manual annotations. |
| Outcome: | The proposed framework improves on 20 numeric attributes extracted from 5 product categories and 3 english marketplaces with language-agnostic performance. |
Distantly Supervised Relation Extraction in Federated Settings (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to label training datasets using distant supervision are expensive and cannot cover all walks of life. |
| Approach: | They propose a federated denoising framework to suppress label noise in federation . they propose to use a multiple instance learning based denoisation method to select reliable sentences . |
| Outcome: | The proposed method can select reliable sentences via cross-platform collaboration. |
Global Relation Embedding for Relation Extraction (N18-1)
Copied to clipboard
| Challenge: | Existing methods to extract textual relations with distant supervision are limited by their reliance on supervised training data. |
| Approach: | They propose to embed relations with global statistics of relations to combat the wrong labeling problem of distant supervision. |
| Outcome: | The proposed method is more robust to training noise introduced by distant supervision and improves relation extraction models. |
DiS-ReX: A Multilingual Dataset for Distantly Supervised Relation Extraction (2022.acl-short)
Copied to clipboard
| Challenge: | Existing benchmarking datasets for multilingual relation extraction have been lacking . |
| Approach: | They propose to use a new benchmark dataset to study multilingual relation extraction task by distant supervision. |
| Outcome: | The proposed task is performed on a multilingual relation extraction dataset using an mBERT encoder. |
Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues. |
| Approach: | They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision. |
| Outcome: | The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses. |
Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing models for yes-no questions are challenging, but they still face challenges. |
| Approach: | They propose an approach grounded on distant supervision and blended training to quickly adapt to a new dialogue domain. |
| Outcome: | The proposed approach improves F1 performance in movie scripts, tennis interviews, and airline customer service domains. |
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent successes of NLP systems require large amounts of labelled data for structured prediction tasks. |
| Approach: | They propose a method for unsupervised transfer from multiple input models for structured prediction using a cross-lingual setup. |
| Outcome: | The proposed method produces less noisy labels for the distant supervision. |
Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to named entity recognition (NER) in Chinese are limited by the lack of annotated data. |
| Approach: | They propose a method which can automatically populate annotated training data without humancost by using distant supervision. |
| Outcome: | The proposed method performs better than comparison systems on two datasets. |
Combining Distant and Direct Supervision for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train relation extraction with distant supervision use noisy labels and implicitly assumes that all the KB facts are mentioned in the text. |
| Approach: | They propose to combine distant supervision data with additional directly-supervised data to train relation extraction models by using sigmoidal attention weights with max pooling. |
| Outcome: | The proposed method achieves state-of-the-art on the widely used FB-NYT dataset. |
Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods ignore the intrinsic noise of distant supervision during the pre-training stage. |
| Approach: | They propose a weighted contrastive learning method that explicitly reduces noise . they leverage supervised data to estimate reliability and reduce noise compared to non-weighted baselines . |
| Outcome: | The proposed method reduces the noise of distant supervision and estimates reliability of pre-training instances. |
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning (P18-1)
Copied to clipboard
| Challenge: | Distant supervision is an efficient method for relation extraction, but it is noisy. |
| Approach: | They propose a deep reinforcement learning strategy to generate false-positive indicators . they redistribute false positives into negative examples to reduce false positive problem . |
| Outcome: | The proposed method significantly improves the performance of distant supervision compared to state-of-the-art systems. |
Heuristically Informed Unsupervised Idiom Usage Recognition (D18-1)
Copied to clipboard
| Challenge: | Existing models for idiom usage recognition have failed to recognize usages without annotated examples. |
| Approach: | They propose an unsupervised method for recognizing the intended usages of idioms by using distributional semantics to identify literal usages. |
| Outcome: | The proposed method performs competitively against supervised methods. |
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
Copied to clipboard
| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
| Approach: | They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting. |
| Outcome: | The proposed methods enable learning when training data is sparse. |
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that results from high-resource languages cannot be easily transferred to realistic, low-resourced scenarios. |
| Approach: | They analyse performance of multilingual transformer models using available resources for Hausa, isiXhosa and NER and topic classification. |
| Outcome: | The proposed models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. |
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision (N19-1)
Copied to clipboard
| Challenge: | Existing methods to train unbiased methods such as REINFORCE take time to train. |
| Approach: | They propose to use posterior regularization to integrate domain-specific rules in instance selection using REINFORCE to improve the performance of the relation classifier trained on cleaned distant supervision datasets. |
| Outcome: | The proposed method improves the performance of the relation classifier trained on cleaned distant supervision dataset as well as the efficiency of the REINFORCE training. |
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
Copied to clipboard
| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
Coarse-to-Fine Query Focused Multi-Document Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on query focused multi-document summarization relies heavily on retrieval-style methods. |
| Approach: | They propose a query-cluster-based model which uses more accurate modules for estimating whether text segments are relevant, likely to contain an answer, and central. |
| Outcome: | The proposed framework outperforms strong comparison systems on benchmark datasets across domains and query types. |
Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
| Approach: | They propose a method that combines the benefits of learning representations and structured learning to predict sentence-level relation mentions given only proposition-level supervision from a KB. |
| Outcome: | The proposed approach outperforms a number of baseline approaches while minimizing label noise. |
ChemNER: Fine-Grained Chemistry Named Entity Recognition with Ontology-Guided Distant Supervision (2021.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a fundamental step in scientific literature analysis to build AI-driven systems for molecular discovery, synthetic strategy designing, and manufacturing. |
| Approach: | They propose an ontology-guided method for fine-grained named entity recognition (NER) it leverages the chemistry type ontologies to generate distant labels with flexible KB-matching . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods with a .25 absolute F1 improvement. |
Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |
Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition suffer from incomplete annotations due to incompleteness of external knowledge bases. |
| Approach: | They propose a method to solve the named entity recognition problem under distant supervision using dictionaries and knowledge bases. |
| Outcome: | The proposed method outperforms existing methods on two benchmark datasets labeled by various knowledge bases. |
Regularized Attentive Capsule Network for Overlapped Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to extract relations from distant supervision contain low-quality instances with noisy words and overlapped relations. |
| Approach: | They propose a Regularized Attentive Capsule Network to better identify overlapped relations in informal sentences . they embed multi-head attention into the capsule network as the low-level capsules . |
| Outcome: | Extensive experiments show that the proposed model improves relation extraction. |
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
| Outcome: | The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming. |
Relation Extraction with Explanation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies focus on improving relation extraction accuracy but little is known about their explanability. |
| Approach: | They propose to automatically generate "distractor" sentences to augment the bags and train the model to ignore the distractors. |
| Outcome: | The proposed model improves extraction accuracy while also explanability. |
Enhanced Distant Supervision with State-Change Information for Relation Extraction (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for enhancing distant supervision with state-change information for relation extraction are limited. |
| Approach: | They propose a method for enhancing distant supervision with state-change information for relation extraction by adding temporal information to a curation dataset. |
| Outcome: | The proposed method reduces noise when used for static relation extraction and can be used to train a relation-extraction system that detects a change of state in relations. |
“A Buster Keaton of Linguistics”: First Automated Approaches for the Extraction of Vossian Antonomasia (D19-1)
Copied to clipboard
| Challenge: | Vossian Antonomasia is a stylistic device which attributes a property to a person by naming another person as a reference point. |
| Approach: | They propose a method for the extraction of Vossian Antonomasias that works completely automatically . they use named entity recognition, distant supervision and a bi-directional LSTM . |
| Outcome: | The proposed method outperforms the only existing semi-automatic method for VA identification by more than 30 percentage points in precision. |
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages (2020.acl-main)
Copied to clipboard
| Challenge: | Existing work on information extraction from semi-structured websites has relied on manual data annotation and learning a model specific to a given template. |
| Approach: | They propose a solution for “zero-shot” open-domain relation extraction from webpages with previously unseen templates using a graph neural network-based approach. |
| Outcome: | The proposed model provides a 31% gain over baseline for zero-shot extraction in a new subject vertical. |