{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/relation-classification/papers/3","list_of":"/task/relation-classification","task":"Relation Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":5,"rows_per_page":100,"rows":[201,300],"of":445,"counts":{"archive_papers_tagged":445,"with_a_code_link":160,"where_syntology_ran_a_sample":29,"not_listed_spam_title":0,"listed":445,"listed_where_code_ran":29,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":4,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/relation-classification","prev":"/task/relation-classification/papers/2","next":"/task/relation-classification/papers/4","papers":[{"url":null,"slug":"visually-grounded-interpretation-of-noun-noun","title":"Visually Grounded Interpretation of Noun-Noun Compounds in English","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"function-words-enhanced-attention-networks","title":"Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification","date":"2022-04-26","arxiv_id":"2204.12111","repositories_listed":0,"syntology":null},{"url":null,"slug":"ergo-event-relational-graph-transformer-for","title":"ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification","date":"2022-04-15","arxiv_id":"2204.07434","repositories_listed":0,"syntology":null},{"url":null,"slug":"eppac-entity-pre-typing-relation","title":"EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing","date":"2022-03-01","arxiv_id":"2203.00193","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatically-generating-counterfactuals-for","title":"Automatically Generating Counterfactuals for Relation Classification","date":"2022-02-22","arxiv_id":"2202.10668","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-domain-few-shot-learning-via-meta","title":"Cross Domain Few-Shot Learning via Meta Adversarial Training","date":"2022-02-11","arxiv_id":"2202.05713","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-protein-protein-interactions","title":"Enhanced Protein-Protein Interactions Extraction from the Literature using Entity Type- and Position-aware Representation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prompt-based-zero-shot-relation-1","title":"Prompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vrdformer-end-to-end-video-visual-relation","title":"VRDFormer: End-to-End Video Visual Relation Detection With Transformers","date":"2022-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"budget-sensitive-reannotation-of-noisy","title":"Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy","date":"2021-12-26","arxiv_id":"2112.13320","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-prompt-tuning-for-relation","title":"Generative Prompt Tuning for Relation Classification","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-general-teacher-for-multi-data-multi-task","title":"One General Teacher for Multi-Data Multi-Task: A New Knowledge Distillation Framework for Discourse Relation Analysis","date":"2021-11-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"serc-syntactic-and-semantic-sequence-based","title":"SERC: Syntactic and Semantic Sequence based Event Relation Classification","date":"2021-11-03","arxiv_id":"2111.02265","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-bootstrapping-recipe-for-low-resource-1","title":"A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-approach-to-discourse-relation","title":"A Unified Approach to Discourse Relation Classification in nine Languages","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/argument-pair-extraction-with-mutual-guidance","slug":"argument-pair-extraction-with-mutual-guidance","title":"Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cvae-based-re-anchoring-for-implicit","title":"CVAE-based Re-anchoring for Implicit Discourse Relation Classification","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-disrpt-2021-shared-task-on-elementary","title":"The DISRPT 2021 Shared Task on Elementary Discourse Unit Segmentation, Connective Detection, and Relation Classification","date":"2021-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-data-bootstrapping-recipe-for-low-resource","title":"A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classification","date":"2021-10-18","arxiv_id":"2110.09570","repositories_listed":0,"syntology":null},{"url":null,"slug":"inconsistent-few-shot-relation-classification","title":"Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning","date":"2021-10-13","arxiv_id":"2110.08254","repositories_listed":0,"syntology":null},{"url":null,"slug":"generate-triggers-in-neural-relation","title":"Generate Triggers in Neural Relation Extraction","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-relation-classification-as-two-way","title":"Supervised Relation Classification as Two-way Span-Prediction","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-interactive-relation","title":"Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-knowledge-help-general-nlu-an-empirical","title":"Does Knowledge Help General NLU? An Empirical Study","date":"2021-09-01","arxiv_id":"2109.00563","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-enhancement-for-implicit-discourse","title":"Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-learning-to-match-to-learning-to","title":"From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lets-be-explicit-about-that-distant","title":"Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ns-hunter-bert-cloze-based-semantic-denoising","title":"NS-Hunter: BERT-Cloze Based Semantic Denoising for Distantly Supervised Relation Classification","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/timers-document-level-temporal-relation","slug":"timers-document-level-temporal-relation","title":"TIMERS: Document-level Temporal Relation Extraction","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"verb-metaphor-detection-via-contextual","title":"Verb Metaphor Detection via Contextual Relation Learning","date":"2021-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-question-generation-with","title":"Enhancing Question Generation with Commonsense Knowledge","date":"2021-06-19","arxiv_id":"2106.10454","repositories_listed":0,"syntology":null},{"url":null,"slug":"let-s-be-explicit-about-that-distant","title":"Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction","date":"2021-06-06","arxiv_id":"2106.03192","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-network-learning-with-partially-aligned","title":"Cross-Network Learning with Partially Aligned Graph Convolutional Networks","date":"2021-06-03","arxiv_id":"2106.01583","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-classification-with-cognitive","title":"Relation Classification with Cognitive Attention Supervision","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"global-context-for-improving-recognition-of","title":"Global Context for improving recognition of Online Handwritten Mathematical Expressions","date":"2021-05-21","arxiv_id":"2105.10156","repositories_listed":0,"syntology":null},{"url":"/paper/relation-classification-with-entity-type","slug":"relation-classification-with-entity-type","title":"Relation Classification with Entity Type Restriction","date":"2021-05-18","arxiv_id":"2105.08393","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-few-shot-relation-classification","title":"Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes","date":"2021-04-17","arxiv_id":"2104.08481","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-prototypical-networks-with-label","title":"Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification","date":"2021-01-10","arxiv_id":"2101.03526","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-incorporating-entity-specific","title":"Towards Incorporating Entity-specific Knowledge Graph Information in Predicting Drug-Drug Interactions","date":"2020-12-21","arxiv_id":"2012.11142","repositories_listed":0,"syntology":null},{"url":null,"slug":"r-2-net-relation-of-relation-learning-network","title":"R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching","date":"2020-12-16","arxiv_id":"2012.08920","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-phase-prototypical-network-model-for","title":"A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"acnlp-at-semeval-2020-task-6-a-supervised","title":"ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-text-classification-with-edge","title":"Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gorynych-transformer-at-semeval-2020-task-6","title":"Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"riva-a-pre-trained-tweet-multimodal-model","title":"RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-a-penalty-based-loss-re-estimation","title":"Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-extraction-of-entity-and-relation-with","title":"Joint Extraction of Entity and Relation with Information Redundancy Elimination","date":"2020-11-27","arxiv_id":"2011.13565","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-recognition-and-relation-extraction","title":"Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian","date":"2020-11-19","arxiv_id":"2011.09817","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigation-of-bert-model-on-biomedical","title":"Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism","date":"2020-11-01","arxiv_id":"2011.00398","repositories_listed":0,"syntology":null},{"url":null,"slug":"relative-and-incomplete-time-expression","title":"Relative and Incomplete Time Expression Anchoring for Clinical Text","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"within-between-lexical-relation","title":"Within-Between Lexical Relation Classification","date":"2020-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"logic-guided-semantic-representation-learning","title":"Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification","date":"2020-10-30","arxiv_id":"2010.16068","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-for-neural-relation","title":"Meta-Learning for Neural Relation Classification with Distant Supervision","date":"2020-10-26","arxiv_id":"2010.13544","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-decouple-relations-few-shot","title":"Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training","date":"2020-10-21","arxiv_id":"2010.10894","repositories_listed":0,"syntology":null},{"url":null,"slug":"tdre-a-tensor-decomposition-based-approach","title":"TDRE: A Tensor Decomposition Based Approach for Relation Extraction","date":"2020-10-15","arxiv_id":"2010.07533","repositories_listed":0,"syntology":null},{"url":"/paper/relation-extraction-as-two-way-span","slug":"relation-extraction-as-two-way-span","title":"Relation Classification as Two-way Span-Prediction","date":"2020-10-09","arxiv_id":"2010.04829","repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-relative-position-representation-based","title":"Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"xiao-yang-ben-guan-xi-fen-lei-yan-jiu-zong","title":"小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)","date":"2020-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autorc-improving-bert-based-relation","title":"AutoRC: Improving BERT Based Relation Classification Models via Architecture Search","date":"2020-09-22","arxiv_id":"2009.10680","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-semantic-relations","title":"Semantic Relations and Deep Learning","date":"2020-09-11","arxiv_id":"2009.05426","repositories_listed":0,"syntology":null},{"url":null,"slug":"hose-net-higher-order-structure-embedded","title":"HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation","date":"2020-08-12","arxiv_id":"2008.05156","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bert-based-one-pass-multi-task-model-for","title":"A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-discourse-relation-classification-we","title":"Implicit Discourse Relation Classification: We Need to Talk about Evaluation","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/modeling-dense-cross-modal-interactions-for","slug":"modeling-dense-cross-modal-interactions-for","title":"Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction","date":"2020-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-brain-activity-for-machine-attention","title":"Human brain activity for machine attention","date":"2020-06-09","arxiv_id":"2006.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-de-relations-pour-l","title":"Classification de relations pour l'intelligence \\'economique et concurrentielle (Relation Classification for Competitive and Economic Intelligence )","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-bert-to-implicit-discourse-relation","title":"Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mpdd-a-multi-party-dialogue-dataset-for","title":"MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships","date":"2020-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/recurrent-interaction-network-for-jointly","slug":"recurrent-interaction-network-for-jointly","title":"Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations","date":"2020-05-01","arxiv_id":"2005.00162","repositories_listed":0,"syntology":null},{"url":null,"slug":"mick-a-meta-learning-framework-for-few-shot","title":"MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data","date":"2020-04-26","arxiv_id":"2004.14164","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-scholarly-knowledge-representation","title":"Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification","date":"2020-04-13","arxiv_id":"2004.06153","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adversarial-domain-adaptation-3","title":"Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification","date":"2020-03-04","arxiv_id":"2003.02244","repositories_listed":0,"syntology":null},{"url":null,"slug":"keml-a-knowledge-enriched-meta-learning","title":"KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification","date":"2020-02-25","arxiv_id":"2002.10903","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-approach-to-discourse-relation","title":"A Neural Approach to Discourse Relation Signal Detection","date":"2020-01-08","arxiv_id":"2001.02380","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-relate-from-captions-and-bounding-1","title":"Learning to Relate from Captions and Bounding Boxes","date":"2019-12-01","arxiv_id":"1912.00311","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-attention-enhanced-selective-gate-with","title":"Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction","date":"2019-11-27","arxiv_id":"1911.11899","repositories_listed":0,"syntology":null},{"url":"/paper/improving-relation-classification-by-entity","slug":"improving-relation-classification-by-entity","title":"Improving Relation Classification by Entity Pair Graph","date":"2019-11-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-relation-classification-by-single","title":"Biomedical Relation Classification by single and multiple source domain adaptation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"next-sentence-prediction-helps-implicit","title":"Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representing-movie-characters-in-dialogues","title":"Representing Movie Characters in Dialogues","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-cross-lingual-semantic","title":"Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-graph-convolutional-network-for","title":"Semantic Graph Convolutional Network for Implicit Discourse Relation Classification","date":"2019-10-21","arxiv_id":"1910.09183","repositories_listed":0,"syntology":null},{"url":null,"slug":"type-aware-convolutional-neural-networks-for","title":"Type-aware Convolutional Neural Networks for Slot Filling","date":"2019-10-01","arxiv_id":"1910.00546","repositories_listed":0,"syntology":null},{"url":null,"slug":"argumentative-relation-classification-as","title":"Argumentative Relation Classification as Plausibility Ranking","date":"2019-09-19","arxiv_id":"1909.09031","repositories_listed":0,"syntology":null},{"url":null,"slug":"discourse-relation-prediction-revisiting-word","title":"Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eoann-lexical-semantic-relation","title":"EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tddiscourse-a-dataset-for-discourse-level","title":"TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"which-aspects-of-discourse-relations-are-hard","title":"Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-transfer-for-implicit-discourse-1","title":"Zero-shot transfer for implicit discourse relation classification","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-extracting-multiple-triplets-with","title":"Jointly Extracting Multiple Triplets with Multilayer Translation Constraints","date":"2019-07-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"arnor-attention-regularization-based-noise","title":"ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/joint-type-inference-on-entities-and","slug":"joint-type-inference-on-entities-and","title":"Joint Type Inference on Entities and Relations via Graph Convolutional Networks","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-meta-learning-for-relation","title":"Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision","date":"2019-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-entity-bio-tag-embeddings-and","title":"Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data","date":"2019-06-21","arxiv_id":"1906.08931","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-noisy-data-in-distant-supervision","title":"Exploiting Noisy Data in Distant Supervision Relation Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-classification-using-segment-level","title":"Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-data-driven-system-for-rhetorical","title":"Towards the Data-driven System for Rhetorical Parsing of Russian Texts","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-symbolic-argumentation-mining-an","title":"Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning","date":"2019-05-22","arxiv_id":"1905.09103","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-difficulty-of-classifying","title":"Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting","date":"2019-05-14","arxiv_id":"1905.05538","repositories_listed":0,"syntology":null},{"url":null,"slug":"frowning-frodo-wincing-leia-and-a-seriously","title":"Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters","date":"2019-03-29","arxiv_id":"1903.12453","repositories_listed":0,"syntology":null}],"record_sha256":"edca9ad42ce71ee598a403bda6bb0c19701fd4a000baa1b37583563707032b1a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}