Papers with Relation Extraction
PolyMinder: A Support System for Entity Annotation and Relation Extraction in Polymer Science Documents (2025.coling-demos)
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| Challenge: | Automated Named Entity Recognition (NER) and Relation Extraction (RE) models are tailored to the polymer domain. |
| Approach: | They propose to automate the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
| Outcome: | The proposed system streamlines the annotation process by providing a web-based interface where users can visualize, verify, and refine the extracted information before finalizing the annotations. |
What Do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification (2022.acl-srw)
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| Challenge: | Existing RE surveys focus on modeling techniques, but there are few that are based on real-world scenarios. |
| Approach: | They propose to survey RE datasets and revisit the task definition and its adoption by the community. |
| Outcome: | The proposed approach improves the reliability of RE evaluations across multiple datasets and reveals significant discrepancies in annotations. |
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)
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| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature. |
| Approach: | They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages. |
| Outcome: | The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text. |
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
Simple Large-scale Relation Extraction from Unstructured Text (L18-1)
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| Challenge: | Knowledge-based question answering relies on the availability of facts, most of which cannot be found in structured sources. |
| Approach: | They propose a method for creating distant (weak) supervision labels for training a large-scale RE system by decoupling the model architecture from the feature design of a state-of-the-art neural network system. |
| Outcome: | The proposed method performs on par with the state-of-the-art model with similar features at 75x reduction in training time. |
Generating Diverse Training Samples for Relation Extraction with Large Language Models (2025.acl-long)
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| Challenge: | Existing models for Relation Extraction (RE) have good results on many benchmarks, but data scarcity is a common problem. |
| Approach: | They propose to use Large Language Models to generate training data for Relation Extraction . they propose to make LLMs produce dissimilar samples by direct instruction . |
| Outcome: | The proposed approach improves the diversity of training samples generated with LLMs while maintaining correctness. |
A Variational Approach for Mitigating Entity Bias in Relation Extraction (2025.acl-short)
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| Challenge: | Relation Extraction (RE) models often rely excessively on entities, resulting in poor generalization. |
| Approach: | They propose a Variational Information Bottleneck (VIB) framework to reduce entity bias in Relation Extraction (RE) . their method extracts relational information from unstructured data to improve generalization . |
| Outcome: | The proposed method achieves state-of-the-art on general and financial domain RE datasets, excelling in in-domain settings and out-of domain. |
An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction (2022.aacl-short)
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| Challenge: | Entity and Relation Extraction tasks are often compared to pipeline approaches . a recent study shows that joint approaches can produce comparable results . |
| Approach: | They propose to use two approaches to the Entity and Relation Extraction task to compare their performance. |
| Outcome: | The proposed approach outperforms the best pipeline model but improperly designed approaches may have poor performance. |
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction (D18-1)
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| Challenge: | Existing methods to extract genre-specific and genre-agnostic features require great human effort. |
| Approach: | They propose to use two encoders to explicitly extract genre-specific and genre-agnostic features. |
| Outcome: | The proposed approach outperforms the state-of-the-art by 1.7% on three distinct genres. |
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement (2020.coling-main)
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| Challenge: | Existing methods for information extraction are based on pipelining to extract entities from unstructured judgment documents . a large number of judgment documents are released on China Judgments Online . |
| Approach: | They propose a legal triplet extraction system for drug-related criminal judgment documents . they annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain . |
| Outcome: | The proposed system extracts entities and semantic relations jointly and benefits from the proposed legal lexicon feature and multi-task learning framework. |
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task (2020.acl-main)
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| 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. |
Improving Relation Extraction with Relational Paraphrase Sentences (2020.coling-main)
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| Challenge: | Existing annotated data is expensive and non-scalable, limiting performance of relation extraction models. |
| Approach: | They propose to enrich relation expressions by relational paraphrase sentences by annotating human-annotated data. |
| Outcome: | The proposed model improves performance even on a strong baseline. |
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (2022.findings-naacl)
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| Challenge: | Recent work shows that Relation Extraction tasks can be recasted as Textual Entailment tasks using verbalizations. |
| Approach: | They propose to recasted RE tasks as Textual Entailment tasks using verbalizations . they show that entailment reduces the need for manual annotation to 50% and 20% . |
| Outcome: | The proposed method reduces the need for manual annotation to 50% and 20% in event argument extraction tasks while achieving the same performance as with full training. |
REBEL: Relation Extraction By End-to-end Language generation (2021.findings-emnlp)
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| Challenge: | Existing approaches to extract relation triplets from text often involve multiple-step pipelines that propagate errors or are limited to a small number of relation types. |
| Approach: | They propose to use autoregressive seq2seq models to simplify Relation Extraction by expressing triplets as a sequence of text and a model that performs end-to-end relation extraction for more than 200 different relation types. |
| Outcome: | The proposed model achieves state-of-the-art on an array of Relation Extraction and Relation Classification benchmarks and achieves top performance in most of them. |
LayoutPointer: A Spatial-Context Adaptive Pointer Network for Visual Information Extraction (2024.naacl-long)
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| Challenge: | Existing models inadequately utilize spatial information of entities, causing incorrectly linking spatially distant entities. |
| Approach: | They propose a Spatial-Context Adaptive Pointer Network to restore semantic order among entities . they propose XFUND-based tail-to-head pointer to restore the semantic order . |
| Outcome: | The proposed method outperforms existing state-of-the-art methods in F1 scores for RE tasks. |
WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation (2021.findings-acl)
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| Challenge: | Existing methods for instance weighting cannot learn the weights which make the model generalize well in target domain. |
| Approach: | They propose a modelagnostic instance weighting algorithm which can learn the instance weights instead of manually designed weighting metrics. |
| Outcome: | The proposed method can learn the instance weights instead of manually designed weighting metrics. |
Weakly-Supervised Questions for Zero-Shot Relation Extraction (2023.eacl-main)
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| Challenge: | Zero-Shot Relation Extraction (ZRE) is a task where the training and test sets have no shared relation types. |
| Approach: | They propose to learn a model that can translate relation descriptions into relevant questions, which are then leveraged to generate the correct tail entity. |
| Outcome: | The proposed model outperforms the state-of-the-art on the fewrel and WikiZSL datasets by more than 16 F1 points without using gold question templates. |
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations (2020.lrec-1)
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| Challenge: | a study of the performance of NLP in relation extraction focuses on a business sector . a morphological dictionary can be used to extract named-entity pairs . |
| Approach: | They propose to use annotated textual corpora to perform Brand-Product relation extraction . they propose to propose query expansion by morpho-syntactically related words . |
| Outcome: | The proposed method improves the performance of the Brand-Product relation extraction task. |
REDFM: a Filtered and Multilingual Relation Extraction Dataset (2023.acl-long)
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| Challenge: | Existing Relation Extraction models rely on small datasets with low coverage of relation types . current systems rely only on small data sets with limited coverage of relationship types - especially when working with languages other than english. |
| Approach: | They propose to use an automatic annotated dataset to train relation extraction systems. |
| Outcome: | The proposed model can extract triplets in multiple languages from a human-revised dataset. |
WEXEA: Wikipedia EXhaustive Entity Annotation (2020.lrec-1)
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| Challenge: | Existing methods for extracting factual knowledge from text are limited to a few subtasks. |
| Approach: | They propose to use Wikipedia to build a corpus with exhaustive annotations of entity mentions. |
| Outcome: | The proposed system can be used to build supervised datasets and can be reproduced by everyone. |
CrossRE: A Cross-Domain Dataset for Relation Extraction (2022.findings-emnlp)
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| Challenge: | Relation Extraction (RE) evaluation is limited to in-domain setups . despite the drought of research on cross-domain RE, its practical importance remains . |
| Approach: | They propose a cross-domain benchmark for relation extraction which includes multi-label annotations and meta-data to include explanations and flags of difficult instances. |
| Outcome: | The proposed model includes explanations and flags of difficult instances. |
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods to extract training instances from unlabeled texts are expensive . sentences that contain the target relations in texts can be scarce and difficult to find . |
| Approach: | They propose a framework that can automatically extract training instances from unlabeled texts for RE. |
| Outcome: | The proposed method can extract training instances from unlabeled texts for RE. |
ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026.eacl-long)
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Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, Pranav Shetty, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
| Challenge: | Existing models for structured information extraction are limited by narrow entity ontologies, simple queries, or homogeneous document types. |
| Approach: | They propose a benchmark dataset for structured Information Extraction (IE) from document images . they analyze open and closed VLMs on this benchmark . |
| Outcome: | The proposed model can perform fine-grained structured extraction across document types and schemas. |
A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction (2026.findings-eacl)
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Marco Martinelli, Stefano Marchesin, Vanessa Bonato, Giorgio Di Nunzio, Nicola Ferro, Ornella Irrera, Laura Menotti, Federica Vezzani, Gianmaria Silvello
| Challenge: | Existing biomedical IE benchmarks are narrow in scope and rely heavily on distantly supervised annotations. |
| Approach: | They propose a benchmark for Information Extraction (IE) that annotates entities, concept-level links, and relations manually from PubMed abstracts. |
| Outcome: | The GutBrainIE benchmark is based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. |
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
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| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
| Approach: | They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase . |
| Outcome: | The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework. |
Enhanced Entity Annotations for Multilingual Corpora (2022.lrec-1)
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| Challenge: | Named Entity Recognition (NER) is a new language for natural language processing. |
| Approach: | They propose to improve the annotation quality of the English Wikipedia tool WEXEA . they propose to use a proven NER system to annotate entities in Wikipedia . |
| Outcome: | The proposed tool can be used to exhaustively annotate entities in Wikipedia articles. |
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning (2023.emnlp-main)
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| Challenge: | Named Entity Recognition, Relation Extraction, Semantic Role Labeling are examples of sequence labeling problems that require finetuning to the target format. |
| Approach: | They propose a dynamic sparse finetuning strategy that selectively focuses on a fraction of parameters, informed by feedback from highly regressing examples. |
| Outcome: | The proposed approach improves performance in low-resource settings and in extreme low-level settings. |
Silver Syntax Pre-training for Cross-Domain Relation Extraction (2023.findings-acl)
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| Challenge: | Relation Extraction (RE) is the task of extracting structured knowledge from unstructured text. |
| Approach: | They exploit the affinity between syntactic structure and semantic RE to obtain low-cost pre-training data. |
| Outcome: | The proposed model outperforms baseline models in five out of six cross-domain setups without additional annotated data. |
MOSAICo: a Multilingual Open-text Semantically Annotated Interlinked Corpus (2024.naacl-long)
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Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli
| Challenge: | Existing approaches to integrate semantics into Natural Language Understanding (NLP) systems are cost-effective and environmental impact-related. |
| Approach: | They propose to provide semantically-annotated corpora for four NLU tasks across five languages and to drop the requirement of closed datasets. |
| Outcome: | The proposed model provides hundreds of millions of silver yet high-quality annotations for four NLU tasks across five languages. |
LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)
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| Challenge: | Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA). |
| Approach: | They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics. |
| Outcome: | The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood. |
S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data. |
| Approach: | They propose a framework for two-stage self-training with synthetic data for relation extraction . |
| Outcome: | The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together. |
CNER: Concept and Named Entity Recognition (2024.naacl-long)
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Approach: | They propose a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
| Outcome: | The proposed task gains +5.4 and +8 macro F1 points when performed as a unified task compared to specialized named entity and concept recognition systems. |
DeepREF: A Framework for Optimized Deep Learning-based Relation Classification (2022.lrec-1)
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| Challenge: | Existing frameworks for relation extraction (RE) are limited due to lack of implementation details. |
| Approach: | They propose to use deep learning to develop relation extraction systems using deep learning models. |
| Outcome: | The proposed framework is inspired by the OpenNRE and REflex existing frameworks. |
Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples (L18-1)
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Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, Elena Simperl
| Challenge: | Existing datasets that provide alignments between natural language and knowledge bases (KB) triples are limited in size, lack coverage and are of unreported quality. |
| Approach: | They propose to build a large scale dataset of alignments between Wikipedia abstracts and Wikidata triples that is two orders of magnitude larger than the largest available alignments dataset. |
| Outcome: | The proposed dataset is two orders of magnitude larger than the largest available dataset and covers 2.5 times more predicates. |
Named Entities in Medical Case Reports: Corpus and Experiments (2020.lrec-1)
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| Challenge: | Only very few annotated corpora in the medical domain exist. |
| Approach: | They propose to annotate medical entities in case reports from PubMed Central's open access library. |
| Outcome: | The proposed corpus is the first of its kind to be made available to the scientific community in English. |
Bio-RFX: Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024.emnlp-main)
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| Challenge: | Existing methods for extracting structured data from unstructured texts neglect unique features of the biomedical literature, such as ambiguous entities and nested proper nouns. |
| Approach: | They propose a model that leverages sentence-level relation classification before entity extraction to tackle entity ambiguity. |
| Outcome: | The proposed model outperforms baselines in both NER and RE tasks and has competitive performance compared to the state-of-the-art fine-tuned baselines for RE. |
Ontology-Style Relation Annotation: A Case Study (2020.lrec-1)
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| Challenge: | Existing methods for Relation Extraction (RE) annotations use links between entities . a domain link connects the relation mention to the source entity while a range link connect the relation to the destination entity. |
| Approach: | They propose an Ontology-Style Relation (OSR) annotation approach to find relation mentions in relation annotations. |
| Outcome: | The proposed approach can be easily converted to Ontology RDF triples to populate an Ontologies. |
Multi-hop Evidence Retrieval for Cross-document Relation Extraction (2023.findings-acl)
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| Challenge: | Relation Extraction (RE) is a task that seeks to identify the relation of entities described according to some context. |
| Approach: | They propose a multi-hop evidence retrieval method based on evidence path mining and ranking to support cross-document relation extraction. |
| Outcome: | The proposed method acquires cross-document evidence and boosts performance in both closed and open environments. |
Entity-centered Cross-document Relation Extraction (2022.emnlp-main)
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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai
| Challenge: | Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents. |
| Approach: | They propose a relation-extraction model that uses cross-path entity relation attention to detect the semantic relations between entities in a given text. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in the dataset CodRED by 10%. |
Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
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| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
| Approach: | They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction. |
| Outcome: | The proposed model outperforms existing models on three RE benchmark datasets. |
Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training (2023.findings-acl)
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| Challenge: | Relation Extraction (RE) is the task of identifying semantic relation between entities mentioned in text. |
| Approach: | They propose a framework to automatically generate labeled data for Relation Extraction . they propose 'reward function' to update pre-trained language model for RE . |
| Outcome: | The proposed framework generates labeled data for relation extraction using a pre-trained language model and a meta learning approach to improve the generated samples. |
AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)
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| Challenge: | State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks. |
| Approach: | They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms. |
| Outcome: | The proposed model performs better on the KIE and RE tasks and is more accurate than existing models. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
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| Challenge: | Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction. |
| Approach: | They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data. |
| Outcome: | The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks. |
Separating Retention from Extraction in the Evaluation of End-to-end Relation Extraction (2021.emnlp-main)
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| Challenge: | State-of-the-art NLP models adopt shallow heuristics that limit their generalization capability. |
| Approach: | They propose to use heuristics that limit their generalization capability to model lexical overlap with the training set in Named-Entity Recognition and Event or Type heuristic in Relation Extraction to test their models. |
| Outcome: | The proposed model can perform better on the two key tasks, while the retention of training relation triples. |
ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget (2024.findings-acl)
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| Challenge: | Entity Linking and Relation Extraction (EL) are fundamental tasks in Natural Language Processing. |
| Approach: | They propose a Retriever-Reader architecture for Entity Linking and Relation Extraction . they propose an input representation that incorporates the candidate entities alongside the text . |
| Outcome: | The proposed architecture achieves state-of-the-art in in- and out-of domain benchmarks while using academic budget training and with 40x inference speed compared to competitors. |
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)
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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu
| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples. |
| Approach: | They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE. |
| Outcome: | The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas. |
Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model (2024.lrec-main)
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| 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. |
Exploring Multimodal Relation Extraction of Hierarchical Tabular Data with Multi-task Learning (2025.acl-long)
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| Challenge: | Existing studies overlook the need of mining relations among multiple columns rather than just the semantic relation between two specific columns in real-world practice. |
| Approach: | They propose a Chain-of-Thought distillation framework with self-correction mechanism to enhance MLLMs’ reasoning capabilities without increasing parameter scale. |
| Outcome: | The proposed method significantly outperforms baselines on wide datasets. |
AnchorAlign: A Novel Anchor Alignment-enhanced Generative Method for Joint Named Entity Recognition and Relation Extraction (2026.findings-acl)
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| Challenge: | Named Entity Recognition and Relation Extraction are interdependent tasks in information extraction. |
| Approach: | They propose a generative method enhanced by anchor alignment to bridge NER and RE tasks . they use anchor entities as semantic pivots to align the two tasks based on their semantic representations . |
| Outcome: | The proposed method outperforms state-of-the-art models on five benchmark datasets. |