Papers with joint models
A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
| Outcome: | The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks. |
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (2020.tacl-1)
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| Challenge: | Chinese word segmentation and dependency parsing suffer from error propagation . a graph-based model can integrate both tasks, but it suffers from performance limitations . |
| Approach: | They propose a graph-based model to integrate Chinese word segmentation and dependency parsing . their model achieves better performance than previous joint models . |
| Outcome: | The proposed model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. |
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling (2021.acl-long)
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| Challenge: | Existing joint models for multi-intent SLU only consider intent detection while ignoring slot filling task. |
| Approach: | They propose a non-autoregressive model for joint multiple intent detection and slot filling . their framework is 11.5 times faster than existing joint models . |
| Outcome: | The proposed model is 11.5 times faster than existing models and is faster than current models. |
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
Ellipsis Resolution as Question Answering: An Evaluation (2021.eacl-main)
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| Challenge: | Existing models for ellipsis resolution in English are expensive and cumbersome . ellipas are hard, open problems in NLP, and can cause errors in translation, question answering, and dialogue understanding. |
| Approach: | They propose an alternative approach to ellipsis resolution based on question answering architectures. |
| Outcome: | The proposed model outperforms the current state of the art for ellipsis resolution in English . it shows that annotations can be useful for a subset of the known ellipas . |
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases (2023.eacl-main)
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| Challenge: | a new method for case outcome classification is being developed for the European Court of Human Rights. |
| Approach: | They propose to use case facts descriptions to classify whether a court finds a violation of conventions. |
| Outcome: | The proposed model improves on single-task and joint models without contrastive loss. |
Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
| Approach: | They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction . |
| Outcome: | The proposed model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance. |
Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure (2024.findings-acl)
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| Challenge: | Existing word-level dependency parsing methods in Chinese lack explicit word boundaries due to the lack of word boundaries. |
| Approach: | They propose to model latent internal structures within Chinese words by constrained Eisner algorithm . they propose to guarantee a single root for intra-word structures and establish inter-word dependencies . |
| Outcome: | The proposed model outperforms existing models on Chinese treebanks and shows that it can predict plausible intra-word structures. |
Paragraph-based Transformer Pre-training for Multi-Sentence Inference (2022.naacl-main)
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| Challenge: | Recent studies show that pre-trained transformers perform poorly for multi-candidate inference tasks. |
| Approach: | They propose a pre-training objective that models paragraph-level semantics across multiple input sentences. |
| Outcome: | The proposed model outperforms existing models on three AS2 and one fact verification datasets. |
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media (N19-1)
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| Challenge: | a number of fact-checking initiatives have been launched, both manual and automatic, but the whole enterprise remains in a state of crisis. |
| Approach: | They propose a multi-task ordinal regression framework that models trustworthiness estimation and political ideology detection of entire news outlets. |
| Outcome: | The proposed model outperforms models that target the problems in isolation. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
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| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers (2021.eacl-main)
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| Challenge: | Numerical tables are used to present experimental results in scientific papers. |
| Approach: | They propose a task to extract metric-types from multi-level header numerical tables . they propose two joint-learning neural classification and generation schemes . |
| Outcome: | The proposed models handle in-header and out-of-headers metric-type identification problems. |
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
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| Challenge: | Question answering systems typically use pipelines that retrieve documents at finer text granularities. |
| Approach: | They propose an architecture for document and snippet ranking that leverages intuition . they modified a natural questions dataset to test their model . |
| Outcome: | The proposed model outperforms pipelines in document retrieval on biomedical data . the proposed model is competitive with the existing model, despite fewer parameters . |
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
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| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
| Approach: | They propose a capsule-based neural network model which performs slot filling and intent detection via a dynamic routing-by-agreement schema. |
| Outcome: | The proposed model performs better than existing models and existing models on real-world datasets. |
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence (2022.emnlp-main)
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| Challenge: | Existing joint models only use training procedure to determine the implicit correlation between intents and slots. |
| Approach: | They propose to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public multi-intent datasets. |
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (P19-1)
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| Challenge: | Existing models for slot filling and intent detection lack bi-directional interrelated connections between the intent and slots. |
| Approach: | They propose a bi-directional interrelated model for slot filling and intent detection that uses an SF-ID network to establish direct connections between the two tasks to promote each other mutually. |
| Outcome: | The proposed model improves on ATIS and Snips datasets in sentence-level semantic frame accuracy and improves performance on the two tasks. |
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain (2020.acl-main)
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| Challenge: | Recent studies show that de-identification is effective in the clinical domain but not in the downstream tasks. |
| Approach: | They propose a stacked model with restricted access to privacy sensitive information and a multitask model to investigate the effect of de-identification on clinical concept extraction. |
| Outcome: | The proposed model is stacked with restricted access to privacy sensitive information and a multitask model. |
A Multitask Learning Approach for Diacritic Restoration (2020.acl-main)
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| Challenge: | Diacritics are used to specify pronunciations and meanings in many languages like Arabic. |
| Approach: | They propose to use multi-task learning to optimize diacritic restoration with related NLP problems . they use Arabic as a case study since it has sufficient data resources for tasks . |
| Outcome: | The proposed model outperforms baseline models and is comparable to the state-of-the-art models. |
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention (2023.findings-emnlp)
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| Challenge: | Existing models for detecting multiple intents and filling slots are based on graphs but face problems . a joint model can exploit the correlations between intents, slots and slot filling tasks . |
| Approach: | They propose a joint model that captures correlations between intents and slot labels . they propose MISCA to incorporate an intent-slot co-attention mechanism and a label attention mechanism . |
| Outcome: | The proposed model outperforms previous models on two benchmark datasets. |
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark (2024.lrec-main)
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| Challenge: | Intent classification and slot-filling tasks are essential tasks of Spoken Language Understanding (SLU). |
| Approach: | They propose to use a MEDIA SLU dataset to train a multilingual model to achieve both tasks jointly. |
| Outcome: | The proposed model can be trained on multiple datasets including the MEDIA dataset and extends to more tasks and use cases. |