Relevant bibliographies by topics / Slot Filling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Slot Filling'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Slot Filling"

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Dai, Yinpei, Yichi Zhang, Hong Liu, Zhijian Ou, Yi Huang, and Junlan Feng. "Elastic CRFs for Open-Ontology Slot Filling." Applied Sciences 11, no. 22 (November 12, 2021): 10675. http://dx.doi.org/10.3390/app112210675.

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Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.
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Khan, Bakhtiar, Faisal Khan, Wasiq Ullah, Muhammad Umair, and Shahid Hussain. "Slot Filling Factor Calculation and Electromagnetic Performance of Single Phase Electrically Excited Flux Switching Motors." Applied Computational Electromagnetics Society 35, no. 8 (October 7, 2020): 922–28. http://dx.doi.org/10.47037/2020.aces.j.350811.

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For variable speed applications, flux controlling capability of electrically excited flux switching motors (EEFSMs) attract researchers’ attention. However, low copper slot filling factor of the EEFSM with standard stator slot vitiates the electromagnetic performance and efficiency. This paper has proposed a new Octane Modular Stator (OMS) EEFSM model that has pentagonal stator slot and high copper slot filling factor. Copper slot filling factor is deliberated analytically for the proposed model and designs with standard stator slots, i.e., trapezoidal and rectangular. Electromagnetic performance of the OMS, Rectangular Stator Slot (RSS) and Trapezoidal Stator Slot (TSS) EEFSM designs are evaluated by finite element analysis (FEA) through JMAG v18.1 FEA solver. The proposed OMS EEFSM model has 9% higher copper slot filling factor in comparison with standard stator slots designs under same geometric parameters. The high copper slot filling factor of the proposed OMS EEFSM model has improved performance in term of low electric and magnetic loading.
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Lee, Sang Yul, and Sang Yong Lee. "Filling and Solidification Characteristics during Thixoforming of Copper Rotor for the Electrical Motors." Solid State Phenomena 116-117 (October 2006): 652–55. http://dx.doi.org/10.4028/www.scientific.net/ssp.116-117.652.

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Filling and solidification phenomena during thixoforming of copper rotor for small sized induction motors have been analyzed and characterized by experiment and computer simulation. Forming defects in slots of thixoformed rotor were examined by microstructural observation. Most filling and solidification failures were mainly related to complexity in slurry flows through slots in rotor core. Computer simulation with single slot model showed the effects of die temperature and ram speed on the filling characteristics clearly. Simulations with flow guide model showed that the control of filling velocity of slurry at slot and the consideration of possible differences in flow velocity in each slot are important.
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Lee, Sang Yul, and Sang Yong Lee. "A Study on the Microstructural Defects in Slots of Thixoformed Copper Rotor." Solid State Phenomena 116-117 (October 2006): 300–303. http://dx.doi.org/10.4028/www.scientific.net/ssp.116-117.300.

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Cu-Ca alloys and the squirrel cage rotors for induction motors of small capacities were used for the development of thixoforming processes. Processing conditions, motor efficiency and forming defects in macro- and microscale for thixoforming of Cu-Ca rotors have been performed to investigate the microstructural features and the filling phenomena in slots of squirrel cage rotor. Inadequate filling due to the complexity of slot structure, separation of solidified metal from the slot, porosities and phase inhomogenitites were typical microstructural defects found in thixoformed Cu-Ca rotors. Exact flow control in terms of billet and die temperatures at slot gate especially, was necessary to prevent significant defects such as incomplete filling of slot.
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Roca, Surya, Sophie Rosset, José García, and Álvaro Alesanco. "A Study on the Impacts of Slot Types and Training Data on Joint Natural Language Understanding in a Spanish Medication Management Assistant Scenario." Sensors 22, no. 6 (March 18, 2022): 2364. http://dx.doi.org/10.3390/s22062364.

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This study evaluates the impacts of slot tagging and training data length on joint natural language understanding (NLU) models for medication management scenarios using chatbots in Spanish. In this study, we define the intents (purposes of the sentences) for medication management scenarios and two types of slot tags. For training the model, we generated four datasets, combining long/short sentences with long/short slots, while for testing, we collect the data from real interactions of users with a chatbot. For the comparative analysis, we chose six joint NLU models (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling, and a joint SLU-LM model) from the literature. The results show that the best performance (with a sentence-level semantic accuracy of 68.6%, an F1-score of 76.4% for slot filling, and an accuracy of 79.3% for intent detection) is achieved using short sentences and short slots. Our results suggest that joint NLU models trained with short slots yield better results than those trained with long slots for the slot filling task. The results also indicate that short slots could be a better choice for the dialog system because of their simplicity. Importantly, the work demonstrates that the performance of the joint NLU models can be improved by selecting the correct slot configuration according to the usage scenario.
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Lim, Jungwoo, Suhyune Son, Songeun Lee, Changwoo Chun, Sungsoo Park, Yuna Hur, and Heuiseok Lim. "Intent Classification and Slot Filling Model for In-Vehicle Services in Korean." Applied Sciences 12, no. 23 (December 5, 2022): 12438. http://dx.doi.org/10.3390/app122312438.

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Since understanding a user’s request has become a critical task for the artificial intelligence speakers, capturing intents and finding correct slots along with corresponding slot value is significant. Despite various studies concentrating on a real-life situation, dialogue system that is adaptive to in-vehicle services are limited. Moreover, the Korean dialogue system specialized in an vehicle domain rarely exists. We propose a dialogue system that captures proper intent and activated slots for Korean in-vehicle services in a multi-tasking manner. We implement our model with a pre-trained language model, and it includes an intent classifier, slot classifier, slot value predictor, and value-refiner. We conduct the experiments on the Korean in-vehicle services dataset and show 90.74% of joint goal accuracy. Also, we analyze the efficacy of each component of our model and inspect the prediction results with qualitative analysis.
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Adel, Heike, and Hinrich Schuetze. "Type-aware Convolutional Neural Networks for Slot Filling." Journal of Artificial Intelligence Research 66 (September 28, 2019): 297–339. http://dx.doi.org/10.1613/jair.1.11725.

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The slot filling task aims at extracting answers for queries about entities from text, such as "Who founded Apple". In this paper, we focus on the relation classification component of a slot filling system. We propose type-aware convolutional neural networks to benefit from the mutual dependencies between entity and relation classification. In particular, we explore different ways of integrating the named entity types of the relation arguments into a neural network for relation classification, including a joint training and a structured prediction approach. To the best of our knowledge, this is the first study on type-aware neural networks for slot filling. The type-aware models lead to the best results of our slot filling pipeline. Joint training performs comparable to structured prediction. To understand the impact of the different components of the slot filling pipeline, we perform a recall analysis, a manual error analysis and several ablation studies. Such analyses are of particular importance to other slot filling researchers since the official slot filling evaluations only assess pipeline outputs. The analyses show that especially coreference resolution and our convolutional neural networks have a large positive impact on the final performance of the slot filling pipeline. The presented models, the source code of our system as well as our coreference resource is publicly available.
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Dietz, Armin, Antonino Oscar Di Tommaso, Fabrizio Marignetti, Rosario Miceli, and Claudio Nevoloso. "Enhanced Flexible Algorithm for the Optimization of Slot Filling Factors in Electrical Machines." Energies 13, no. 5 (February 26, 2020): 1041. http://dx.doi.org/10.3390/en13051041.

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The continuous development in the field of industrial automation and electric mobility has led to the need for more efficient electrical machines with a high power density. The improvement of electrical machines’ slot filling factors is one of the measures to satisfy these requirements. In recent years, this topic has aroused greater interest in the industrial sector, since the evolution of the winding technological manufacturing processes allows an economically sustainable realization of ordered winding arrangements, rather than random ones. Moreover, the manufacture of electrical machines’ windings must be preceded by an accurate design phase in which it is possible to evaluate the maximum slot filling factor obtainable for a given wire shape and for its dimensions. For this purpose, this paper presents an algorithmic approach for the evaluation of maximum slot filling factors in electrical machines under an ideal geometric premise. In particular, this algorithm has a greater degree of flexibility with respect to the algorithm approaches found in the literature, since the study has been extended to round, rectangular and hexagonal wire sections. Furthermore, the slot filling factor calculation was carried out both for standard and non-standard slots. The algorithmic approach proposed can be considered as an additional useful tool for the fast design of electrical machine windings.
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Sun, Rui, Lu Rao, and Xingfa Zhou. "A Joint Model of Natural Language Understanding for Human-Computer Conversation in IoT." Wireless Communications and Mobile Computing 2022 (August 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2074035.

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Natural language understanding (NLU) technologies for human-computer conversation is becoming a hot topic in the Internet of Things (IoT). Intent detection and slot filling are two fundamental NLU subtasks. Current approaches to these two subtasks include joint training methods and pipeline methods. Whether treating intent detection and slot filling as two separate tasks or training the two tasks as a joint model utilizing neural networks, most methods fail to build a complete correlation between the intent and slots. Some studies indicate that the intent and slots have a strong relationship because slots often highly depend on intent and also give clues to intent. Thus, recent joint models connect the two subtasks by sharing an intermediate network representation, but we argue that precise label information from one task is more helpful in improving the performance of another task. It is difficult to achieve complete information interaction between intent and slots because the extracted features in existing methods do not contain sufficient label information. Therefore, a novel bidirectional information transfer model is proposed in order to create a sufficient interaction between intent detection and slot filling with type-aware information enhancement. Such a framework collects more explicit label information from the network’s top layer and learns discriminative features from labels. According to the experimental results, our model greatly outperforms previous models and achieves the state-of-the-art performance on the two datasets: ATIS and SNIPS.
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Cheng, Shih Ping, Chang Chou Hwang, and Chia Ming Chang. "Design Techniques for Reducing Cogging Torque in SPM Motors Using Shifted Slot Opening." Applied Mechanics and Materials 284-287 (January 2013): 692–96. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.692.

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Skewing is one of the most common methods to reduce the cogging torque of permanent magnet synchronous motors. This technique may cause manufacturing troubles such as impossible automatic slot filling due to skewing stator slot. One method to overcome this problem is to use the step twisted stator structure with shifted slot opening. Compared to other methods of skewing, in the presented method, the stator slots are still straight slots and the skewing effect is implemented by shifting slot openings coupled with a step twisted stator design. This paper discusses the use of the step twisted stator structure with shifted slot opening and the associated effects on machine performance. We address the cogging torque, average torque, torque ripple, and back-EMF and its total harmonic distortion (THD). We examine the results using finite element analysis (FEA).
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Dissertations / Theses on the topic "Slot Filling"

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Pink, Glen Alan. "Slot Filling." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17055.

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Slot filling (SF) is the task of automatically extracting facts about particular entities from unstructured text, and populating a knowledge base (KB) with these facts. These structured KBs enable applications such as structured web queries and question answering. SF is typically framed as a query-oriented setting of the related task of relation extraction. Throughout this thesis, we reflect on how SF is a task with many distinct problems. We demonstrate that recall is a major limiter on SF system performance. We contribute an analysis of typical SF recall loss, and find a substantial amount of loss occurs early in the SF pipeline. We confirm that accurate NER and coreference resolution are required for high-recall SF. We measure upper bounds using a naïve graph-based semi-supervised bootstrapping technique, and find that only 39% of results are reachable using a typical feature space. We expect that this graph-based technique will be directly useful for extraction, and this leads us to frame SF as a label propagation task. We focus on a detailed graph representation of the task which reflects the behaviour and assumptions we want to model based on our analysis, including modifying the label propagation process to model multiple types of label interaction. Analysing the graph, we find that a large number of errors occur in very close proximity to training data, and identify that this is of major concern for propagation. While there are some conflicts caused by a lack of sufficient disambiguating context—we explore adding additional contextual features to address this—many of these conflicts are caused by subtle annotation problems. We find that lack of a standard for how explicit expressions of relations must be in text makes consistent annotation difficult. Using a strict definition of explicitness results in 20% of correct annotations being removed from a standard dataset. We contribute several annotation-driven analyses of this problem, exploring the definition of slots and the effect of the lack of a concrete definition of explicitness: annotation schema do not detail how explicit expressions of relations need to be, and there is large scope for disagreement between annotators. Additionally, applications may require relatively strict or relaxed evidence for extractions, but this is not considered in annotation tasks. We demonstrate that annotators frequently disagree on instances, dependent on differences in annotator world knowledge and thresholds on making probabilistic inference. SF is fundamental to enabling many knowledge-based applications, and this work motivates modelling and evaluating SF to better target these tasks.
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Kim, Joo-Kyung. "Linguistic Knowledge Transfer for Enriching Vector Representations." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500571436042414.

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Novotný, Jiří. "Analýza možností zvýšení účinnosti asynchronních motorů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220919.

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In the first part of the master’s thesis dealing with the increasing efficiency of induction motors there are briefly presented basic information about induction motors, followed by an overview of the losses of induction motors. The next part deals with the ways to increase efficiency of induction motors without increasing tooling costs. The practical part consists of four measurements of four induction motors, with their various mechanical adjustments to make comparing benefits of these modifications possible. The measured results are compared by a finite element method in Maxwell 2D Design program, in which the same motors are simulated as measured. Theoretical knowledge about the increase of efficiency is practically applied while being implemented in the simulations.
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Chen, Chung Chen, and 陳鍾誠. "XML Retrieval - A Slot-Filling Approach." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/28440213557073524161.

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博士
國立臺灣大學
資訊工程學研究所
90
Extensible Markup Language (XML) is widely used in data exchanging and knowledge representation. A retrieval system that understands the content of XML documents is strongly desired. In order to improve the efficiency of XML retrieval systems, we design a set of methods based on a ontology called slot-tree, and use the slot-tree to help the XML retrieval process. One problem for us to building smart computer is that computer cannot understand natural language as good as human. This is called the semantic gap between human and computer. For XML retrieval systems, semantic gap lies on both the query side and document side. The semantic gap in query side is due to the difficulty for human to write structuralized query. The semantic gap in document side is due to the difficulty for computer to understand XML documents. In order to reduce semantic gaps, we design a XML retrieval system based on the slot-tree ontology. The slot-tree ontology is an object-based knowledge representation. First, we design the slot-tree ontology to represent the inner structure of an object. Next, we design a slot-filling algorithm that maps XML documents into the slot-tree ontology in order to catch the semantics. After that, we design a XML retrieval system to reduce the semantic gap based on the slot-tree ontology and slot-filling algorithm. The system contains a slot-based query interface, a semantic retrieval model for XML, and a program that extract summary for browsing. However, the construction of slot-tree is not an easy job, so we design a slot-mining algorithm to construct slot-tree automatically. Slot-mining algorithm is a statistical approach that based on the correlation analysis between tags and words. The highly correlated terms are filled into the slot-tree as values. This algorithm eases the construction process of slot-tree. Two XML collections are used as the test bed of our XML retrieval system, one about butterflies and another about proteins. We found that our XML retrieval system is easy to use and performs well in the retrieval effectiveness and the quality of browsing. Besides, the slot-mining algorithm can fill important words into each slot. However, the mining result should be modified in order to improve the quality of slot-tree. Finally, we conclude our contribution on XML retrieval and compare our methods to some other methods. A qualitative analysis is given in the last chapter. After that, we propose our direction for the future research.
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Lo, Kuo-Lung, and 羅國隆. "Ink Drop Filling by a Slot Coating Die for an Electrowetting Display Panel." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/r2wfdx.

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Books on the topic "Slot Filling"

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Ultimate Vegetarian Slow Cooker Cookbook: 200 Flavorful and Filling Meatless Recipes That Prep Fast and Cook Slow. Arcas Publishing, 2016.

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Eastman, B. R. Bimbofication of Wing Mao: From Nerd to Slut, an Interracial Asian Lust Tale of Rear Action, Stuffing and Filling. Independently Published, 2017.

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Jones, Michael, Norman Qureshi, and Kim Rajappan. Atrial fibrillation. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0116.

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Atrial fibrillation is a tachycardia arising in the atria, with atrial electrical activity occurring chaotically and continuously, without any effective atrial contraction occurring. The effects of this are an irregular ventricular rate, loss of the atrial contribution to ventricular filling, and the pooling of blood in the atria, thus increasing the risk of thrombus formation. The ventricular rate may be fast, slow, or of normal speed, depending on the state of the patient’s atrioventricular conduction. Atrial fibrillation is classified as paroxysmal (self-terminating within 7 days), persistent (lasting longer than 1 week, or requiring cardioversion to terminate) or permanent (cardioversion unable to terminate durably to sinus rhythm).
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Book chapters on the topic "Slot Filling"

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Liu, In-Mao. "A slot-filling model of sentence comprehension." In Cognitive Processing of the Chinese and the Japanese Languages, 101–12. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9161-4_6.

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Liu, Shudong, Peijie Huang, Zhanbiao Zhu, Hualin Zhang, and Jianying Tan. "Cross-domain Slot Filling with Distinct Slot Entity and Type Prediction." In Natural Language Processing and Chinese Computing, 517–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88480-2_41.

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Tang, Yanxi, Jianzong Wang, Xiaoyang Qu, Nan Zhang, and Jing Xiao. "D-GHNAS for Joint Intent Classification and Slot Filling." In Web and Big Data, 794–807. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_58.

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Liu, Gaoshuo, Shenggen Ju, and Yu Chen. "Binary Label-Aware Transfer Learning for Cross-Domain Slot Filling." In Neural Information Processing, 359–68. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92270-2_31.

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Chen, Zheng, Suzanne Tamang, Adam Lee, Xiang Li, Marissa Passantino, and Heng Ji. "Top-Down and Bottom-Up: A Combined Approach to Slot Filling." In Information Retrieval Technology, 300–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_29.

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Kong, Junsheng, Yi Cai, Da Ren, and Zilu Li. "Deep Multi-task Learning with Cross Connected Layer for Slot Filling." In Natural Language Processing and Chinese Computing, 308–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32236-6_27.

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Wu, Tongtong, Meng Wang, Huan Gao, Guilin Qi, and Weizhuo Li. "Zero-Shot Slot Filling via Latent Question Representation and Reading Comprehension." In PRICAI 2019: Trends in Artificial Intelligence, 123–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29894-4_10.

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Kale, Mihir, and Aditya Siddhant. "MixOut: A Simple Yet Effective Data Augmentation Scheme for Slot-Filling." In Lecture Notes in Electrical Engineering, 279–88. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8395-7_21.

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Wakabayashi, Kei, Johane Takeuchi, and Mikio Nakano. "Segmentation-Based Formulation of Slot Filling Task for Better Generative Modeling." In Lecture Notes in Electrical Engineering, 17–31. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5538-9_2.

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Weichselbraun, Albert, Roger Waldvogel, Andreas Fraefel, Alexander van Schie, and Philipp Kuntschik. "Slot Filling for Extracting Reskilling and Upskilling Options from the Web." In Natural Language Processing and Information Systems, 279–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08473-7_25.

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Conference papers on the topic "Slot Filling"

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Lu, Hengtong, Zhuoxin Han, Caixia Yuan, Xiaojie Wang, Shuyu Lei, Huixing Jiang, and Wei Wu. "Slot Transferability for Cross-domain Slot Filling." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.440.

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Yu, Mengshi, Jian Liu, Yufeng Chen, Jinan Xu, and Yujie Zhang. "Cross-Domain Slot Filling as Machine Reading Comprehension." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/550.

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With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.
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Dou, Quansheng, and Panpan Cui. "Slot Filling Using En-Training." In 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2019. http://dx.doi.org/10.1109/cyberc.2019.00053.

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Shin, Youhyun, Kang Min Yoo, and Sang-goo Lee. "Slot Filling with Delexicalized Sentence Generation." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1808.

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Yan, Yang, Junda Ye, Zhongbao Zhang, and Liwen Wang. "AISFG: Abundant Information Slot Filling Generator." In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.naacl-main.308.

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Jiang, Chengyue, Zijian Jin, and Kewei Tu. "Neuralizing Regular Expressions for Slot Filling." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.747.

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Papanikolaou, Yannis, Marlene Staib, Justin Joshua Grace, and Francine Bennett. "Slot Filling for Biomedical Information Extraction." In Proceedings of the 21st Workshop on Biomedical Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.bionlp-1.7.

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Goo, Chih-Wen, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, and Yun-Nung Chen. "Slot-Gated Modeling for Joint Slot Filling and Intent Prediction." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-2118.

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Georgiadou, Despoina, Vassilios Diakoloukas, Vassilios Tsiaras, and Vassilios Digalakis. "ClockWork-RNN Based Architectures for Slot Filling." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1075.

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Dao, Mai Hoang, Thinh Hung Truong, and Dat Quoc Nguyen. "Intent Detection and Slot Filling for Vietnamese." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-618.

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