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Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks [article]

Endri Kacupaj, Joan Plepi, Kuldeep Singh, Harsh Thakkar, Jens Lehmann, Maria Maleshkova
2021 arXiv   pre-print
This paper addresses the task of (complex) conversational question answering over a knowledge graph.  ...  For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).  ...  Acknowledgments The project leading to this publication has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.  ... 
arXiv:2104.01569v2 fatcat:nksqgjhp45cpbck56i6ld6bgrm

Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks

Endri Kacupaj, Joan Plepi, Kuldeep Singh, Harsh Thakkar, Jens Lehmann, Maria Maleshkova
2021 Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume   unpublished
This paper addresses the task of (complex) conversational question answering over a knowledge graph.  ...  For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).  ...  Acknowledgments The project leading to this publication has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.  ... 
doi:10.18653/v1/2021.eacl-main.72 fatcat:eqynso7nrrhqpp4e6nvctnjity

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs [article]

Joan Plepi, Endri Kacupaj, Kuldeep Singh, Harsh Thakkar, Jens Lehmann
2021 arXiv   pre-print
Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs.  ...  In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph.  ...  Acknowledgments The project leading to this publication has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.  ... 
arXiv:2103.07766v2 fatcat:m4dobg5ygfet3bqnaymezypaxm

Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices [article]

Hariom A. Pandya, Brijesh S. Bhatt
2021 arXiv   pre-print
Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering  ...  The usage and amount of information available on the internet increase over the past decade.  ...  In the second phase these confidence scores are propagated and aggregated over the structure of the knowledge Graph, to provide a confidence distribution over the set of possible answers.  ... 
arXiv:2112.03572v1 fatcat:c6ena24xondevec54u7f524p3m

Using a KG-Copy Network for Non-Goal Oriented Dialogues [article]

Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Simon Jordan, Jens Lehmann
2019 arXiv   pre-print
A dataset for nongoal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams.  ...  Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.  ...  The movie dialogues can utilize this provided knowledge graph for recommendation and question answering purposes.  ... 
arXiv:1910.07834v1 fatcat:wxmgv337nnhpxjbmd32a4aieyq

XAI Language Tutor - A XAI-based Language Learning Chatbot using Ontology and Transfer Learning Techniques

Nuobei SHI, Qin Zeng, Raymond Lee
2020 International Journal on Natural Language Computing  
the connections of neural network in bionics, and explain the output sentence from language model.  ...  by ontology graph.  ...  ACKNOWLEDGEMENTS The authors would like to thank for UIC DST for the provision of computer equipment and facilities. This project is supported by UIC research grant R202008.  ... 
doi:10.5121/ijnlc.2020.9501 fatcat:vjxfhmnmf5hxjbao2rj4sxglyi

Answering Conversational Questions on Structured Data without Logical Forms

Thomas Mueller, Francesco Piccinno, Peter Shaw, Massimo Nicosia, Yasemin Altun
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network.  ...  This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references  ...  Encoder We use a Graph Neural Network (GNN) encoder based on the Transformer (Vaswani et al., 2017) .  ... 
doi:10.18653/v1/d19-1603 dblp:conf/emnlp/MullerPSNA19 fatcat:mhgark75k5hhncp44hsl4oxooi

The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning [article]

Nuobei Shi, Qin Zeng, Raymond Lee
2020 arXiv   pre-print
of neural network in bionics, and explain the output sentence from language model.  ...  ontology graph.  ...  ACKNOWLEDGEMENTS The authors would like to thank for UIC DST for the provision of computer equipment and facilities. This project is supported by UIC research grant R202008.  ... 
arXiv:2009.13984v1 fatcat:jrt6rykpsngejnvndgjzbcfxqa

Towards information-rich, logical text generation with knowledge-enhanced neural models [article]

Hao Wang, Bin Guo, Wei Wu, Zhiwen Yu
2020 arXiv   pre-print
However, existing end-to-end neural models suffer from the problem of tending to generate uninformative and generic text because they cannot ground input context with background knowledge.  ...  The challenges of knowledge enhanced text generation including how to select the appropriate knowledge from large-scale knowledge bases, how to read and understand extracted knowledge, and how to integrate  ...  Acknowledgments This work was supported by the National Key R&D Program of China (2017YFB1001800) and the National Natural Science Foundation of China (No. 61772428, 61725205).  ... 
arXiv:2003.00814v1 fatcat:5fllyakwqzf4vnmar3a6zjoewe

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue [article]

Gaurav Kumar, Rishabh Joshi, Jaspreet Singh, Promod Yenigalla
2019 arXiv   pre-print
and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse.  ...  A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through  ...  We have used this loss to bring the query and response representations closer in the conversational space. Questions with similar answers should be closer to each other and the correct response.  ... 
arXiv:1912.10160v1 fatcat:xhmmpnsz2fbqtexdhuhgk7p3a4

Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers [article]

Shijie Geng, Peng Gao, Moitreya Chatterjee, Chiori Hori, Jonathan Le Roux, Yongfeng Zhang, Hongsheng Li, Anoop Cherian
2021 arXiv   pre-print
Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual  ...  To solve this task, we introduce a semantics-controlled multi-modal shuffled Transformer reasoning framework, consisting of a sequence of Transformer modules, each taking a modality as input and producing  ...  Acknowledgements: Shijie Geng, Peng Gao, and Moitreya Chatterjee worked on this project during MERL internships.  ... 
arXiv:2007.03848v2 fatcat:bqvz6lk3szfv7frgcq4fvfz2ji

A Survey of Knowledge-Enhanced Text Generation [article]

Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang
2022 arXiv   pre-print
In this survey, we present a comprehensive review of the research on knowledge enhanced text generation over the past five years.  ...  The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge  ...  reasoning over knowledge graph via path finding strategies; and (M4) improve the graph embeddings with graph neural networks.  ... 
arXiv:2010.04389v3 fatcat:vzdtlz4j65g2va7gwkbmzyxkhq

An Inference Approach To Question Answering Over Knowledge Graphs [article]

Aayushee Gupta, K.M. Annervaz, Ambedkar Dukkipati, Shubhashis Sengupta
2021 arXiv   pre-print
Direct answering models over knowledge graphs in literature are very few.  ...  The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph.  ...  An approach to convert Question Answering(QA) problem over a Knowledge Graph to that of Inference. 2.  ... 
arXiv:2112.11070v1 fatcat:i2bb7zl2tvadpdlp25tmjc5kby

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce [article]

Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo
2020 arXiv   pre-print
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.  ...  Specifically, we first construct a unified knowledge graph and extract key entities between user--product pairs, which serve as the skeleton of a conversation.  ...  To this end, we present a novel corpus called COOKIE: COnversational recommendation Over Knowledge graphs In E-commerce platforms.  ... 
arXiv:2008.09237v1 fatcat:agnch5bxxjcebcswpy3mkbltte

Answering Conversational Questions on Structured Data without Logical Forms [article]

Thomas Müller, Francesco Piccinno, Massimo Nicosia, Peter Shaw, Yasemin Altun
2019 arXiv   pre-print
We encode tables as graphs using a graph neural network model based on the Transformer architecture. The answers are then selected from the encoded graph using a pointer network.  ...  This model is appropriate for processing conversations around structured data, where the attention mechanism that selects the answers to a question can also be used to resolve conversational references  ...  Encoder We use a Graph Neural Network (GNN) encoder based on the Transformer (Vaswani et al., 2017) .  ... 
arXiv:1908.11787v1 fatcat:rk6cv2gns5fr7itim4x6iq2qfe
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