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End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
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We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end. ...
We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure. ...
We thank Stephan Baier, Siemens CT members and the anonymous reviewers for valuable feedback. This research was supported by Bundeswirtschaftsministerium (bmwi. de), grant 01MD15010A (Smart Data Web). ...
doi:10.18653/v1/e17-2119
dblp:conf/eacl/SchutzeWK17
fatcat:frulzitxgrdsvbaohokyxwtxf4
Hierarchical Attention and Knowledge Matching Networks With Information Enhancement for End-to-End Task-Oriented Dialog Systems
2019
IEEE Access
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Nowadays, most end-to-end task-oriented dialog systems are based on sequence-to-sequence (Seq2seq), which is an encoder-decoder framework. ...
To solve this problem, we introduce hierarchical attention and knowledge matching networks with information enhancement (HAMI) for task-oriented dialog systems. ...
The whole system is end-to-end trainable. The framework of our system is illustrated in Figure 1 .
A. ...
doi:10.1109/access.2019.2892730
fatcat:rt2d4j77xrhmtlmzg6a42utruu
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. ...
Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. ...
We would like to thank Prof. Xiaoyan Zhu for her generous support. ...
doi:10.1609/aaai.v33i01.33016473
fatcat:thvza6vddvafvjerc7dzcom4dq
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
[article]
2018
arXiv
pre-print
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In addition, commonsense knowledge is applied through multi-source attention to facilitate story comprehension, and thus to help generate coherent and reasonable endings. ...
Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. ...
We would like to thank Prof. Xiaoyan Zhu for her generous support. ...
arXiv:1808.10113v3
fatcat:ydhklq5qkzdkflrfircl4tmhim
Visual Relationship Detection Using Part-and-Sum Transformers with Composite Queries
[article]
2021
arXiv
pre-print
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In this paper, we present a new approach, denoted Part-and-Sum detection Transformer (PST), to perform end-to-end visual composite set detection. ...
be detected in a hierarchical fashion. ...
Algorithms performing inference hierarchically exist [57, 34, 38] but they are not trained end-to-end. ...
arXiv:2105.02170v2
fatcat:z5dn6rrbxffkditvnh6icazkra
Deep Neural Networks for Relation Extraction
[article]
2021
arXiv
pre-print
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Finally, we propose a hierarchical entity graph convolutional network for relation extraction across documents. ...
In this thesis, we first propose a syntax-focused multi-factor attention network model for finding the relation between two entities. ...
This decoder consists of two pointer networks which find the start and end location of the two entities in a sentence, and a classification net-work which identifies the relation between them. ...
arXiv:2104.01799v1
fatcat:vmatz7gxazd4xnm2oprncd5mm4
A Survey on Dialogue Systems: Recent Advances and New Frontiers
[article]
2018
arXiv
pre-print
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Dialogue systems have attracted more and more attention. ...
For dialogue systems, deep learning can leverage a massive amount of data to learn meaningful feature representations and response generation strategies, while requiring a minimum amount of hand-crafting ...
With the advance of end-to-end neural generative models in recent years, many attempts have been made to construct an end-to-end trainable framework for task-oriented dialogue systems. ...
arXiv:1711.01731v3
fatcat:6wuovcynqbhlzmuorchn4mn6ma
Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking
[article]
2021
arXiv
pre-print
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Based on the encoder-decoder framework, we adopt a hierarchical copy approach that calculates two levels of attention at the word- and turn-level, which are then renormalized to obtain the final copy distribution ...
A focus loss term is employed to encourage the model to assign the highest turn-level attention weight to the most informative turn. ...
Hierarchical Attention Network -Our hierarchical dynamic copy mechanism draws inspiration from Hierarchical Attention Network (HAN) (Yang et al., 2016) , which was first proposed for text classification ...
arXiv:2107.11778v1
fatcat:5wxiswur7beunhfum4okukrrpm
Iterative-Deepening Conflict-Based Search
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF). CBS variants typically compute MAPF solutions using some form of A* search. ...
However, they often do so under strict time limits so as to avoid exhausting the available memory. ...
We thank all anonymous reviewers for their constructive comments. ...
doi:10.24963/ijcai.2020/561
dblp:conf/ijcai/YuanZPZSG20
fatcat:j7ygfwlc4zgybnjhuqf243dsa4
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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We use a novel sentence-level policy gradient method to bridge the nondifferentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. ...
., compresses and paraphrases) to generate a concise overall summary. ...
Acknowledgments We thank the anonymous reviewers for their helpful comments. ...
doi:10.18653/v1/p18-1063
dblp:conf/acl/BansalC18
fatcat:7zhltuzqq5frdfhnkzyjmmdhia
Phrase-based Image Captioning with Hierarchical LSTM Model
[article]
2017
arXiv
pre-print
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In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. ...
It consists of a phrase decoder at the bottom hierarchy to decode noun phrases of variable length, and an abbreviated sentence decoder at the upper hierarchy to decode an abbreviated form of the image ...
To this end, we design a phrase-based hierarchical LSTM model, namely phi-LSTM that consists of a phrase decoder and an abbreviated sentence (AS) decoder to generate image description from phrase to arXiv ...
arXiv:1711.05557v1
fatcat:slhffic5tvgxrh3tftpfe7c4ju
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.01569v1 fatcat:hnlbmxr23jfi7a64s4srgsq5ee
LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations ...
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
Learning to Memorize in Neural Task-Oriented Dialogue Systems
[article]
2019
arXiv
pre-print
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We also propose a recorded delexicalization copy strategy to replace real entity values with ordered entity types. ...
Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism. GLMP further introduces the concept of response sketching and double pointers copying. ...
Short Summary We present an end-to-end trainable memory-to-sequence (Mem2Seq) model for task-oriented dialogue systems. ...
arXiv:1905.07687v1
fatcat:upvqqczyajdtnf45bcszxs6nai
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
[article]
2018
arXiv
pre-print
Preserved Fulltext
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We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. ...
., compresses and paraphrases) to generate a concise overall summary. ...
Acknowledgments We thank the anonymous reviewers for their helpful comments. ...
arXiv:1805.11080v1
fatcat:4w3iawlhp5fhvp2yclghygsr2e
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding
[article]
2020
arXiv
pre-print
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Our findings include: links can lead to good classification outcomes without text; conservative voice is under-represented on Twitter; follow is the most important relation to predict ideology; retweet ...
Experimental results showed that TIMME is overall better than the state-of-the-art models for ideology detection on Twitter. ...
Figure 3 : The two types of decoder in our multi-task framework, referred to as TIMME and TIMME-hierarchical. ...
arXiv:2006.01321v2
fatcat:efvsu7r2ijgp3pab5c3bwubvxu
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