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Weakly-Supervised Neural Semantic Parsing with a Generative Ranker
2018
Proceedings of the 22nd Conference on Computational Natural Language Learning
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Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving state-of-the-art results. ...
., whether they are likely to execute to the correct denotation and the degree to which they preserve the meaning of the utterance; (b) a scheduled training procedure effectively balances the contribution ...
Acknowledgments This research is supported by a Google PhD Fellowship and an AdeptMind Scolar Fellowship to the first author. ...
doi:10.18653/v1/k18-1035
dblp:conf/conll/0001L18
fatcat:dr7ox2u5b5agdfawmrrji2d4da
Weakly-supervised Neural Semantic Parsing with a Generative Ranker
[article]
2018
arXiv
pre-print
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Experiments on three Freebase datasets demonstrate the effectiveness of our semantic parser, achieving results within the state-of-the-art range. ...
In this paper we introduce a neural parser-ranker system for weakly-supervised semantic parsing. ...
Acknowledgments This research is supported by a Google PhD Fellowship and an AdeptMind Scolar Fellowship to the first author. ...
arXiv:1808.07625v1
fatcat:6tg5z4rjfzbijfj7mcbxwheccu
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
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)
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Leveraging external knowledge is an emerging trend in machine comprehension task. ...
To this end, we propose a Structural Knowledge Graph-aware Network (SKG) model, constructing sub-graphs for entities in the machine comprehension context. ...
Acknowledgements This work was supported by National Key R&D Program of China under Grant 2018YFB1005100, the National Natural Science Foundation of China (No.61533018, No.U1605251). ...
doi:10.18653/v1/d19-1602
dblp:conf/emnlp/QiuZFLJLLZ19
fatcat:ctjguxojo5h55f7mulg6zrpxb4
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
2016
Transactions of the Association for Computational Linguistics
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We present a method for unsupervised open-domain relation discovery. ...
The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities ...
The authors thank 5 github.com/diegma/relation-autoencoder 242 the action editor and the anonymous reviewers for their valuable suggestions and Limin Yao for answering our questions about data and baselines ...
doi:10.1162/tacl_a_00095
fatcat:xwr5n3h23navrejh4q72ep4gzi
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
[article]
2018
arXiv
pre-print
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Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse ...
Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answering. ...
Acknowledgement We gratefully acknowledge the support of the European Research Council for this research (ERC Advanced Grant NonSequeToR, # 740516). ...
arXiv:1806.04523v1
fatcat:5nprpxdqprh6pp44s6lv2n5q7m
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
[article]
2020
arXiv
pre-print
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We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine ...
systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for ...
CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding SPARQL query against the Freebase knowledge base (Bollacker ...
arXiv:1912.09713v2
fatcat:qpqftadxqbaengbo3dq726dgem
Structured Knowledge Discovery from Massive Text Corpus
[article]
2019
arXiv
pre-print
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In this dissertation, I will introduce principles, models, and algorithms for effective structured knowledge discovery from the massive text corpus. ...
In particular, four problems are studied in this dissertation: Structured Intent Detection for Natural Language Understanding, Structure-aware Natural Language Modeling, Generative Structured Knowledge ...
For example, online question answering websites are able to offer globally accessible information via human-human interactions. ...
arXiv:1908.01837v1
fatcat:j46srlxblfd35cd4z6jkl43iiu
Semantic Parsing with Dual Learning
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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Semantic parsing converts natural language queries into structured logical forms. The paucity of annotated training samples is a fundamental challenge in this field. ...
In this work, we develop a semantic parsing framework with the dual learning algorithm, which enables a semantic parser to make full use of data (labeled and even unlabeled) through a dual-learning game ...
Learning to collaborate for question answering and asking. ...
doi:10.18653/v1/p19-1007
dblp:conf/acl/CaoZLLY19
fatcat:tbsrr24ij5exphjctkmwwwf4l4
Question Answering over Knowledge Graphs via Structural Query Patterns
[article]
2019
arXiv
pre-print
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Natural language question answering over knowledge graphs is an important and interesting task as it enables common users to gain accurate answers in an easy and intuitive manner. ...
Searching the structured query over the knowledge graph can produce answers to the question. ...
In this paper, we focus on constructing query graphs for answering natural language questions over a knowledge graph. ...
arXiv:1910.09760v2
fatcat:oghjct4335grvnyuhhk4tovdxy
GraphQ IR: Unifying Semantic Parsing of Graph Query Language with Intermediate Representation
[article]
2022
arXiv
pre-print
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With the IR's natural-language-like representation that bridges the semantic gap and its formally defined syntax that maintains the graph structure, neural semantic parser can more effectively convert ...
In this paper, we propose a unified intermediate representation (IR) for graph query languages, namely GraphQ IR. ...
., 2020b ) is a large-scale dataset for complex question answering over Wikidata knowledge base (Vrandečić and Krötzsch, 2014) . ...
arXiv:2205.12078v1
fatcat:ewv6iyoyvjfepko2cbw4v7il5m
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
2016
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). ...
We evaluated the proposed model on a 20 Question Game conversational game simulator. ...
Also there is a 5% chance that the simulator will consider unknown as an attribute and thus it will answer with unknown intent for any question related to it. ...
doi:10.18653/v1/w16-3601
dblp:conf/sigdial/ZhaoE16
fatcat:aundy2dhmjdldndc2fveom7foy
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
[article]
2016
arXiv
pre-print
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This paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent Q-Networks (DRQN). ...
We evaluated the proposed model on a 20 Question Game conversational game simulator. ...
We would also like to thank Alan W Black for discussions on this paper. ...
arXiv:1606.02560v2
fatcat:i4akopezyzchzbonjfx57pecdi
Neural Approaches to Conversational AI
[article]
2019
arXiv
pre-print
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We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. ...
For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still ...
Figure 6 . 3 : 63 Given a complex question Q, we decompose it to a sequence of simple questions Q 1 , Q 2 , ..., use a Web-scale KB-QA agent to generate for each Q i an answer A i , from which we compute ...
arXiv:1809.08267v3
fatcat:j57xlm4ogferdnrpfs4f2jporq
Benchmarking Knowledge Graphs on the Web
[article]
2020
arXiv
pre-print
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The growing interest in making use of Knowledge Graphs for developing explainable artificial intelligence, there is an increasing need for a comparable and repeatable comparison of the performance of Knowledge ...
This paper gives an overview of benchmarks used to evaluate systems that process Knowledge Graphs. ...
Matching URIs and labels can be a particularly difficult task for a question answering benchmarking framework. • C2KB: This sub-task aims to identify all relevant resources for the given question. ...
arXiv:2002.06039v1
fatcat:tc25o6rplbekndjng4cqh2oxoe
The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. ...
Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. ...
[30] propose a VQA framework named "Ahab" that uses explicit reasoning over an RDF (Resource Description Framework) Knowledge Base to derive the answer, which naturally gives rise to a reasoning chain ...
doi:10.1109/cvpr.2017.416
dblp:conf/cvpr/WangWSH17
fatcat:m7f32lzpsrd73iys2xawv7rr7e
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