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Summarizing Source Code using a Neural Attention Model
Srinivasan Iyer
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Ioannis Konstas
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Alvin Cheung
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Luke Zettlemoyer
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Paper Details:
Month: August
Year: 2016
Location: Berlin, Germany
Venue:
ACL |
Citations
URL
Obj2Text: Generating Visually Descriptive Language from Object Layouts
Xuwang Yin
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Vicente Ordonez
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SQL-to-Text Generation with Graph-to-Sequence Model
Kun Xu
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Lingfei Wu
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Zhiguo Wang
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Yansong Feng
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Vadim Sheinin
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A Parallel Corpus of Python Functions and Documentation Strings for Automated Code Documentation and Code Generation
Antonio Valerio Miceli Barone
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Rico Sennrich
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NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
Xi Victoria Lin
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Chenglong Wang
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Luke Zettlemoyer
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Michael D. Ernst
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A Repository of Corpora for Summarization
Franck Dernoncourt
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Mohammad Ghassemi
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Walter Chang
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Polyglot Semantic Parsing in APIs
Kyle Richardson
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Jonathan Berant
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Jonas Kuhn
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A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization
Parag Jain
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Anirban Laha
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Karthik Sankaranarayanan
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Preksha Nema
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Mitesh M. Khapra
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Shreyas Shetty
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A Syntactic Neural Model for General-Purpose Code Generation
Pengcheng Yin
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Graham Neubig
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Learning Semantic Correspondences in Technical Documentation
Kyle Richardson
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Jonas Kuhn
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The Code2Text Challenge: Text Generation in Source Libraries
Kyle Richardson
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Sina Zarrieß
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Jonas Kuhn
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Content Aware Source Code Change Description Generation
Pablo Loyola
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Edison Marrese-Taylor
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Jorge Balazs
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Yutaka Matsuo
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Fumiko Satoh
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http://stackoverflow.com
https://github.com/
http://archive.org/details/stackexchange
http://zql.sourceforge.net
http://torch.ch
Field Of Study
Linguistic Trends
Embeddings
Task
Information Retrieval
Language Generation
Summarization
Machine Translation
Language
Chinese
English
Dataset
Spoken Dialog
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