Anirban Laha

According to our database1, Anirban Laha authored at least 13 papers between 2016 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2019
What is deemed computationally creative?
IBM J. Res. Dev., 2019

Scalable Micro-planned Generation of Discourse from Structured Data.
Comput. Linguistics, 2019

Unsupervised Neural Text Simplification.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective.
Proceedings of the 57th Conference of the Association for Computational Linguistics: Tutorial Abstracts, 2019

2018
On Controllable Sparse Alternatives to Softmax.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

A Mixed Hierarchical Attention Based Encoder-Decoder Approach for Standard Table Summarization.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

2017
A Machine Learning Approach for Evaluating Creative Artifacts.
CoRR, 2017

Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks.
CoRR, 2017

Story Generation from Sequence of Independent Short Descriptions.
CoRR, 2017

Joint Learning of Correlated Sequence Labeling Tasks Using Bidirectional Recurrent Neural Networks.
Proceedings of the Interspeech 2017, 2017

Diversity driven attention model for query-based abstractive summarization.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

2016
An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks.
Proceedings of the COLING 2016, 2016


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