Search Results for author: Anirban Laha

Found 11 papers, 5 papers with code

Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective

no code implementations ACL 2019 Abhijit Mishra, Anirban Laha, Karthik Sankaranarayanan, Parag Jain, Saravanan Krishnan

In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language.

Knowledge Graphs Translation

On Controllable Sparse Alternatives to Softmax

no code implementations NeurIPS 2018 Anirban Laha, Saneem A. Chemmengath, Priyanka Agrawal, Mitesh M. Khapra, Karthik Sankaranarayanan, Harish G. Ramaswamy

Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc.

Abstractive Text Summarization Classification +3

Unsupervised Neural Text Simplification

1 code implementation ACL 2019 Sai Surya, Abhijit Mishra, Anirban Laha, Parag Jain, Karthik Sankaranarayanan

The paper presents a first attempt towards unsupervised neural text simplification that relies only on unlabeled text corpora.

Denoising Text Simplification

Scalable Micro-planned Generation of Discourse from Structured Data

1 code implementation CL 2019 Anirban Laha, Parag Jain, Abhijit Mishra, Karthik Sankaranarayanan

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG).

Knowledge Graphs Text Generation

Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization

2 code implementations NAACL 2018 Preksha Nema, Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Mitesh M. Khapra

For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level).

A Machine Learning Approach for Evaluating Creative Artifacts

no code implementations18 Jul 2017 Disha Shrivastava, Saneem Ahmed CG, Anirban Laha, Karthik Sankaranarayanan

Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity.

BIG-bench Machine Learning

Story Generation from Sequence of Independent Short Descriptions

no code implementations18 Jul 2017 Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, Karthik Sankaranarayanan

Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity.

Machine Translation Story Generation +1

An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks

no code implementations COLING 2016 Anirban Laha, Vikas Raykar

Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification).

Classification General Classification +2

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