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.
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.
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.
Ranked #2 on Text Simplification on ASSET
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).
1 code implementation • NAACL 2018 • Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, Shreyas Shetty
Structured data summarization involves generation of natural language summaries from structured input data.
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).
no code implementations • 18 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.
no code implementations • 18 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.
2 code implementations • ACL 2017 • Preksha Nema, Mitesh Khapra, Anirban Laha, Balaraman Ravindran
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion.
Ranked #2 on Query-Based Extractive Summarization on Debatepedia
no code implementations • 14 Mar 2017 • Vardaan Pahuja, Anirban Laha, Shachar Mirkin, Vikas Raykar, Lili Kotlerman, Guy Lev
The stream of words produced by Automatic Speech Recognition (ASR) systems is typically devoid of punctuations and formatting.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
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).