Search Results for author: Vivek Gupta

Found 25 papers, 13 papers with code

Unsupervised Contextualized Document Representation

1 code implementation22 Sep 2021 Ankur Gupta, Vivek Gupta

In this paper, we address this issue by proposing SCDV+BERT(ctxd), a simple and effective unsupervised representation that combines contextualized BERT (Devlin et al., 2019) based word embedding for word sense disambiguation with SCDV soft clustering approach.

Sentence Similarity Word Sense Disambiguation

RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing

no code implementations21 Sep 2021 Vivek Gupta, Akshat Shrivastava, Adithya Sagar, Armen Aghajanyan, Denis Savenkov

While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering.

Question Answering Semantic Parsing

Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

no code implementations2 Aug 2021 Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Srivastava, Maneesh Singh, Vivek Srikumar

Our experiments demonstrate that a BERT-based model representative of today's state-of-the-art fails to properly reason on the following counts: it often (a) misses the relevant evidence, (b) suffers from hypothesis and knowledge biases, and, (c) relies on annotation artifacts and knowledge from pre-trained language models as primary evidence rather than relying on reasoning on the premises in the tabular input.

Language Modelling

TabPert: An Effective Platform for Tabular Perturbation

1 code implementation2 Aug 2021 Nupur Jain, Vivek Gupta, Anshul Rai, Gaurav Kumar

To truly grasp reasoning ability, a Natural Language Inference model should be evaluated on counterfactual data.

Natural Language Inference

Incorporating External Knowledge to Enhance Tabular Reasoning

1 code implementation NAACL 2021 J. Neeraja, Vivek Gupta, Vivek Srikumar

Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text.

Natural Language Inference

P-SIF: Document Embeddings Using Partition Averaging

1 code implementation18 May 2020 Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar

One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation.

INFOTABS: Inference on Tables as Semi-structured Data

no code implementations ACL 2020 Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar

In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them.

DeepSumm -- Deep Code Summaries using Neural Transformer Architecture

no code implementations31 Mar 2020 Vivek Gupta

In this paper, we employ neural techniques to solve the task of source code summarizing and specifically compare NMT based techniques to more simplified and appealing Transformer architecture on a dataset of Java methods and comments.

Improving Document Classification with Multi-Sense Embeddings

1 code implementation18 Nov 2019 Vivek Gupta, Ankit Saw, Pegah Nokhiz, Harshit Gupta, Partha Talukdar

Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks.

Classification Document Classification +3

On Dimensional Linguistic Properties of the Word Embedding Space

2 code implementations WS 2020 Vikas Raunak, Vaibhav Kumar, Vivek Gupta, Florian Metze

Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.

Machine Translation Sentence Classification +2

Equalizing Recourse across Groups

no code implementations7 Sep 2019 Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, Suresh Venkatasubramanian

We measure recourse as the distance of an individual from the decision boundary of a classifier.

Decision Making Fairness

A Logic-Driven Framework for Consistency of Neural Models

1 code implementation IJCNLP 2019 Tao Li, Vivek Gupta, Maitrey Mehta, Vivek Srikumar

While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples.

Natural Language Inference

Effective Dimensionality Reduction for Word Embeddings

1 code implementation WS 2019 Vikas Raunak, Vivek Gupta, Florian Metze

Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents.

Dimensionality Reduction Word Embeddings

Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles

no code implementations15 Nov 2017 Dhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sellamanickam Sundararajan, Shravan Narayanamurthy, Rahul Kidambi

We also demonstrate their usefulness in making design choices such as the number of classifiers in the ensemble and the size of a subset of data used for training that is needed to achieve a certain value of generalization error.

Leveraging Distributional Semantics for Multi-Label Learning

no code implementations18 Sep 2017 Rahul Wadbude, Vivek Gupta, Piyush Rai, Nagarajan Natarajan, Harish Karnick, Prateek Jain

Our approach is novel in that it highlights interesting connections between label embedding methods used for multi-label learning and paragraph/document embedding methods commonly used for learning representations of text data.

Document Embedding Multi-Label Learning +1

Text Summarization using Abstract Meaning Representation

2 code implementations6 Jun 2017 Shibhansh Dohare, Harish Karnick, Vivek Gupta

With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding.

Natural Language Understanding Text Summarization

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

4 code implementations EMNLP 2017 Dheeraj Mekala, Vivek Gupta, Bhargavi Paranjape, Harish Karnick

We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.

Information Retrieval Multi-Label Classification +1

User Bias Removal in Review Score Prediction

no code implementations20 Dec 2016 Rahul Wadbude, Vivek Gupta, Dheeraj Mekala, Harish Karnick

Review score prediction of text reviews has recently gained a lot of attention in recommendation systems.

Recommendation Systems

Product Classification in E-Commerce using Distributional Semantics

no code implementations COLING 2016 Vivek Gupta, Harish Karnick, Ashendra Bansal, Pradhuman Jhala

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title.

Classification General Classification

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