Search Results for author: Nitish Gupta

Found 18 papers, 5 papers with code

Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance: A Study Of German Manufacturing Firms

no code implementations16 Aug 2021 Nitish Gupta, Jay Shah, Satwik Gupta, Ruchir Kaul

In this paper, we estimate the causal impact (i. e. Average Treatment Effect, ATT) of the EU ETS on GHG emissions and firm competitiveness (primarily measured by employment, turnover, and exports levels) by combining a difference-in-differences approach with semi-parametric matching techniques and estimators an to investigate the effect of the EU ETS on the economic performance of these German manufacturing firms using a Stochastic Production Frontier model.

Study Of German Manufacturing Firms: Causal Impact Of European Union Emission Trading Scheme On Firm Behaviour And Economic Performance

no code implementations16 Aug 2021 Nitish Gupta, Ruchir Kaul, Satwik Gupta, Jay Shah

The results based on the nonparametric nearest neighbor matching suggest a statistically significant positive effect of the EU ETS on the economic performance of the regulated firms during Phase I of the EU ETS.

Enforcing Consistency in Weakly Supervised Semantic Parsing

1 code implementation ACL 2021 Nitish Gupta, Sameer Singh, Matt Gardner

The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons.

Semantic Parsing Visual Reasoning

Paired Examples as Indirect Supervision in Latent Decision Models

no code implementations EMNLP 2021 Nitish Gupta, Sameer Singh, Matt Gardner, Dan Roth

Such an objective does not require external supervision for the values of the latent output, or even the end task, yet provides an additional training signal to that provided by individual training examples themselves.

Question Answering Question Generation

What do we expect from Multiple-choice QA Systems?

no code implementations Findings of the Association for Computational Linguistics 2020 Krunal Shah, Nitish Gupta, Dan Roth

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.

Language understanding Multiple choice QA

Evaluating NLP Models via Contrast Sets

no code implementations1 Oct 2020 Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, A. Zhang, Ben Zhou

Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.

Reading Comprehension Sentiment Analysis

Overestimation of Syntactic Representation in Neural Language Models

no code implementations ACL 2020 Jordan Kodner, Nitish Gupta

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.

Obtaining Faithful Interpretations from Compositional Neural Networks

1 code implementation ACL 2020 Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, Matt Gardner

Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture.

Overestimation of Syntactic Representationin Neural Language Models

no code implementations10 Apr 2020 Jordan Kodner, Nitish Gupta

With the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful.

Robust Named Entity Recognition with Truecasing Pretraining

no code implementations15 Dec 2019 Stephen Mayhew, Nitish Gupta, Dan Roth

Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data.

Named Entity Recognition NER

Neural Module Networks for Reasoning over Text

2 code implementations ICLR 2020 Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, Matt Gardner

Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations.

Joint Multilingual Supervision for Cross-lingual Entity Linking

1 code implementation EMNLP 2018 Shyam Upadhyay, Nitish Gupta, Dan Roth

This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language.

Cross-Lingual Entity Linking Entity Linking

Entity Linking via Joint Encoding of Types, Descriptions, and Context

no code implementations EMNLP 2017 Nitish Gupta, Sameer Singh, Dan Roth

For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge.

Entity Linking

Collectively Embedding Multi-Relational Data for Predicting User Preferences

no code implementations23 Apr 2015 Nitish Gupta, Sameer Singh

Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations.

Collaborative Filtering

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