Search Results for author: Ashim Gupta

Found 9 papers, 4 papers with code

A Graph-Based Framework for Structured Prediction Tasks in Sanskrit

no code implementations CL (ACL) 2020 Amrith Krishna, Bishal Santra, Ashim Gupta, Pavankumar Satuluri, Pawan Goyal

Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes.

Dependency Parsing Structured Prediction

Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study

1 code implementation28 Jul 2021 Mattia Medina Grespan, Ashim Gupta, Vivek Srikumar

Symbolic knowledge can provide crucial inductive bias for training neural models, especially in low data regimes.

Chunking Inductive Bias

X-FACT: A New Benchmark Dataset for Multilingual Fact Checking

no code implementations ACL 2021 Ashim Gupta, Vivek Srikumar

In this work, we introduce X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing real-world claims.

Domain Generalization Fact Checking

BERT & Family Eat Word Salad: Experiments with Text Understanding

1 code implementation10 Jan 2021 Ashim Gupta, Giorgi Kvernadze, Vivek Srikumar

In this paper, we study the response of large models from the BERT family to incoherent inputs that should confuse any model that claims to understand natural language.

Evaluating Neural Morphological Taggers for Sanskrit

1 code implementation WS 2020 Ashim Gupta, Amrith Krishna, Pawan Goyal, Oliver Hellwig

Neural sequence labelling approaches have achieved state of the art results in morphological tagging.

Morphological Tagging

Neural Approaches for Data Driven Dependency Parsing in Sanskrit

no code implementations17 Apr 2020 Amrith Krishna, Ashim Gupta, Deepak Garasangi, Jivnesh Sandhan, Pavankumar Satuluri, Pawan Goyal

We compare the performance of each of the models in a low-resource setting, with 1, 500 sentences for training.

Dependency Parsing

An LSTM-CRF Based Approach to Token-Level Metaphor Detection

no code implementations WS 2018 Malay Pramanick, Ashim Gupta, Pabitra Mitra

In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF.

Machine Translation

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