no code implementations • Findings (ACL) 2022 • Navita Goyal, Roodram Paneri, Ayush Agarwal, Udit Kalani, Abhilasha Sancheti, Niyati Chhaya
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
no code implementations • 7 Apr 2024 • Ishani Mondal, Abhilasha Sancheti
In this paper, we assess the robustness (reliability) of ChatGPT under input perturbations for one of the most fundamental tasks of Information Extraction (IE) i. e. Named Entity Recognition (NER).
no code implementations • 19 Dec 2022 • Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger
In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties.
no code implementations • 23 Nov 2022 • Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger
Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties.
1 code implementation • 24 May 2022 • Divya Kothandaraman, Sumit Shekhar, Abhilasha Sancheti, Manoj Ghuhan, Tripti Shukla, Dinesh Manocha
SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation.
no code implementations • 20 Mar 2022 • Abhilasha Sancheti, Balaji Vasan Srinivasan, Rachel Rudinger
We introduce a new task of entailment relation aware paraphrase generation which aims at generating a paraphrase conforming to a given entailment relation (e. g. equivalent, forward entailing, or reverse entailing) with respect to a given input.
1 code implementation • *SEM (NAACL) 2022 • Abhilasha Sancheti, Rachel Rudinger
SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage.
no code implementations • 12 Nov 2020 • Samarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin
With this assumption, we would like to assist the analytics process of a user through command recommendations.
no code implementations • 24 Oct 2020 • Navita Goyal, Roodram Paneri, Ayush Agarwal, Udit Kalani, Abhilasha Sancheti, Niyati Chhaya
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
no code implementations • NAACL 2021 • Navita Goyal, Balaji Vasan Srinivasan, Anandhavelu Natarajan, Abhilasha Sancheti
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus.
no code implementations • EMNLP (WNUT) 2020 • Abhilasha Sancheti, Kushal Chawla, Gaurav Verma
We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets.
no code implementations • 11 May 2020 • Abhilasha Sancheti, Kundan Krishna, Balaji Vasan Srinivasan, Anandhavelu Natarajan
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved.
no code implementations • 28 Nov 2017 • Biswarup Bhattacharya, Iftikhar Burhanuddin, Abhilasha Sancheti, Kushal Satya
Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations.