Search Results for author: Abhilasha Sancheti

Found 12 papers, 2 papers with code

CaM-Gen: Causally Aware Metric-Guided Text Generation

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.

Causal Inference Text Generation

What to Read in a Contract? Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents

no code implementations19 Dec 2022 Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

In this paper, we address the task of summarizing legal contracts for each of the contracting parties, to enable faster reviewing and improved understanding of them.

Extractive Summarization

Agent-Specific Deontic Modality Detection in Legal Language

no code implementations23 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.

Natural Language Understanding Transfer Learning

SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

1 code implementation24 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.

Active Learning Domain Adaptation +2

Entailment Relation Aware Paraphrase Generation

no code implementations20 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.

Natural Language Inference Paraphrase Generation +2

What do Large Language Models Learn about Scripts?

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.

CaM-Gen:Causally-aware Metric-guided Text Generation

no code implementations24 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.

Causal Inference Text Generation

Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus

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.

Language Modelling Style Transfer +1

Reinforced Rewards Framework for Text Style Transfer

no code implementations11 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.

Style Transfer Text Style Transfer

Intent-Aware Contextual Recommendation System

no code implementations28 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.

Recommendation Systems

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