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Found 13 papers, 2 papers with code
CaM-Gen: Causally Aware Metric-Guided Text Generation
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.
How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations?
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).
What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions
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.
Agent-Specific Deontic Modality Detection in Legal Language
Legal documents are typically long and written in legalese, which makes it particularly difficult for laypeople to understand their rights and duties.
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection
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.
Entailment Relation Aware Paraphrase Generation
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.
What do Large Language Models Learn about Scripts?
SIF is a two-staged framework that fine-tunes LM on a small set of ESD examples in the first stage.
Goal-driven Command Recommendations for Analysts
With this assumption, we would like to assist the analytics process of a user through command recommendations.
CaM-Gen:Causally-aware Metric-guided Text Generation
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.
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus
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.
LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets
We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets.
Reinforced Rewards Framework for Text Style Transfer
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.
Intent-Aware Contextual Recommendation System
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.
