Search Results for author:
Found 21 papers, 3 papers with code
IGA: An Intent-Guided Authoring Assistant
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.
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.
CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring
We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context.
MIMOQA: Multimodal Input Multimodal Output Question Answering
In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.
IGA : An Intent-Guided Authoring Assistant
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
In this paper, we propose a Director-Generator framework to rewrite content in the target author's style, specifically focusing on certain target attributes.
Incorporating Stylistic Lexical Preferences in Generative Language Models
While recent advances in language modeling have resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style.
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.
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.
Towards Transparent and Explainable Attention Models
To make attention mechanisms more faithful and plausible, we propose a modified LSTM cell with a diversity-driven training objective that ensures that the hidden representations learned at different time steps are diverse.
Generating summaries tailored to target characteristics
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries.
Generating Formality-Tuned Summaries Using Input-Dependent Rewards
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content.
Adapting Language Models for Non-Parallel Author-Stylized Rewriting
Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style.
A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text
With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic.
Let's Ask Again: Refine Network for Automatic Question Generation
It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer.
Improving generation quality of pointer networks via guided attention
The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries.
When science journalism meets artificial intelligence : An interactive demonstration
We present an online interactive tool that generates titles of blog titles and thus take the first step toward automating science journalism.
Vocabulary Tailored Summary Generation
Neural sequence-to-sequence models have been successfully extended for summary generation. However, existing frameworks generate a single summary for a given input and do not tune the summaries towards any additional constraints/preferences.
Corpus-based Content Construction
Enterprise content writers are engaged in writing textual content for various purposes.
Generating Topic-Oriented Summaries Using Neural Attention
Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers.
