Search Results for author:
Found 30 papers, 3 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Generating summaries tailored to target characteristics
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Learning with Difference Attention for Visually Grounded Self-supervised Representations
Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by their poor visual grounding.
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
First, our attention segregation loss reduces the cross-attention overlap between attention maps of different concepts in the text prompt, thereby reducing the confusion/conflict among various concepts and the eventual capture of all concepts in the generated output.
CoPL: Contextual Prompt Learning for Vision-Language Understanding
Our key innovations over earlier works include using local image features as part of the prompt learning process, and more crucially, learning to weight these prompts based on local features that are appropriate for the task at hand.
Learning with Multi-modal Gradient Attention for Explainable Composed Image Retrieval
To this end, our key novelty is a new gradient-attention-based learning objective that explicitly forces the model to focus on the local regions of interest being modified in each retrieval step.
Iterative Multi-granular Image Editing using Diffusion Models
We hope our work would attract attention to this newly identified, pragmatic problem setting.
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e. g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement.
Social Media Ready Caption Generation for Brands
Our architecture is based on two parts: a the first part contains an image captioning model that takes in an image that the brand wants to post online and gives a plain English caption; b the second part takes in the generated caption along with the target brand personality and outputs a catchy personality-aligned social media caption.
