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Found 18 papers, 11 papers with code
A Design Space for Intelligent and Interactive Writing Assistants
In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities.
mEdIT: Multilingual Text Editing via Instruction Tuning
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance.
Threads of Subtlety: Detecting Machine-Generated Texts Through Discourse Motifs
With the advent of large language models (LLM), the line between human-crafted and machine-generated texts has become increasingly blurred.
Personalized Text Generation with Fine-Grained Linguistic Control
As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized.
ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models
In recent times, large language models (LLMs) have shown impressive performance on various document-level tasks such as document classification, summarization, and question-answering.
Speakerly: A Voice-based Writing Assistant for Text Composition
We present Speakerly, a new real-time voice-based writing assistance system that helps users with text composition across various use cases such as emails, instant messages, and notes.
Benchmarking Cognitive Biases in Large Language Models as Evaluators
According to our findings, LLMs may still be unable to be utilized for automatic annotation aligned with human preferences.
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation.
CoEdIT: Text Editing by Task-Specific Instruction Tuning
We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).
Writing Assistants Should Model Social Factors of Language
Intelligent writing assistants powered by large language models (LLMs) are more popular today than ever before, but their further widespread adoption is precluded by sub-optimal performance.
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision
Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants.
Understanding Iterative Revision from Human-Written Text
Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process.
Text Simplification by Tagging
Edit-based approaches have recently shown promising results on multiple monolingual sequence transduction tasks.
Ranked #1 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
Adversarial Grammatical Error Correction
The discriminator is a sentence-pair classification model, trained to judge a given pair of grammatically incorrect-correct sentences on the quality of grammatical correction.
The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction
Recent work on Grammatical Error Correction (GEC) has highlighted the importance of language modeling in that it is certainly possible to achieve good performance by comparing the probabilities of the proposed edits.
Dialogue Act Classification with Context-Aware Self-Attention
Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks.
Ranked #3 on
Dialogue Act Classification
on Switchboard corpus
