Wluper

Dive into the nuances of human-human interactions in dialog systems, where politeness takes center stage. 

Learning From Free-Text Human Feedback: Examples of Human-Human Dialogs

Delve into the core of dialog system experimentation, exploring hyperparameters and baseline results.

Hyperparameters and Baseline Experiments in Dialog Systems

Embark on the journey of annotating dialog datasets, tasked with identifying user dissatisfaction, new concepts, corrections, and alternative responses. 

Dialog Datasets Annotation Guidelines

With a focus on non-native speaker annotators, the study unveils the nuances in identifying errors, emphasizing differences across datasets.

A Detailed Analysis of Inter-Annotator Agreement

A statistical analysis of automatic filtering's effectiveness in identifying errors within different similarity ranges. 

A Detailed Analysis on the Effectiveness of Automatic Filtering

Learn the structured nature of ask-oriented dialogues, as seen with BABI dataset's scripted conversations and the more natural flow found in SGD, MWoZ datasets.

Examples of Task-Oriented Dialogs 

Grasp automatic filtering at the sentence level, exploring the visual representation of similarity ranges between user responses and error-indicating sentences.

Automatic Filtering: Sentence-Level Analysis

Explore the seamless implementation of automatic filtering using PyTorch and the Transformers library.

Implementing Automatic Filtering with PyTorch and Transformers

From deciphering subtle cues to improving communication skills, this guide provides practical insights for effective digital interaction.

Error-Indicating Sentences And
Phrases

This article explores the innovative approach of identifying potentially relevant user responses with sentence transformer technology. 

Notes on Automatic Filtering for Potentially Relevant Dialogs

Uncover the layers of utterance, response, context, and society-level errors, gaining valuable insights into language-generation challenges. 

Integrated Error Taxonomy: Form Violation vs Content Violation

Embark on a comprehensive exploration of AI dialog datasets, uncovering insights into errors in system utterances and user responses.

Conclusion and Beyond: Navigating the Landscape of Errors and Feedback in AI Conversations

Explore the intricacies of learning from errors in AI dialog systems, uncovering challenges in extracting signals from human-human polite disagreements.

Discussion: User Reactions in Human-Bot Dialogs as Key to Learning from Errors

Witness the impact of a refined Integrated Error Taxonomy through a human evaluation, shedding light on its effectiveness in human-bot dialog understanding

Human Assessment of Enhanced Integrated Error Taxonomy

Embark on a statistical expedition through the heart of AI dialog datasets, exploring the distribution of errors and user responses. 

Statistical Analysis of Dialog Datasets for Error and Response Patterns  

This article unveils modifications based on observed weaknesses, creating a more nuanced classification system. 

Manual Error Type Analysis and
Taxonomies

Explore conversational datasets as we dissect six distinct types of dialogs, spanning task-oriented, open-domain, and knowledge-grounded interactions. 

Dialog Datasets: Navigating Task-Oriented, Open-Domain, and Knowledge-Grounded Conversations

Explore the landscape of dialog datasets enriched with free-text human feedback and delve into the intricacies of error taxonomies in conversational AI. 

Exploring Dialog Datasets Annotated with Free-Text Human Feedback

Explore the potential of synthetic dialog generation to augment existing datasets with annotations for errors and free-text human feedback. 

Learning From Free-Text Human Feedback—Collect New Datasets Or Extend Existing Ones?

“Send out and order it to the front—with chickens. And let them understand that we don't want any more of this cold indifference.”

ITALIAN WITH GRAMMAR

All successful startups have something in common — their product or service solves a problem or fills a business or retail consumer need. Thus an important part of identifying your startup’s space in the economy is understanding the market that you are targeting.

Building a Market Model for Your Startup’s Product

Per <a href="http://nexsis.co" target="_blank">Nexsis </a>&amp; <a href="http://lupercalcapital.com" target="_blank">Lupercal Capital</a>:

How Hardware Touch-points could be a Key to Sustainable Crypto Token Prices

Although 2018 has seen many successful ICOs, with total capital raised having already <a href="https://www.coindesk.com/6-3-billion-2018-ico-funding-already-outpaced-2017/" target="_blank">surpassed the 2017 total</a>, the market has changed significantly, and running a successful ICO is becoming more difficult.

How regulation has made obtaining ICO funding harder

Week 6: Reading list & news to keep up-to-date with Javascript, Python, DevOps, Cloud, Serverless…

Teaching machines to understand natural language is extremely complex and is considered the holy grail in the field of Artificial Intelligence

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Learning From Free-Text Human Feedback: Examples of Human-Human Dialogs

Hyperparameters and Baseline Experiments in Dialog Systems

Dialog Datasets Annotation Guidelines

A Detailed Analysis of Inter-Annotator Agreement

A Detailed Analysis on the Effectiveness of Automatic Filtering

Examples of Task-Oriented Dialogs

Automatic Filtering: Sentence-Level Analysis

Implementing Automatic Filtering with PyTorch and Transformers

Error-Indicating Sentences And Phrases

Notes on Automatic Filtering for Potentially Relevant Dialogs

Integrated Error Taxonomy: Form Violation vs Content Violation

Conclusion and Beyond: Navigating the Landscape of Errors and Feedback in AI Conversations

Discussion: User Reactions in Human-Bot Dialogs as Key to Learning from Errors

Human Assessment of Enhanced Integrated Error Taxonomy

Statistical Analysis of Dialog Datasets for Error and Response Patterns

Manual Error Type Analysis and Taxonomies

Dialog Datasets: Navigating Task-Oriented, Open-Domain, and Knowledge-Grounded Conversations

Exploring Dialog Datasets Annotated with Free-Text Human Feedback

Learning From Free-Text Human Feedback—Collect New Datasets Or Extend Existing Ones?

ITALIAN WITH GRAMMAR

Building a Market Model for Your Startup’s Product

How Hardware Touch-points could be a Key to Sustainable Crypto Token Prices

How regulation has made obtaining ICO funding harder

Week 6: Reading list & news to keep up-to-date with Javascript, Python, DevOps, Cloud, Serverless…

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