Tag: AI
Muse v2.0 Update Summary
Control the “difficulty” of responses Adjusting the level of Musio’s responses to suit the user’s level of English, following the Common European Framework of Reference for Languages guideline – the proficiency of the speaker is categorized into one of A1, A2, B1, B2, C1, and C2: A1 being the most elementary and C2 being the most advanced. The level of Musio’s responses can be determined by the user (needs work on frontend / Musio OS), or be automatically determined by analysing the level of the user’s English. in Easy Mode, the level of Musio’s responses are determined at the utterance level. Musio’s response will not exceed a given user’s level. Examples: Specified level: A1 u: how are you m: [A1] […]
Open Domain Dialogue Dataset Comparison Report
Bach vs. Others This document presents a comparison between curated open-domain dialogue datasets available in the public domain and the data produced by AKA’s Bach data platform. The current report focuses on quantitative measurement which could be done in a transparent manner and represent objective differences found in the data. The analysis was performed using the following criteria: Total Number of Tokens Number of tokens is a measure of the overall size of the dataset. It is very important for training the modern Deep Learning-based models. Bach dataset displays clear superiority to others. Higher is better. Vocabulary Size Vocabulary size is the number of unique tokens appearing in the dataset. It represents the variety of speech in dialogues. Our dataset […]
AKA’s Paper (ReSmart) is accepted by HIMS 2020
AKA’s paper is accepted by International Conference HIMS (Health Informatics and Medical System) //americancse.org/events/csce2020/conferences/hims20 (July, 2020)
Abusive Language Detection for Muse Engine
Overview Bach, multiple linked dialogue data platform for Muse engine, has utilized multiple resources – artificial intelligence, human reviewers, automated rating system, etc. – in an effort to generate best human-machine conversations, and a noisy data follows as a necessity from the development process. Noisy data is meaningless data, and its meaning can be expanded to include abusive language which causes challenges that we encountered when developing Muse engine. In this blog post, we will describe a development process of Muse engine ‘s abusive language detection system and demonstrate the efficacy by comparing the system with different models in detecting abusive language . To be brief, AKA’s abusive language detection system has shown a good performance by extracting additional features […]