Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Scalable Framework for Learning From Implicit User Feedback to Improve Natural Language Understanding in Large-Scale Conversational AI Systems
إطار قابل للتطوير للتعلم من ملاحظات المستخدم الضمنية لتحسين فهم اللغة الطبيعية في أنظمة منظمة العفو الدولية على نطاق واسع
251
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
فهم اللغة الطبيعية (NLU) هو عنصر ثابت داخل نظام مساعد AI أو مساعد رقمي، وهو مسؤول عن إنتاج فهم دلالي لطلب المستخدم.نقترح مقاربة قابلة للتطوير والتلقائي لتحسين NLU في نظام AI محادثة على نطاق واسع من خلال الاستفادة من تعليقات المستخدم الضمنية، مع وجود نظرة ثاقبة لبيانات تفاعل المستخدم وسياق الحوار معلومات غنية بما يمكن استنتاج رضا المستخدم ونيةه.على وجه الخصوص، نقترح إطارا أمرا مدرجا لمنح بيانات الإشراف الجديدة لتحسين NLU من حركة الإنتاج الحي.مع مجموعة واسعة من التجارب، نعرض نتائج تطبيق الإطار وتحسين NLU لنظام إنتاج واسع النطاق عبر 10 مجالات.
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request. We propose a scalable and automatic approach for improving NLU in a large-scale conversational AI system by leveraging implicit user feedback, with an insight that user interaction data and dialog context have rich information embedded from which user satisfaction and intention can be inferred. In particular, we propose a domain-agnostic framework for curating new supervision data for improving NLU from live production traffic. With an extensive set of experiments, we show the results of applying the framework and improving NLU for a large-scale production system across 10 domains.
References used
https://aclanthology.org/
rate research
Read More
This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions relate
d to the use of unlabeled data in production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training, and 2) how does the selected data affect the performance of different state-of-the-art SSL techniques. We compare four widely used SSL techniques, Pseudo-label (PL), Knowledge Distillation (KD), Virtual Adversarial Training (VAT) and Cross-View Training (CVT) in conjunction with two data selection methods including committee-based selection and submodular optimization based selection. We further examine the benefits and drawbacks of these techniques when applied to intent classification (IC) and named entity recognition (NER) tasks, and provide guidelines specifying when each of these methods might be beneficial to improve large scale NLU systems.
We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience repo
rt for data preparation and training. This paper introduces the first large-scale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian. For additional background and access to the data, models, and software, please see: http://norlm.nlpl.eu
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, t
he users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments. In this work, we compa
re human assessment data from the last two WMT evaluation campaigns collected via two different methods for document-level evaluation. Our analysis shows that a document-centric approach to evaluation where the annotator is presented with the entire document context on a screen leads to higher quality segment and document level assessments. It improves the correlation between segment and document scores and increases inter-annotator agreement for document scores but is considerably more time consuming for annotators.
In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via publi
c crowdsourcing marketplaces and will present the key components of efficient label collection. This will be followed by a practical session, where participants address a real-world language resource production task, experiment with selecting settings for the labeling process, and launch their label collection project on one of the largest crowdsourcing marketplaces. The projects will be run on real crowds within the tutorial session and we will present useful quality control techniques and provide the attendees with an opportunity to discuss their own annotation ideas.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
