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Found 13 papers, 1 papers with code
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots
In this work, we investigate the problem of satisfaction states tracking and its effects on CSP in E-commerce service chatbots.
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Mining Interest Trends and Adaptively Assigning SampleWeight for Session-based Recommendation
Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms.
An Online Question Answering System based on Sub-graph Searching
Knowledge graphs (KGs) have been widely used for question answering (QA) applications, especially the entity based QA.
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention.
An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary
In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses.
TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based.
MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning
In this paper, we propose the conversational query rewriting model - MLR, which is a Multi-task model on sequence Labeling and query Rewriting.
Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce
In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain.
A Feature-based Classification Technique for Answering Multi-choice World History Questions
Our FRDC_QA team participated in the QA-Lab English subtask of the NTCIR-11.
Detecting Concept-level Emotion Cause in Microblogging
In this paper, we propose a Concept-level Emotion Cause Model (CECM), instead of the mere word-level models, to discover causes of microblogging users' diversified emotions on specific hot event.
