Search Results for author:
Found 14 papers, 4 papers with code
Contextual Dialogue Act Classification for Open-Domain Conversational Agents
Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents
Our results show that ConCET significantly improves topic classification performance on both datasets, including 8-10% improvements over state-of-the-art deep learning methods.
Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems
The insights from our study can enable more intelligent conversational systems, which could adapt in real-time to the inferred user satisfaction and engagement.
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
In addition, using ISS-MULT could finely improve the MULT method for question answering tasks, and these improvements prove more significant in the answer triggering task.
User Intent Inference for Web Search and Conversational Agents
To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain.
Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents
A topic suggested by the agent should be relevant to the person, appropriate for the conversation context, and the agent should have something interesting to say about it.
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.
Emora: An Inquisitive Social Chatbot Who Cares For You
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
CRAB: Class Representation Attentive BERT for Hate Speech Identification in Social Media
In recent years, social media platforms have hosted an explosion of hate speech and objectionable content.
APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
Query categorization is an essential part of query intent understanding in e-commerce search.
DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search
Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e. g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
Making Large Language Models Interactive: A Pioneer Study on Supporting Complex Information-Seeking Tasks with Implicit Constraints
Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e. g.,"find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.
Ericson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs.
