44 citations · 71 across the 15 of their papers we have counts for
8 papers · 1 filter
Lexical Entrainment for Conversational Systems
Zhengxiang Shi, Procheta Sen, Aldo Lipani
Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenome…
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Zhengxiang Shi, Aldo Lipani
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various t…
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
Zhengxiang Shi, Aldo Lipani
In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP). However, the impact of data augmentation (DA) tech…
Self Contrastive Learning for Session-based Recommendation
Zhengxiang Shi, Xi Wang, Aldo Lipani
Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contras…
A Survey on Asking Clarification Questions Datasets in Conversational Systems
Hossein A. Rahmani, Xi Wang, Yue Feng +3
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Aski…
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
Yue Feng, Hossein A. Rahmani, Aldo Lipani +1
Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to acc…
When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
Zhengxiang Shi, Jerome Ramos, To Eun Kim +3
In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to con…
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Zhengxiang Shi, Aldo Lipani
Language models (LMs) trained on vast quantities of unlabelled data have greatly advanced the field of natural language processing (NLP). In this study, we re-visit the widely acce…