41 citations · 100 across the 12 of their papers we have counts for
12 papers
Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants
Cheng Qian, Haode Qi, Gengyu Wang +2
Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately…
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
Hui Wan, Siva Sankalp Patel, J. William Murdock +2
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no m…
Improved Text Classification via Contrastive Adversarial Training
Lin Pan, Chung-Wei Hang, Avirup Sil +1
We propose a simple and general method to regularize the fine-tuning of Transformer-based encoders for text classification tasks. Specifically, during fine-tuning we generate adver…
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Xiangyang Mou, Chenghao Yang, Mo Yu +4
Recent advancements in open-domain question answering (ODQA), i.e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datase…
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Haode Qi, Lin Pan, Atin Sood +4
Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challe…
Multilingual BERT Post-Pretraining Alignment
Lin Pan, Chung-Wei Hang, Haode Qi +3
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Usi…
Frustratingly Hard Evidence Retrieval for QA Over Books
Xiangyang Mou, Mo Yu, Bingsheng Yao +4
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We for…
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Ming Tan, Yang Yu, Haoyu Wang +4
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training da…
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Haoyu Wang, Ming Tan, Mo Yu +5
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph. In practice, multiple passes are computationally expensive and this ma…
Diverse Few-Shot Text Classification with Multiple Metrics
Mo Yu, Xiaoxiao Guo, Jinfeng Yi +6
We study few-shot learning in natural language domains. Compared to many existing works that apply either metric-based or optimization-based meta-learning to image domain with low…
Robust Task Clustering for Deep Many-Task Learning
Mo Yu, Xiaoxiao Guo, Jinfeng Yi +5
We investigate task clustering for deep-learning based multi-task and few-shot learning in a many-task setting. We propose a new method to measure task similarities with cross-task…
Neural Models for Sequence Chunking
Feifei Zhai, Saloni Potdar, Bing Xiang +1
Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the mea…