activity
20182021
most citedExtracting Multiple-Relations in One-Pass with Pre-Trained Transformers

18 citations · 20 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL20211 cited

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…

cs.CL2020

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…

cs.CL2020

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…

cs.CL20201 cited

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…

cs.CL2019

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…

cs.CL201918 cited

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…