activity
20162022
most citedExploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge

18 citations · 67 across the 10 of their papers we have counts for

collaborators

18 papers

cs.CL20221 cited

WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition

Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu +5

Zero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data…

cs.CL20213 cited

Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

Jia-Chen Gu, Hui Liu, Zhen-Hua Ling +3

Persona can function as the prior knowledge for maintaining the consistency of dialogue systems. Most of previous studies adopted the self persona in dialogue whose response was ab…

cs.CV20215 cited

SimTriplet: Simple Triplet Representation Learning with a Single GPU

Quan Liu, Peter C. Louis, Yuzhe Lu +9

Contrastive learning is a key technique of modern self-supervised learning. The broader accessibility of earlier approaches is hindered by the need of heavy computational resources…

eess.IV2021

ASIST: Annotation-free Synthetic Instance Segmentation and Tracking by Adversarial Simulations

Quan Liu, Isabella M. Gaeta, Mengyang Zhao +6

Background: The quantitative analysis of microscope videos often requires instance segmentation and tracking of cellular and subcellular objects. The traditional method consists of…

cs.CL20204 cited

Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems

Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng +7

Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to genera…

cs.CL2020

Exploring End-to-End Differentiable Natural Logic Modeling

Yufei Feng, Zi'ou Zheng, Quan Liu +2

We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natura…