83 citations · 264 across the 31 of their papers we have counts for
8 papers · 1 filter
Dialogue Response Selection with Hierarchical Curriculum Learning
Yixuan Su, Deng Cai, Qingyu Zhou +6
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-wo…
COMETA: A Corpus for Medical Entity Linking in the Social Media
Marco Basaldella, Fangyu Liu, Ehsan Shareghi +1
Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.…
Self-Alignment Pretraining for Biomedical Entity Representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng +2
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remai…
Visual Pivoting for (Unsupervised) Entity Alignment
Fangyu Liu, Muhao Chen, Dan Roth +1
This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combi…
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
Victor Prokhorov, Yingzhen Li, Ehsan Shareghi +1
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in…
Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter
Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar +3
We present a new challenging stance detection dataset, called Will-They-Won't-They (WT-WT), which contains 51,284 tweets in English, making it by far the largest available dataset…