252 citations · 658 across the 50 of their papers we have counts for
19 papers · 1 filter
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences
Yifan Chen, Qi Zeng, Dilek Hakkani-Tur +3
Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules. To address this limitation, Li…
MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding
Revanth Gangi Reddy, Xilin Rui, Manling Li +9
Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images…
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
Revanth Gangi Reddy, Sai Chetan, Zhenhailong Wang +8
Claim detection and verification are crucial for news understanding and have emerged as promising technologies for mitigating misinformation and disinformation in the news. However…
Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification
Zhenhailong Wang, Heng Ji
State-of-the-art brain-to-text systems have achieved great success in decoding language directly from brain signals using neural networks. However, current approaches are limited t…
Joint Multimedia Event Extraction from Video and Article
Brian Chen, Xudong Lin, Christopher Thomas +5
Visual and textual modalities contribute complementary information about events described in multimedia documents. Videos contain rich dynamics and detailed unfoldings of events, w…
Chemical-Reaction-Aware Molecule Representation Learning
Hongwei Wang, Weijiang Li, Xiaomeng Jin +4
Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN…
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
Yu Meng, Yunyi Zhang, Jiaxin Huang +4
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw…
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks
Tuan Lai, Heng Ji, ChengXiang Zhai
Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base. Recently, many BERT-based models have been intro…
Corpus-based Open-Domain Event Type Induction
Jiaming Shen, Yunyi Zhang, Heng Ji +1
Traditional event extraction methods require predefined event types and their corresponding annotations to learn event extractors. These prerequisites are often hard to be satisfie…
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
Chenkai Sun, Weijiang Li, Jinfeng Xiao +3
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research. How to extract detailed knowledge about chemical reactions from…
A Unified Transformer-based Framework for Duplex Text Normalization
Tuan Manh Lai, Yang Zhang, Evelina Bakhturina +2
Text normalization (TN) and inverse text normalization (ITN) are essential preprocessing and postprocessing steps for text-to-speech synthesis and automatic speech recognition, res…
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
Tuan Lai, Heng Ji, ChengXiang Zhai +1
Compared to the general news domain, information extraction (IE) from biomedical text requires much broader domain knowledge. However, many previous IE methods do not utilize any e…
Stage-wise Fine-tuning for Graph-to-Text Generation
Qingyun Wang, Semih Yavuz, Victoria Lin +2
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize t…
Towards Robust Neural Retrieval Models with Synthetic Pre-Training
Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan +4
Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However,…
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
Ying Lin, Han Wang, Jiangning Chen +5
The growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specif…
Document-Level Event Argument Extraction by Conditional Generation
Sha Li, Heng Ji, Jiawei Han
Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human information-seeking behavior and leads to incom…
Efficient Attentions for Long Document Summarization
Luyang Huang, Shuyang Cao, Nikolaus Parulian +2
The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel e…
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution
Tuan Lai, Heng Ji, Trung Bui +3
Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pretrained language models, we argue that it is still…
The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction
Manling Li, Sha Li, Zhenhailong Wang +5
Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event sc…