Papers by Tuan Lai
Translation between Molecules and Natural Language (2022.emnlp-main)
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| Challenge: | MolT5 pretrains models on unlabeled natural language text and molecule strings . bringing a new drug to market can cost over a billion dollars and take over ten years . |
| Approach: | They propose a self-supervised learning framework for pretraining models on unlabeled natural language text and molecule strings. |
| Outcome: | The proposed framework pretrains models on unlabeled natural language text and molecule strings, and it generates high quality outputs. |
The Context-Dependent Additive Recurrent Neural Net (N18-1)
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| Challenge: | Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP). |
| Approach: | They propose a new family of Recurrent Neural Networks that address contextual sequence mapping . they propose to use contextual signals to control the flow of information . |
| Outcome: | The proposed architecture outperforms existing methods on dialog problem and language model . the proposed architectures are based on a novel family of recurrent neural networks . |
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference (2021.acl-long)
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| Challenge: | Existing methods for information extraction from biomedical texts do not utilize external knowledge . despite the exponential growth of biomedically published articles, many existing methods fall behind . |
| Approach: | They propose a framework that utilizes external knowledge for entity and relation extraction . KECI uses an initial span graph to construct a knowledge graph containing relevant background knowledge . |
| Outcome: | The proposed framework achieves state-of-the-art results in two biomedical datasets . it achieves 4.59% and 4.91% improvement in F1 scores over the state- of-the art methods . |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks (2021.findings-emnlp)
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| Challenge: | Biomedical entity linking is a task of linking entities in biomedical documents to referent entities in a knowledge base. |
| Approach: | They propose an efficient convolutional neural network with residual connections for biomedical entity linking. |
| Outcome: | The proposed model achieves comparable or even better linking accuracy on five public datasets while having about 60 times fewer parameters. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents (2020.coling-main)
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| Challenge: | Existing methods for keyphrase extraction are limited by the number of annotated documents. |
| Approach: | They propose a joint learning approach that uses the idea of self-distillation to extract keyphrases from unlabeled articles. |
| Outcome: | The proposed approach outperforms baseline models on two public benchmarks: Inspec and SemEval-2017. |
ISA: An Intelligent Shopping Assistant (2020.aacl-demo)
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| Challenge: | In-store users only need to take a picture or scan the barcode of the product of interest, and then the user can talk to the assistant about the product. |
| Approach: | They present a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. |
| Outcome: | The proposed system can improve shopping experience in physical stores by leveraging advanced techniques in computer vision, speech processing, and natural language processing. |
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System (2022.acl-demo)
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Manling Li, Revanth Gangi Reddy, Ziqi Wang, Yi-shyuan Chiang, Tuan Lai, Pengfei Yu, Zixuan Zhang, Heng Ji
| Challenge: | a new system extracts supporting and refuting claims from COVID-19 related news . the system is publicly available at GitHub and DockerHub, with complete documentation. |
| Approach: | They propose a COVID-19 Claim Radar system that extracts supporting and refuting claims . the system leverages Wikidata as the hub to consolidate coreferential knowledge elements . |
| Outcome: | The system extracts supporting and refuting claims from COVID-19 pandemic information . it leverages Wikidata as the hub to merge coreferential knowledge elements . |
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)
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| Challenge: | Existing studies focus on Wikipedia-derived KBs, but there is little work on EL over Wikidata . EL systems have found applications in many tasks such as question answering . |
| Approach: | They propose a novel approach to linking entity mentions to referent entities in a knowledge base . they use a sequence-to-sequence model to generate the profile of the target entity . |
| Outcome: | The proposed approach achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010. |
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
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| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
| Outcome: | The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use. |
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution (2021.naacl-main)
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| Challenge: | Existing methods for event coreference resolution use symbolic features, but they are noisy and contain errors. |
| Approach: | They propose a context-dependent gated module to adaptively control the information flows from the input symbolic features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets: ACE 2005 and KBP 2016 . |
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
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| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
| Approach: | They propose to design an explainable, evidence-based memory network architecture that connects current sample with seen samples and bases its decision on these samples. |
| Outcome: | The proposed model can trace errors to training instances that might have caused errors . the proposed model achieves state-of-the-art performance on two popular datasets . |
A Gated Self-attention Memory Network for Answer Selection (D19-1)
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| Challenge: | Existing deep learning approaches for answer selection use word-level comparison followed by aggregation. |
| Approach: | They propose a new gated self-attention memory network for answer selection task . they combine a transfer learning technique from a large-scale online corpus to create a gated network . |
| Outcome: | The proposed model outperforms existing methods on two standard answer selection datasets: TrecQA and WikiQA. |