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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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publications
Detecting personal attributes through analyzing online forums
Published in Cambridge Language Sciences Early Careers Researchers Symposium, 2019
Please read the paper for more details
Recommended citation: Wang, Z., Wu, X., Lin, W., Rastorgueva, E. (2019). Detecting personal attributes through analyzing online forums. In Cambridge Language Sciences Early Careers Researchers Symposium. https://github.com/Zhilin123/Publications/blob/master/Cambridge%20Language%20Sciences%20ECR.pdf
No you’re not alone A better way to find people with similar experiences on Reddit
Published in Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text, 2019
Please read the paper for more details
Recommended citation: Wang, Z., Rastorgueva, E., Lin, W., Wu, X. (2019). No you're not alone A better way to find people with similar experiences on Reddit. In Proceedings of the 2019 EMNLP Workshop W-NUT: The 5th Workshop on Noisy User-generated Text. https://www.aclweb.org/anthology/D19-5540
Author2Vec: A Novel Framework for Generating User Embedding
Published in on Arxiv, 2020
Please read the paper for more details
Recommended citation: Lin, W., Wu, X., Wang, Z., Rastorgueva, E. (2019). Author2Vec: A Novel Framework for Generating User Embedding. On Arxiv. https://arxiv.org/pdf/2003.11627.pdf
Automatic Detection of Self-Adaptors for Psychological Distress
Published in Proceedings of 2020 15th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2020), 2020
Please read the paper for more details
Recommended citation: Lin, W., Orton, I., Liu, M., Mahmoud, M. (2020). Automatic Detection of Self-Adaptors for Psychological Distress. In Proceedings of 2020 15th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2020). https://www.computer.org/csdl/proceedings-article/fg/2020/307900a214/1kecI2z8ccU
Multimodal Deep Learning Framework for Mental Disorder Recognition
Published in Proceedings of 2020 15th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2020), 2020
Please read the paper for more details
Recommended citation: Zhang, Z., Lin, W. (equal contribution), Liu, M., Mahmoud, M. (2020). Multimodal Deep Learning Framework for Mental Disorder Recognition. In Proceedings of 2020 15th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2020). https://www.computer.org/csdl/proceedings-article/fg/2020/307900a222/1kecI2YXH2M
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans [as collaborative authors]
Published in Nature Machine Intelligence volume 3, pages199�217 (2021), 2021
Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
Recommended citation: Roberts, M., Driggs, D., Thorpe, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3, 199�217 (2021). https://doi.org/10.1038/s42256-021-00307-0 https://www.nature.com/articles/s42256-021-00307-0
Message-Aware Graph Attention Networks for Large Scale Multi-Robot Path Planning
Published in IEEE Robotics and Automation Letters (RA-L), 2021
Please read the paper for more details
Recommended citation: Li, Q., Lin, W. (equal contribution), Liu, Z., Prorok, A. (2021). Message-Aware Graph Attention Networks for Large Scale Multi-Robot Path Planning. In IEEE Robotics and Automation Letters (RA-L). https://ieeexplore.ieee.org/abstract/document/9424371
Learning similarity between movie characters and its potential implications on understanding human experiences
Published in Proceedings of the 2021 NAACL Workshop WNU: 3rd Workshop on Narrative Understanding, 2021
Please read the paper for more details
Recommended citation: Wang, Z., Lin, W., Wu, X. (2021). Learning similarity between movie characters and its potential implications on understanding human experiences. In Proceedings of the 2021 NAACL Workshop WNU: 3rd Workshop on Narrative Understanding. https://arxiv.org/abs/2010.12183
Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress
Published in IEEE Transactions on Affective Computing, 2021
Please read the paper for more details
Recommended citation: Lin, W., Orton, I., Li, Q., Pavarini, G., Mahmoud, M. (2021). Looking At The Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress. In IEEE Transactions on Affective Computing. https://ieeexplore.ieee.org/abstract/document/9506822
Knowledge-Aware Graph-Enhanced GPT2 for Dialogue State Tracking
Published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Please read the paper for more details
Recommended citation: Lin, W., Tseng, B.-H., Byrne, B. (2021). Knowledge-Aware Graph-Enhanced GPT2 for Dialogue State Tracking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://aclanthology.org/2021.emnlp-main.620/
Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets
Published in Proceedings of the RecSys 2022: Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS), 2022
Please read the paper for more details
Recommended citation: Lin, W., Shou, L., Gong, M., Pei, J., Wang, Z., Byrne, B., Jiang, D. (2022). Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets. In Proceedings of the RecSys 2022: Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). https://ceur-ws.org/Vol-3294/short5.pdf
Transformer-Empowered Content-Aware Collaborative Filtering
Published in Proceedings of the RecSys 2022: Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS), 2022
Please read the paper for more details
Recommended citation: Lin, W., Shou, L., Gong, M., Pei, J., Wang, Z., Byrne, B., Jiang, D. (2022). Transformer-Empowered Content-Aware Collaborative Filtering. In Proceedings of the RecSys 2022: Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS). https://ceur-ws.org/Vol-3294/long3.pdf
Retrieval Augmented Visual Question Answering with Outside Knowledge
Published in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
Please read the paper for more details
Recommended citation: Lin, W., Byrne, B. (2022). Retrieval Augmented Visual Question Answering with Outside Knowledge. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://aclanthology.org/2022.emnlp-main.772/
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking
Published in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Findings (EACL), 2023
Please read the paper for more details
Recommended citation: Coca, A., Tseng, B.-H., Lin, W., Byrne, B. (2023). More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Findings (EACL). https://aclanthology.org/2023.findings-eacl.106/
FVQA 2.0: Introducing Adversarial Samples for Fact-based Visual Question Answering
Published in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Findings (EACL), 2023
Please read the paper for more details
Recommended citation: Lin, W., Wang, Z., Byrne, B. (2023). FVQA 2.0: Introducing Adversarial Samples for Fact-based Visual Question Answering. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Findings (EACL). https://arxiv.org/abs/2303.10699
An Inner Table Retriever for Robust Table Question Answering
Published in Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Please read the paper for more details
Recommended citation: Lin, W., Blloshmi, R., Byrne, B., de Gispert, A., Iglesias, G. (2023). An Inner Table Retriever for Robust Table Question Answering. In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL). https://www.amazon.science/publications/an-inner-table-retriever-for-robust-table-question-answering
LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering
Published in Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL), 2023
Please read the paper for more details
Recommended citation: Lin, W., Blloshmi, R., Byrne, B., de Gispert, A., Iglesias, G. (2023). LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering. In Proceedings of the 61th Annual Meeting of the Association for Computational Linguistics (ACL). https://www.amazon.science/publications/li-rage-late-interaction-retrieval-augmented-generation-with-explicit-signals-for-open-domain-table-question-answering
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
Published in 24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), 2023
Please read the paper for more details
Recommended citation: Coca, A., Tseng, B.-H., Chen, J., Lin, W., Zhang, W., Anders, T., Byrne, B. (2023). Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns. In 24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL). https://aclanthology.org/2023.sigdial-1.42/
Finer-grained Late-interaction Multimodal Retrieval for Knowledge-based Visual Question Answering
Published in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023
Please read the paper for more details
Recommended citation: Lin, W., Chen, J., Mei, J., Coca, A., Byrne, B. (2023). Finer-grained Late-interaction Multimodal Retrieval for Knowledge-based Visual Question Answering. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). https://openreview.net/forum?id=IWWWulAX7g
PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024
Please read the paper for more details
Recommended citation: Lin, W., Mei, J., Chen, J., Byrne, B. (2024). PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024). https://arxiv.org/abs/2402.08327
CONTROL-DAG: Efficient Controlled Decoding for Directed Acyclic Non-Autoregressive Text Generation
Published in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2024), 2024
Please read the paper for more details
Recommended citation: Chen, J., Lin, W., Byrne, B. (2024). CONTROL-DAG: Efficient Controlled Decoding for Directed Acyclic Non-Autoregressive Text Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2024). https://aclanthology.org/2024.naacl-short.42/
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding
Published in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2024), 2024
Please read the paper for more details
Recommended citation: Yang, G., Chen, J., Lin, W., Byrne, B. (2024). Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2024). https://aclanthology.org/2024.naacl-short.34/
Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning
Published in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 2024
Please read the paper for more details
Recommended citation: Mei, J., Chen, J., Lin, W., Byrne, B., Tomalin, M. (2024). Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024). https://arxiv.org/abs/2311.08110