1 code implementation • 31 Jan 2024 • Florian Le Bronnec, Song Duong, Mathieu Ravaut, Alexandre Allauzen, Nancy F. Chen, Vincent Guigue, Alberto Lumbreras, Laure Soulier, Patrick Gallinari
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies.
1 code implementation • 25 Jan 2024 • Mathieu Ravaut, Hao Zhang, Lu Xu, Aixin Sun, Yong liu
Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation.
1 code implementation • 28 Nov 2023 • Hailin Chen, Fangkai Jiao, Xingxuan Li, Chengwei Qin, Mathieu Ravaut, Ruochen Zhao, Caiming Xiong, Shafiq Joty
Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce.
no code implementations • 16 Oct 2023 • Mathieu Ravaut, Aixin Sun, Nancy F. Chen, Shafiq Joty
However, in question answering, language models exhibit uneven utilization of their input context.
no code implementations • 6 Aug 2023 • Mathieu Ravaut, Hailin Chen, Ruochen Zhao, Chengwei Qin, Shafiq Joty, Nancy Chen
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-resource scenarios.
1 code implementation • 2 Apr 2023 • Iva Bojic, Josef Halim, Verena Suharman, Sreeja Tar, Qi Chwen Ong, Duy Phung, Mathieu Ravaut, Shafiq Joty, Josip Car
We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.
2 code implementations • 19 Dec 2022 • Mathieu Ravaut, Shafiq Joty, Nancy Chen
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks.
1 code implementation • 17 Oct 2022 • Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
To bypass this limitation, we propose a new paradigm in second-stage abstractive summarization called SummaFusion that fuses several summary candidates to produce a novel abstractive second-stage summary.
1 code implementation • ACL 2022 • Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset.
Ranked #2 on Document Summarization on CNN / Daily Mail
no code implementations • 8 Apr 2019 • Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung, Maksims Volkovs, Laura C. Rosella
We perform one of the first large-scale machine learning studies with this data to study the task of predicting diabetes in a range of 1-10 years ahead, which requires no additional screening of individuals. In the best setup, we reach a test AUC of 80. 3 with a single-model trained on an observation window of 5 years with a one-year buffer using all datasets.
no code implementations • 8 May 2018 • Aparna Balagopalan, Satya Gorti, Mathieu Ravaut, Raeid Saqur
Although GANs have had a lot of success in producing more realistic images than other approaches, they have only seen limited use for text sequences.
1 code implementation • 27 Jan 2018 • Mathieu Ravaut, Satya Gorti
Any gradient descent optimization requires to choose a learning rate.
1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
no code implementations • 26 May 2017 • Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Huiling Chen, Jie Lin, Babar Nazir, Cen Chen, Tse Chiang Howe, Zeng Zeng, Vijay Chandrasekhar
We present a deep learning framework for computer-aided lung cancer diagnosis.