Search Results for author: Mathieu Ravaut

Found 14 papers, 9 papers with code

Parameter-Efficient Conversational Recommender System as a Language Processing Task

1 code implementation25 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.

Dialogue Generation Knowledge Graphs +2

PromptSum: Parameter-Efficient Controllable Abstractive Summarization

no code implementations6 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.

Abstractive Text Summarization Language Modelling

A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

1 code implementation2 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.

Machine Reading Comprehension Retrieval

Unsupervised Summarization Re-ranking

2 code implementations19 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.

Abstractive Text Summarization Re-Ranking

Towards Summary Candidates Fusion

1 code implementation17 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.

Abstractive Text Summarization Re-Ranking

SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization

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.

Abstractive Text Summarization Document Summarization +1

Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada

no code implementations8 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.

BIG-bench Machine Learning

ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs

no code implementations8 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.

Image Generation

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