Search Results for author: Syrine Krichene

Found 6 papers, 4 papers with code

DoT: An efficient Double Transformer for NLP tasks with tables

1 code implementation Findings (ACL) 2021 Syrine Krichene, Thomas Müller, Julian Martin Eisenschlos

To improve efficiency while maintaining a high accuracy, we propose a new architecture, DoT, a double transformer model, that decomposes the problem into two sub-tasks: A shallow pruning transformer that selects the top-K tokens, followed by a deep task-specific transformer that takes as input those K tokens.

Question Answering

Open Domain Question Answering over Tables via Dense Retrieval

1 code implementation NAACL 2021 Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Martin Eisenschlos

Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages.

Open-Domain Question Answering

Understanding tables with intermediate pre-training

1 code implementation Findings of the Association for Computational Linguistics 2020 Julian Martin Eisenschlos, Syrine Krichene, Thomas Müller

To be able to use long examples as input of BERT models, we evaluate table pruning techniques as a pre-processing step to drastically improve the training and prediction efficiency at a moderate drop in accuracy.

Data Augmentation Natural Language Inference +1

Embedding models for recommendation under contextual constraints

no code implementations21 Jun 2019 Syrine Krichene, Mike Gartrell, Clement Calauzenes

For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i. e., less popular items).

Recommendation Systems

Learning Nonsymmetric Determinantal Point Processes

1 code implementation NeurIPS 2019 Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene

Our method imposes a particular decomposition of the nonsymmetric kernel that enables such tractable learning algorithms, which we analyze both theoretically and experimentally.

Information Retrieval Point Processes +1

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