Search Results for author: Fréderic Godin

Found 9 papers, 5 papers with code

Zero-Shot Cross-Lingual Sentiment Classification under Distribution Shift: an Exploratory Study

no code implementations11 Nov 2023 Maarten De Raedt, Semere Kiros Bitew, Fréderic Godin, Thomas Demeester, Chris Develder

The brittleness of finetuned language model performance on out-of-distribution (OOD) test samples in unseen domains has been well-studied for English, yet is unexplored for multi-lingual models.

Cross-Lingual Sentiment Classification Sentiment Analysis +3

IDAS: Intent Discovery with Abstractive Summarization

1 code implementation31 May 2023 Maarten De Raedt, Fréderic Godin, Thomas Demeester, Chris Develder

Intent discovery is the task of inferring latent intents from a set of unlabeled utterances, and is a useful step towards the efficient creation of new conversational agents.

Abstractive Text Summarization Descriptive +4

Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals

1 code implementation21 Oct 2022 Maarten De Raedt, Fréderic Godin, Chris Develder, Thomas Demeester

We demonstrate the effectiveness of our approach in sentiment classification, using IMDb data for training and other sets for OOD tests (i. e., Amazon, SemEval and Yelp).

counterfactual Sentiment Analysis +3

Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering

no code implementations NAACL 2019 Fréderic Godin, Anjishnu Kumar, Arpit Mittal

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications.

Knowledge Graphs Question Answering +2

Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?

1 code implementation EMNLP 2018 Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, Thomas Demeester

In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.

Morphological Tagging

Predefined Sparseness in Recurrent Sequence Models

1 code implementation CONLL 2018 Thomas Demeester, Johannes Deleu, Fréderic Godin, Chris Develder

Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models.

Language Modelling Word Embeddings

Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation Functions in Quasi-Recurrent Neural Networks

2 code implementations25 Jul 2017 Fréderic Godin, Jonas Degrave, Joni Dambre, Wesley De Neve

A DReLU, which comes with an unbounded positive and negative image, can be used as a drop-in replacement for a tanh activation function in the recurrent step of Quasi-Recurrent Neural Networks (QRNNs) (Bradbury et al. (2017)).

Language Modelling Sentiment Analysis +1

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