Papers by Fréderic Godin
Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering (N19-2)
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| Challenge: | Existing methods for question answering over knowledge graphs use reinforcement learning to reason over a knowledge graph. |
| Approach: | They propose a new performance metric for question-answering agents that extends the binary reward structure to a ternary reward structure which rewards an agent for not answering a question rather than giving an incorrect answer. |
| Outcome: | The proposed method significantly improves the precision of answered questions while only not answering a limited number of correctly answered questions. |
Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals (2022.emnlp-main)
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| Challenge: | a recent study found that finetuned language models rely on spurious patterns in training data . this limitation limits their performance on out-of-distribution (OOD) test data. |
| Approach: | They propose a method that only requires annotation of a small fraction of training data . they add 1% manual counterfactuals to training data and generate extra counterfacts in vector space . |
| Outcome: | The proposed approach improves sentiment classification using IMDb data and other sets for OOD tests. |
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders (2021.emnlp-main)
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| Challenge: | Existing studies have used probing tasks to verify the presence of linguistic properties in vector representations, but it is unclear whether they can be manipulated to indirectly steer them. |
| Approach: | They validate a geometric mapping technique to transform linguistic properties without tuning . they use a pre-trained multilingual autoencoder to transform three linguistic property . |
| Outcome: | The proposed method can be used without tuning of the pre-trained autoencoder . the results are validated in monolingual and cross-lingual settings . |
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules? (D18-1)
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| Challenge: | Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn. |
| Approach: | They extend contextual decomposition technique to convolutional neural networks and bidirectional long-term memory networks to evaluate and compare these models for morphological tagging on three morphology-dependent languages. |
| Outcome: | The proposed models implicitly discover understandable linguistic rules for morphological tagging on three morphology-dependent languages. |