Papers by Julien Kloetzer
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks (2021.acl-long)
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| Challenge: | Existing models of NLP are fading away, but new ones are needed to maintain their dominance. |
| Approach: | They propose a method to pretrain a CNN using Wikipedia data and integrate it with standard TLMs. |
| Outcome: | The proposed method outperforms the original ALBERT on GLUE tasks and achieves similar performance to SOTA on open-domain QA tasks. |
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge (D19-1)
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| Challenge: | Existing methods for recognizing event causality written in web texts ignore each annotator's independent judgments, but we exploit each anorator''s judgments to predict the majority vote labels. |
| Approach: | They propose to grasp each annotator's policy by training multiple classifiers that predict the labels given by a single annotators and combine the outputs to predict the final labels determined by majority vote. |
| Outcome: | The proposed methods grasp each annotator's policy and combine the outputs to predict the final labels determined by majority vote. |
Understanding User Utterances in a Dialog System for Caregiving (2020.lrec-1)
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| Challenge: | a dialog system that can monitor the health status of seniors has a huge potential for solving the labor shortage in the caregiving industry in aging societies. |
| Approach: | They are developing a yes/no response classifier and an entailment recognizer to correctly interpret user utterances. |
| Outcome: | The proposed system can correctly interpret user utterances and can monitor the health of seniors. |
Open-Domain Why-Question Answering with Adversarial Learning to Encode Answer Texts (P19-1)
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| Challenge: | Existing why-QA methods retrieve “answer passages” that consist of several sentences . AGR is a vector representation of the non-redundant reason sought by a why-question . |
| Approach: | They propose a method for why-question answering that uses an adversarial learning framework. |
| Outcome: | The proposed method improves state-of-the-art open-domain QA on Japanese datasets . it also improves a state- of-the art method on publicly available English datasets. |