@article {liAssessingCapacityTransformer2023, title = {Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement}, journal = {Transactions of the Association for Computational Linguistics}, volume = {10}, year = {2023}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @article {7482, title = {Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement}, journal = {Transactions of the Association for Computational Linguistics}, volume = {11}, year = {2023}, pages = {18-33}, doi = {10.1162/tacl_a_00531}, url = {https://hal.science/hal-03957265}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @inproceedings {wisniewski:hal-03912438, title = {Analyzing Gender Translation Errors to Identify Information Flows between the Encoder and Decoder of a NMT System}, year = {2022}, month = {Dec}, address = {Abu Dhabi, United Arab Emirates}, url = {https://hal.science/hal-03912438} } @inproceedings {tytgat:hal-03911956, title = {Apprentissage automatique pour la surveillance de marques}, year = {2021}, address = {Saint-Etienne, France}, url = {https://hal.science/hal-03911956} } @inproceedings {7232, title = {Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense}, year = {2021}, pages = {3080{\textendash}3089}, publisher = {Association for Computational Linguistics}, address = {Online}, abstract = {

We evaluate the ability of Bert embeddings to represent tense information, taking French and Chinese as a case study. In French, the tense information is expressed by verb morphology and can be captured by simple surface information. On the contrary, tense interpretation in Chinese is driven by abstract, lexical, syntactic and even pragmatic information. We show that while French tenses can easily be predicted from sentence representations, results drop sharply for Chinese, which suggests that Bert is more likely to memorize shallow patterns from the training data rather than uncover abstract properties.

}, doi = {10.18653/v1/2021.eacl-main.269}, url = {https://aclanthology.org/2021.eacl-main.269}, author = {Li, Bingzhi and Wisniewski, Guillaume} } @inproceedings {li:halshs-03197072, title = {Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense}, year = {2021}, address = {Kiev (on line), Ukraine}, url = {https://halshs.archives-ouvertes.fr/halshs-03197072}, author = {Li, Bingzhi and Wisniewski, Guillaume} } @inproceedings {li:halshs-03197072, title = {Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense}, year = {2021}, address = {Kiev (on line), Ukraine}, url = {https://shs.hal.science/halshs-03197072}, author = {Li, Bingzhi and Wisniewski, Guillaume} } @inproceedings {li:halshs-03755089, title = {Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement}, year = {2021}, address = {Punta Cana, Dominican Republic}, doi = {10.18653/v1/2021.emnlp-main.377}, url = {https://halshs.archives-ouvertes.fr/halshs-03755089}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @inproceedings {li:halshs-03755089, title = {Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement}, year = {2021}, month = {Nov}, address = {Punta Cana, Dominican Republic}, doi = {10.18653/v1/2021.emnlp-main.377}, url = {https://shs.hal.science/halshs-03755089}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @inproceedings {liAreTransformersModern2021, title = {Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement}, year = {2021}, month = {nov}, pages = {4599{\textendash}4610}, publisher = {Association for Computational Linguistics}, address = {Online and Punta Cana, Dominican Republic}, abstract = {

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks\&$\#$39; syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.

}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @inproceedings {7231, title = {Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement}, year = {2021}, pages = {4599{\textendash}4610}, publisher = {Association for Computational Linguistics}, address = {Online and Punta Cana, Dominican Republic}, abstract = {

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject. We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks\&$\#$39; syntactic ability. Our fine-grained analyses of results on the long-range French object-verb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.

}, doi = {10.18653/v1/2021.emnlp-main.377}, url = {https://aclanthology.org/2021.emnlp-main.377}, author = {Li, Bingzhi and Wisniewski, Guillaume and Beno{\^\i}t Crabb{\'e}} } @inproceedings {michaud:hal-02798572, title = {Analyse d{\textquoteright}erreurs de transcriptions phon{\'e}miques automatiques d{\textquoteright}une langue {\textquoteleft}{\textquoteleft} rare {\textquoteright}{\textquoteright} : le na (mosuo)}, year = {2020}, pages = {451-462}, publisher = {ATALA}, address = {Nancy, France}, keywords = {analyse d{\textquoteright}erreurs, Apprentissage machine, Computational Language Documentation, Documentation linguistique assist{\'e}e par ordinateur, error analysis, Interdisciplinarit{\'e}, interdisciplinarity, machine learning, phonological transcription, Reconnaissance de la parole, Speech recognition, Traitement automatique des langues naturelles, transcription phonologique}, url = {https://hal.archives-ouvertes.fr/hal-02798572}, author = {Michaud, Alexis and Adams, Oliver and Guillaume, S{\'e}verine and Wisniewski, Guillaume}, editor = {Benzitoun and Christophe and Braud and Chlo{\'e} and Huber and Laurine and Langlois and David and Ouni and Slim and Pogodalla and Sylvain and Schneider and St{\'e}phane} }