Papers by Guillaume Wisniewski
Beyond Surprisal: A Dual Metric Framework for Lexical Skill Acquisition in LLMs (2025.coling-main)
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| Challenge: | Existing learning curves capture when and how a model learns to use words correctly, but they neglect the equally important skill of avoiding incorrect usage. |
| Approach: | They propose a new metric which measures a model's capacity to refrain from using words in unexpected or unexpected contexts. |
| Outcome: | The proposed metric measures the model's ability to refrain from using words in unexpected or unexpected contexts. |
What Do Neural Speech Models Know About Phonology? Evidence from Structured Phoneme Confusions (2026.findings-acl)
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| Challenge: | acoustic and phonological models of speech recognition are often limited to the phoneme level . a recent study has shown that phoneme confusions are strongly structured in phonology space . |
| Approach: | They adopt a featural representation of phonemes grounded in phonological theory which models speech sounds as structured bundles of distinctive articulatory and acoustic properties. |
| Outcome: | The proposed model allows us to analyse phoneme confusions at a finer granularity and to investigate whether certain phonological features are more vulnerable than others. |
Establishing degrees of closeness between audio recordings along different dimensions using large-scale cross-lingual models (2024.findings-eacl)
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| Challenge: | Existing methods to analyze speech representations using pretraining data are difficult to achieve for endangered languages. |
| Approach: | They propose an unsupervised method to examine the level of abstraction in vector representations of speech from a pretrained model to determine their level of abstractness. |
| Outcome: | The proposed method is fully unsupervised and could be used in comparative studies on under-documented languages. |
Phonetic Normalization for Machine Translation of User Generated Content (D19-55)
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| Challenge: | a method to correct noisy User Generated Content (UGC) in French is proposed . it leverages on the existence of UGC specific noise due to the misuse of words with similar pronunciations. |
| Approach: | They propose a phonetizer-based method to correct noisy User Generated Content (UGC) they use phonetic similarity to generate IPA pronunciations of words . |
| Outcome: | The proposed method improves translation quality of noisy User Generated Content (UGC) in french. |
Using Artificial French Data to Understand the Emergence of Gender Bias in Transformer Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have demonstrated the ability of neural language models to learn linguistic properties without direct supervision. |
| Approach: | They propose to use an artificial corpus generated by a PCFG to control the gender distribution in training data and determine under which conditions a model correctly captures gender information. |
| Outcome: | The proposed approach allows to control the gender distribution in training data and determine under which conditions a model correctly captures gender information or appears gender-biased. |
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (N18-2)
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| Challenge: | Several strategies have been proposed to overcome the projectivity constraint by introducing transition-based dependency parsers that can build non-projective dependencies. |
| Approach: | They propose a modification of dynamic oracles to allow use of non-projective data . their method consistently outperforms traditional projectivization and pseudo-projectivisation approaches . |
| Outcome: | The proposed method outperforms projectivization and pseudo-projectivisation methods on 73 treebanks and achieves significant gains for non-projective languages. |
Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles (N18-2)
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| Challenge: | Multiple annotation conventions have been proposed for representing dependency structures. |
| Approach: | They propose to consider a set of syntactic references encoding alternative syntak representations to train a parser with a dynamic oracle. |
| Outcome: | The proposed approach can predict the best syntactic representation among all possible references. |
Errator: a Tool to Help Detect Annotation Errors in the Universal Dependencies Project (L18-1)
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| Challenge: | UD project aims to develop cross-linguistically consistent treebank annotations for a wide array of languages. |
| Approach: | They introduce tools that implement the annotation variation principle to help annotators find and correct errors in UD treebanks. |
| Outcome: | The proposed tools can be used to correct errors in UD treebank annotations. |
Quantifying training challenges of dependency parsers (C18-1)
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| Challenge: | a new metric is introduced to evaluate the difficulty to learn a given class of dependencies . a series of systematic computations using that metric have revealed interesting properties of the 3 considered parsing algorithms . |
| Approach: | They introduce a new metric to evaluate the difficulty to learn a given class of dependencies . they use it to characterize the information conveyed by cross-lingual parsers . |
| Outcome: | The proposed metric reveals the kind of dependencies that require high effort during training . it also shows that cross-lingual parsers can provide better quality information . |
Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement (2021.emnlp-main)
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| Challenge: | Recent studies have shown 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. |
| Approach: | They propose to take an alternative look at these results by studying whether neural networks are able to build an abstract sentence representation rather than capture surface statistical regularities. |
| Outcome: | The proposed model can achieve high accuracy on the long-range French object-verb agreement, indicating a possible flaw in the model's syntactic ability. |
Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation (N18-2)
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| Challenge: | Existing machine translation evaluation metrics use synonyms and paraphrases to reward meaning-equivalent but lexically divergent translations. |
| Approach: | They propose a machine translation evaluation metric which exploits reference translations enriched with meaning equivalent expressions. |
| Outcome: | The proposed metric achieves medium performance on large and noisier datasets . it is compared with the existing HyTER evaluation metric . |
How Bad are PoS Tagger in Cross-Corpora Settings? Evaluating Annotation Divergence in the UD Project. (N19-1)
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| Challenge: | Using annotation variation principles, Part-of-Speech tagging performance degrades when applied to test sentences that depart from training data. |
| Approach: | They propose to use the annotation variation principle to identify inconsistencies between annotations . they also evaluate their impact on prediction performance . |
| Outcome: | The proposed method can detect errors in gold standard annotations and improve prediction performance. |
Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense (2021.eacl-main)
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| Challenge: | a recent study has shown that neural networks can learn from linguistic representations without supervision . many studies have tried to identify which linguistic properties are encoded in the embeddings . |
| Approach: | They evaluate the ability of Bert embeddings to represent tense information . they use a multilingual linguistic probe to predict the morphology of a word . |
| Outcome: | The proposed model can predict tenses in French and Chinese, but the results drop sharply for Chinese. |
Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement (2023.tacl-1)
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| Challenge: | Existing studies have shown that transformers are able to predict subject-verb agreement, demonstrating their ability to uncover an abstract representation of the sentence in an unsupervised way. |
| Approach: | They propose to compare how transformers handle subject-verb and object-past participle agreements in French using probing and counterfactual analysis methods. |
| Outcome: | The proposed model handles subject-verb and object-past participle agreements in a way consistent with their modeling in theoretical linguistics. |
How Distributed are Distributed Representations? An Observation on the Locality of Syntactic Information in Verb Agreement Tasks (2022.acl-short)
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| Challenge: | Using probing, causal analysis and feature selection, we find that syntactic information is encoded locally in the transformers representations consistent with the French grammar. |
| Approach: | They address the question of the localization of syntactic information encoded in transformers representations by probing, causal analysis and feature selection methods. |
| Outcome: | The proposed representations are consistent with the object-past participle agreement in French and are consistent in both languages. |