Papers by Pascal Denis
WordNet Is All You Need: A Surprisingly Effective Unsupervised Method for Graded Lexical Entailment (2023.findings-emnlp)
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| Challenge: | a simple unsupervised method for predicting graded lexical entailment in English relies on WordNet . despite its simplicity, our method outperforms all previous methods using WordNet as weak supervision. |
| Approach: | They propose an unsupervised method which relies exclusively on WordNet for predicting graded lexical entailment in English. |
| Outcome: | The proposed method outperforms existing methods on the largest GLE dataset using WordNet. |
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (D18-1)
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| Challenge: | Existing approaches combine language and perception to infer word embeddings . however, the embeddables produced by such models do not reflect the actual word representations. |
| Approach: | They propose a probabilistic model that integrates linguistic and perceptual inputs to explain observed word-context pairs in a text corpus. |
| Outcome: | The proposed model achieves competitive or stronger results on tasks of assessing pairwise word similarity and image/caption retrieval compared to other state-of-the-art models. |
Chop and Change: Anaphora Resolution in Instructional Cooking Videos (2022.findings-aacl)
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| Challenge: | temporally evolving entities present challenges for anaphora resolution tasks . recipes provide rich source for referring expressions of transformed entities . |
| Approach: | They propose to use annotations to annotate recipes for anaphora resolution task . they propose to employ temporal features to improve anamorphic resolution . |
| Outcome: | The proposed annotation scheme improves the performance of the anaphora resolution task. |
MMAR: Multilingual and Multimodal Anaphora Resolution in Instructional Videos (2024.findings-emnlp)
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| Challenge: | Existing approaches to multilingual anaphora resolution include images and video inputs. |
| Approach: | They propose to include multimodal information in the form of images in anaphora resolution tasks. |
| Outcome: | The proposed approach improves resolution by 10% for unseen languages. |
Phylogenic Multi-Lingual Dependency Parsing (N19-1)
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| Challenge: | phylogenetic learning is beneficial for low resourced languages and well furnished languages families. |
| Approach: | They propose to use the phylogenetic tree to guide the learning of multi-lingual dependency parsers . they use a phylogy tree to train models that leverage languages structural similarities . |
| Outcome: | The proposed model outperforms independently learned models on zero-shot parsing of unseen languages. |
To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models (2024.emnlp-main)
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| Challenge: | Word Sense Disambiguiation and Word sense Induction are considered independent problems, but they are often neglected in practice. |
| Approach: | They propose an unsupervised task of learning a soft clustering amongwords that defines a set of concepts directly from data. |
| Outcome: | The proposed approach leverages both a local and global cross-lexicon view to induce concepts and also senses in the context of the proposed task. |
Find-2-Find: Multitask Learning for Anaphora Resolution and Object Localization (2023.emnlp-main)
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| Challenge: | Existing systems require large number of accurate annotations, such as image-level labels and location-level labeling. |
| Approach: | They propose a joint anaphora resolution and object localization dataset targeting visual-linguistic ambiguity. |
| Outcome: | The proposed framework improves visual-linguistic alignment and object localization with one joint model compared to a strong single-task baseline. |
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing (D18-1)
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| Challenge: | a measure of morphological complexity is used to characterize syntactic information in word embeddings. |
| Approach: | They propose a measure of morphological complexity in terms of governor-dependent preferential attachment that explains parsing performance. |
| Outcome: | The proposed framework improves parsing performance on morphologically rich languages using morphology as a syntactic marker. |
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
| Approach: | They propose to use BERT to probe contextual language models for predicting typicality scores. |
| Outcome: | The proposed methods improve on previous studies on word embeddings and their ability to predict typicality scores. |
Fair NLP Models with Differentially Private Text Encoders (2022.findings-emnlp)
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| Challenge: | Encoded text representations often capture sensitive attributes about individuals, raising privacy concerns and making models unfair to certain groups. |
| Approach: | They propose an approach that combines privacy and adversarial training to learn private representations which induces fairer models. |
| Outcome: | The proposed approach improves on four NLP datasets and shows that privacy and fairness can positively reinforce each other. |
A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models (2023.starsem-1)
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| Challenge: | Lexical Semantic Change is the study of how the meaning of words evolves through time. |
| Approach: | They propose to use distributional models to detect whether LD or LPC operate for given word pairs. |
| Outcome: | The proposed frameworks achieve a balanced accuracy above 0.6 on the dataset. |
Fair Without Leveling Down: A New Intersectional Fairness Definition (2023.emnlp-main)
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| Challenge: | Existing approaches to capture intersectional group fairness lack significant unfairness at intersection levels. |
| Approach: | They propose a new definition of intersectional fairness that combines absolute and relative performance across sensitive groups. |
| Outcome: | The proposed definition does not improve on a simple baseline. |