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Found 11 papers, 3 papers with code
Shared Task in Evaluating Accuracy: Leveraging Pre-Annotations in the Validation Process
Our submission consists in fact of two submissions; we first analyze solely the performance of the rules and classifiers (pre-annotations), and then the human evaluation aided by the former pre-annotations using the web interface (hybrid).
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively.
MuLan: A Study of Fact Mutability in Language Models
Facts are subject to contingencies and can be true or false in different circumstances.
Word Order and World Knowledge
Word order is an important concept in natural language, and in this work, we study how word order affects the induction of world knowledge from raw text using language models.
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development
To this end, we release a multinational English legal corpus (LeXFiles) and a legal knowledge probing benchmark (LegalLAMA) to facilitate training and detailed analysis of legal-oriented PLMs.
Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations
This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations.
Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations
Plumitifs (dockets) were initially a tool for law clerks.
Attending Form and Context to Generate Specialized Out-of-VocabularyWords Representations
We propose a new contextual-compositional neural network layer that handles out-of-vocabulary (OOV) words in natural language processing (NLP) tagging tasks.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings.
Predicting and interpreting embeddings for out of vocabulary words in downstream tasks
We propose a novel way to handle out of vocabulary (OOV) words in downstream natural language processing (NLP) tasks.
