activity
20212024
most citedOn the Context-Free Ambiguity of Emoji

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Symbolic Autoencoding for Self-Supervised Sequence Learning

Mohammad Hossein Amani, Nicolas Mario Baldwin, Amin Mansouri +3

Traditional language models, adept at next-token prediction in text sequences, often struggle with transduction tasks between distinct symbolic systems, particularly when parallel…

cs.CL2022

The Glass Ceiling of Automatic Evaluation in Natural Language Generation

Pierre Colombo, Maxime Peyrard, Nathan Noiry +2

Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods. Thus, numerous research efforts have focused on crafting…

cs.LG2022

Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning

Damien Teney, Maxime Peyrard, Ehsan Abbasnejad

Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model,…

cs.CL20221 cited

On the Context-Free Ambiguity of Emoji

Justyna Czestochowska, Kristina Gligoric, Maxime Peyrard +6

Emojis come with prepacked semantics making them great candidates to create new forms of more accessible communications. Yet, little is known about how much of this emojis semantic…

cs.CL2021

GenIE: Generative Information Extraction

Martin Josifoski, Nicola De Cao, Maxime Peyrard +2

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object)…