activity
20162023
most citedUsing millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

431 citations · 857 across the 69 of their papers we have counts for

collaborators
Showing 2017 · cs.CLShow all

9 papers · 2 filters

cs.CL2017

Limitations of Cross-Lingual Learning from Image Search

Mareike Hartmann, Anders Soegaard

Cross-lingual representation learning is an important step in making NLP scale to all the world's languages. Recent work on bilingual lexicon induction suggests that it is possible…

cs.CL2017

Is writing style predictive of scientific fraud?

Chloé Braud, Anders Søgaard

The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators…

cs.CL2017★ 268 cited

A Survey Of Cross-lingual Word Embedding Models

Sebastian Ruder, Ivan Vulić, Anders Søgaard

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural l…

cs.CL2017★ 3 cited

Multi-Task Learning of Keyphrase Boundary Classification

Isabelle Augenstein, Anders Søgaard

Keyphrase boundary classification (KBC) is the task of detecting keyphrases in scientific articles and labelling them with respect to predefined types. Although important in practi…

cs.CL2017

Cross-lingual and cross-domain discourse segmentation of entire documents

Chloé Braud, Ophélie Lacroix, Anders Søgaard

Discourse segmentation is a crucial step in building end-to-end discourse parsers. However, discourse segmenters only exist for a few languages and domains. Typically they only det…

cs.CL2017★ 12 cited

Identifying beneficial task relations for multi-task learning in deep neural networks

Joachim Bingel, Anders Søgaard

Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regulari…