3 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Hate Personified: Investigating the role of LLMs in content moderation
Sarah Masud, Sahajpreet Singh, Viktor Hangya +2
For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including a…
Style-Specific Neurons for Steering LLMs in Text Style Transfer
Wen Lai, Viktor Hangya, Alexander Fraser
Text style transfer (TST) aims to modify the style of a text without altering its original meaning. Large language models (LLMs) demonstrate superior performance across multiple ta…
Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages
Viktor Hangya, Silvia Severini, Radoslav Ralev +2
Very low-resource languages, having only a few million tokens worth of data, are not well-supported by multilingual NLP approaches due to poor quality cross-lingual word representa…
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings
Silvia Severini, Viktor Hangya, Masoud Jalili Sabet +2
Bilingual Word Embeddings (BWEs) are one of the cornerstones of cross-lingual transfer of NLP models. They can be built using only monolingual corpora without supervision leading t…
Addressing the Challenges of Cross-Lingual Hate Speech Detection
Irina Bigoulaeva, Viktor Hangya, Iryna Gurevych +1
The goal of hate speech detection is to filter negative online content aiming at certain groups of people. Due to the easy accessibility of social media platforms it is crucial to…
The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task
Alexandra Chronopoulou, Dario Stojanovski, Viktor Hangya +1
This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions, German<->Upper Sorbian. Our core unsupervised neural machine…