no code implementations • EACL (WASSA) 2021 • Ilia Markov, Nikola Ljubešić, Darja Fišer, Walter Daelemans
In this paper, we describe experiments designed to evaluate the impact of stylometric and emotion-based features on hate speech detection: the task of classifying textual content into hate or non-hate speech classes.
no code implementations • COLING (PEOPLES) 2020 • Nikola Ljubešić, Ilia Markov, Darja Fišer, Walter Daelemans
We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT).
no code implementations • NAACL (NLP4IF) 2021 • Ilia Markov, Walter Daelemans
Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results.
no code implementations • NAACL (NLP4IF) 2021 • Jens Lemmens, Ilia Markov, Walter Daelemans
We study the usefulness of hateful metaphorsas features for the identification of the type and target of hate speech in Dutch Facebook comments.
no code implementations • TRAC (COLING) 2022 • Ilia Markov, Walter Daelemans
Online hate speech detection is an inherently challenging task that has recently received much attention from the natural language processing community.
1 code implementation • 23 Oct 2023 • Stefan F. Schouten, Peter Bloem, Ilia Markov, Piek Vossen
But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language.
1 code implementation • 13 Oct 2023 • Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
We show, for the first time, that the majority of inference computations for large generative models such as LLaMA, OPT, and Falcon can be performed with both weights and activations being cast to 4 bits, in a way that leads to practical speedups, while at the same time maintaining good accuracy.
1 code implementation • 16 Jun 2023 • Stefan F. Schouten, Baran Barbarestani, Wondimagegnhue Tufa, Piek Vossen, Ilia Markov
Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift.
no code implementations • 5 Feb 2023 • Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh
Communication-reduction techniques are a popular way to improve scalability in data-parallel training of deep neural networks (DNNs).
1 code implementation • 31 Oct 2022 • Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar, Dan Alistarh
Data-parallel distributed training of deep neural networks (DNN) has gained very widespread adoption, but can still experience communication bottlenecks.
1 code implementation • 16 Nov 2021 • Ilia Markov, Hamidreza Ramezanikebrya, Dan Alistarh
CGX is based on two technical advances: \emph{At the system level}, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication.
no code implementations • COLING 2020 • Ehsan Lotfi, Ilia Markov, Walter Daelemans
Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications.
1 code implementation • NeurIPS 2020 • Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel Roy, Ali Ramezani-Kebrya
Many communication-efficient variants of SGD use gradient quantization schemes.
no code implementations • WS 2020 • Jens Lemmens, Ben Burtenshaw, Ehsan Lotfi, Ilia Markov, Walter Daelemans
We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020.
no code implementations • 16 Jan 2020 • Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee, Vyacheslav Kungurtsev, Dan Alistarh
Our framework, called elastic consistency enables us to derive convergence bounds for a variety of distributed SGD methods used in practice to train large-scale machine learning models.
no code implementations • 25 Sep 2019 • Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh.
We prove that, under standard assumptions, SGD can converge even in this extremely loose, decentralized setting, for both convex and non-convex objectives.
no code implementations • WS 2019 • Ilia Markov, Vivi Nastase, Carlo Strapparava
In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2).
no code implementations • SEMEVAL 2019 • Ilia Markov, Eric Villemonte de la Clergerie
We present the INRIA approach to the suggestion mining task at SemEval 2019.
no code implementations • WS 2018 • Ilia Markov, Vivi Nastase, Carlo Strapparava, Grigori Sidorov
We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language.
no code implementations • COLING 2018 • Ilia Markov, Vivi Nastase, Carlo Strapparava
In this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification.
no code implementations • WS 2017 • Ilia Markov, Lingzhen Chen, Carlo Strapparava, Grigori Sidorov
We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017.
no code implementations • WS 2017 • Helena Gomez, Ilia Markov, Jorge Baptista, Grigori Sidorov, David Pinto
This year{'}s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts.