Search Results for author: Momchil Hardalov

Found 16 papers, 7 papers with code

Leaf: Multiple-Choice Question Generation

1 code implementation22 Jan 2022 Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov

Testing with quiz questions has proven to be an effective way to assess and improve the educational process.

Multiple-choice Question Answering +1

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

1 code implementation13 Sep 2021 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection.

Stance Detection

Cross-Domain Label-Adaptive Stance Detection

1 code implementation EMNLP 2021 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.

Domain Adaptation Stance Detection

Detecting Abusive Language on Online Platforms: A Critical Analysis

no code implementations27 Feb 2021 Preslav Nakov, Vibha Nayak, Kyle Dent, Ameya Bhatawdekar, Sheikh Muhammad Sarwar, Momchil Hardalov, Yoan Dinkov, Dimitrina Zlatkova, Guillaume Bouchard, Isabelle Augenstein

Abusive language on online platforms is a major societal problem, often leading to important societal problems such as the marginalisation of underrepresented minorities.

Abusive Language

A Survey on Stance Detection for Mis- and Disinformation Identification

no code implementations Findings (NAACL) 2022 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).

Fact Checking Misinformation +3

EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering

2 code implementations EMNLP 2020 Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov

We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.

Question Answering Transfer Learning

Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection

no code implementations30 Apr 2020 Momchil Hardalov, Ivan Koychev, Preslav Nakov

Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset.

Intent Detection Natural Language Understanding +1

In Search of Credible News

1 code implementation19 Nov 2019 Momchil Hardalov, Ivan Koychev, Preslav Nakov

As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources.

Recursive Style Breach Detection with Multifaceted Ensemble Learning

no code implementations17 Jun 2019 Daniel Kopev, Dimitrina Zlatkova, Kristiyan Mitov, Atanas Atanasov, Momchil Hardalov, Ivan Koychev, Preslav Nakov

We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur.

Change Detection Ensemble Learning +1

Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots

no code implementations12 Feb 2019 Momchil Hardalov, Ivan Koychev, Preslav Nakov

Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents.

Information Retrieval Language Modelling +3

Towards Automated Customer Support

1 code implementation2 Sep 2018 Momchil Hardalov, Ivan Koychev, Preslav Nakov

Recent years have seen growing interest in conversational agents, such as chatbots, which are a very good fit for automated customer support because the domain in which they need to operate is narrow.

Information Retrieval Machine Translation +2

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