2 code implementations • 4 Jun 2023 • Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Ves Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
1 code implementation • 26 May 2023 • Pietro Lesci, Yoshinari Fujinuma, Momchil Hardalov, Chao Shang, Yassine Benajiba, Lluis Marquez
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
1 code implementation • 24 May 2023 • Petar Ivanov, Ivan Koychev, Momchil Hardalov, Preslav Nakov
Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers.
1 code implementation • 10 Oct 2022 • Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry Ilvovsky, Preslav Nakov
Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision.
1 code implementation • 22 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.
no code implementations • SemEval (ACL) 2016 • Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova, Galia Angelova
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
1 code implementation • 13 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.
no code implementations • RANLP 2021 • Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students.
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.
no code implementations • 31 Mar 2021 • Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov, Yoan Dinkov, Isabelle Augenstein, Preslav Nakov
The framework is based on a nearest-neighbour architecture.
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).
no code implementations • 27 Feb 2021 • Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
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.
no code implementations • 30 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.
1 code implementation • 19 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.
1 code implementation • RANLP 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
no code implementations • 17 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.
no code implementations • 12 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.
1 code implementation • 2 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.
no code implementations • SEMEVAL 2018 • Daniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov, Ivan Koychev, Ivelina Nikolova, Galia Angelova
We present the system built for SemEval-2018 Task 2 on Emoji Prediction.