Momchil Hardalov


2023

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Diable: Efficient Dialogue State Tracking as Operations on Tables
Pietro Lesci | Yoshinari Fujinuma | Momchil Hardalov | Chao Shang | Yassine Benajiba | Lluis Marquez
Findings of the Association for Computational Linguistics: ACL 2023

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. This approach is inefficient, especially when the number of slots is large and the conversation is long. We propose Diable, a new task formalisation that simplifies the design and implementation of efficient DST systems and allows one to easily plug and play large language models. We represent the dialogue state as a table and formalise DST as a table manipulation task. At each turn, the system updates the previous state by generating table operations based on the dialogue context. Extensive experimentation on the MultiWoz datasets demonstrates that Diable (i) outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient than current state-of-the-art methods while retaining competitive Joint Goal Accuracy, and (iii) is robust to noisy data annotations due to the table operations approach.

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Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection
Momchil Hardalov | Ivan Koychev | Preslav Nakov
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing

Detecting the user’s intent and finding the corresponding slots among the utterance’s words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. 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. Here, we leverage such models, and we design a novel architecture on top of them. Moreover, we propose an intent pooling attention mechanism, and we reinforce the slot filling task by fusing intent distributions, word features, and token representations. The experimental results on standard datasets show that our model outperforms both the current non-BERT state of the art as well as stronger BERT-based baselines.

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Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
Momchil Hardalov | Zara Kancheva | Boris Velichkov | Ivelina Nikolova-Koleva | Milena Slavcheva
Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing

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Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion
Prasanna Kumar Kumaresan | Bharathi Raja Chakravarthi | Subalalitha Cn | Miguel Ángel García-Cumbreras | Salud María Jiménez Zafra | José Antonio García-Díaz | Rafael Valencia-García | Momchil Hardalov | Ivan Koychev | Preslav Nakov | Daniel García-Baena | Kishore Kumar Ponnusamy
Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion

Hope serves as a powerful driving force that encourages individuals to persevere in the face of the unpredictable nature of human existence. It instills motivation within us to remain steadfast in our pursuit of important goals, regardless of the uncertainties that lie ahead. In today’s digital age, platforms such as Facebook, Twitter, Instagram, and YouTube have emerged as prominent social media outlets where people freely express their views and opinions. These platforms have also become crucial for marginalized individuals seeking online assistance and support[1][2][3]. The outbreak of the pandemic has exacerbated people’s fears around the world, as they grapple with the possibility of losing loved ones and the lack of access to essential services such as schools, hospitals, and mental health facilities.

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bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
Momchil Hardalov | Pepa Atanasova | Todor Mihaylov | Galia Angelova | Kiril Simov | Petya Osenova | Veselin Stoyanov | Ivan Koychev | Preslav Nakov | Dragomir Radev
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present bgGLUE (Bulgarian General Language Understanding Evaluation), a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. Our benchmark includes NLU tasks targeting a variety of NLP problems (e.g., natural language inference, fact-checking, named entity recognition, sentiment analysis, question answering, etc.) and machine learning tasks (sequence labeling, document-level classification, and regression). We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark. The evaluation results show strong performance on sequence labeling tasks, but there is a lot of room for improvement for tasks that require more complex reasoning. We make bgGLUE publicly available together with the fine-tuning and the evaluation code, as well as a public leaderboard at https://bgglue.github.io, and we hope that it will enable further advancements in developing NLU models for Bulgarian.

2022

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A Neighborhood Framework for Resource-Lean Content Flagging
Sheikh Muhammad Sarwar | Dimitrina Zlatkova | Momchil Hardalov | Yoan Dinkov | Isabelle Augenstein | Preslav Nakov
Transactions of the Association for Computational Linguistics, Volume 10

We propose a novel framework for cross- lingual content flagging with limited target- language data, which significantly outperforms prior work in terms of predictive performance. The framework is based on a nearest-neighbor architecture. It is a modern instantiation of the vanilla k-nearest neighbor model, as we use Transformer representations in all its components. Our framework can adapt to new source- language instances, without the need to be retrained from scratch. Unlike prior work on neighborhood-based approaches, we encode the neighborhood information based on query– neighbor interactions. We propose two encoding schemes and we show their effectiveness using both qualitative and quantitative analysis. Our evaluation results on eight languages from two different datasets for abusive language detection show sizable improvements of up to 9.5 F1 points absolute (for Italian) over strong baselines. On average, we achieve 3.6 absolute F1 points of improvement for the three languages in the Jigsaw Multilingual dataset and 2.14 points for the WUL dataset.

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A Survey on Stance Detection for Mis- and Disinformation Identification
Momchil Hardalov | Arnav Arora | Preslav Nakov | Isabelle Augenstein
Findings of the Association for Computational Linguistics: NAACL 2022

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). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.

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CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Momchil Hardalov | Anton Chernyavskiy | Ivan Koychev | Dmitry Ilvovsky | Preslav Nakov
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

While there has been substantial progress in developing systems to automate fact-checking, they still lack credibility in the eyes of the users. 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. This is a sensible approach as people trust manual fact-checking, and as many claims are repeated multiple times. Yet, a major issue when building such systems is the small number of known tweet–verifying article pairs available for training. Here, we aim to bridge this gap by making use of crowd fact-checking, i.e., mining claims in social media for which users have responded with a link to a fact-checking article. In particular, we mine a large-scale collection of 330,000 tweets paired with a corresponding fact-checking article. We further propose an end-to-end framework to learn from this noisy data based on modified self-adaptive training, in a distant supervision scenario. Our experiments on the CLEF’21 CheckThat! test set show improvements over the state of the art by two points absolute. Our code and datasets are available at https://github.com/mhardalov/crowdchecked-claims

2021

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Generating Answer Candidates for Quizzes and Answer-Aware Question Generators
Kristiyan Vachev | Momchil Hardalov | Georgi Karadzhov | Georgi Georgiev | Ivan Koychev | Preslav Nakov
Proceedings of the Student Research Workshop Associated with RANLP 2021

In education, quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.

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Cross-Domain Label-Adaptive Stance Detection
Momchil Hardalov | Arnav Arora | Preslav Nakov | Isabelle Augenstein
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. 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. Moreover, we propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the cross-domain results, and we highlight the important factors influencing the model performance.

2020

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EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering
Momchil Hardalov | Todor Mihaylov | Dimitrina Zlatkova | Yoan Dinkov | Ivan Koychev | Preslav Nakov
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We propose EXAMS – a new benchmark dataset for cross-lingual and multilingual question answering for high school examinations. We collected more than 24,000 high-quality high school exam questions in 16 languages, covering 8 language families and 24 school subjects from Natural Sciences and Social Sciences, among others.EXAMS offers unique fine-grained evaluation framework across multiple languages and subjects, which allows precise analysis and comparison of the proposed models. We perform various experiments with existing top-performing multilingual pre-trained models and show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains. We hope that EXAMS will enable researchers to explore challenging reasoning and knowledge transfer methods and pre-trained models for school question answering in various languages which was not possible by now. The data, code, pre-trained models, and evaluation are available at http://github.com/mhardalov/exams-qa.

2019

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Beyond English-Only Reading Comprehension: Experiments in Zero-shot Multilingual Transfer for Bulgarian
Momchil Hardalov | Ivan Koychev | Preslav Nakov
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc. This is largely due to the release of pre-trained contextualized representations such as BERT and ELMo, which can be fine-tuned for the target task. Despite those advances and the creation of more challenging datasets, most of the work is still done for English. Here, we study the effectiveness of multilingual BERT fine-tuned on large-scale English datasets for reading comprehension (e.g., for RACE), and we apply it to Bulgarian multiple-choice reading comprehension. We propose a new dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects —history, biology, geography and philosophy—, and 412 additional questions from online quizzes in history. While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option. Moreover, we experiment with different indexing and pre-training strategies. The evaluation results show accuracy of 42.23%, which is well above the baseline of 24.89%.

2018

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Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine
Daniel Kopev | Atanas Atanasov | Dimitrina Zlatkova | Momchil Hardalov | Ivan Koychev | Ivelina Nikolova | Galia Angelova
Proceedings of the 12th International Workshop on Semantic Evaluation

We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related ones. We investigated different methods of text preprocessing including replacing text emojis with respective tokens and splitting hashtags to capture more meaning. To represent text we used word n-grams and word embeddings. We experimented with a wide range of classifiers and our best results were achieved using a SVM-based classifier and a Hierarchical Attention Neural Network.

2016

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SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering
Tsvetomila Mihaylova | Pepa Gencheva | Martin Boyanov | Ivana Yovcheva | Todor Mihaylov | Momchil Hardalov | Yasen Kiprov | Daniel Balchev | Ivan Koychev | Preslav Nakov | Ivelina Nikolova | Galia Angelova
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)