Elena Simperl


Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Mubashara Akhtar | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics: EACL 2023

Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video. While there is increasing interest in using images for AFC, previous works mostly focus on detecting manipulated or fake images. We propose a novel task, chart-based fact-checking, and introduce ChartBERT as the first model for AFC against chart evidence. ChartBERT leverages textual, structural and visual information of charts to determine the veracity of textual claims. For evaluation, we create ChartFC, a new dataset of 15,886 charts. We systematically evaluate 75 different vision-language (VL) baselines and show that ChartBERT outperforms VL models, achieving 63.8% accuracy. Our results suggest that the task is complex yet feasible, with many challenges ahead.


A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider | Tim Schopf | Juraj Vladika | Mikhail Galkin | Elena Simperl | Florian Matthes
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)

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.

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PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking
Mubashara Akhtar | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics: NAACL 2022

Inspired by human fact checkers, who use different types of evidence (e.g. tables, images, audio) in addition to text, several datasets with tabular evidence data have been released in recent years. Whilst the datasets encourage research on table fact-checking, they rely on information from restricted data sources, such as Wikipedia for creating claims and extracting evidence data, making the fact-checking process different from the real-world process used by fact checkers. In this paper, we introduce PubHealthTab, a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by real fact checkers. We outline our approach for collecting evidence data from various websites and present an in-depth analysis of our dataset. Finally, we evaluate state-of-the-art table representation and pre-trained models fine-tuned on our dataset, achieving an overall F1 score of 0.73.


T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples
Hady Elsahar | Pavlos Vougiouklis | Arslen Remaci | Christophe Gravier | Jonathon Hare | Frederique Laforest | Elena Simperl
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Learning to Generate Wikipedia Summaries for Underserved Languages from Wikidata
Lucie-Aimée Kaffee | Hady Elsahar | Pavlos Vougiouklis | Christophe Gravier | Frédérique Laforest | Jonathon Hare | Elena Simperl
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

While Wikipedia exists in 287 languages, its content is unevenly distributed among them. In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata. To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples. We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.


Aligning Texts and Knowledge Bases with Semantic Sentence Simplification
Yassine Mrabet | Pavlos Vougiouklis | Halil Kilicoglu | Claire Gardent | Dina Demner-Fushman | Jonathon Hare | Elena Simperl
Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016)

A Neural Network Approach for Knowledge-Driven Response Generation
Pavlos Vougiouklis | Jonathon Hare | Elena Simperl
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present a novel response generation system. The system assumes the hypothesis that participants in a conversation base their response not only on previous dialog utterances but also on their background knowledge. Our model is based on a Recurrent Neural Network (RNN) that is trained over concatenated sequences of comments, a Convolution Neural Network that is trained over Wikipedia sentences and a formulation that couples the two trained embeddings in a multimodal space. We create a dataset of aligned Wikipedia sentences and sequences of Reddit utterances, which we we use to train our model. Given a sequence of past utterances and a set of sentences that represent the background knowledge, our end-to-end learnable model is able to generate context-sensitive and knowledge-driven responses by leveraging the alignment of two different data sources. Our approach achieves up to 55% improvement in perplexity compared to purely sequential models based on RNNs that are trained only on sequences of utterances.