Adam Jatowt


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MAKED: Multi-lingual Automatic Keyword Extraction Dataset
Yash Verma | Anubhav Jangra | Sriparna Saha | Adam Jatowt | Dwaipayan Roy
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Keyword extraction is an integral task for many downstream problems like clustering, recommendation, search and classification. Development and evaluation of keyword extraction techniques require an exhaustive dataset; however, currently, the community lacks large-scale multi-lingual datasets. In this paper, we present MAKED, a large-scale multi-lingual keyword extraction dataset comprising of 540K+ news articles from British Broadcasting Corporation News (BBC News) spanning 20 languages. It is the first keyword extraction dataset for 11 of these 20 languages. The quality of the dataset is examined by experimentation with several baselines. We believe that the proposed dataset will help advance the field of automatic keyword extraction given its size, diversity in terms of languages used, topics covered and time periods as well as its focus on under-studied languages.

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Fine-tuning de modèles de langues pour la veille épidémiologique multilingue avec peu de ressources (Fine-tuning Language Models for Low-resource Multilingual Epidemic Surveillance)
Stephen Mutuvi | Emanuela Boros | Antoine Doucet | Adam Jatowt | Gaël Lejeune | Moses Odeo
Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Les modèles de langues pré-entraînés connaissent un très grand succès en TAL, en particulier dans les situations où l’on dispose de suffisamment de données d’entraînement. Cependant, il reste difficile d’obtenir des résultats similaires dans des environnements multilingues avec peu de données d’entraînement, en particulier dans des domaines spécialisés tels que la surveillance des épidémies. Dans cet article, nous explorons plusieurs hypothèses concernant les facteurs qui pourraient avoir une influence sur les performances d’un système d’extraction d’événements épidémiologiques dans un scénario multilingue à faibles ressources : le type de modèle pré-entraîné, la qualité du tokenizer ainsi que les caractéristiques des entités à extraire. Nous proposons une analyse exhaustive de ces facteurs et observons une corrélation importante, quoique variable ; entre ces caractéristiques et les performances observées sur la base d’une tâche de veille épidémiologique multilingue à faibles ressources. Nous proposons aussi d’adapter les modèles de langues à cette tâche en étendant le vocabulaire du tokenizer pré-entraîné avec les entités continues, qui sont des entités qui ont été divisées en plusieurs sous-mots. Suite à cette adaptation, nous observons une amélioration notable des performances pour la plupart des modèles et des langues évalués.

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Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
Yao Zhang | Peiyao Li | Hongru Liang | Adam Jatowt | Zhenglu Yang
Findings of the Association for Computational Linguistics: ACL 2022

Current Question Answering over Knowledge Graphs (KGQA) task mainly focuses on performing answer reasoning upon KGs with binary facts. However, it neglects the n-ary facts, which contain more than two entities. In this work, we highlight a more challenging but under-explored task: n-ary KGQA, i.e., answering n-ary facts questions upon n-ary KGs. Nevertheless, the multi-hop reasoning framework popular in binary KGQA task is not directly applicable on n-ary KGQA. We propose two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact, and 2) upgrade the reasoning structure from chain to tree. Therefore, we propose a novel fact-tree reasoning framework, FacTree, which integrates the above two upgrades. FacTree transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer. Experimental results on the n-ary KGQA dataset we constructed and two binary KGQA benchmarks demonstrate the effectiveness of FacTree compared with state-of-the-art methods.

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Topic-aware Multimodal Summarization
Sourajit Mukherjee | Anubhav Jangra | Sriparna Saha | Adam Jatowt
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

Multimodal Summarization (MS) has attracted research interest in the past few years due to the ease with which users perceive multimodal summaries. It is important for MS models to consider the topic a given target content belongs to. In the current paper, we propose a topic-aware MS system which performs two tasks simultaneously: differentiating the images into “on-topic” and “off-topic” categories and further utilizing the “on-topic” images to generate multimodal summaries. The hypothesis is that, the proposed topic similarity classifier will help in generating better multimodal summary by focusing on important components of images and text which are specific to a particular topic. To develop the topic similarity classifier, we have augmented the existing popular MS data set, MSMO, with similar “on-topic” and dissimilar “off-topic” images for each sample. Our experimental results establish that the focus on “on-topic” features helps in generating topic-aware multimodal summaries, which outperforms the state of the art approach by 1.7 % in ROUGE-L metric.

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Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections
Nicolas Gutehrlé | Antoine Doucet | Adam Jatowt
Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may be, however, impaired by some aspects of search engines, such as the overwhelming number of documents returned or the lack of contextual knowledge provided. New methods that could work independently or in combination with search engines are then required to access these collections. In this position paper, we propose to extend TimeLine Summarization (TLS) methods on archive collections to assist in their studies. We provide an overview of existing TLS methods and we describe a conceptual framework for an Archive TimeLine Summarization (ATLS) system, which aims to generate informative, readable and interpretable timelines.

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A Survey of Automatic Text Summarization Using Graph Neural Networks
Marco Ferdinand Salchner | Adam Jatowt
Proceedings of the 29th International Conference on Computational Linguistics

Although automatic text summarization (ATS) has been researched for several decades, the application of graph neural networks (GNNs) to this task started relatively recently. In this survey we provide an overview on the rapidly evolving approach of using GNNs for the task of automatic text summarization. In particular we provide detailed information on the functionality of GNNs in the context of ATS, and a comprehensive overview of models utilizing this approach.

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Transfer Learning for Humor Detection by Twin Masked Yellow Muppets
Aseem Arora | Gaël Dias | Adam Jatowt | Asif Ekbal
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

Humorous texts can be of different forms such as punchlines, puns, or funny stories. Existing humor classification systems have been dealing with such diverse forms by treating them independently. In this paper, we argue that different forms of humor share a common background either in terms of vocabulary or constructs. As a consequence, it is likely that classification performance can be improved by jointly tackling different humor types. Hence, we design a shared-private multitask architecture following a transfer learning paradigm and perform experiments over four gold standard datasets. Empirical results steadily confirm our hypothesis by demonstrating statistically-significant improvements over baselines and accounting for new state-of-the-art figures for two datasets.


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Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries
Yi Yu | Adam Jatowt | Antoine Doucet | Kazunari Sugiyama | Masatoshi Yoshikawa
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story.To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.

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Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021
Nina Tahmasebi | Adam Jatowt | Yang Xu | Simon Hengchen | Syrielle Montariol | Haim Dubossarsky
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

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GMH: A General Multi-hop Reasoning Model for KG Completion
Yao Zhang | Hongru Liang | Adam Jatowt | Wenqiang Lei | Xin Wei | Ning Jiang | Zhenglu Yang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios. We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.


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Multilingual Epidemiological Text Classification: A Comparative Study
Stephen Mutuvi | Emanuela Boros | Antoine Doucet | Adam Jatowt | Gaël Lejeune | Moses Odeo
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we approach the multilingual text classification task in the context of the epidemiological field. Multilingual text classification models tend to perform differently across different languages (low- or high-resourced), more particularly when the dataset is highly imbalanced, which is the case for epidemiological datasets. We conduct a comparative study of different machine and deep learning text classification models using a dataset comprising news articles related to epidemic outbreaks from six languages, four low-resourced and two high-resourced, in order to analyze the influence of the nature of the language, the structure of the document, and the size of the data. Our findings indicate that the performance of the models based on fine-tuned language models exceeds by more than 50% the chosen baseline models that include a specialized epidemiological news surveillance system and several machine learning models. Also, low-resource languages are highly influenced not only by the typology of the languages on which the models have been pre-trained or/and fine-tuned but also by their size. Furthermore, we discover that the beginning and the end of documents provide the most salient features for this task and, as expected, the performance of the models was proportionate to the training data size.

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Dataset for Temporal Analysis of English-French Cognates
Esteban Frossard | Mickael Coustaty | Antoine Doucet | Adam Jatowt | Simon Hengchen
Proceedings of the Twelfth Language Resources and Evaluation Conference

Languages change over time and, thanks to the abundance of digital corpora, their evolutionary analysis using computational techniques has recently gained much research attention. In this paper, we focus on creating a dataset to support investigating the similarity in evolution between different languages. We look in particular into the similarities and differences between the use of corresponding words across time in English and French, two languages from different linguistic families yet with shared syntax and close contact. For this we select a set of cognates in both languages and study their frequency changes and correlations over time. We propose a new dataset for computational approaches of synchronized diachronic investigation of language pairs, and subsequently show novel findings stemming from the cognate-focused diachronic comparison of the two chosen languages. To the best of our knowledge, the present study is the first in the literature to use computational approaches and large data to make a cross-language diachronic analysis.

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Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing
Sora Lim | Adam Jatowt | Michael Färber | Masatoshi Yoshikawa
Proceedings of the Twelfth Language Resources and Evaluation Conference

The spread of biased news and its consumption by the readers has become a considerable issue. Researchers from multiple domains including social science and media studies have made efforts to mitigate this media bias issue. Specifically, various techniques ranging from natural language processing to machine learning have been used to help determine news bias automatically. However, due to the lack of publicly available datasets in this field, especially ones containing labels concerning bias on a fine-grained level (e.g., on sentence level), it is still challenging to develop methods for effectively identifying bias embedded in new articles. In this paper, we propose a novel news bias dataset which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences. Our dataset consists of 966 sentences from 46 English-language news articles covering 4 different events and contains labels concerning bias on the sentence level. For scalability reasons, the labels were obtained based on crowd-sourcing. Our dataset can be used for analyzing news bias, as well as for developing and evaluating methods for news bias detection. It can also serve as resource for related researches including ones focusing on fake news detection.


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Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change
Nina Tahmasebi | Lars Borin | Adam Jatowt | Yang Xu
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

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Spatio-Temporal Prediction of Dialectal Variant Usage
Péter Jeszenszky | Panote Siriaraya | Philipp Stoeckle | Adam Jatowt
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

The distribution of most dialectal variants have not only spatial but also temporal patterns. Based on the ‘apparent time hypothesis’, much of dialect change is happening through younger speakers accepting innovations. Thus, synchronic diversity can be interpreted diachronically. With the assumption of the ‘contact effect’, i.e. contact possibility (contact and isolation) between speaker communities being responsible for language change, and the apparent time hypothesis, we aim to predict the usage of dialectal variants. In this paper we model the contact possibility based on two of the most important factors in sociolinguistics to be affecting language change: age and distance. The first steps of the approach involve modeling contact possibility using a logistic predictor, taking the age of respondents into account. We test the global, and the local role of age for variation where the local level means spatial subsets around each survey site, chosen based on k nearest neighbors. The prediction approach is tested on Swiss German syntactic survey data, featuring multiple respondents from different age cohorts at survey sites. The results show the relative success of the logistic prediction approach and the limitations of the method, therefore further proposals are made to develop the methodology.


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A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task
Qian Li | Ziwei Li | Jin-Mao Wei | Yanhui Gu | Adam Jatowt | Zhenglu Yang
Proceedings of the 27th International Conference on Computational Linguistics

Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task.

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A High-Quality Gold Standard for Citation-based Tasks
Michael Färber | Alexander Thiemann | Adam Jatowt
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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HistoryComparator: Interactive Across-Time Comparison in Document Archives
Adam Jatowt | Marc Bron
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Recent years have witnessed significant increase in the number of large scale digital collections of archival documents such as news articles, books, etc. Typically, users access these collections through searching or browsing. In this paper we investigate another way of accessing temporal collections - across-time comparison, i.e., comparing query-relevant information at different periods in the past. We propose an interactive framework called HistoryComparator for contrastively analyzing concepts in archival document collections at different time periods.


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Omnia Mutantur, Nihil Interit: Connecting Past with Present by Finding Corresponding Terms across Time
Yating Zhang | Adam Jatowt | Sourav Bhowmick | Katsumi Tanaka
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)