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  • Authors: The 28th International Conference on Computational Linguistics 2020; Bohn, Tanner;

    To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Bing;

    Anthology paper link: https://aclanthology.org/2021.emnlp-main.270/ Abstract: Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods ��� neural EA models ��� rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.

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  • Authors: Benjamin, Geoffrey;

    Video of Sanggar Warisan Pulai Penyengat rehearsing their music and dance performance of 'Bilik 44' for competitions in various parts of Indonesia.For more information about this recording, see Chapter 12, 'Malay art music composers and performers of Tanjungpinang and Pulau Penyengat’ by Geoffrey Benjamin, in Margaret Kartomi (ed), Performing the Arts of Indonesia: Malay Identity and Politics in the Music, Dance and Theatre of the Riau Islands, Copenhagen: Nias Press, 2019.

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  • Authors: Association for Computational Linguistics 2023; Koreeda, Yuta; Ozaki, Hiroaki; Sogawa, Yasuhiro; +3 Authors

    This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup." Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Murauer, Benjamin;

    We present a collection of diverse datasets and aggregated scores of dataset aspects to help develop more generalizable findings in authorship attribution research.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    On this auspicious day, in a sunny and level place, fixing it up to (the elders') liking, a place for their sons and daughters, demonstrating their filial piety. Wishes of peace and good luck for all.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NAKALA; CoCoONarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Audiovisual . 2022
    License: CC BY NC SA
    Data sources: Datacite; CoCoON
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NAKALA; CoCoON
    Sound . 2022
    License: CC BY NC SA
    Data sources: Datacite; CoCoON
    https://doi.org/10.24397/pangl...
    Audiovisual . 2022
    License: CC BY NC SA
    Data sources: Datacite
    https://doi.org/10.24397/pangl...
    Sound . 2022
    License: CC BY NC SA
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NAKALA; CoCoONarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NAKALA; CoCoON
      Audiovisual . 2022
      License: CC BY NC SA
      Data sources: Datacite; CoCoON
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NAKALA; CoCoON
      Sound . 2022
      License: CC BY NC SA
      Data sources: Datacite; CoCoON
      https://doi.org/10.24397/pangl...
      Audiovisual . 2022
      License: CC BY NC SA
      Data sources: Datacite
      https://doi.org/10.24397/pangl...
      Sound . 2022
      License: CC BY NC SA
      Data sources: Datacite
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  • Authors: Association for Computational Linguistics 2022; Cohen, Daniel; Eickhoff, Carsten; Rekabsaz, Navid; +1 Authors

    Contrastive learning has been the dominant approach to training dense retrieval models. In this work, we investigate the impact of ranking context - an often overlooked aspect of learning dense retrieval models. In particular, we examine the effect of its constituent parts: jointly scoring a large number of negatives per query, using retrieved (query-specific) instead of random negatives, and a fully list-wise loss. To incorporate these factors into training, we introduce Contextual Document Embedding Reranking (CODER), a highly efficient retrieval framework. When reranking, it incurs only a negligible computational overhead on top of a first-stage method at run time (approx. 5 ms delay per query), allowing it to be easily combined with any state-of-the-art dual encoder method. Models trained through CODER can also be used as stand-alone retrievers. Evaluating CODER in a large set of experiments on the MS MARCO and TripClick collections, we show that the contextual reranking of precomputed document embeddings leads to a significant improvement in retrieval performance. This improvement becomes even more pronounced when more relevance information per query is available, shown in the TripClick collection, where we establish new state-of-the-art results by a large margin.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; lu, shuqi;

    Anthology paper link: https://aclanthology.org/2021.emnlp-main.220/ Abstract: I'll introduce our work, Less is More: Pre-train a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder, which employs a weak decoder with restricted capacity and attention span and bring significant improvement in dense retrieval.

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  • Authors: Kartomi, Margaret J.; Kartomi, Hidris;

    For male participants. The rapa'i (a medium-size frame drum) is used in the performance of daboh (Arabic: dabbus, awl) in which religiously inspired performers stab themselves with an awl or dagger, with no pain felt, to show their religious concentration and invulnerability. Daboh is accompanied by mass rapa'i playing and songs of praise of Allah and the Prophets. This image presents a close-up of one of the men playing the rapa'i in a competitive performance of daboh between two groups. Copyright 1982. Notes prepared by Bronia Kornhauser with Margaret Kartomi, School of Music-Conservatorium, Monash University. Photography by Hidris Kartomi.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Yuji; Baldwin, Timothy; Iwata, Tomoharu; +2 Authors

    We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus (e.g. a few hundred sentence pairs). Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence. Through sharing model parameters among different languages, our model jointly trains the word embeddings in a common cross-lingual space. We also propose to combine word and subword embeddings to make use of orthographic similarities across different languages. We base our experiments on real-world data from endangered languages, namely Yongning Na, Shipibo-Konibo, and Griko. Our experiments on bilingual lexicon induction and word alignment tasks show that our model outperforms existing methods by a large margin for most language pairs. These results demonstrate that, contrary to common belief, an encoder-decoder translation model is beneficial for learning cross-lingual representations even in extremely low-resource conditions. Furthermore, our model also works well on high-resource conditions, achieving state-of-the-art performance on a German-English word-alignment task.

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8,506 Research products
  • Authors: The 28th International Conference on Computational Linguistics 2020; Bohn, Tanner;

    To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Bing;

    Anthology paper link: https://aclanthology.org/2021.emnlp-main.270/ Abstract: Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods ��� neural EA models ��� rely on training with seed alignment, i.e., a set of pre-aligned entity pairs which are very costly to annotate. In this paper, we devise a novel Active Learning (AL) framework for neural EA, aiming to create highly informative seed alignment to obtain more effective EA models with less annotation cost. Our framework tackles two main challenges encountered when applying AL to EA: (1) How to exploit dependencies between entities within the AL strategy. Most AL strategies assume that the data instances to sample are independent and identically distributed. However, entities in KGs are related. To address this challenge, we propose a structure-aware uncertainty sampling strategy that can measure the uncertainty of each entity as well as its impact on its neighbour entities in the KG. (2) How to recognise entities that appear in one KG but not in the other KG (i.e., bachelors). Identifying bachelors would likely save annotation budget. To address this challenge, we devise a bachelor recognizer paying attention to alleviate the effect of sampling bias. Empirical results show that our proposed AL strategy can significantly improve sampling quality with good generality across different datasets, EA models and amount of bachelors.

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  • Authors: Benjamin, Geoffrey;

    Video of Sanggar Warisan Pulai Penyengat rehearsing their music and dance performance of 'Bilik 44' for competitions in various parts of Indonesia.For more information about this recording, see Chapter 12, 'Malay art music composers and performers of Tanjungpinang and Pulau Penyengat’ by Geoffrey Benjamin, in Margaret Kartomi (ed), Performing the Arts of Indonesia: Malay Identity and Politics in the Music, Dance and Theatre of the Riau Islands, Copenhagen: Nias Press, 2019.

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  • Authors: Association for Computational Linguistics 2023; Koreeda, Yuta; Ozaki, Hiroaki; Sogawa, Yasuhiro; +3 Authors

    This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup." Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.

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  • Authors: The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Murauer, Benjamin;

    We present a collection of diverse datasets and aggregated scores of dataset aspects to help develop more generalizable findings in authorship attribution research.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    On this auspicious day, in a sunny and level place, fixing it up to (the elders') liking, a place for their sons and daughters, demonstrating their filial piety. Wishes of peace and good luck for all.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NAKALA; CoCoONarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NAKALA; CoCoON
    Audiovisual . 2022
    License: CC BY NC SA
    Data sources: Datacite; CoCoON
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NAKALA; CoCoON
    Sound . 2022
    License: CC BY NC SA
    Data sources: Datacite; CoCoON
    https://doi.org/10.24397/pangl...
    Audiovisual . 2022
    License: CC BY NC SA
    Data sources: Datacite