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Temporal Natural Language Inference: Evidence-Based Evaluation of Temporal Text Validity

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13980))

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Abstract

It is important to learn whether text information remains valid or not for various applications including story comprehension, information retrieval, and user state tracking on microblogs and via chatbot conversations. It is also beneficial to deeply understand the story. However, this kind of inference is still difficult for computers as it requires temporal commonsense. We propose a novel task, Temporal Natural Language Inference, inspired by traditional natural language reasoning to determine the temporal validity of text content. The task requires inference and judgment whether an action expressed in a sentence is still ongoing or rather completed, hence, whether the sentence still remains valid, given its supplementary content. We first construct our own dataset for this task and train several machine learning models. Then we propose an effective method for learning information from an external knowledge base that gives hints on temporal commonsense knowledge. Using prepared dataset, we introduce a new machine learning model that incorporates the information from the knowledge base and demonstrate that our model outperforms state-of-the-art approaches in the proposed task.

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Notes

  1. 1.

    Note that it is not always easy to determine the correct answer as the context or necessary details might be missing, and in such cases humans seem to rely on probabilistic reasoning besides the commonsense base.

  2. 2.

    The dataset will be made freely available after paper publication.

  3. 3.

    Note that \(s_1\) and \(s_2\) may have temporal order: \(t_{s_1} \le t_{s_2}\), where \(t_{s_{id}}\) \((id=1,2)\) is the creation time (or a reading order) of a sentence \(s_{id}\). This may be for example in the case of receiving microblog posts issued by the user (or when reading next sentences of a story or a novel).

  4. 4.

    SNLI dataset is licensed under CC-BY-SA 4.0.

  5. 5.

    https://www.mturk.com/.

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Authors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Taishi Hosokawa

  2. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

  3. Osaka Seikei University, Osaka, Japan

    Kazunari Sugiyama

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  1. Taishi Hosokawa
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  2. Adam Jatowt
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  3. Kazunari Sugiyama
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Corresponding author

Correspondence to Adam Jatowt .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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Hosokawa, T., Jatowt, A., Sugiyama, K. (2023). Temporal Natural Language Inference: Evidence-Based Evaluation of Temporal Text Validity. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13980. Springer, Cham. https://doi.org/10.1007/978-3-031-28244-7_28

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