Puerto, Haritz ; Şahin, Gözde Gül ; Gurevych, Iryna (2023)
MetaQA: Combining Expert Agents for Multi-Skill Question Answering.
17th Conference of the European Chapter of the Association for Computational Linguistics. Dubrovnik, Croatia (02.-06.05.2023)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The recent explosion of question-answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer predictions. Through quantitative and qualitative experiments, we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future research of multi-agent systems.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Puerto, Haritz ; Şahin, Gözde Gül ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | MetaQA: Combining Expert Agents for Multi-Skill Question Answering |
| Sprache: | Englisch |
| Publikationsjahr: | 2 Mai 2023 |
| Verlag: | ACL |
| Buchtitel: | The 17th Conference of the European Chapter of the Association for Computational Linguistics - proceedings of the conference |
| Veranstaltungstitel: | 17th Conference of the European Chapter of the Association for Computational Linguistics |
| Veranstaltungsort: | Dubrovnik, Croatia |
| Veranstaltungsdatum: | 02.-06.05.2023 |
| URL / URN: | https://aclanthology.org/2023.eacl-main.259/ |
| Kurzbeschreibung (Abstract): | The recent explosion of question-answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer predictions. Through quantitative and qualitative experiments, we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future research of multi-agent systems. |
| Freie Schlagworte: | UKP_p_square, UKP_p_seditrah_factcheck |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 12 Jun 2023 12:27 |
| Letzte Änderung: | 04 Aug 2023 09:33 |
| PPN: | 510358594 |
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| Suche nach Titel in: | TUfind oder in Google |
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