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        <title>Commonsense for Generative Multi-Hop Question Answering Tasks</title>
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        <namePart type="given">Lisa</namePart>
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    <name type="personal">
        <namePart type="given">Yicheng</namePart>
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    <name type="personal">
        <namePart type="given">Mohit</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model’s performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1454</identifier>
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        <date>2018-oct-nov</date>
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            <start>4220</start>
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%0 Conference Proceedings
%T Commonsense for Generative Multi-Hop Question Answering Tasks
%A Bauer, Lisa
%A Wang, Yicheng
%A Bansal, Mohit
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F bauer-etal-2018-commonsense
%X Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model’s performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show that our background knowledge enhancements are generalizable and improve performance on QAngaroo-WikiHop, another multi-hop reasoning dataset.
%R 10.18653/v1/D18-1454
%U https://aclanthology.org/D18-1454
%U https://doi.org/10.18653/v1/D18-1454
%P 4220-4230

Markdown (Informal)

[Commonsense for Generative Multi-Hop Question Answering Tasks](https://aclanthology.org/D18-1454) (Bauer et al., EMNLP 2018)

• Commonsense for Generative Multi-Hop Question Answering Tasks (Bauer et al., EMNLP 2018)

ACL

• Lisa Bauer, Yicheng Wang, and Mohit Bansal. 2018. Commonsense for Generative Multi-Hop Question Answering Tasks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4220–4230, Brussels, Belgium. Association for Computational Linguistics.