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task ID: multihopquestionanswering

Related Papers

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractA multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do …...

3788

Breadth First Reasoning Graph for Multi-hop Question Answering

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractRecently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span …...

21286

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractComplex question answering often requires finding a reasoning chain that consists of multiple evidence pieces. Current approaches incorporate the strengths of structured knowledge and unstructured text, assuming text corpora is …...

21482

Robustifying Multi-hop QA through Pseudo-Evidentiality Training

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThis paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer …...

8166

TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractMulti-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of …...

31763

Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMulti-hop Question Answering is an agent task for testing the reasoning ability. With the development of pre-trained models, the implicit reasoning ability has been surprisingly improved and can even surpass …...

30798

Graph-Induced Transformers for Efficient Multi-Hop Question Answering

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractA graph is a suitable data structure to represent the structural information of text. Recently, multi-hop question answering (MHQA) tasks, which require inter-paragraph/sentence linkages, have come to exploit such properties …...

30067

Prompt-based Conservation Learning for Multi-hop Question Answering

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMulti-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a …...

5065

Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractTo explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets. However, it remains an …...

33370

Ask to Understand: Question Generation for Multi-hop Question Answering

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

Abstract“Multi-hop Question Answering (QA) requires the machine to answer complex questions by find-ing scattering clues and reasoning from multiple documents. Graph Network (GN) and Ques-tion Decomposition (QD) are two common …...

5557

Rethinking Label Smoothing on Multi-hop Question Answering

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

Abstract“Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first …...

5554