@article{sultan-etal-2016-joint,
title = "A Joint Model for Answer Sentence Ranking and Answer Extraction",
author = "Sultan, Md Arafat and
Castelli, Vittorio and
Florian, Radu",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina",
journal = "Transactions of the Association for Computational Linguistics",
volume = "4",
year = "2016",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q16-1009",
doi = "10.1162/tacl_a_00087",
pages = "113--125",
abstract = "Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.",
}
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<abstract>Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.</abstract>
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%0 Journal Article
%T A Joint Model for Answer Sentence Ranking and Answer Extraction
%A Sultan, Md Arafat
%A Castelli, Vittorio
%A Florian, Radu
%J Transactions of the Association for Computational Linguistics
%D 2016
%V 4
%I MIT Press
%C Cambridge, MA
%F sultan-etal-2016-joint
%X Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.
%R 10.1162/tacl_a_00087
%U https://aclanthology.org/Q16-1009
%U https://doi.org/10.1162/tacl_a_00087
%P 113-125
Markdown (Informal)
[A Joint Model for Answer Sentence Ranking and Answer Extraction](https://aclanthology.org/Q16-1009) (Sultan et al., TACL 2016)
ACL