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CMU OAQA at TREC 2016 LiveQA: An Attentional Neural Encoder-Decoder Approach for Answer Ranking
2016
Text Retrieval Conference
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The main improvement this year is the introduction of a novel answer passage ranking method based on attentional encoder-decoder recurrent neural networks (RNN). ...
In the TREC 2016 LiveQA evaluations, human assessors gave our system an average score of 1.1547 on a three-point scale and the average score was .5766 for all the 26 systems evaluated. ...
Here we describe the attention-based neural encoder-decoder model we used for ranking in greater detail. ...
dblp:conf/trec/WangN16
fatcat:ifbytcfxx5gy7ai64p7ggedsou
CMU OAQA at TREC 2017 LiveQA: A Neural Dual Entailment Approach for Question Paraphrase Identification
2017
Text Retrieval Conference
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In this paper, we present CMU's question answering system that was evaluated in the TREC 2017 LiveQA Challenge. Our overall approach this year is similar to the one used in 2015 and 2016. ...
In the TREC 2017 LiveQA evaluations, human assessors gave our system an average score of 1.139 on a three-point scale and the median score was 0.777 for all the systems evaluated. ...
During the official run, our QA server received one question per minute for 24 hours and Figure 1 : Architecture of the CMU-OAQA LiveQA system provided answers within one minute for 98% of the input questions ...
dblp:conf/trec/WangN17
fatcat:yfpimgedavfnzme26dmjeirpnq
A question-entailment approach to question answering
2019
BMC Bioinformatics
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
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Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. ...
Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. ...
Shooshan (NLM/NIH) for her help with the judgment of the retrieved answers, and Ellen Voorhees (NIST) for her help with the TREC LiveQA evaluation. ...
doi:10.1186/s12859-019-3119-4
fatcat:ztrn5jaiwjdizmicuscn466ghq
A Question-Entailment Approach to Question Answering
[article]
2019
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. ...
Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. ...
The CMU-OAQA system [48] achieved the best performance of 0.637 average score on the medical task by using an attentional encoder-decoder model for paraphrase identification and answer ranking. ...
arXiv:1901.08079v1
fatcat:kwtxhoxuvnbovgxve33vb4d6b4