Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1979-1983 |
| Number of pages | 5 |
| ISBN (electronic) | 9781450356404 |
| Publication status | Published - 23 Apr 2018 |
| Event | 27th International World Wide Web, WWW 2018 - Lyon, France Duration: 23 Apr 2018 → 27 Apr 2018 |
Publication series
| Name | The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 |
|---|---|
| Volume | 2018-January |
Abstract
In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.
Keywords
- Non-factoid question answering, representation learning
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
Cite this
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The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018. Association for Computing Machinery (ACM), 2018. p. 1979-1983 (The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018; Vol. 2018-January).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Neural Network-based Framework for Non-factoid Question Answering
AU - Tran, Nam Khanh
AU - Niederée, Claudia
N1 - Funding Information: This work was partially funded by the German Federal Ministry of Education and Research (BMBF) for the project eLabour (01UG1512C).
PY - 2018/4/23
Y1 - 2018/4/23
N2 - In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.
AB - In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.
KW - Non-factoid question answering
KW - representation learning
UR - http://www.scopus.com/inward/record.url?scp=85110423076&partnerID=8YFLogxK
U2 - 10.1145/3184558.3191830
DO - 10.1145/3184558.3191830
M3 - Conference contribution
AN - SCOPUS:85110423076
T3 - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
SP - 1979
EP - 1983
BT - The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
PB - Association for Computing Machinery (ACM)
T2 - 27th International World Wide Web, WWW 2018
Y2 - 23 April 2018 through 27 April 2018
ER -