A Joint Model for Answer Sentence Ranking and Answer Extraction

By Md Arafat Sultan, Vittorio Castelli, Radu Florian. 2016

14 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Retrieval & Search, Question Answering & Reading Comprehension

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tomasz Jurczyk
2. Jinho D. Choi

In Information Retrieval & Search

1. Tomasz Jurczyk
2. Jinho D. Choi

In Question Answering & Reading Comprehension

1. Tomasz Jurczyk
2. Jinho D. Choi

Citations in the corpus

• Analysis of Wikipedia-based Corpora for Question Answering (2018, background): “Given a document, answer extraction (Shen and Klakow, 2006; Sultan et al., 2016) finds answer phrases whereas answer selection (Wang et al.”