Question: What methods did the writer present for RQE?
Evidence: With the availability of rich data on users' locations, profiles and search history, personalization has become the leading trend in large-scale information retrieval. However, efficiency through personalization is not yet the most suitable model when tackling domain-specific searches. This is due to several factors, such as the lexical and semantic challenges of domain-specific data that often include advanced argumentation and complex contextual information, the higher sparseness of relevant information sources, and the more pronounced lack of similarities between users' searches.
A recent study on expert search strategies among healthcare information professionals BIBREF0 showed that, for a given search task, they spend an average of 60 minutes per collection or database, 3 minutes to examine the relevance of each document, and 4 hours of total search time. When written in steps, their search strategy spans over 15 lines and can reach up to 105 lines.
With the abundance of information sources in the medical domain, consumers are more and more faced with a similar challenge, one that needs dedicated solutions that can adapt to the heterogeneity and specifics of health-related information.
Dedicated Question Answering (QA) systems are one of the viable solutions to this problem as they are designed to understand natural language questions without relying on external information on the users.
In the context of QA, the goal of Recognizing Question Entailment (RQE) is to retrieve answers to a premise question ( INLINEFORM0 ) by retrieving inferred or entailed questions, called hypothesis questions ( INLINEFORM1 ) that already have associated answers. Therefore, we define the entailment relation between two questions as: a question INLINEFORM2 entails a question INLINEFORM3 if every answer to INLINEFORM4 is also a correct answer to INLINEFORM5 BIBREF1 .
RQE is particularly relevant due to the increasing numbers of similar questions posted online BIBREF2 and its ability to solve differently the challenging issues of question understanding and answer extraction. In addition to being used to find relevant answers, these resources can also be used in training models able to recognize inference relations and similarity between questions.
Question similarity has recently attracted international challenges BIBREF3 , BIBREF4 and several research efforts proposing a wide range of approaches, including Logistic Regression, Recurrent Neural Networks (RNNs), Long Short Term Memory cells (LSTMs), and Convolutional Neural Networks (CNNs) BIBREF5 , BIBREF6 , BIBREF1 , BIBREF7 .
In this paper, we study question entailment in the medical domain and the effectiveness of the end-to-end RQE-based QA approach by evaluating the relevance of the retrieved answers. Although entailment was attempted in QA before BIBREF8 , BIBREF9 , BIBREF10 , as far as we know, we are the first to introduce and evaluate a full medical question answering approach based on question entailment for free-text questions. Our contributions are:
The next section is dedicated to related work on question answering, question similarity and entailment. In Section SECREF3 , we present two machine learning (ML) and deep learning (DL) methods for RQE and compare their performance using open-domain and clinical datasets. Section SECREF4 describes the new collection of medical question-answer pairs. In Section SECREF5 , we describe our RQE-based approach for QA. Section SECREF6 presents our evaluation of the retrieved answers and the results obtained on TREC 2017 LiveQA medical questions.
In Section 3, we present two machine learning (ML) and deep learning (DL) methods for RQE and compare their performance using open-domain and clinical datasets.
Answer: The authors present two machines learning (ML) and deep learning (DL) methods for RQE.