Full machine translation for factoid question answering

In this paper we present an SMT-based approach to Question Answering (QA). QA is the task of extracting exact answers in response to natural language questions. In our approach, the answer is a translation of the question obtained with an SMT system. We use the n-best translations of a given question to find similar sentences in the document collection that contain the real answer. Although it is not the first time that SMT inspires a QA system, it is the first approach that uses a full Machine Translation system for generating answers. Our approach is validated with the datasets of the TREC QA evaluation.Peer ReviewedPreprin

An analysis of machine translation errors on the effectiveness of an Arabic-English QA system

The aim of this paper is to investigate how much the effectiveness of a Question Answering (QA) system was affected by the performance of Machine Translation (MT) based question translation. Nearly 200 questions were selected from TREC QA tracks and ran through a question answering system. It was able to answer 42.6% of the questions correctly in a monolingual run. These questions were then translated manually from English into Arabic and back into English using an MT system, and then re-applied to the QA system. The system was able to answer 10.2% of the translated questions. An analysis of what sort of translation error affected which questions was conducted, concluding that factoid type questions are less prone to translation error than others

The effects of topic familiarity on user search behavior in question answering systems

This paper reports on experiments that attempt to characterize the relationship between users and their knowledge of the search topic in a Question Answering (QA) system. It also investigates user search behavior with respect to the length of answers presented by a QA system. Two lengths of answers were compared; snippets (one to two sentences of text) and exact answers. A user test was conducted, 92 factoid questions were judged by 44 participants, to explore the participants’ preferences, feelings and opinions about QA system tasks. The conclusions drawn from the results were that participants preferred and obtained higher accuracy in finding answers from the snippets set. However, accuracy varied according to users’ topic familiarity; users were only substantially helped by the wider context of a snippet if they were already familiar with the topic of the question, without such familiarity, users were about as accurate at locating answers from the snippets as they were in exact set

Dublin City University at QA@CLEF 2008

We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework

A hybrid filtering approach for question answering

We describe a question answering system that took part in the bilingual CLEFQA task (German-English) where German is the source language and English the target language.We used the BableFish online translation system to translate the German questions into English. The system is targeted at Factoid and Denition questions. Our focus in designing the current system is on testing our online methods which are based on information extraction and linguistic ltering methods. Our system does not make use of precompiled tables or Gazetteers but uses Web snippets to rerank candidate answers extracted from the document collections. WordNet is also used as a lexical resource in the system. Our question answering system consists of the following core components: Question Anal- ysis, Passage Retrieval, Sentence Analysis and Answer Selection. These components employ various Natural Language Processing (NLP) and Machine Learning (ML) tools, a set of heuristics and dierent lexical resources. Seamless integration of the various components is one of the major challenges of QA system development. In order to facilitate our develop- ment process, we used the Unstructured Information Management Architecture (UIMA) as our underlying framework

Reading Wikipedia to Answer Open-Domain Questions

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article. This task of machine reading at scale combines the challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer spans from those articles). Our approach combines a search component based on bigram hashing and TF-IDF matching with a multi-layer recurrent neural network model trained to detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA datasets indicate that (1) both modules are highly competitive with respect to existing counterparts and (2) multitask learning using distant supervision on their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page