17,330 research outputs found
Improving Retrieval-Based Question Answering with Deep Inference Models
Question answering is one of the most important and difficult applications at
the border of information retrieval and natural language processing, especially
when we talk about complex science questions which require some form of
inference to determine the correct answer. In this paper, we present a two-step
method that combines information retrieval techniques optimized for question
answering with deep learning models for natural language inference in order to
tackle the multi-choice question answering in the science domain. For each
question-answer pair, we use standard retrieval-based models to find relevant
candidate contexts and decompose the main problem into two different
sub-problems. First, assign correctness scores for each candidate answer based
on the context using retrieval models from Lucene. Second, we use deep learning
architectures to compute if a candidate answer can be inferred from some
well-chosen context consisting of sentences retrieved from the knowledge base.
In the end, all these solvers are combined using a simple neural network to
predict the correct answer. This proposed two-step model outperforms the best
retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201
Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering
We propose an unsupervised strategy for the selection of justification
sentences for multi-hop question answering (QA) that (a) maximizes the
relevance of the selected sentences, (b) minimizes the overlap between the
selected facts, and (c) maximizes the coverage of both question and answer.
This unsupervised sentence selection method can be coupled with any supervised
QA approach. We show that the sentences selected by our method improve the
performance of a state-of-the-art supervised QA model on two multi-hop QA
datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading
Comprehension (MultiRC). We obtain new state-of-the-art performance on both
datasets among approaches that do not use external resources for training the
QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1%
EM0 on MultiRC. Our justification sentences have higher quality than the
justifications selected by a strong information retrieval baseline, e.g., by
5.4% F1 in MultiRC. We also show that our unsupervised selection of
justification sentences is more stable across domains than a state-of-the-art
supervised sentence selection method.Comment: Published at EMNLP-IJCNLP 2019 as long conference paper. Corrected
the name reference for Speer et.al, 201
- …