Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors

By Peter D. Turney. 2017

4 citations recorded by the source corpus.

While open-domain question answering (QA) systems have proven effective for answering simple questions, they struggle with more complex questions. Our goal is to answer more complex questions reliably, without incurring a significant cost in knowledge resource construction to support the QA. One readily available knowledge resource is a term bank, enumerating the key concepts in a domain. We have developed an unsupervised learning approach that leverages a term bank to guide a QA system, by representing the terminological knowledge with thousands of specialized vector spaces. In experiments with complex science questions, we show that this approach significantly outperforms several state-of-the-art QA systems, demonstrating that significant leverage can be gained from continuous vector representations of domain terminology.

Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search

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Citations in the corpus

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering (2018, methodology): “We ﬁrst experiment with a simple logistic regression model (Mihaylov and Nakov, 2016; Mihaylov and Frank, 2016, 2017) that uses centroid vectors r emb s of the word embeddings of tokens in s , and then computes the cosine similarities between the question and each answer choice, r cos q,c i : For…”
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project (2019, neutral): “Further details are provided in (Turney, 2017).”