123,525 research outputs found
Controlling Risk of Web Question Answering
Web question answering (QA) has become an indispensable component in modern
search systems, which can significantly improve users' search experience by
providing a direct answer to users' information need. This could be achieved by
applying machine reading comprehension (MRC) models over the retrieved passages
to extract answers with respect to the search query. With the development of
deep learning techniques, state-of-the-art MRC performances have been achieved
by recent deep methods. However, existing studies on MRC seldom address the
predictive uncertainty issue, i.e., how likely the prediction of an MRC model
is wrong, leading to uncontrollable risks in real-world Web QA applications. In
this work, we first conduct an in-depth investigation over the risk of Web QA.
We then introduce a novel risk control framework, which consists of a qualify
model for uncertainty estimation using the probe idea, and a decision model for
selectively output. For evaluation, we introduce risk-related metrics, rather
than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA.
The empirical results over both the real-world Web QA dataset and the academic
MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development
in Information Retrieva
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for answering the questions. We show that, in comparison to
other recently introduced large-scale datasets, TriviaQA (1) has relatively
complex, compositional questions, (2) has considerable syntactic and lexical
variability between questions and corresponding answer-evidence sentences, and
(3) requires more cross sentence reasoning to find answers. We also present two
baseline algorithms: a feature-based classifier and a state-of-the-art neural
network, that performs well on SQuAD reading comprehension. Neither approach
comes close to human performance (23% and 40% vs. 80%), suggesting that
TriviaQA is a challenging testbed that is worth significant future study. Data
and code available at -- http://nlp.cs.washington.edu/triviaqa/Comment: Added references, fixed typos, minor baseline updat
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