7,628 research outputs found
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Recognising objects according to a pre-defined fixed set of class labels has
been well studied in the Computer Vision. There are a great many practical
applications where the subjects that may be of interest are not known
beforehand, or so easily delineated, however. In many of these cases natural
language dialog is a natural way to specify the subject of interest, and the
task achieving this capability (a.k.a, Referring Expression Comprehension) has
recently attracted attention. To this end we propose a unified framework, the
ParalleL AttentioN (PLAN) network, to discover the object in an image that is
being referred to in variable length natural expression descriptions, from
short phrases query to long multi-round dialogs. The PLAN network has two
attention mechanisms that relate parts of the expressions to both the global
visual content and also directly to object candidates. Furthermore, the
attention mechanisms are recurrent, making the referring process visualizable
and explainable. The attended information from these dual sources are combined
to reason about the referred object. These two attention mechanisms can be
trained in parallel and we find the combined system outperforms the
state-of-art on several benchmarked datasets with different length language
input, such as RefCOCO, RefCOCO+ and GuessWhat?!.Comment: 11 page
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