16,979 research outputs found
Generating Natural Questions About an Image
There has been an explosion of work in the vision & language community during
the past few years from image captioning to video transcription, and answering
questions about images. These tasks have focused on literal descriptions of the
image. To move beyond the literal, we choose to explore how questions about an
image are often directed at commonsense inference and the abstract events
evoked by objects in the image. In this paper, we introduce the novel task of
Visual Question Generation (VQG), where the system is tasked with asking a
natural and engaging question when shown an image. We provide three datasets
which cover a variety of images from object-centric to event-centric, with
considerably more abstract training data than provided to state-of-the-art
captioning systems thus far. We train and test several generative and retrieval
models to tackle the task of VQG. Evaluation results show that while such
models ask reasonable questions for a variety of images, there is still a wide
gap with human performance which motivates further work on connecting images
with commonsense knowledge and pragmatics. Our proposed task offers a new
challenge to the community which we hope furthers interest in exploring deeper
connections between vision & language.Comment: Proceedings of the 54th Annual Meeting of the Association for
Computational Linguistic
A Deep Relevance Matching Model for Ad-hoc Retrieval
In recent years, deep neural networks have led to exciting breakthroughs in
speech recognition, computer vision, and natural language processing (NLP)
tasks. However, there have been few positive results of deep models on ad-hoc
retrieval tasks. This is partially due to the fact that many important
characteristics of the ad-hoc retrieval task have not been well addressed in
deep models yet. Typically, the ad-hoc retrieval task is formalized as a
matching problem between two pieces of text in existing work using deep models,
and treated equivalent to many NLP tasks such as paraphrase identification,
question answering and automatic conversation. However, we argue that the
ad-hoc retrieval task is mainly about relevance matching while most NLP
matching tasks concern semantic matching, and there are some fundamental
differences between these two matching tasks. Successful relevance matching
requires proper handling of the exact matching signals, query term importance,
and diverse matching requirements. In this paper, we propose a novel deep
relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model
employs a joint deep architecture at the query term level for relevance
matching. By using matching histogram mapping, a feed forward matching network,
and a term gating network, we can effectively deal with the three relevance
matching factors mentioned above. Experimental results on two representative
benchmark collections show that our model can significantly outperform some
well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape
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