4,328 research outputs found
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
Most Reading Comprehension methods limit themselves to queries which can be
answered using a single sentence, paragraph, or document. Enabling models to
combine disjoint pieces of textual evidence would extend the scope of machine
comprehension methods, but currently there exist no resources to train and test
this capability. We propose a novel task to encourage the development of models
for text understanding across multiple documents and to investigate the limits
of existing methods. In our task, a model learns to seek and combine evidence -
effectively performing multi-hop (alias multi-step) inference. We devise a
methodology to produce datasets for this task, given a collection of
query-answer pairs and thematically linked documents. Two datasets from
different domains are induced, and we identify potential pitfalls and devise
circumvention strategies. We evaluate two previously proposed competitive
models and find that one can integrate information across documents. However,
both models struggle to select relevant information, as providing documents
guaranteed to be relevant greatly improves their performance. While the models
outperform several strong baselines, their best accuracy reaches 42.9% compared
to human performance at 74.0% - leaving ample room for improvement.Comment: This paper directly corresponds to the TACL version
(https://transacl.org/ojs/index.php/tacl/article/view/1325) apart from minor
changes in wording, additional footnotes, and appendice
Adaptive image retrieval using a graph model for semantic feature integration
The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the
retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe
how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)
Unsupervised Spoken Term Detection with Spoken Queries by Multi-level Acoustic Patterns with Varying Model Granularity
This paper presents a new approach for unsupervised Spoken Term Detection
with spoken queries using multiple sets of acoustic patterns automatically
discovered from the target corpus. The different pattern HMM
configurations(number of states per model, number of distinct models, number of
Gaussians per state)form a three-dimensional model granularity space. Different
sets of acoustic patterns automatically discovered on different points properly
distributed over this three-dimensional space are complementary to one another,
thus can jointly capture the characteristics of the spoken terms. By
representing the spoken content and spoken query as sequences of acoustic
patterns, a series of approaches for matching the pattern index sequences while
considering the signal variations are developed. In this way, not only the
on-line computation load can be reduced, but the signal distributions caused by
different speakers and acoustic conditions can be reasonably taken care of. The
results indicate that this approach significantly outperformed the unsupervised
feature-based DTW baseline by 16.16\% in mean average precision on the TIMIT
corpus.Comment: Accepted by ICASSP 201
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
Modeling Documents with Deep Boltzmann Machines
We introduce a Deep Boltzmann Machine model suitable for modeling and
extracting latent semantic representations from a large unstructured collection
of documents. We overcome the apparent difficulty of training a DBM with
judicious parameter tying. This parameter tying enables an efficient
pretraining algorithm and a state initialization scheme that aids inference.
The model can be trained just as efficiently as a standard Restricted Boltzmann
Machine. Our experiments show that the model assigns better log probability to
unseen data than the Replicated Softmax model. Features extracted from our
model outperform LDA, Replicated Softmax, and DocNADE models on document
retrieval and document classification tasks.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
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