640 research outputs found
Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models
Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed
skepticism that neural ranking models were actually improving ad hoc retrieval
effectiveness in limited data scenarios. He provided anecdotal evidence that
authors of neural IR papers demonstrate "wins" by comparing against weak
baselines. This paper provides a rigorous evaluation of those claims in two
ways: First, we conducted a meta-analysis of papers that have reported
experimental results on the TREC Robust04 test collection. We do not find
evidence of an upward trend in effectiveness over time. In fact, the best
reported results are from a decade ago and no recent neural approach comes
close. Second, we applied five recent neural models to rerank the strong
baselines that Lin used to make his arguments. A significant improvement was
observed for one of the models, demonstrating additivity in gains. While there
appears to be merit to neural IR approaches, at least some of the gains
reported in the literature appear illusory.Comment: Published in the Proceedings of the 42nd Annual International ACM
SIGIR Conference on Research and Development in Information Retrieval (SIGIR
2019
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Leveraging on the recent developments in convolutional neural networks
(CNNs), matching dense correspondence from a stereo pair has been cast as a
learning problem, with performance exceeding traditional approaches. However,
it remains challenging to generate high-quality disparities for the inherently
ill-posed regions. To tackle this problem, we propose a novel cascade CNN
architecture composing of two stages. The first stage advances the recently
proposed DispNet by equipping it with extra up-convolution modules, leading to
disparity images with more details. The second stage explicitly rectifies the
disparity initialized by the first stage; it couples with the first-stage and
generates residual signals across multiple scales. The summation of the outputs
from the two stages gives the final disparity. As opposed to directly learning
the disparity at the second stage, we show that residual learning provides more
effective refinement. Moreover, it also benefits the training of the overall
cascade network. Experimentation shows that our cascade residual learning
scheme provides state-of-the-art performance for matching stereo
correspondence. By the time of the submission of this paper, our method ranks
first in the KITTI 2015 stereo benchmark, surpassing the prior works by a
noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to
this pape
Protected Methionine and Heat-Treated Soybean Meal for High-Producing Dairy Cows
The effectiveness of a rumen-protected methionine preparation was studied as an amino acid source for high-producing dairy cows during wk 4 through 16 postpartum. Twenty-four Holstein cows (14 primiparous and 10 multiparous) were fed concentrate containing heat-treated· soybean meal without or with 50 g/cow/day of added ruminally protected methionine product which provided 15 g of added DL-methionine daily. Cows were fed 16% crude protein mixed diets containing 30% of dry matter as corn silage, 15% as alfalfa hay, and 55% as concentrate. Milk production and composition were adjusted for pretreatment values (3 wk postpartum) by analysis of covariance. Adjusted milk yields (34.6 and 33.1 kg/day) were higher for cows fed heated soybean meal, but this difference was accounted for by higher pretreatment production (32.6 and 36.9 kg/day) of multiparous cows fed supplemental methionine. Production of 4% fat-corrected milk (28.5 and 27.6 kg/day) and solids-corrected milk (29.0 and 28.5 kg/day) was similar for cows fed both diets. Percentages of fat (2.81 and 2.92) and protein (2.88 and 2.92) were similar, while total solids (11.49 and 12.69) and solids-not-fat (8.68 and 8.77) were higher when cows were fed supplemental methionine. Milk protein percent (2.89 and 2.99) and milk protein production (.97 and 1.00 kg/day) were increased for primiparous cows fed supplemental methionine. Fatty acid composition in milk was similar. Dry matter intakes (20.2 and 21.0 kg/day) were higher especially in multiparous cows (21.5 and 23.8 kg/day) when fed supplemental methionine. Body weights (602 and 598 kg) and body weight changes were similar for the two treatments. Ruminal pH, volatile fatty acids, and ammonia, as well as blood serum urea and glucose were generally unaffected by methionine supplementation. Concentrations of methionine in arterial and venous plasma were elevated slightly when fed additional methionine, but the first in arterial and venous plasma were elevated fed additional methionine, but the first limiting amino acid for milk production, as calculated by several methods, was not changed by feeding supplemental ruminally protected methionine
Judea Captured
This coin is a part of the “Judea Captured” collection minted in Caesarea. Through thorough imaging and the decoding of Greek inscriptions we believe that we can prove the significance of the coin in correlation to the First Jewish War in battle against the Romans. We believe that it celebrates the victory of the Romans during the First Jewish War (66 – 69 AD) , under the Emperor Vespasian (68 – 79 AD). But the question does arise about the significance of certain symbols; especially the palm tree and crown made of palm leaves and how they relate to the War as well
Repeatability Corner Cases in Document Ranking: The Impact of Score Ties
Document ranking experiments should be repeatable. However, the interaction
between multi-threaded indexing and score ties during retrieval may yield
non-deterministic rankings, making repeatability not as trivial as one might
imagine. In the context of the open-source Lucene search engine, score ties are
broken by internal document ids, which are assigned at index time. Due to
multi-threaded indexing, which makes experimentation with large modern document
collections practical, internal document ids are not assigned consistently
between different index instances of the same collection, and thus score ties
are broken unpredictably. This short paper examines the effectiveness impact of
such score ties, quantifying the variability that can be attributed to this
phenomenon. The obvious solution to this non-determinism and to ensure
repeatable document ranking is to break score ties using external collection
document ids. This approach, however, comes with measurable efficiency costs
due to the necessity of consulting external identifiers during query
evaluation.Comment: Published in the Proceedings of the 42nd Annual International ACM
SIGIR Conference on Research and Development in Information Retrieval (SIGIR
2019
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