SIGIR是一个展示信息检索领域中各种新技术和新成果的重要国际论坛。

【跨模态哈希方法研究】Supervised Hierarchical Cross-Modal Hashing
#论文解读# #SIGIR 2019# 针对跨模态哈希方法的研究,作者创新性地利用了数据类别标签的层次性,提出了一种端到端的有监督层次跨模态哈希。通过分层监督,更好地在汉明空间中保留了数据在原始空间中的语义相似性关系。在跨模态哈希...全文

0+
0+

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.

1+
0+
下载
预览

【用户注意力指导的多模态对话系统】User Attention-guided Multimodal Dialog Systems
#论文解读# #开源论文# 本文是山东大学发表于SIGIR 2019的工作。针对任务型对话系统,作者利用用户的注意力信息,通过从属性角度对商品进行细分,分层的建模顾客的兴趣,从而最终实现精准的推荐;另外加入了图像信...全文

2+
0+

This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41st Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: \url{https://github.com/WING-NUS/scisumm-corpus}.

2+
0+
下载
预览
0+
0+
0+
0+
Top