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

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017

As an empirical discipline, information access and retrieval research requires substantial software infrastructure to index and search large collections. This workshop is motivated by the desire to better align information retrieval research with the practice of building search applications from the perspective of open-source information retrieval systems. Our goal is to promote the use of Lucene for information access and retrieval research

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape

Searching Spontaneous Conversational Speech

The ACM SIGIR Workshop on Searching Spontaneous Conversational Speech was held as part of the 2007 ACM SIGIR Conference in Amsterdam.\ud The workshop program was a mix of elements, including a keynote speech, paper presentations and panel discussions. This brief report describes the organization of this workshop and summarizes the discussions

SIGIR: scholar vs. scholars' interpretation

Google Scholar allows researchers to search through a free and extensive source of information on scientific publications. In this paper we show that within the limited context of SIGIR proceedings, the rankings created by Google Scholar are both significantly different and very negatively correlated with those of domain experts

Fourth International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse

© ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM SIGIR Forum (2011) http://doi.acm.org/10.1145/1988852.1988860[EN] The Fourth International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 10) was held in conjunction with the 2010 Conference on Multilingual and Multimodal Information Access Evaluation (CLEF-10) in Padua, Italy. The workshop was organized as a competition covering two tasks: plagiarism detection and Wikipedia vandalism detection. This report gives a short overview of the plagiarism detection task. Detailed analyses of both tasks have been published as CLEF Notebook Papers [3, 6], which can be downloaded at www.webis.de/publications.Our special thanks go to the participants of the competition for their devoted work. We also thank Yahoo! Research for their sponsorship. This work is partially funded by CONACYTMexico and the MICINN project TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 (Plan I+D+i).Stein, B.; Rosso, P.; Stamatatos, E.; Potthast, M.; Barrón Cedeño, LA.; Koppel, M. (2011). Fourth International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse. ACM SIGIR Forum. 45(1):45-48. https://doi.org/10.1145/1988852.1988860S454845

Benchmarking news recommendations: the CLEF NewsREEL use case

The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016

Online Forum Thread Retrieval using Pseudo Cluster Selection and Voting Techniques

Online forums facilitate knowledge seeking and sharing on the Web. However, the shared knowledge is not fully utilized due to information overload. Thread retrieval is one method to overcome information overload. In this paper, we propose a model that combines two existing approaches: the Pseudo Cluster Selection and the Voting Techniques. In both, a retrieval system first scores a list of messages and then ranks threads by aggregating their scored messages. They differ on what and how to aggregate. The pseudo cluster selection focuses on input, while voting techniques focus on the aggregation method. Our combined models focus on the input and the aggregation methods. The result shows that some combined models are statistically superior to baseline methods.Comment: The original publication is available at http://www.springerlink.com/. arXiv admin note: substantial text overlap with arXiv:1212.533

Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)

The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not yet widely used. This workshop is intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, text mining and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The BIRNDL workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the third edition of the Computational Linguistics (CL) Scientific Summarization Shared Task.Comment: 2 pages, workshop paper accepted at the SIGIR 201