Highlights of the 40th European Conference on Information Retrieval (ECIR '18) Date: April 6, 2018 Venue: Stavanger, Norway. Symposium at the IAI group Please cite, link to or credit this presentation when using it or part of it in your work.

1. A summary of ECIR'18
Darío Garigliotti
April 6, 2018

2. A word cloud
• Frequent terms:
• Reproducibility
• Interpretability
• Accountability
• Explainability

3. Points from the main panel
Panel on predicting/explaining performance
• What is performance? For whom?
• Users like explainability, determinism...
• Users: minimum explanation and "give me the results in SERP";
• Engineers: happy to deliver a non-black-box = detailed
description of all decisions and settings
• Usual vs novice user

4. Points from the main panel
• Users hate failure, IR strong in failure analysis
• Researcher intuitions not always explicitly reported (e.g., Carsten's paper)
• Statistical vs practical significance
• IR problems become complex, so the metrics involved (and their optimization)
• It might need a breakthrough in UI, leading to new metrics
• Different risks in academia vs industry
• E.g., failure prediction ASAP

5. Points from the main panel
• Over-optimizations, e.g., on user groups
• current -e.g., Salesforce-;
• future per-person customization? -and Edgar' keynote-)
• Overall moving to a ML paradigm? Metamodels (i.e., not IR retrieval models,
but models for learning models)?
• Model the whole info need rather than single queries
• No transparency => no ability for users to realize her complex task
• Explainability for diversity and bias in complex needs vs how the system
decided these results
• IR goal: get info need => so maybe rather than guess, ask (CAIR)

6. Industry day panel
• Again, explainability of, e.g., end-2-end DL models
• So what is need for reproducibility?
• Example: CAIR? community: surviving without
problem definition, metric, or dataset
• If we aim reproducibility, we can't stop working on
developing datasets (vs decreasing/missing
availability of large datasets from companies)

7. Gabriella Kazai keynote -
Evaluation
• History
• INEX evaluation pipeline (Experts; altruism)
• Offline metrics and crowdsourcing (Crowds; $)
• Online metrics (Users; self-interest)
• Crowdsourcing for offline evaluation
• Potential: to access large source of expertise
• Current: challenges in costs, time, quality
• Factors: payment, task - guidelines, judge population...

8. Gabriella Kazai keynote -
Evaluation
• Future of IR
• Pervasive
• Personal
• Conversational-oriented

9. Some papers
• Reproducibility + Best paper awardee:
• Gianmaria Silvello, Maristella Agosti, Riccardo Bucco, Giulio Busato,
Giacomo Fornari, Andrea Langeli, Alberto Purpura, Giacomo Rocco and
Alessandro Tezza. Statistical Stemmers: A Reproducibility Study
• Reproducibility of NN approaches
• Alexander Dür, Andreas Rauber and Peter Filzmoser. Reproducing a
Neural Question Answering Architecture applied to the SQuAD
Benchmark Dataset: Challenges and Lessons Learned
• Hybrid embedding (+ related? to QA)
• Daniel Cohen and W Bruce Croft. A Hybrid Embedding Approach to
Noisy Answer Passage Retrieval

10. Some papers
• Entities in topic modeling
• Andreas Spitz and Michael Gertz. Entity-centric Topic Extraction and Exploration: A Network-based
Approach
• Original snippets
• Martin Potthast, Wei-Fan Chen, Matthias Hagen and Benno Stein. A Plan for Ancillary Copyright:
Original Snippets
• Music recommendation
• Kartik Gupta, Noveen Sachdeva and Vikram Pudi. Explicit Modelling of the Implicit Short Term User
Preferences for Music Recommendation
• Multilinguality?
• Georgios Balikas, Charlotte Laclau, Ievgen Redko and Massih-Reza Amini. Cross-lingual Document
Retrieval using Regularized Wasserstein Distance