Neu-IR 2016: Lessons from the Trenches

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Slides from the Lessons from the Trenches session at the SIGIR 2016 workshop on Neural Information Retrieval (Neu-IR 2016).

Lessons from
the Trenches
Neu-IR workshop 2016

Ground
Rules
0.5 mins for setup
+
3 mins for presentation
+
2 mins for questions
This presentation is timed and will
progress automatically
So just follow along!
X 7 speakers
Sergey Nikolenko
Qingyao Ai
Debasis Ganguly
Alessandro Moschitti
Jun Xu
Grady Simon
Bhaskar Mitra

The End!

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(Slides from my talk at SEA: Search Engines Amsterdam) Online information access systems, like recommender systems and search, mediate what information gets exposure and thereby influence their consumption at scale. There is a growing body of evidence that information retrieval (IR) algorithms that narrowly focus on maximizing ranking utility of retrieved items may disparately expose items of similar relevance from the collection. Such disparities in exposure outcome raise concerns of algorithmic fairness and bias of moral import, and may contribute to both representational harms—by reinforcing negative stereotypes and perpetuating inequities in representation of women and other historically marginalized peoples—and allocative harms, from disparate exposure to economic opportunities. In this talk, we present a framework of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in retrieval.

Joint Multisided Exposure Fairness for Search and Recommendation

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Joint Multisided Exposure Fairness for Search and Recommendation

In this talk, I will share some of my personal reflections on the progress in the field of neural IR and some of the ongoing and future research directions that I am personally excited about. This talk will be informed by my own research in this area as well as my experience both as a developer/organizer of the MS MARCO benchmark and the TREC Deep Learning Track and as an applied researcher previously working on web scale search systems at Bing. My goal in this talk would be to move the conversation beyond neural reranking models towards a richer and bolder vision of search powered by deep learning.

What’s next for deep learning for Search?

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In this talk, I share some of my personal reflections and learnings on benchmark development and community building for making robust scientific progress. This talk is informed by my experience as a developer of the MS MARCO benchmark and as an organizer of the TREC Deep Learning Track. My goal in this talk is to situate the act of releasing a dataset in the context of broader research visions and to draw due attention to considerations of scientific and social outcomes that are invariably salient in the acts of dataset creation and distribution.

So, You Want to Release a Dataset? Reflections on Benchmark Development, Comm...

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Emerging machine learning approaches, including deep learning methods, for information retrieval (IR) have recently demonstrated significant improvements in accuracy of relevance estimation at the cost of increasing model complexity and corresponding rise in computational and environmental costs of training and inference. In web search, these costs are further compounded by the necessity to train on large-scale datasets, consume long documents as inputs, and retrieve relevant documents from web-scale collections within milliseconds in response to high volume query traffic. A typical playbook for developing deep learning models for IR involves largely ignoring efficiency concerns during model development and then later scaling these methods by either finding faster approximations of the same models or employing heuristics to reduce the input space over which these models operate. Domain knowledge about the specific IR task and deeper understanding of system design and data structures in whose context these models are deployed can significantly help with not only model simplification but also to inform data-structure specific machine learning model design. Alternatively, predictive machine learning can also be employed specifically to improve efficiency in large scale IR settings. In this talk, I will cover several case studies for both improving efficiency of machine learning models for IR as well as direct application of machine learning to improve retrieval efficiency, and conclude with a brief discussion on potential future directions for efficiency-sensitive benchmarking of machine learning models for IR.

Efficient Machine Learning and Machine Learning for Efficiency in Information...

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Online information access systems, like recommender systems and search, mediate what information gets exposure and thereby influence their consumption at scale. There is a growing body of evidence that information retrieval (IR) algorithms that narrowly focus on maximizing ranking utility of retrieved items may disparately expose items of similar relevance from the collection. Such disparities in exposure outcome raise concerns of algorithmic fairness and bias of moral import, and may contribute to both representational harms—by reinforcing negative stereotypes and perpetuating inequities in representation of women and other historically marginalized peoples—and allocative harms, from disparate exposure to economic opportunities. In this talk, we present a framework of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers. Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in retrieval. The development of expected exposure based metrics also opens up new opportunities and challenges for model optimization. We demonstrate how stochastic ranking policies can be optimized towards target expected exposure and highlight the trade-offs that may exist in optimizing for different fairness dimensions.

Multisided Exposure Fairness for Search and Recommendation

Multisided Exposure Fairness for Search and Recommendation

Multisided Exposure Fairness for Search and Recommendation

Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as webpages, in response to user's need, which may be expressed as a query. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this lecture will be on the fundamentals of neural networks and their applications to learning to rank.

Neural Learning to Rank

Neural Learning to Rank

Neural Learning to Rank

The emergence of deep learning based methods for search poses several challenges and opportunities not just for modeling, but also for benchmarking and measuring progress in the field. Some of these challenges are new, while others have evolved from existing challenges in IR benchmarking exacerbated by the scale at which deep learning models operate. Evaluation efforts such as the TREC Deep Learning track and the MS MARCO public leaderboard are intended to encourage research and track our progress, addressing big questions in our field. The goal is not simply to identify which run is "best" but to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This entails a wider conversation in the IR community about what constitutes meaningful progress, how benchmark design can encourage or discourage certain outcomes, and about the validity of our findings. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track--and reflect on the state of the field and the road ahead.

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We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.

Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning Track

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Lecture slides presented at Northeastern University (December, 2020). Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as webpages, in response to user's need, which may be expressed as a query. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this lecture will be on the fundamentals of neural networks and their applications to learning to rank.

Neural Learning to Rank

Neural Learning to Rank

Neural Learning to Rank

This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.

Duet @ TREC 2019 Deep Learning Track

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Duet @ TREC 2019 Deep Learning Track

The emergence of deep learning-based methods for information retrieval (IR) poses several challenges and opportunities for benchmarking. Some of these are new, while others have evolved from existing challenges in IR exacerbated by the scale at which deep learning models operate. In this talk, I will present a brief overview of what we have learned from our work on MS MARCO and the TREC Deep Learning track, and reflect on the road ahead.

Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond

Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond

Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond

Deep Neural Methods for Retrieval

Deep Neural Methods for Retrieval

Deep Neural Methods for Retrieval

Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user's need. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this tutorial will be on the fundamentals of neural networks and their applications to learning to rank.

Neural Learning to Rank

Neural Learning to Rank

Neural Learning to Rank

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Learning to Rank with Neural Networks

Learning to Rank with Neural Networks

Deep Learning for Search

Deep Learning for Search

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Neural Learning to Rank

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A fundamental goal of search engines is to identify, given a query, documents that have relevant text. This is intrinsically difficult because the query and the document may use different vocabulary, or the document may contain query words without being relevant. We investigate neural word embeddings as a source of evidence in document ranking. We train a word2vec embedding model on a large unlabelled query corpus, but in contrast to how the model is commonly used, we retain both the input and the output projections, allowing us to leverage both the embedding spaces to derive richer distributional relationships. During ranking we map the query words into the input space and the document words into the output space, and compute a query-document relevance score by aggregating the cosine similarities across all the query-document word pairs. We postulate that the proposed Dual Embedding Space Model (DESM) captures evidence on whether a document is about a query term in addition to what is modelled by traditional term-frequency based approaches. Our experiments show that the DESM can re-rank top documents returned by a commercial Web search engine, like Bing, better than a term-matching based signal like TF-IDF. However, when ranking a larger set of candidate documents, we find the embeddings-based approach is prone to false positives, retrieving documents that are only loosely related to the query. We demonstrate that this problem can be solved effectively by ranking based on a linear mixture of the DESM and the word counting features.

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Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.

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2. Ground Rules 0.5 mins for setup + 3 mins for presentation + 2 mins for questions This presentation is timed and will progress automatically So just follow along! X 7 speakers Sergey Nikolenko Qingyao Ai Debasis Ganguly Alessandro Moschitti Jun Xu Grady Simon Bhaskar Mitra

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