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

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., ... TREC 2020 Deep Learning track The TREC 2020 Deep Learning track [Craswell et al., 2020c] uses the same training data as the previous year [Craswell et al., 2020b] , which was originally derived from ...

arXiv:2011.07368v2 fatcat:vzcm4qubzvflbluntvlhvn6fsy

Multiple Versions

We benchmark our models under the strictly blind evaluation setting of the TREC 2020 Deep Learning track and find that our proposed architecture changes lead to improved retrieval quality over TKL. ... Furthermore, we incorporate query term independence and explicit term matching to extend the model to the full retrieval setting. ... The TREC 2020 Deep Learning track provided participants with a click log dataset called ORCAS [9] . ...

arXiv:2104.09393v1 fatcat:styghwxkwfhnzgi5ukr3lmgotq

The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less effective) alternative ... In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption. ... Deep Learning track . ...

arXiv:2007.10434v1 fatcat:vxpfn3fnwbge3fx5kiejezvhj4

This paper describes a click data release related to the TREC Deep Learning Track document corpus. ... We perform some preliminary experiments using the click data to augment the TREC DL training data, offering by comparison: 28x more queries, with 49x more connections to 4.4x more URLs in the corpus. ... ranking task of the TREC Deep Learning Track are evaluated with ORCAS labels vs. actual Qrel from the track. ...

arXiv:2006.05324v2 fatcat:koqr3mrifngn7b6uoe24gva4gm

Multiple Versions

Using Model 1 we produced best neural and non-neural runs on the MS MARCO document ranking leaderboard in late 2020. ... We use the neural Model1 as an aggregator layer applied to context-free or contextualized query/document embeddings. ... These queries are only sparsely judged (about one relevant passage per query In addition to large query sets with sparse judgments, we use two evaluation sets from TREC 2019/2020 deep learning tracks ...

arXiv:2102.06815v2 fatcat:bg74b25ks5e4lk7za25j6s6ace

Multiple Versions

Supervised machine learning methods that use neural networks ("deep learning") have yielded substantial improvements to a multitude of Natural Language Processing (NLP) tasks in the past decade. ... However, I observe that these techniques are impractical due to their high query-time computational costs. ... Due to the massive scale of this dataset, it is the base of the upcoming 2019 TREC Deep Learning track. ...

doi:10.1145/3476415.3476432 fatcat:fdjy53sggvhgxo5fa5hzpede2i

Open Access

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query for a particular task. ... This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. ... TREC 2019 Deep Learning Track. Due to the nature of TREC planning cycles, the organization of the Deep Learning Track at TREC 2019 predated the advent of BERT for text ranking. ...

doi:10.1145/3437963.3441667 fatcat:6teqmlndtrgfvk5mneq5l7ecvq

This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. ... The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. ... TREC 2019/2020 Deep Learning Tracks. Due to the nature of TREC planning cycles, the organization of the Deep Learning Track at TREC 2019 predated the advent of BERT for text ranking. ...

arXiv:2010.06467v3 fatcat:obla6reejzemvlqhvgvj77fgoy

Multiple Versions

In recent years, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods ... Moreover, we discuss some open challenges and highlight several promising directions, with the hope of inspiring and facilitating more works on these topics for future research. ... FSR, AR, QR AR, QR, KE DS DS SG, DS SG, DS SG, DS Source TREC Robust Track TREC Million Query track Web pages TREC web track TREC Deep Learning track AOL Query logs Sogou Query logs Sogou Query logs Sogou ...

arXiv:2111.13853v3 fatcat:pilemnpphrgv5ksaktvctqdi4y

Multiple Versions

Deep reinforce learning Basic idea of deep reinforce learning Deep reinforcement learning [165] combines the perception of deep learning with the decision-making ability of reinforcement learning, ... As with machine learning methods, deep machine learning methods also have supervised learning and unsupervised learning. ... We can find that adversary B successfully and efficiently simulates A's view in attacking the IND-sID-CPA security of the proposed CIBPRE scheme, except the reencryption key query Q RK PRE ðID, S 0 , αÞ ...

doi:10.5772/intechopen.79903 fatcat:kvdisoudirgdhd7tvscnhsb6gm

Open Access

At the core, the system is a hybrid human-machine database engine where the machine interacts with the SMEs as part of a feedback loop to gather, infer, ascertain and enhance the database knowledge and ... Another body of literature focuses on developing languages for querying Tweets. ... Acknowledgments We would like to thank the participants of the RealStream2013 workshop at ECMLPKDD2013 in Prague, and in particular Bernhard Pfahringer and George Forman, for suggestions and discussions ...

doi:10.1145/2674026.2674028 fatcat:y3bozzeohveibgxb5wmiwfcogm

In particular, we experiment with a variety of popular or novel deep learning models for salience estimation in a single document summarization setting, and design several ablation experiments to gain ... Overall, we find that when simple, position based heuristics are available, as in single document news or research summarization, deep learning models of salience often exploit them to make predictions ... 2018) , deep learning-based language generation models still produce errors (Dušek et al., 2020) . ...

doi:10.7916/d8-61n8-mg23 fatcat:sztaye4ftvhubjibjd3ty5dqy4

There is a multitude of problems that need to be dealt with to solve this task. ... With the goal of improving document understanding, we identify three main problems to study within the scope of this thesis. ... a context c with the assumption that query terms are independent. ...

doi:10.15488/4440 fatcat:2igvuxyo6vcyffhspf3xtwbiru

However, in recent years, researchers have been facing massive scholarly documents published at an exponentially increasing rate. ... The second part of this dissertation examines the problem of analyzing the interdependencies between terms in scholarly literature. ... With the rapid popularity of deep learning technologies, Deep learning techniques have been applied to network embedding. ...

doi:10.25781/kaust-6iir9 fatcat:qryzpwfphzepvgzhx2guibecji

Thank you to all the interns and temporal team members; I also share some incredible memories with them: Giovanni, Alperen, Stefano, Dimitry, Giulio, Liviu, Amjad (now colleague at Inselspital!) ... ROIs: Regions of interest Table 6 . 6 4: Performance measures computed with trec eval software for the deep learning based features. ... fit into the context of automatic PCa grading with deep learning. ...

doi:10.13097/archive-ouverte/unige:160358 fatcat:o7pm7mj3uzb7bezjkqrwyy3pnm

Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track [article]

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Conformer-Kernel with Query Term Independence for Document Retrieval [article]

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ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search [article]

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