SIGIR 2016 Workshop WebQA II

Moschitti, Alessandro; Márquez, Lluiís; Nakov, Preslav; Agichtein, Eugene; Clarke, Charles L. A.; Szpektor, Idan

doi:10.1145/2911451.2917767

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Answers site. There have been also relevant workshops, e.g., at SIGIR (Moschitti et al 2016). The most relevant initiative was that on cQA at SemEval-2015 and 2016, i.e., Task 3 (Nakov et al 2015, 2016b), which focused on answering new questions using a cQA forum (Qatar Living).…”

Section: Related Workmentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

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We analyze resources and models for Arabic community Question Answering (cQA). In particular, we focus on CQA-MD, our cQA corpus for Arabic in the domain of medical forums. We describe the corpus and the main challenges it poses due to its mix of informal and formal language, and of different Arabic dialects, as well as due to its medical nature. We further present a shared task on cQA at SemEval, the International Workshop on Semantic Evaluation, based on this corpus. We discuss the features and the machine learning approaches used by the teams who participated in the task, with focus on the models that exploit syntactic information using convolutional tree kernels and neural word embeddings. We further analyze and extend the outcome of the SemEval challenge by training a meta-classifier combining the output of several systems. This allows us to compare different features and different learning algorithms in an indirect way. Finally, we analyze the most frequent errors common to all approaches, categorizing them into prototypical cases, and zooming into the way syntactic information in tree kernel approaches can help solve some of the most difficult cases. We believe that our analysis and the lessons learned from the process of corpus creation as well as from the shared task analysis will be helpful for future research on Arabic cQA.

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Section: Related Workmentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

Self Cite

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SemEval-2017 Task 3: Community Question Answering

Nakov¹,

Hoogeveen²,

Màrquez³

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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180

157

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We describe SemEval2017 Task 3 on Community Question Answering.This year, we reran the four subtasks from SemEval-2016: (A) Question-Comment Similarity, (B) Question-Question Similarity, (C) QuestionExternal Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88. 43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C.

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Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

Moro

Valgimigli

2021

Sensors

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The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised train sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our.

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SIGIR 2016 Workshop WebQA II

Cited by 4 publications

References 19 publications

Arabic community question answering

Arabic community question answering

SemEval-2017 Task 3: Community Question Answering

Efficient Self-Supervised Metric Information Retrieval: A Bibliography Based Method Applied to COVID Literature

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